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The use of hand-held

electrical moisture meters

with commercially important

Australian hardwoods

Part 1

© 2003 Forest & Wood Products Research & Development Corporation All rights reserved. Publication: The use of hand-held electrical moisture meters with commercially important Australian hardwoods The Forest and Wood Products Research and Development Corporation (“FWPRDC”) makes no warranties or assurances with respect to this publication including merchantability, fitness for purpose or otherwise. FWPRDC and all persons associated with it exclude all liability (including liability for negligence) in relation to any opinion, advice or information contained in this publication or for any consequences arising from the use of such opinion, advice or information. This work is copyright and protected under the Copyright Act 1968 (Cth). All material except the FWPRDC logo may be reproduced in whole or in part, provided that it is not sold or used for commercial benefit and its source (Forest and Wood Products Research and Development Corporation) is acknowledged. Reproduction or copying for other purposes, which is strictly reserved only for the owner or licensee of copyright under the Copyright Act, is prohibited without the prior written consent of the Forest and Wood Products Research and Development Corporation. Project no: PN01.1306 Researchers: Philip Blakemore CSIRO Forestry and Forest Products PO Box E4008, Kingston ACT 2604 Ph: +61 2 6281 8211 Fax: +61 2 6281 8312 Email: Enquiries@ffp.csiro.au Forest and Wood Products Research and Development Corporation PO Box 69, World Trade Centre, Victoria 8005 Phone: 03 9614 7544 Fax: 03 9614 6822 Email: info@fwprdc.org.au Web: www.fwprdc.org.au

The use of hand-held electrical moisture meters with commercially important

Australian hardwoods

(Part 1 – Executive summary, methods, results, conclusion & recommendations)

Prepared for the

Forest & Wood Products Research & Development Corporation

by

Philip Blakemore

The FWPRDC is jointly funded by the Australian forest and wood products industry and the Australian Government.

2

EXECUTIVE SUMMARY

Objectives • Establish the scientific and technical basis for Australian Standards on

drying of Australian hardwoods, in particular AS/NZS 1080.1 (1996) and AS 2796 (1999);

• Establish accuracy limits for both resistance and capacitance moisture meters based on the procedure prescribed under AS/NZS 1080.1 (1996);

• Benchmark the accuracy limits against the Oven Dry method under AS/NZS 1080.1 (1996);

• Develop protocols to enable moisture meters to be calibrated to meet the requirements of the Australian Standards; and

• Develop appropriate correction factors for the main commercial hardwood species.

Key Results The major results of the study were: The selection of species and sample material covered a wide range of basic densities ranging from a mean of 414 kgm-3 for plantation grown Shining Gum (Eucalyptus nitens) up to 865 kgm-3 for mature Lemon Scented Gum (Corymbia citriodora).

• The expected accuracy (95% confidence intervals for predicted OD MC%) for most of the species corrections on boards within the MC% range of between 7-22% were:

o mostly between ±1.5% MC and ±3% MC for resistance type meters. The species corrections for the low density species were generally more accurate than for the higher density species. ! Due to the characteristics of the meters, the expected

accuracy locally around the critical 10-12% MC range could possibly be improved to around ±0.5-1.0% MC for most species.

o mostly between ±3% MC and ±6% for dielectric (capacitance) type meters. The accuracy limits depend on thickness as well for dielectric meters.

• The main reason that the accuracy of the resistance meters was about twice that for the dielectric meters was the effect of sample density on meter readings. For most meters and species, when boards were equilibrated under the same conditions, density explained as much or more of the variation in meter readings as oven dry MC% and thickness.

3

• When using moisture meters to assess drying quality, the approach taken in AS/NZS 4787 (2001) of defining allowable ranges for meter readings is much more practical and achievable than the absolute OD MC% limits set in AS 2796.

Application of Results The species corrections developed in this project should be included in a revision of AS/NZS 1080.1 (1996). Given that accuracy limits are provided with the species corrections, dielectric type meters can be included in this revised standard, but they are only likely to be of practical use to industry when used to estimate the mean moisture content of a number of boards.

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TABLE OF CONTENTS

PART ONE

EXECUTIVE SUMMARY ................................................................................................2 Objectives ..................................................................................................................2 Key Results................................................................................................................2 Application of Results...............................................................................................3

TABLE OF CONTENTS .................................................................................................4 INTRODUCTION.............................................................................................................6 MATERIALS AND METHODS .......................................................................................6

Measuring Moisture Content ....................................................................................6 Resistance type meters.........................................................................................7 Principle of operation ..............................................................................................7 Electrode.................................................................................................................7 Calibration...............................................................................................................9 Grain orientation ...................................................................................................10 Temperature .........................................................................................................11 Moisture gradients ................................................................................................12 Density..................................................................................................................13 Species corrections ..............................................................................................13 Wood preservatives, adhesives and coatings ......................................................17 Meter reading drift.................................................................................................17

Dielectric (capacitance) type meters..................................................................18 Principle of operation ............................................................................................18 Electrode...............................................................................................................19 Calibration.............................................................................................................19 Grain orientation ...................................................................................................20 Temperature .........................................................................................................20 Moisture gradients ................................................................................................20 Thickness..............................................................................................................20 Species corrections ..............................................................................................21 Density..................................................................................................................21 Wood preservatives, adhesives and coating ........................................................24

Sample Material .......................................................................................................25 Drying and Conditioning.........................................................................................26 Moisture Meters.......................................................................................................28

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Calibration of meters...............................................................................................29 Sampling procedure................................................................................................29 Calculating new corrections and accuracy limits ................................................31

RESULTS AND DISCUSSION .....................................................................................35 Species Properties ..................................................................................................35 Meter Calibrations ...................................................................................................36 Species corrections ................................................................................................37

Resistance meters ...............................................................................................37 Dielectric meters..................................................................................................50 Hand pressure ......................................................................................................83

CONCLUSIONS AND RECOMMENDATIONS ............................................................84 ACKNOWLEDGMENTS...............................................................................................88 REFERENCES..............................................................................................................88 PART TWO APPENDIX A BASIC DENSITY AND GREEN MC% INFORMATION....................90 APPENDIX B METER CALIBRATIONS..................................................................91 APPENDIX C RESISTANCE TYPE METER LINEAR REGRESSIONS ...............104 APPENDIX D DIELECTRIC SPECIES AND THICKNESS CORRECTIONS........171 APPENDIX E CSIRO EMC CHART ......................................................................191

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INTRODUCTION Electrical moisture meters are a critical tool used by the timber industry to monitor kiln drying, and to assess the final moisture content (MC) of dried timber products. High quality drying with timber at a uniform MC, appropriate for the in-service conditions, is vital if expensive in service movement problems are to be avoided. The standard procedure for measuring MC is to cut a small sample of timber and dry it in an oven with accurate temperature control. This process is both destructive to the timber and can take a considerable amount of time. Moisture meters provide a means of instantly estimating the MC% that would otherwise be obtained using the oven dry method. While the fundamental relationships between the electrical properties measured and the MC of the timber are strong, there are many other factors that can affect the measured electrical property. Some of these factors are known and easily accounted for, while others are less well understood or poorly accounted for. For many commercial species the calibration of meters for particular species has been questioned in recent years, particularly with changing timber resources from old growth to regrowth being available to industry. This project seeks to establish appropriate protocols and new corrections for the hardwood industry to use meters with greater confidence.

MATERIALS AND METHODS

Measuring Moisture Content For industrial purposes, the most accurate and accepted method for determining the MC of a piece of timber is the oven-dry (OD) method. The relevant standard in Australia that covers this method is AS/NZS 1080.1 (1996). This method requires two pieces of equipment; an accurate set of scales (e.g. to 0.1 g for test specimens with a mass greater then 50 g, and to 0.01 for test specimens less than 50 g) and a ventilated or forced convection (preferably forced convection) oven capable of maintaining a temperature of 103oC ±2oC. The equations for calculating MC% are shown in equations 1 & 2.

( ) 100M

MMMC%2

21 ×

−= (1)

or 1001MMMC%

2

1 ×

−

= (2)

Where: MC% = Moisture Content (% of oven dry weight) M1 = Initial mass (water and wood) M2 = Mass of oven dry wood

7

The limitation of the method is that it generally requires a minimum of 12-24 hours (maybe considerably longer for larger test pieces) of drying for the test specimens to reach a constant oven-dry weight. Electrical moisture meters offer a rapid means of measuring MC and can be broadly categorized into two different types: resistance (or conductance) and dielectric (or capacitance). The advantages and disadvantages of each type are briefly summarized in Table 1 below. Resistance type meters

Principle of operation Resistance meters work on the principle that dry timber is a poor conductor of electricity. Water, on the other hand, is a good conductor of electricity due to ionic flows. Thus, the measured resistance (or conductance, which is the inverse of resistance) to a flow of current through timber is a measure of the amount of moisture in that timber. Effectively then, a resistance type moisture meter is a form of Ohmmeter. Indeed, James (1988) notes that a conductance-type (resistance-type) moisture meter differs from an ordinary ohmmeter only in the unusually high values of resistance (low conductance) that must be measured when checking wood with MC below about 10 percent.

Electrode Resistance meters generally require electrodes that penetrate, and damage, the timber sample. The vast majority of meters currently sold use two nail like pins that are driven in to the timber sample. Other electrode arrangements, such as 4 pins or 2 blades, are used but are less common in recent times. James (1988) notes that the measured conductance when using a two pin electrode is mostly related to the small shell of timber in immediate contact with the pins. This is because conductance in wood is largely due to localized ionic flows around the pins. The consequence of this is that, neither the distance between the pins nor the conductivity of the wood between the pins has any significant affect on readings. The pins will also measure the wettest wood in contact with both pins. Put another way, quoting James (1988), if the moisture content of the wettest wood in contact with one pole of the electrode differs from the moisture content of the wettest wood in contact with the other pole, the drier of the two loci will limit the current and thus be responsible for the reading.

8

Table 1: Summary of three main methods for measuring MC in timber (Adapted from James, 1994)

OVEN DRY RESISTANCE DIELECTRIC

DETAILS Standard method for measuring wood moisture content. • Based on weight loss in

sample placed in oven (preferably fan forced at 1030C ± 20C) for 24-48 hours or until constant weight is observed.

• Expressed as a percentage of oven dry weight.

Measures the resistance (or conductance) between 2 electrode pins.

The contact resistance between pin and wood is predominantly what is measured.

The meter measures the wettest wood that contacts both pins.

Take two relatively similar forms:

Either based on measurement of power loss factor (Power loss type) or in conjunction with the dielectric constant (Capacitive admittance type) of the specimen.

Both use surface contact electrodes

ADVANTAGES

• Very strong effect of moisture content on conductance.

• Overshadows other variables

• Surface electrodes make no holes in specimen.

• Moisture content estimates are possible at moisture contents less than 6%. (Upper limit is about the same as for conductance type, but capacitive admittance type can give ballpark data at moisture content greater than fiber saturation).

DISADVANTAGES • Extremely low conductance, at low moisture

content, and upper limit is essentially fiber saturation point, because at higher moisture content, conductance is not well correlated with moisture content

• Requires electrodes that make holes in specimen

• Readings are influenced by variables other than moisture content to a greater degree than for resistance types.

• Surface electrodes strongly influenced by material near the surface. Material below about 0.003m below the surface is not properly weighted in the reading.

ADDITIONAL OBSERVATIONS

Electric moisture meters are affected by factors other than moisture content such as wood density, water soluble electrolytes in the wood, temperature, species and uneven moisture distribution. So even though the meters respond quite precisely to the electric properties that founds their calibration, these other factors can introduce variance into the data.

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Traditionally, in both America (James, 1988) and Australia (Edwards, 1974) the majority of species data corrections have been generated using four pin electrodes. In Australias case an unknown number may also have been generated using a blade electrode. James (1988) recommends that a correction of 0.5 to 1 % needs to be added to measurements taken with a two pin electrode where the species correction was generated using a four pin electrode, and the indicated reading is greater than 15%. He comments that the reason for this is that despite the larger pins used in a two pin electrode, doubling the contact area of a single pin is substantially less effective in increasing the net conductance than duplicating the contact area with a second pin. While James (1988) does not mention blade electrodes, assumedly given the larger contact area with blades, a similar correction for two pin electrode corrections needs to be applied to species corrections generated with blade electrodes. The use of nails as electrodes, either as permanent electrodes or for thicker material, is common practice. For convenience, it is often easier to locate the nails about the same distance apart as the pins on the meter so that the pins can be placed on the nail heads to take a measurement. However, as noted above the exact distance between the pins is not critical and flexible cable connections can be made to the nails. Where permanent electrodes (nails or otherwise) are placed in the wood, the readings may become unreliable after a few hours (James, 1988). However, after the MC of the timber is below about 15% the readings of the permanent electrodes are as reliable as those taken with electrodes freshly driven into the timber.

Calibration Most resistance moisture meters use Douglas-Fir (Pseudotsuga menziesii) as their default species calibration. Table 2 shows the Australian resistance values used as the Douglas-Fir standard (Edwards, 1974 and AS/NZS 1080.1: 1997). Note, there is a slight discrepancy between the figures shown for meter readings of 6 and 7 % between Edwards (1974) and AS/NZS 1080.1 (1997). Confounding the issue of Douglas-Fir calibration, is that different world regions and different meter manufacturers can, and do, use different standard resistances for Douglas-Fir. To illustrate this point, resistance values for Douglas-Fir (Coastal) from James (1988) are included in Table 2. Skaar (1988) confirms that these are the resistance values used by most US made resistance moisture meters. Many meter manufacturers do not actually provide or specify the standard resistances they use as their Douglas-Fir calibration. Figure 1 plots the resistance values provided by Edwards (1974) that are shown in Table 2. It is useful for visualizing why resistance meters work best between the range of 7 and about 24%. Below 7% the resistances that need to be measured are too high to be measured accurately. Above 24% the line relating resistance to MC% flattens out considerably illustrating that the relationship is poor and that small errors in measuring the resistance of the timber sample can translate into large errors in the estimate of the timbers MC%.

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Grain orientation The USDA FPL (1974) provides approximate ratios of 1.0 : 0.55 : 0.5 for electrical conductivity in the longitudinal, radial and tangential directions respectively. However, James (1988) notes that in practical terms, below about 15 % MC the effect of grain direction on meter readings is negligible. At MC above 20% differences of up to 2% MC on the meter reading may occur. AS/NZS 1080.1 (1997) provides no direction as to which orientation the electrodes should be placed into the timber except to say that the meter should be used in accordance with the manufacturers instructions. Currently most manufacturers recommend placing the pins into the wood parallel with the grain of the timber along the length of the board. This practice of placing the pins parallel with the length of the board is well established in Australian industry, despite the fact that the majority of species corrections, or at least the ones generated by CSIRO (Edwards, 1974 and CSIRO, 1974) were likely to have been generated with measurements taken across the grain. Neither document actually specifies which direction the species correction measurements were taken, but it is likely they were taken across the grain as the blade type electrodes which where used for some corrections were recommended to be used in this manner, as it made penetration of the blades into the timber considerably easier. However they were taken, as noted above, in the critical MCs below 15% MC the differences between meter measurements taken along or across the grain should be negligible.

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Table 2: Australian Douglas fir standard resistances

Resistance MΩ (megohm)

Meter Reading (MC%)

(Edwards, Unpublished)

(AS/NZS 1080.1)

(James, 1988)

5 376000 6 79400 66000 7 22400 22000 22400 8 5010 5010 4780 9 1990 1990 1660 10 850 850 630 11 380 380 265 12 180 180 120 13 98 98 60 14 55 55 33 15 32 32 18.6 16 19 19 11.2 17 12 12 7.1 18 7.4 7.4 4.6 19 5 5 3.09 20 3.4 3.4 2.14 21 2.3 2.3 1.51 22 1.6 1.6 1.10 23 1 1 0.79 24 0.77 0.77 0.6 26 0.6 0.6 28 0.48 0.48 30 0.38 0.38 32 0.315 0.315 34 0.26 0.26 36 0.215 0.215 38 0.185 0.185 40 0.16 0.16 50 0.105 60 0.08 70 0.065

Temperature The temperature of the wood (not air temperature) has an important effect on resistance type meter readings. Table 3 shows the temperature corrections for resistance moisture meter readings that are provided in AS/NZS 1080.1 (1997). These corrections should be applied to meter readings before a species correction is applied. Temperature corrections are applied first as most species corrections are based on measurements taken at room temperature (20-21oC).

Figure 1: Graph of standard resistance data (Edwards, 1974) against MC% for Douglas-Fir.

0.01

0.1

1

10

100

1000

10000

100000

1000000

0 20 40 60 80

MC%R

esis

tanc

e (m

egoh

ms)

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Table 3: Temperature corrections for use with resistance type moisture meters (AS/NZS 1080.1, 1997)

MR Temperature 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

5 7 8 9 11 12 13 14 15 16 17 19 20 21 22 10 7 8 9 10 11 12 13 14 16 17 18 19 20 21 22 15 6 7 8 9 11 12 13 14 15 16 17 18 19 20 22 20 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 25 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 30 6 7 8 9 10 11 12 12 13 14 15 16 17 18 19 20 21 22 35 6 7 8 9 10 11 12 13 14 15 15 16 17 18 19 20 21 22 40 6 7 8 9 10 11 12 13 14 15 16 16 17 18 19 20 21 22 50 6 7 8 9 10 11 11 12 13 14 15 16 17 18 19 18 20 2160 6 7 8 8 9 10 11 12 13 14 14 15 16 17 18 19 2070 3 7 8 9 10 11 11 12 13 14 15 16 16 17 1880 6 7 8 9 9 10 11 12 13 13 14 15 16 1790 6 7 8 8 9 10 11 11 12 13 14 15 15

The basis of the temperature corrections shown in Table 3 is not entirely clear. The version of this table in CSIRO (1974) does acknowledge that advantage has been taken of the work of Keylwerth and Noack (1956) in the compilation of this data. But it provides no details on how the data was compiled. Similarly, Figure 2 shows the commonly referred to chart produced by James (1988) which he also acknowledges is based on the combined data from several investigators. Once again though, he provides few details on which particular sets of data were combined or how. There does appear to be a reasonable agreement between the corrections provided by either Table 3 or Figure 2.

Figure 2: Temperature corrections for use with resistance type moisture meters (James, 1988)

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Moisture gradients In most drying situations the moisture gradient in the board is such that the surface of the board is drier than the core of the board. Given the commentary on resistance meters measuring the wettest timber in contact with either electrode above, it follows that in most situations the wood that is being measured is near the tip of pins. Nevertheless, there are times when rain, condensate or water spray may collect on the surface. In such cases, insulated pins are required to ensure the electrodes are measuring the wettest wood near the tip of the pins. Most two pin electrodes are currently supplied with insulated pins. If free water has collected or pooled on the board surface then not even the use of insulated pins will prevent high readings as it is likely for water to flow with the pins as they are driven into the timber. As resistance-type meters in most situations (always with insulated pins) measure the timber near the tip of the pins they can be used to measure the moisture gradients that are typically found in a timber board during and after drying. This feature is recognized in AS/NZS 4787 (2001) where gradients are measured as the difference between the measured MC with pin tips inserted to 1/2 the thickness of the board (core MC) and the MC at 1/6 the thickness of the board (surface MC). The mean MC of the board is approximated by driving the pins into a depth of 1/3 the thickness of the board being measured. This 1/3 thickness rule is more conservative, in being more likely to read high, than the 1/4 or 1/5 rule (i.e. pins driven in to 1/4 or 1/5 the thickness of board) referred to by James (1988).

Density Density itself has no significant effect on the meter readings of resistance-type moisture meters. However, density may be associated with levels of extractives such as water soluble electrolytes that do affect meter readings.

Species corrections Edwards (1974) outlines the procedure that was used historically by CSIRO to generate species corrections. Put simply, the procedure involves drying and conditioning a set of sample boards down through a number of equilibrium moisture content (EMC) conditions (normally spaced between 7% and about 24%) such that minimal MC gradients are present at each condition. Meter readings are then taken once the boards are equalized and finally the boards are oven-dried to determine the oven-dry MC at each set of conditions.

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A linear regression (in the form of equation 3) is then established between the oven-dry MC% and the meter readings using the least squares method. Linear regression can be visualised by plotting the two variables against each other in a graph (Figure 3A). By convention it is important that the meter readings be plotted on the Y-axis (ordinate) and the oven dry MC% on the X-axis (abscissa). This is done to represent the assumed relationship that one variable (the dependent variable) is functionally dependent on the other variable (the independent variable). Clearly, in this case the meter readings are dependent on the oven-dry MC of the wood. The black or dark line Figure 3(A) represents the linear part of equation 3 (c + (m x OD)) or the explained component of the variation in the dependent variable due to the linear relationship to the independent variable. The vertical lines connecting the individual data points to this regression line are the errors (ε) or the unexplained component of the variation in the dependent variable. It is important to note that these errors are defined in the vertical direction, i.e. the errors all occur in the dependent variable and not in the independent variable. The least squares method of calculating equation 3 is such that these errors are minimised; the average of the errors also has to equal zero. ε+×= OD)(m+c MR (3)

Where: MR = Meter reading moisture content c = Y intercept m = gradient of line OD = Oven-dry moisture content (%)

ε = error Other curvilinear relationships are possible, but over the range of MCs considered important linear relationships have generally been considered satisfactory. This acceptance of using a linear relationship between meter readings and oven dry MC% is supported by its continued use around the world. Recent examples include Shupe et al (2002), Gills (2000), Milota (1996), Milota and Gupta (1996), Milota (1994) and Simpson (1994). Equation 3 can then be rearranged to provide either a corrected reading (equation 4) or a correction factor (equation 5). Note well, in practice as equations 4 and 5 are intended as a predictive tool, the actual OD MC and associated error (ε) are unknown and hence are removed from equation 4 and 5.

m

)cR M(Reading Corrected −= (4)

MRm

c)R (MFactor Correction −−

= (5)

It is tempting to swap the MR and OD in equation 3 (as shown in Figure 3B) to avoid the re-arrangements of equation 4. Afterall, it is easy to see that equation 4 is functionally the same as equation 3 (See equation 6).

15

( )MRbaReading Corrected ×+= (6)

Where: a = mc

−

b = m1

However, Figure 3(B) clearly shows that due to the nature of how the errors are attributed to the dependent variable, the regression line calculated with oven-dry MC% on the Y axis is slightly different from the re-arranged line calculated in Figure 3(A) (shown as the red or darker line in Figure 3(B). To quickly demonstrate how the equations shown in Figure 3 are used to calculate a corrected reading, a meter reading of 22% will be used as an example. Using equation 4 or 5:

12.5) .65(21 Reading Corrected

3.89 0.7522 Reading Correctedor

1.33)18.522(Reading Corrected

≈=⇒

−×=

−=

This corrected reading can be read directly from Figure 3 by following point from 22% on the MR scale to the regression line and then reading the corresponding point on the OD MC% scale (as shown by the dotted line). In the example data shown, a meter reading of 22% was included and its actual OD MC% was 11%. Hence the prediction error for that board and that meter reading is 1.5%. In the normal use of the species corrections, this prediction error would be unknown. However, it is possible, from the known unexplained variance (ε) in the data used to calculate the linear regression, to calculate the expected range (confidence interval) of the prediction errors or accuracy that can be expected. How this expected accuracy is calculated is discussed in the latter section on the statistical methodology used in the experimental work undertaken here. The only point to note here is that the explicit assumption that all the error in the model occurs in the meter reading variable also has important consequences for calculating the expected accuracy or confidence limits of the species corrections.

16

y = 1.33x + 5.18R2 = 0.85

10

12

14

16

18

20

22

24

26

28

30

32

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

OD MC%

Met

er R

eadi

ng (M

C%

)

y = 0.64x - 1.43R2 = 0.85

6789

1011121314151617181920

10 12 14 16 18 20 22 24 26 28 30 32 34

Meter Reading (MC%)

OD

MC

%

y = (1/1.33) x - (5.18/1.33) = 0.75x - 3.89

Figure 3: (A) A fictitious example of a linear regression. (B) The same data with the axis swapped. An explanation of the difference between the two linear regression lines shown is provided in the text.

Figure 4 below shows the type of data generated by Edwards (1974) and the linear relationship used to generate the species correction data. Unfortunately, this data for Grey Ironbark is the only type of its kind that still exists for all the original CSIRO generated species corrections. For all other species corrections the data has been lost and only the corrections, which are based on the straight line shown in the graph, remain. This is unfortunate for as mentioned above, it is the spread of points above and below the straight line (ε error) that allows an estimation of the accuracy a user can have in the corrections provided by the straight line equation.

B

A

17

Figure 4: Example data collected for CSIRO species corrections (Edwards, 1974)

Wood preservatives, adhesives and coatings Generally the presence of any of these chemicals can affect the reliability of meter readings (AS/NZS 1080.1, 1997). Corrections have been generated for certain wood preservatives with some species (e.g. CCA treated radiata pine (CSIRO, 1974)) but they are of lesser interest to the hardwood industry where preservative treatments are less common due to the difficulty of treating the impermeable heartwood.

Meter reading drift With most meters with continuous displays, it is readily apparent that immediately after the pins are driven into the timber the readings start to drift down. Figure 5 shows measurements of this drift taken by Simpson (1994). Figure 5 (B) clearly shows a similar percentage drift when used on a board at a low MC (10%) as at a higher MC (21%). This means that drift is less of a significant problem at lower MC as the actual change in meter readings is a lot less. The practical consideration with drift is that meter readings need to be taken at a consistent time after the final effort of driving the pins into the timber sample. Normally this would mean taking a reading within the first 2-3 seconds after driving the pins into the timber sample.

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Figure 5: Drift in meter readings with time. (A) Actual meter reading and (B) percentage of immediate reading (Reproduced from Simpson, 1994).

Dielectric (capacitance) type meters

Principle of operation Dielectric (or capacitance) type meters can be further broken down in to three sub-types depending on which property they measure. The three types of meters are: capacitance (dielectric constant) type, power-loss type and a capacitance admittance type (James, 1988). Technical problems and cost means that almost no capacitance type (dielectric constant) meters are made commercially. The capacitance admittance type is effectively a combination of the other two types of meters. James (1988) provides the following description for how the three types of meters operate.

Capacitance type moisture meters use the relationship between moisture content and dielectric constant. The wood specimen is penetrated by the electric field associated with the capacitor of the frequency determining circuit of an oscillator when the electrode of the meter contacts the wood. The frequency of the oscillator is changed according to the effect of the specimen on the capacitance of this capacitor or, in other words, according to the dielectric constant of the specimen. A frequency discriminator generates a signal, read on a meter, proportional to the changes in frequency. Using the relation between dielectric constant and moisture, the meter can be calibrated to read moisture content

Power-loss type meters use the relation between moisture content and loss factor. The wood specimen is penetrated by the electric field radiating from an electrode that is coupled to a low-power oscillator in the meter. Power absorbed by the specimen loads the oscillator and reduces its amplitude of oscillation, which is in turn indicated by the meter dial. Since the loss factor depends on moisture content, the meter dial can be related to percent moisture.”

A B

19

Capacitance admittance type - The electrode of this meter is a capacitive element in a resistance-capacitance bridge circuit. When a wood specimen contacts the electrode, its capacitance and losses (admittance) are increased so that the bridge is unbalanced in proportion to the dielectric constant and loss factor of the specimen. The meter dial reads the amount of bridge imbalance, which can be related to the moisture content of the wood specimen causing the imbalance.

While most resistance type meters are in principle similar to each other, there is much greater variation between capacitance meters. Even where meters are based on the same operating principle as discussed above, they may differ in what frequencies (Figure 6) they use or in their electrode design.

Electrode One of the major advantages of dielectric type meters is that the electrodes are non-penetrating or non-damaging. Most meters currently have what James (1988) refers to as a general purpose electrode for use on either rough or smooth surfaces. The electrode itself is commonly separated from the timber by 2-3 mm of insulation which makes the surface condition less influential on meter readings. The actual shape and design of electrode varies from one meter to another and is an integral part of the operation of the meter.

Calibration Similar to resistance type meters, most dielectric type meters use Douglas-Fir as the default calibration. Figure 6 shows examples of the relationship between MC and loss factor and dielectric constant at two different frequencies for Douglas-Fir. The different meter measuring principles and characteristics means that most dielectric meters, even if they are the same sub-type meter, are to some degree different from each other.

Figure 6: Examples of (A) relationship between power-loss factor and MC% and (B) relationship between dielectric constant and MC%. (James, 1988).

A B

20

Grain orientation James (1988) states that grain orientation has no effect on dielectric meters because the electrodes are symmetrical.

Temperature Temperature corrections for dielectric meters are much more complicated than those for resistance meters (James, 1988) and need to be supplied by the meter manufacturer.

Moisture gradients Hand-held dielectric type meters are only capable of providing a single average MC meter reading. The effect of MC gradients on the average reading is mostly dependent on how well the measuring field penetrates the sample timber. James (1988) observes that the moisture content of the material nearest the electrode has a predominant effect on the reading; in extreme cases (such as wet surfaces), the reading could differ greatly from the average moisture content. Mackay (1976) determined that the surface thickness or depth that most affected meter readings with power-loss type meters was only 2.5 mm (0.1 inch). James (1994) further summarised that the surface effect for most types of dielectric meters is about 3 mm (0.125 inch). However, while Quarles (1991) supported this summary for power loss type meters, he suggested that the depth of field penetration of capacitance-admittance type meters may be more sensitive to wet material up 9 mm (0.375 inch) from the surface. Regardless of how predominant the surface effect on average readings is, James (1988) concluded that irregular drying gradients have unpredictable effects on the readings of dielectric meters

Thickness If the MC gradients are minimal then the effect of thickness depends on the thickness of the boards used to calibrate the meter. In general, thicker boards will have little effect on meter readings, while thinner boards will result in low meter readings (James, 1988). With thin timber boards the material on which the board is placed can also affect the meter readings. As such, for all dielectric meters, recommended usage is for the board being measured to have at least a 30-50 mm air gap below the part of the board being measured. Where MC gradients are present the effect of thickness on meter readings is related to those moisture gradients. In the same way that resistance meters need to measure at 1/3 depth to estimate the mean MC of boards, the type of dielectric meters and electrode need to match the thickness of the material being measured to ensure appropriate field penetration for the thickness of board. Some dielectric type meters have a built in thickness adjustment. It is unclear from accompanying manuals if the meters simply make a mathematical adjustment for thickness or if the depth of the field penetration is adjusted. Given that electrode arrangements are largely fixed, and the predominance of the surface layers in readings, it is likely that the thickness corrections are mathematical.

21

Species corrections The technique of using linear regression equations to generate species corrections is as applicable for dielectric moisture meters as it is for resistance meters. Milota (1996) is a recent example of where species corrections were generated for both a resistance and a dielectric type meter using the same method at the same time (See Figure 7). Figure 7 is useful in illustrating how the spread in data points affects the accuracy of the meters with different species. For example it is clear that the corrections would be much more reliable for Bigleaf Maple than they would be for California-Laurel. It can also be observed that for Bigleaf Maple (dielectric meter) that the relationship between meter reading and oven dry MC% is slightly curvilinear implying there would be a small bias in the corrections within certain ranges of meter readings.

Figure 7: Examples of species corrections for resistance type meters (A, B & C) and a dielectric type meter (D, E & F). Reproduced from Milota (1996)

Density Density affects the readings of all dielectric meters; how it affects meter readings is different for each type of dielectric meter. Generally, the effect is probably stronger with dielectric-constant and capacitance-admittance type meters than with power-loss type meters (James, 1988 and Quarles and Breiner, 1989).

22

Because of the dependence of dielectric moisture meters on density, the vast majority of dielectric meter manufacturers use this relationship as a means of approximating the species corrections that would otherwise be generated. Most meters either have a density correction or a density grouping built into the meters, or correction tables are supplied with the meter. The species corrections are applied by using the average density figure for a species. Only one meter manufacturer (Wagner Inc) that the author is aware of has published the basis for their density based species corrections (Milota, 1994). In this instance the derived equation was: GRGR SM0.62S86.51 M0.249 8.77 CF ××−×−×+= (7)

Where: CF = Correction Factor (= (corrected meter reading) (meter reading)) MR = Meter Reading SG = Specific Gravity While the methodology used to develop multiple regression equations of this sort is sound, care needs to be taken in extrapolating the equation for densities outside those used to generate the regression. Hence, Milota (1994) notes the corrections provided by this equation should only be used as a first approximation in the absence of more accurate individual species corrections. For example, Figure 8 shows the differences between the density based corrections and the individual species corrections that were used to generate the regression equation on which the density corrections are based (Milota, 1994). The graph shows the bias errors that should be expected when using a density based correction. Around the important 12% MC these expected bias errors should be less than ±1%. An important point to note is that the majority of species used to generate the density based corrections were low density softwoods. Milota (1996) and Milota and Gupta (1996) subsequently published a number of individual species corrections for other species including a number of USA hardwood species. Figure 9 shows the expected bias errors between the density based corrections using Equation 7 and the individual species corrections. This clearly shows that while the expected bias errors in using the density based corrections are again within ±1.5% at meter readings of around 10-12%, the expected biases at meter readings of 20-25% could be up to 6%. The expected bias errors demonstrated here with higher density hardwood species means that the applicability of this particular density based correction equation is doubtful when extrapolating its use with much higher density Australian eucalypt species. The multiple regression equation shown in Equation 7 was recalculated by including all the subsequently published correction values by Milota (1996) and Milota & Gupta (1996). The resultant equation is shown in Equation 8. Unfortunately, it showed (Figure 10) little improvement in the expected bias errors identified in Figure 9. GRGR SM0.343S62.20 M0.115 11.003 CF ××−×−×+= (8)

23

As none of the other manufacturers of dielectric meters appear to have published the basis of their density corrections (either built-in or in the form of tables) it is impossible to do a similar assessment of their suitability for use with high density Australian eucalypt species. However, by placing a dielectric meter on a uniform test sample (calibration plate or piece of thick composite board) and adjusting the density setting it is possible to gain an insight into the type of relationship the density corrections are based on. Some of the meters used in this study appear to use a linear density correction relationship similar to the Wagner meter while others appear to use a curvilinear relationship.

Comparison of Original Milota Species(Softwoods except White Oak)

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

0 5 10 15 20 25 30 35

Meter Reading

SG E

qn. -

Spe

cies

Cor

rect

ions

Cedar, WesternRed (0.33)

Spruce,Engelmann (0.37)

Fir, White (0.38)

Hemlock,Western (0.38)

Pine, Lodgepole (0.44)Fir, Douglas (0.49)

Larch, Western (0.54)Oak, White (0.66)

Figure 8: Differences between density (in this case specific gravity was used) based corrections and the individual species corrections used to generate the regression used to generate the density based corrections (numbers in brackets indicate average species specific gravity as determined in the study) (Milota, 1994)

24

Comparison of Subsequent Milota Species(Hardwoods except Douglas Fir and Dahurian Larch)

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

0 5 10 15 20 25 30 35

Meter Reading

SG

Eqn

. - S

peci

es C

orre

ctio

ns

Alder, Red (0.44)

Giant Chinkapin (0.46)Fir, Douglas (0.52)

Maple, Bigleaf (0.52)Larch, Dahurian (0.55)Ash, Oregan (0.56)

Laurel, California (0.56)Oak, CaliforniaBlack (0.58)Madrone, Pacific (0.61)Tanoak (0.66)

Oak, Oregan white (0.68)

Figure 9: Differences between density (in this case specific gravity was used) based corrections and the individual species corrections (Milota, 1996 and Milota & Gupta (1996))

Comparison of Subsequent Milota Species(Hardwoods except Douglas Fir and Dahurian Larch)

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

0 5 10 15 20 25 30 35

Meter Reading

SG E

qn. -

Spe

cies

Cor

rect

ions

Alder, Red (0.44)

Giant Chinkapin (0.46)Fir, Douglas (0.52)

Maple, Bigleaf (0.52)Larch, Dahurian (0.55)Ash, Oregan (0.56)

Laurel, California (0.56)Oak, CaliforniaBlack (0.58)Madrone, Pacific (0.61)Tanoak (0.66)

Oak, Oregan white (0.68)

Figure 10: Differences between density based corrections (Using equation 8 instead of equation 7) and the individual species corrections (Milota, 1996 and Milota & Gupta (1996))

Wood preservatives, adhesives and coating Similarly to resistance type meters, the presence of any of these chemicals can affect the reliability of dielectric type meter readings. Correction factors should be possible at lower meter readings (James, 1988), but given the differences between meters highlighted earlier, these would need to be supplied on a species by treatment by meter basis.

25

Sample Material This project attempted to generate new species corrections for most of the commercially important species in Australia. Table 4 lists the species or resource types included in the project and the supplying mill. Each co-operating mill was asked to supply 30 green sample boards (approximately 25 mm thick x 100 mm wide x 400 mm long) of a variety of growth ring or sawn orientations, and representing as wide a variation in source logs as possible. Difficulties in sourcing plantation material for E. nitens and E. globulus meant that only 20 and 11 boards respectively, were able to be collected for these species. Upon receipt at least 50 mm was cut to waste from one end and then a 20 mm sample section was cut from the freshly cut end. The 20 mm section and the remaining sample board were then immediately weighed. The volume of the 20 mm section was then determined using the water displacement method. The basic density of the sample boards were estimated based on these measurements of the 20 mm section. The oven dry mass of the sample boards was then estimated from the oven dry mass of the 20 mm sections. The oven dry mass of the sample boards was estimated in this way so that the MC of the sample boards through the various conditioning stages could be estimated. The actual oven dry mass of the boards was determined at the completion of conditioning.

Table 4: List of species/resource types incorporated into project.

Species/Resource Type (Trade or Common name) Supplier

Regrowth E. regnans (Vic Ash) Drouin West (Vic)

Regrowth E. delegatensis (Vic Ash) JL Gould (Vic)

Regrowth E. obliqua (Tas Oak) FFIC (Tas)

Regrowth E. marginata (Jarrah) Sotico (WA)

Regrowth E. pilularis (Blackbutt) Boral (NSW)

Mature C. maculata (Mill supplied C. citriodora as that is all they cut from Mature resource in Queensland)

(Spotted gum) Boral (Queensland)

Regrowth C. maculata (Spotted gum) Boral (NSW)

Plantation E. globulus (Southern blue gum)

Woakwine Forests (SA)

Plantation E. nitens (Shining Gum) Gunns (Tas)

Mature native forest E. camaldulensis (Red gum) Merbein Sawmill

26

Drying and Conditioning The drying and equalising conditions used in this study are summarized in Table 5. To minimise drying degrade, boards were initially dried from green under mild conditions in a conditioning room running at 250C and as high a relative humidity as the conditioning room could manage; airflow was minimal. Measuring relative humidity at this high range is difficult: as it was estimated to be above 97%, corresponding to an EMC of >>25% (Appendix E). The boards were dried for between 4-8 months prior to the first set of measurements being taken, depending on when the boards were supplied. Weighings over several weeks showed that drying for most species had slowed considerably and while moisture gradients were likely to present, a set of readings was taken to give some feel for how accurate or inaccurate the meters are likely to be at these higher MCs at the limit of, or beyond, the useful range of the meters. Due to difficulties obtaining material, the E. globulus sample boards did not arrive in time to be included in this first set of measurements.

Table 5: Summary of drying and equalizing conditions used

Set of measurements Temperature RH% Estimated

(EMC)1 1 25oC 97% 30%+

2 (400C) 25oC (95%) 85% (22%) 18%

3 25oC 95% >23%

4 25oC 80% 15.5%

5 25oC 67% 12%

6 25oC 50% 9%

7 25oC 35% 6.5%

1 Based on EMC chart produced by CSIRO Forestry and Forest Products

27

To speed up the drying, the temperature in the conditioning room was raised to 40oC and RH set at 94% (EMC of ~22%); airflow was still minimal. After the board masses had stabilized, 25 of the 30 boards were reconditioned for 5 hrs to recover collapse. Despite the average MC being relatively high for reconditioning (normally conducted at an average MC of 18%) that treatment was nevertheless applied as MC gradients should have been minimal and the core MC should have been low enough to allow reconditioning. The boards were then re-equilibrated for a week and one face was machined to provide a smooth surface. At this stage the room conditions were changed to 25oC and 93% RH (EMC of 22%) to minimize the temperature difference between the conditioning room and the laboratory (~20-21oC). It was noticed within 24 hrs that the boards were picking up a significant amount of moisture. Ovendry samples cut from some spare sample boards confirmed that the EMC under the conditions at 400C were likely to have been closer to 18%. The discrepancy was again likely to have been due to the difficulty of measuring and controlling RH at such high levels, especially at the elevated temperature. The RH of the room was then adjusted to 85% (EMC of 18%) and the boards re-equalized for a further 2 weeks before the second set of measurements were taken. For the next set of measurements the RH of the conditioning room was brought back up to 95% (EMC of >23%). Given that the sample boards were reabsorbing moisture up to the EMC, they were likely to equilibrate at a slightly lower MC of around 21%. For this and the remainder of the EMC set conditions the boards were placed into forced air cabinets inside the conditioning room to speed up the equalizing process and to try and ensure more uniform equalizing conditions for all boards. During the 4th and 5th set of measurements, it became apparent that for most species the boards were settling at an EMC 1-3% higher than expected. As the sample boards were on a desorption curve, this was not entirely unexpected as the CSIRO EMC chart is for the average of a number of species, as well as for the average of the sorption hysteresis effect2. However, the extent of the differences was surprising and implies one of two things. Either the CSIRO EMC chart is inadequate for a number of these species, or the control room conditions were being inaccurately measured and controlled. It is likely that both factors had an some effect in this project. However, this was not explored further as the main concern in this project was not the absolute MCs achieved, but rather that the stability and uniformity of the air conditions for each set of conditions was optimised and that an adequate range of MCs was achieved. Therefore, to ensure that the meter readings adequately covered the critical 10-12% MC range an additional set of measurements was added and the intended EMC of the final 3 sets of measurements adjusted as shown in Table 5.

2 Timber will equalise at a different moisture content depending on whether it is gaining or losing moisture; the difference between the two is known as sorption hysteresis.

28

For deciding when equilibrium had been achieved, boards from a number of different species (such that the range of densities present was adequately represented) were periodically weighed. Ideally, the sample boards would have been left to equilibrate in the controlled environment room until the masses started to fluctuate (masses will fluctuate with the fluctuations in relative humidity in the room due to the difficulty of tightly controlling RH%). Because of time constraints, the general rule of thumb used for this project was that measurements were taken after the slowest drying species was on average losing 0.1% MC or less over a 7 day period. For each of the last four sets of measurements (15.5%, 12%, 9% and 6.5% EMC) this took approximately 40 days. It is therefore possible that some minor moisture gradients existed in the sample boards. No attempt to measure these gradients was undertaken as, given the length of equalising, these gradients were expected to be minimal and well within what occurs in industrial practice. Also, any variance that may have been added to meter measurements, by these small gradients, only added to the industrial robustness of the accuracy limits for any of the species corrections.

Moisture Meters The major suppliers of moisture meters in Australia were identified and were requested to supply for the duration of the project the most relevant meter in their range for the hardwood sawmilling industry in Australia. Table 6 shows the type, brand and model of meters that were included in this study as well as the supplying company.

Table 6: List of meters supplied for inclusion in this project

Meter Brand Model Type Supplier/Distributor BES Bollmann Combo 100 Resistance Regal Services Carrel and Carrel C901 Resistance Carrel and Carrel Ltd

Delmhorst J2000 Resistance The Moisture Meter Company Pty Ltd

Deltron DCR 22 Resistance Deltron Moisture Meters

Electrophysics MT 700, CT808 Resistance, Dielectric J.A.S. Instruments

Kett MT 700, MOCO 2 HM530

Resistance, Dielectric Agelec Enterprises

Merlin HM8 WS25HD, PM 1-E Dielectric Hawleys Pty Ltd

Protimeter Timbermaster Resistance PCWI International Pty Ltd

Wagner MMI 1100 Dielectric The Moisture Meter Company Pty Ltd

29

Calibration of meters The supplying companies were asked to check the calibration of their meters before they were supplied. The meters were then sent back to the suppliers for them to re-check the calibration after the 3rd set of measurements and again when the meters were returned at the completion of measurements. Additionally, the calibration of all the resistance-type meters was checked on a CSIRO built calibration box. The box consists of a set of resistors that correspond to all the resistances shown in Table 2 for Douglas fir from 6 to 40% MC. The calibration check was conducted immediately before and after each set of measurements was undertaken to check for calibration drift. The box also allows an evaluation of how different each meters Douglas fir calibration is from the Australian Douglas fir standard.

Figure 11: Calibration box for resistance meters

Calibration plates were also supplied with the Electrophysics and the Wagner dielectric meters and so the calibration of all the dielectric meters was checked on these two plates immediately before and after each set of measurements to again check for calibration drift.

Sampling procedure For each set of measurements boards were brought out of the conditioning rooms and weighed and the thickness and width measured. Thickness was measured in the middle of the face, directly under where the dielectric type meters were placed. Boards were measured with each of the resistance meters first and then with each of the dielectric meters. Measurements were taken on both faces of each board with all meters. The pins of the resistance meters were driven into the boards towards either end of the board to allow the dielectric boards to measure the undamaged central portion of each board.

30

All of the resistance type meters were supplied with insulated two pin (sliding hammer type) probes except for the Protimeter Timbermaster (small uninsulated two pin probe without a hammer attachment) and the Kett MT 700 (small uninsulated four pin probe without a hammer attachment). All of the insulated two-pin probes were driven into the timber so that the uninsulated tips were 1-2 mm below the surface. Given that most of the uninsulated tips were 6-8 mm long this corresponded with the tips being approximately 1/3 the depth of the sample boards (which ranged in thickness from 20-28 mm depending on sawing overcut and amount of shrinkage). All measurements were taken along the grain. For all of the dielectric meters the boards were supported on timber blocks or boards at either end to provide an air gap under the boards. The minimum depth of the air gap under each board was 80 mm. All meters were placed on the boards in line or parallel with the long axis or length of each board. In keeping with earlier studies (Shupe et al (2002), Gills et al (2000), Milota (1996), Milota and Gupta (1996) and Milota (1994)) the dielectric meters were placed on the sample boards with no hand pressure. This was to minimise variation from different users applying variable amounts of pressure. However, recommended practice by most manufacturers is for light to moderate hand pressure to be applied. The effect of hand pressure on the results obtained here will be discussed in detail later. The temperature of the laboratory where measurements were taken was routinely measured and was consistently between 20 and 220C. Even though the boards were conditioned in a room at 250C no temperature corrections were applied. The weighing and measurement of board dimensions of a set of boards generally took between 5-10 minutes and the full set of meter measurements took 30-40 minutes. In this way it is likely that the board temperature may have been up to 1-20C above room temperature. It was considered that errors due to these possible temperature differences were within those expected for normal operational use. Table 7 shows the default setting that was used on each meter to generate the new species corrections.

31

Table 7: Default meter settings used to generate species corrections.

Meter Species

or density setting

Temperature

Thickness

BES Bollmann (Combo 100) 2 200C -

Carrel and Carrel (C 901) - - -

Delmhorst (J 2000) 1 700F -

Deltron (DCR 22) - - -

Electrophysics (MT 700) - - -

Electrophysics (CT 808) 0.5 - 1.00

Kett (MT 700) 6 - -

Kett (MOCO 2 HM530) 0.5 Auto 25

Merlin (HM8 WS 25HD) 2 - -

Merlin (PM 1E) 0.5 - 20 mm

Protimeter (Timbermaster) B - -

Wagner (MMI 1100) 0.5 - -

Calculating new corrections and accuracy limits A number of reasonably complicated statistical equations are presented in this section. Providing a full description of the statistical notation used would require an overly extensive amount of detail that can otherwise be obtained from standard text books on statistics. The equations shown here are presented to enable readers with sufficient statistical knowledge to understand the exact statistical basis of the methods used, but in general the discussion is limited to the practical implications of these equations. The notation used here is consistent with that of Zar (1984), but should also be consistent with most other similar textbooks. For those with limited statistical knowledge who do not wish to read this section in full the following summary is provided.

32

• Linear regression is used to calculate the species corrections for the resistance type meters.

o The confidence intervals for the accuracy of resistance type meters are not symmetrical.

• Multiple linear regression is used to calculate the species corrections for the dielectric type meters as the thickness of the boards also has to be taken into account.

o The confidence intervals for the accuracy of dielectric type meters are symmetrical, but vary with both MR and board thickness.

For the resistance type meters, a simple linear regression was used to relate the meter readings to the OD MC%. The general form of the simple linear regression equation generated is shown in equation 3 (page 14). The 95% confidence intervals (CI) for the linear regressions were calculated using equation 9.

( )iYDF (2), 0.05i St YInterval Confidence 95% ×±= (9)

Where: )ˆ(n1SS 2

22

XYiY

−+=

∑• xXX i (10)

Or for the 95% CI for an additional individual meter reading

)ˆ(

n11SS 2

22

XYiY

−++=

∑• xXX i (11)

To help visualise what these 95% CI represent they are shown in Figure 12 as calculated for the same fictitious data as shown in Figure 3. The important thing to note is that due to the assumptions about the error term (ε) in equation 3 (these assumptions are discussed more fully in the species corrections section starting on page 13), the 95% CI are symmetric vertically above and below the regression line (as shown in Figure 12). The other important point to note from Figure 12 is that the 95% CI become greater or wider with increasing distance from the midpoint of the linear regression. The midpoint is the point on the linear regression line that represents the mean of the MR and OD MC% data used to calculate the linear regression.

33

y = 1.33x + 5.18R2 = 0.85

101214161820222426283032343638

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

OD MC%

Met

er R

eadi

ng (M

C%

)

MidpointOD MC%mean = 13.0MRmean = 22.5

Figure 12: 95% CI intervals (inner dotted lines around the solid line are for the regression equation and the outer dotted lines are for an additional meter reading) for the same example linear regression as shown Figure 3.

A standard measure for assessing the usefulness of a linear regression is the coefficient of determination (r2). This is a proportion (or percentage) of the variation present in the dependent variable that is explained by the linear regression as a percentage of the total variation present. This proportion is affected by a number of variables such as the range of oven dry MCs the linear regression is based on, MC gradients and their interaction with thickness, species and drying treatments. Jamroz (1994) suggests that the r2 value is not a quantitative measure of the accuracy of a meter and that a better measure of meter accuracy is the Moisture Sorting Parameter (MSP)

mcMSP sd= (12)

Where: csd = The standard deviation of the constant c (See equation 3) m = The slope of the linear relationship (See equation 3) The advantage of the MSP is that it provides a measure of accuracy in units of oven-dry MC and is therefore independent of whatever units the meter reading is in. Unfortunately, it still has the same limitations as r2 in that the value is still dependent on factors such as range of oven-dry MCs used, species, MC gradients and board dimensions and drying treatments.

34

Once equation 3 has been established, equations 4 and 5 (page 14) can then be used to calculate the species corrections (or predicted OD MC%) for a given meter reading. These equations are known as inverse prediction equations because based on a measure of the dependent variable (meter reading) we are trying to predict the independent variable (oven-dry MC). The 95% CI for these inverse prediction equations can be visualised in Figure 12 as the horizontal distance from the regression line to the 95% CI for an additional meter reading (outer dotted lines). These prediction CI are not symmetrical (except at the midpoint) and hence, equation 13 is needed to calculate the non-symmetrical 95% CI for the predicted oven-dry MC.

( ) ( )

++

−±

−+

∑• n11K

xYYS

Kt

KYY bX 2

22

XYii (13)

Where: 222 btbK −=

For the dielectric meters multiple linear regression was used to relate the OD MC% to meter readings, the thickness of the boards and the interaction effect between meter reading and board thickness. The species correction equation that results from this process is shown in equation 14.

TMRm TmMmc CR 321 ××+×+×+= R (14)

Where: CR = Corrected meter reading or predicted oven-dry moisture content (%) MR = Meter reading moisture content (%) T = Thickness of board (mm) c = Y intercept m1 = partial regression coefficient for MR m2 = partial regression coefficient for T m3 = partial regression coefficient for interaction between MR and T Multiple regression can be thought of as an extension of simple linear regression except that more than one independent variable is involved. As the multiple regression generated here is intended solely as a prediction model, unlike for linear regressions, it is convention and easier to directly have the predicted variable (in this case oven-dry mass) as the dependent variable. Equation 9 is again varied to calculate the 95% CI for a predicted oven-dry MC from a single meter reading for a given thickness of board. The equation 10 component of equation 9 is replaced with equation 15. The important thing to note here is that the prediction CI calculated here are symmetrical and are specific for values of both the meter reading and board thickness.

xxcn1SS

m

1i

m

1kkiij

2m2,.....,,1YY

+= ∑∑

= =• (15)

35

RESULTS AND DISCUSSION

Species Properties The mean basic density and green MC data from the small test sections cut from the supplied sample boards are shown in Figure 13. The individual board basic densities are plotted against MC in Appendix A. A typical inverse (curved) relationship between basic density and MC is shown in these plots.

131 94 94 89 66 54 59 57 3892

865

705702

691

645

563527507

453414

0

100

200

300

400

500

600

700

800

900

1000

Shining

Gum

Mounta

in Ash

Blue G

umAlpi

ne A

shMes

smate

Jarra

hBlac

kbutt

River R

ed G

umSpo

tted G

um

Lemon

Sce

nted G

um

Bas

ic D

ensi

ty (K

g/m

3)

0

50

100

150

200

250

300

350

400

450

500

Gre

en M

C%

Average of MC%Average of Basic Density

Figure 13: Mean basic density and green MC% of the small sections cut from the sample boards for each species. Error bars indicate the 95% CI for the means. Individual markers (circles and squares) indicate the maximum and minimum values for each species.

36

Meter Calibrations Appendix B shows the calibration check data collected for all the moisture meters during the sampling process. These show that the calibration of the 3 Australian or New Zealand made meters (Carrel & Carrel, Deltron and Electrophysics) approximately matched the Australian Douglas-Fir resistances (AS/NZS 1080.1: 1997) shown in Table 2 and Figure 1. The calibration of the BES Bollmann and the Protimenter appears to be slightly different from the Australian Douglas-Fir standard. The Delmhorst and the Kett appear to have a calibration that differs from the Australian Douglas-Fir standard in 2 stages. The first part of the Delmhorst calibration (Meter Readings <21%) is similar to the Australian Douglas-Fir standard, but it differs significantly above this point. During the final set of measurements, a fault was suspected in the Electrophysics MT700. At the completion of the first day, the meter was re-checked against the calibration box and a fault confirmed. As it was unclear as to when the fault had occurred during the day, all data for that meter collected for that day was excluded from the data set. It was not possible to get the meter fixed before the completion of that set of measurements and so no data for MC below about 11% MC are included for this meter for any species. The dielectric meters appear to have shown more variation between calibration checks than the resistance type meters, but this is possibly due to differences in the surfaces the calibration plates were placed on for each calibration check.

37

Species corrections Resistance meters Table 8 to Table 17 show the new species corrections that apply with each of the different resistance type moisture meters. Most of the differences between the meters are directly due to the differences in the Douglas-Fir calibrations of the different meters. Figure 15 to Figure 24 show the expected level of accuracy or confidence in the new species corrections. These figures need to be interpreted a little cautiously as they reflect both the variation in measurements over the whole of the range of the linear relationship as well as any slight non-linear parts of the relationship. To assist in this interpretation, the actual plots of meter readings against oven dry MC% are shown in Appendix C. The measurements taken at the higher MC levels (>30%) are also shown in Appendix C. These measurements give a feel for how inaccurate the meters are at the higher MCs and the danger of extrapolating the corrections provided much above about 24-26% MC. Looking at the plots in Appendix C it is apparent that for many of the species the spread or variation of data points around the 12% MC level is less than for the whole of the regression (e.g. Figure A-1). This suggests that for timber at 12% MC it should be possible to get accuracy limits much less than those indicated in Figure 15 to Figure 24. Figure 14(A) plots just the ~12% EMC data from Figure A-1. Figure 14(B) shows the difference between the predicted OD MC% from the linear regression in Figure 14(A) as opposed to using the main regression as shown in Figure A-1 as well as the 95% Confidence Interval for a single measurement using the 12% linear regression. Figure 14 shows that at its midpoint the 95% confidence interval for predicted values is approximately ± 0.7%, less than half the ± 1.8% CI for the main linear regression. Figure A-1 and Figure 14(B) also shows that the actual relationship for the main data (Figure A-1) is not quite linear, and that a slight bias of approximately 0.5% (i.e. the predicted OD MC% using the main regression would on average be 0.5% high) could be expected when measuring the boards at about 12% MC. A difficulty with looking at just this set of data is that as the range of oven-dry MC% is restricted the R2 value for the linear regression is also much lower and the expected accuracy of the prediction OD MC% decreases rapidly away from the mean OD MC% of the sample boards used to generate it. Therefore, the regression equations and corrections shown in Table 8 to Table 17 are the most generally robust and simple to use, but it is important to note that mills may be able to improve their confidence in predicting the OD MC% of their timber (possibly to around ± 0.5% to ± 1.0%) if they continue to do oven dry checks and build up their data set and do their own linear regression in the 10-12% MC range.

38

y = 0.8078x + 1.0626R2 = 0.3212

8.0

8.5

9.0

9.5

10.0

10.5

11.0

11.5

12.0

10.2 10.4 10.6 10.8 11.0 11.2 11.4 11.6 11.8 12.0 12.2

Oven Dry MC%

Met

er R

eadi

ng (M

C%

)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

9.1 9.6 10.1 10.6 11.1

Meter Reading (MC%)

Pred

icte

d O

D M

C%

rela

tive

to P

redi

cted

O

D M

C%

from

the

mai

n lin

ear r

egre

ssio

n.

ODpred(12%) -ODpred(main) -ve 95% CI

+ve 95% CI

Figure 14: (A) The BES Bolmann meter readings plotted against oven dry MC% for the Alpine Ash sample boards approximately equalized to 12% MC. (B) The difference between the predicted OD MC% from the 12% regression and the main regression as well as the prediction intervals (PI) for an individual meter reading.

The levels of accuracy with each meter varied considerably from species to species. The two main problems were the inconsistent variation in the meter readings at different OD MC% and any departures from linearity in the relationship between meter readings and OD MC%. The inconsistent variation in meter readings at different OD MC% is actually a violation of one of the assumptions of linear regression. That is, the variation in meter readings should be the same at all points along the linear regression (homoscedasticity). For these meter readings, generally the variation increases with increasing OD MC%. In such cases, standard statistical practice might be to transform the data (logarithmic transform in this case) and conduct the linear regression analysis on the transformed data. However, in this case the transformation may increase the non-linearity problems and in a number of cases the variation at the lowest OD MC% is also starting to increase relative to the meter readings at 12%. The logarithmic transformation in such cases would exaggerate the variation of these low meter readings relative to the higher readings.

39

In a number of cases, it appeared that some of the extreme measurements at the higher MC% may have been outliers. In some cases it was found by anatomical examination (conducted by Jugo Ilic3) that some of the supplied boards were not of the correct species. In this way about 10 of the Spotted Gum sample boards, and 2-3 of the Blackbutt sample boards, were excluded from analysis. In most other cases, the high or low reading boards that might otherwise have been excluded as outliers (based on being outside the 95% confidence intervals for the linear regression), were left in as in almost all cases the same boards were consistently measured high or low, relative to the other sample boards, across all the sets of EMC conditions. While it is possible to fit a polynomial equation to the plots of the meter readings against OD MC%, the departures from linearity, where they occur, are typically small and the much simpler and easy to use linear regression relationship is considered adequate.

3 Senior Research Scientist and expert wood anatomist at CSIRO FFP

40

Alpine Ash

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C% Bollman

Carrel & CarrelDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 15: Comparative 95% CI for all the resistance type moisture meters when used on regrowth Alpine Ash (E. delegatensis)

Table 8: Regrowth Alpine Ash (E. delegatensis) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC% AS/NZS 1080.1 1.0809 -2.9202 8 9 10 11 12 13 14 15 16 17 18 18 19 20 21 22 23 24 25 Bollmann 0.7538 1.3033 0.968 1.0% 8 9 10 12 13 14 16 17 18 19 21 22 Carrel & Carrel 1.0452 -0.6610 0.972 0.9% 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Delmhorst 0.9138 0.3216 0.964 1.0% 7 8 9 11 12 13 14 15 16 17 18 19 20 22 Deltron 1.0924 -1.1478 0.976 0.8% 7 8 9 10 11 12 13 14 15 16 17 18 18 19 20 21 22 Electrophysics 0.8530 1.4484 0.935 1.5% 8 9 10 11 12 14 15 16 17 18 19 21 22 Kett 0.8780 -1.7648 0.960 1.1% 9 10 11 12 13 15 16 17 18 19 20 21 Protimeter 0.8270 3.1222 0.960 1.1% 7 8 10 11 12 13 14 16 17 18 19 20 22

41

Blackbutt

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C% Bollman

Carrel & CarrelDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 16: Comparative 95% CI for all the resistance type moisture meters when used on regrowth Blackbutt (E. pilularis)

Table 9: Regrowth Blackbutt (E. pilularis) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.0000 -1.0000 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Bollmann 0.6629 1.9789 0.959 1.2% 8 9 11 12 14 15 17 18 20 21 Carrel & Carrel 0.9988 -0.8363 0.950 1.2% 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Delmhorst 0.8849 -0.2007 0.948 1.3% 7 8 9 10 12 13 14 15 16 17 18 19 21 22 Deltron 1.0512 -1.4045 0.955 1.2% 7 8 9 10 11 12 13 14 15 16 17 18 18 19 20 21 22 Electrophysics 0.7617 2.2814 0.879 2.2% 8 9 10 11 13 14 15 17 18 19 21 22 Kett 0.8218 -2.0376 0.928 1.5% 10 11 12 13 15 16 17 18 20 21 22 Protimeter 0.9798 -0.3658 0.952 1.2% 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

42

Blue Gum

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC% )

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C% Bollman

Carrel & CarrelDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 17: Comparative 95% CI for all the resistance type moisture meters when used on plantation grown Blue Gum (E. globulus)

Table 10: Plantation grown Blue Gum (E. globulus) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.1829 -2.6187 7 8 9 10 11 12 12 13 14 15 16 17 17 18 19 20 21 22 23 Bollmann 0.9624 -0.5314 0.8895 3.4% 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Carrel & Carrel 1.2197 -1.9763 0.9345 2.3% 7 8 9 10 11 11 12 13 14 15 16 16 17 18 19 20 20 21 Delmhorst 1.0336 -0.6196 0.9536 1.9% 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Deltron 1.2928 -2.8118 0.9025 2.9% 8 8 9 10 11 11 12 13 14 15 15 16 17 18 18 19 20 21 Electrophysics 1.1052 -1.1876 0.9173 3.2% 7 8 9 10 11 12 13 14 15 16 16 17 18 19 20 21 22 Kett 1.0294 -3.3462 0.9530 1.9% 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Protimeter 0.9032 2.6300 0.9485 2.0% 7 8 9 10 11 13 14 15 16 17 18 19 20 21

43

Jarrah

-4

-3

-2

-1

0

1

2

3

4

0 5 10 15 20 25 30

MR

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C% Bollman

Carrell & CarrellDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 18: Comparative 95% CI for all the resistance type moisture meters when used on regrowth Jarrah (E. marginata)

Table 11: Regrowth Jarrah (E. marginata) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.0000 -1.0000 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Bollmann 0.6330 2.6779 0.960 1.2% 8 10 12 13 15 16 18 19 21 Carrell & Carrell 0.8739 1.2213 0.950 1.2% 8 9 10 11 12 13 15 16 17 18 19 20 21 Delmhorst 0.7594 1.8550 0.948 1.2% 8 9 11 12 13 15 16 17 19 20 21 Deltron 0.9512 0.0322 0.936 1.4% 7 8 9 10 12 13 14 15 16 17 18 19 20 21 22 Electrophysics 0.7217 2.9006 0.894 2.0% 7 8 10 11 13 14 15 17 18 20 21 22 Kett 0.7196 -0.2279 0.908 1.7% 9 10 11 13 14 16 17 18 20 21 Protimeter 0.6740 4.7133 0.916 1.6% 8 9 11 12 14 15 17 18 20 21

44

Lemon Scented Gum

-3

-2

-1

0

1

2

3

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C% Bollman

Carrel & CarrelDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 19: Comparative 95% CI for all the resistance type moisture meters when used on mature Lemon Scented Gum (C. citriodora)

Table 12: Mature Lemon Scented Gum (C. citriodora) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.2695 -1.1027 7 8 9 10 10 11 12 13 13 14 15 16 17 17 18 19 20 Bollmann 1.0395 -0.8557 0.8713 2.2% 8 9 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Carrel & Carrel 1.3260 -2.2251 0.8905 1.9% 8 8 9 10 11 11 12 13 14 14 15 16 17 18 18 19 20 Delmhorst 1.2845 -3.3116 0.8814 1.9% 7 8 9 10 10 11 12 13 13 14 15 16 17 17 18 19 20 20 21 Deltron 1.4991 -4.9555 0.8777 2.0% 7 8 9 9 10 11 11 12 13 13 14 15 15 16 17 17 18 19 19 Electrophysics 1.1104 -0.1077 0.7902 3.0% 7 8 9 10 11 12 13 14 15 15 16 17 18 19 20 21 22 Kett 0.9672 -3.2046 0.8914 1.8% 10 11 12 13 14 15 16 17 18 19 20 21 22 Protimeter 0.9812 1.4829 0.8858 1.9% 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

45

Messmate

-3

-2

-1

0

1

2

3

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C% Bollman

Carrel & CarrelDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 20: Comparative 95% CI for all the resistance type moisture meters when used on regrowth Messmate (E. obliqua)

Table 13: Regrowth Messmate (E. obliqua) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.0000 -1.0000 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Bollmann 0.8193 0.7539 0.9353 1.5% 8 9 10 11 13 14 15 16 17 19 20 21 22 Carrel & Carrel 1.0523 -0.5337 0.9650 1.0% 7 8 9 10 11 12 13 14 15 16 17 18 19 20 20 21 22 Delmhorst 0.9335 0.1409 0.9635 1.0% 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 Deltron 1.1466 -1.6032 0.9624 1.0% 8 8 9 10 11 12 13 14 14 15 16 17 18 19 20 21 21 22 Electrophysics 0.9538 -0.2478 0.9140 1.8% 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Kett 0.9256 -2.3226 0.9526 1.2% 9 10 11 12 13 14 15 17 18 19 20 21 22 Protimeter 0.8558 2.8067 0.9690 0.9% 7 8 10 11 12 13 14 15 17 18 19 20 21 22

46

Mountain Ash

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C% Bollman

Carrel & CarrelDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 21: Comparative 95% CI for all the resistance type moisture meters when used on regrowth Mountain Ash (E. regnans)

Table 14: Regrowth Mountain Ash (E. regnans) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.0809 -2.9202 8 9 10 11 12 13 14 15 16 17 18 18 19 20 21 22 23 24 25 Bollmann 0.7408 1.8690 0.9796 0.9% 7 9 10 12 13 14 16 17 18 20 21 22 Carrel & Carrel 1.0322 0.2267 0.9781 0.8% 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Delmhorst 0.8642 1.3422 0.9729 0.9% 7 8 9 11 12 13 14 15 16 18 19 20 21 22 Deltron 1.1070 -0.7262 0.9760 0.8% 8 8 9 10 11 12 13 14 15 16 17 18 18 19 20 21 22 Electrophysics 0.8280 2.1310 0.9447 1.5% 8 9 10 11 12 14 15 16 17 19 20 21 22 Kett 0.9353 -1.8372 0.9636 1.0% 9 10 11 12 13 14 15 16 18 19 20 21 22 Protimeter 0.8299 3.5707 0.9577 1.1% 7 8 10 11 12 13 14 16 17 18 19 20 22

47

River Red Gum

-3

-2

-1

0

1

2

3

0 5 10 15 20 25 30

MR

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C% Bollman

Carrell & CarrellDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 22: Comparative 95% CI for all the resistance type moisture meters when used on regrowth River Red Gum (E. camaldulensis) Table 15: Regrowth River Red Gum (E. camaldulensis) species corrections for the various resistance type moisture meters

Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.0000 -3.0000 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Bollmann 0.6032 2.1822 0.9268 1.6% 8 10 11 13 15 16 18 20 21 Carrel & Carrel 0.9247 -0.8068 0.9439 1.3% 7 8 10 11 12 13 14 15 16 17 18 19 20 21 Delmhorst 0.7922 0.1236 0.9328 1.4% 7 9 10 11 12 14 15 16 18 19 20 21 Deltron 0.9937 -1.7325 0.9351 1.4% 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Electrophysics 0.6836 2.6003 0.8220 2.7% 8 9 11 12 14 15 17 18 20 21 Kett 0.6934 -0.8225 0.9096 1.7% 10 11 13 14 16 17 18 20 21 Protimeter 0.6999 3.3452 0.9044 1.7% 8 10 11 12 14 15 17 18 20 21 22

48

Shining Gum

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30

MR

95%

Con

fiden

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terv

al fo

r Pr

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ted

Ove

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C% Bollman

Carrell & CarrellDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 23: Comparative 95% CI for all the resistance type moisture meters when used on plantation grown Shining Gum (E. nitens)

Table 16: Plantation Shining Gum (E. nitens) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.1249 -1.9320 7 8 9 10 11 11 12 13 14 15 16 17 18 19 19 20 21 22 23 Bollmann 0.8341 0.5417 0.9646 1.4% 8 9 10 11 13 14 15 16 17 19 20 21 22 Carrel & Carrel 1.1015 -1.0553 0.9745 1.0% 7 8 9 10 11 12 13 14 15 15 16 17 18 19 20 21 22 Delmhorst 0.9544 0.1254 0.9643 1.2% 7 8 9 10 11 12 13 15 16 17 18 19 20 21 22 Deltron 1.1738 -1.8001 0.9751 1.0% 7 8 9 10 11 12 13 13 14 15 16 17 18 19 19 20 21 22 Electrophysics 0.9477 0.3805 0.9256 2.0% 8 9 10 11 12 13 14 15 16 18 19 20 21 22 Kett 1.0064 -2.9364 0.9616 1.3% 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Protimeter 0.9196 2.4327 0.9758 1.0% 7 8 9 10 11 13 14 15 16 17 18 19 20 21 22

49

Spotted gum

-4

-3

-2

-1

0

1

2

3

4

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

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terv

al fo

r Pr

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ted

Ove

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C% Bollman

Carrel & CarrelDelmhorstDeltronElectrophysicsKett MT700Protimeter

Figure 24: Comparative 95% CI for all the resistance type moisture meters when used on regrowth Spotted Gum (C. maculata)

Table 17: Regrowth Spotted Gum (C. maculata) species corrections for the various resistance type moisture meters Meter Reading % 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Meter m c r2 MSP Corrected MC%

AS/NZS 1080.1 1.7738 -6.4896 7 8 8 9 9 10 10 11 12 12 13 13 14 14 15 15 16 17 17 Bollmann 1.4211 -4.5755 0.8674 2.8% 7 8 9 10 10 11 12 12 13 14 14 15 16 17 17 18 19 19 20 Carrel & Carrel 1.5086 -3.4243 0.9277 1.8% 8 8 9 10 10 11 12 12 13 14 14 15 16 16 17 18 18 Delmhorst 1.2454 -1.7268 0.9622 1.3% 7 8 9 9 10 11 12 13 13 14 15 16 17 17 18 19 20 21 Deltron 1.6705 -5.7701 0.9432 1.6% 7 8 8 9 9 10 11 11 12 12 13 14 14 15 15 16 17 17 18 Electrophysics 1.4957 -4.8398 0.8400 3.1% 7 8 9 9 10 11 11 12 13 13 14 15 15 16 17 17 18 19 19 Kett 1.2146 -5.1891 0.9302 1.8% 9 10 11 12 13 13 14 15 16 17 17 18 19 20 21 22 22 Protimeter 1.0273 1.9806 0.9507 1.5% 8 9 10 11 12 13 14 15 16 17 18 19 19 20 21

50

Dielectric meters Generating species corrections for dielectric type meters is more difficult than for resistance type meters due to the complicating effect of thickness on meter readings. Due to shrinkage during the drying and conditioning process the thickness of boards must decrease from one set of measurements to the next as the boards dry out at each set of EMC conditions. One way of avoiding this problem, especially with low density conifers, has been to use sample boards that are considerably thicker than the depth of field (>50 mm). Indeed a couple of dielectric meters included in this study are supplied without thickness corrections on the basis that they are used only on thick dimension timber. Given the low permeability of Australian eucalypts the time for equalizing at each set of EMC conditions with such thick material would have been very long. Instead, the decision was made to use material approximately 25 mm thick. This corresponds to a common rough sawn thickness that gets machined into high value appearance products such as 19 mm thick strip flooring. The thickness of boards used in this study also allowed a limited (due to the narrow range of board thicknesses within each set of species sample boards) evaluation of the effect of thickness on meter readings. In this case a multiple regression model was used that assumed that thickness has a linear effect on meter readings. It appears that most of the meters with built in corrections do not use a linear type relationship. By placing the meters with thickness corrections on a uniform piece of material and adjusting the thickness corrections it was possible to observe that in most instances the thickness corrections were likely to be either non-linear or linear in sections (Appendix B). In the majority of cases though, the corrections are close to linear with boards between 20-30 mm thick. Indeed the main deviation from linearity appears to occur with most meters for boards less than 10 mm thick. As the range of thickness included in the multiple regression for each species is narrow, the assumption of linearity is also fairly safe as long as the multiple regression corrections are not extrapolated too far beyond the range of board thicknesses used to derive it (i.e. for 20-30 mm thick). Table 18 to Table 37 show the partial regression coefficients calculated for each meter, with each species, for both the sawn and machined finishes. The meter readings and thickness settings used to calculate the shown species correction tables are only intended to provide an indication of the corrections derived. In most cases it would be preferable to use the multiple regression coefficients provided in the tables to calculate the corrections using a spreadsheet or calculator. Nevertheless, more complete correction tables are included in Appendix D. In all cases the thickness value used is approximately the mean value of all thickness measurements used to generate the multiple regression. The thickness values used are shown on each chart. The CI will widen when used with boards thicker or thinner than the thickness shown on the figures.

51

It is clear from Figure 26 to Figure 35 that the 95% CI for the dielectric meters are considerably greater than for the resistance type meters. The main reason for this poor level of accuracy is the effect of within species density variation on meter readings. Figure 25 shows plots of meter readings against the basic density of the Alpine Ash boards at 12% EMC. Part of the reason that the R2 values in Figure 25 (A & B) are so high is that the actual EMC of the boards is partly related to density. Hence, Figure 25 (C & D) plots the residuals (the unexplained variation in the meter readings after actual OD MC% and board thickness are taken into account using multiple regression) against BD. This clearly demonstrates that, for a set of measurements on boards at the same EMC conditions, the affect of density on meters is as great if not greater than the effect of OD MC% (which is what we are trying to predict). Clearly, this is the main reason why the 95% CI for the predicted OD MC% using the dielectric meters are about double those for the resistance moisture meters. If the density of the sample boards was known at the time of measuring then it could be taken into account. Unfortunately, this currently is not practical in a mill situation and common practice for all meters is to use an average species density value as a type of species correction. In the absence of such individual board density corrections, the expected accuracy of individual measurements with dielectric meters, when used on Australian eucalypt species, is poor.

52

Alpine Ash (Sawn Finish)

y = 0.0224x + 0.7032R2 = 0.7985

6

8

10

12

14

16

18

300.0 350.0 400.0 450.0 500.0 550.0 600.0 650.0 700.0

Basic Density(kg/m3)

Met

er R

eadi

ngs

(MC

%)

Alpine Ash (Sawn Finish)

y = 0.0161x - 8.4993R2 = 0.5083

-5

-4

-3

-2

-1

0

1

2

3

4

300.0 350.0 400.0 450.0 500.0 550.0 600.0 650.0 700.0

Basic Density(kg/m3)

Res

idua

ls

Alpine Ash (Machined Finish)

y = 0.0271x - 0.8981R2 = 0.798

6

8

10

12

14

16

18

300.0 350.0 400.0 450.0 500.0 550.0 600.0 650.0 700.0

Basic Density(kg/m3)

Met

er R

eadi

ngs

(MC

%)

Alpine Ash (Machined Finish)

y = 0.0163x - 8.6145R2 = 0.4449

-5

-4

-3

-2

-1

0

1

2

3

4

300.0 350.0 400.0 450.0 500.0 550.0 600.0 650.0 700.0

Basic Density(kg/m3)

Res

idua

ls

Figure 25: (A & B) Plot of meter readings against basic density for the Alpine Ash (E. delegatensis) for the sample boards at approximately 12% MC using the Wagner MM1 1100 moisture meter. (C & D) Plot of the residuals from multiple regression analysis on meter readings taken to exclude the effect of OD MC% and thickness on meter readings.

A

B

C

D

53

Alpine Ash - Sawn Finish(Thickness = 25 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Alpine Ash - Machined Finish( Thickness = 25 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 26: Comparative 95% CI for all the dielectric type moisture meters when used on regrowth Alpine Ash (E. delegatensis). The 95% CI shown are only for 25 mm thick boards.

54

Table 18: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the regrowth Alpine Ash (E. delegatensis) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(M

eter

R

eadi

ng)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

23 6 10 13 17 20 24 25 8 11 14 17 20 23 Electrophysics 0.5 1 2.3323 1.3049 -0.0627 -29.3377 0.857527 10 12 15 17 20 22 23 6 9 12 14 17 20 25 7 10 13 16 19 22 Kett 0.5 25 0.2083 0.4031 0.0214 -7.5297 0.862927 8 11 14 17 21 24 23 5 10 14 18 22 27 25 7 11 15 19 22 26 Merlin HMB Group

2 _ 2.1268 1.1145 -0.0465 -26.5951 0.861427 9 12 16 19 23 26 23 4 9 13 17 21 26 25 6 10 14 18 23 27 Merlin PM 1E 0.5 20 1.3316 0.8726 -0.0114 -22.1854 0.759427 8 12 16 20 24 28 23 3 8 12 17 22 27 25 4 9 14 18 23 28 Wagner 0.5 _ 1.2964 0.7157 -0.0048 -20.6120 0.852527 6 10 15 20 24 29

55

Table 19: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the regrowth Alpine Ash (E. delegatensis) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

23 7 9 12 14 17 20 25 8 10 13 15 18 20 Electrophysics 0.5 1 0.8984 0.6560 -0.0107 -12.3762 0.8575

27 9 11 14 16 19 21 23 6 8 10 12 15 17 25 7 9 11 14 16 19 Kett 0.5 25 0.0120 0.3091 0.0245 -4.9670 0.8348

27 7 10 13 15 18 21 23 6 9 13 17 20 24 25 7 11 14 18 21 25 Merlin HMB Group

2 _ 1.2403 0.7803 -0.0145 -17.7394 0.8558

27 8 12 15 19 22 25 23 4 7 10 13 16 19 25 4 8 11 14 18 21 Merlin PM 1E 0.5 20 -

0.3030 0.0496 0.0460 -2.0121 0.7730

27 5 9 12 16 20 24 23 4 7 11 15 19 23 25 5 9 13 17 21 25 Wagner 0.5 _ 0.3817 0.3533 0.0249 -10.2535 0.8459

27 6 10 14 18 22 27

56

Blackbutt- Sawn Finish(Thickness = 26 mm)

-8

-6

-4

-2

0

2

4

6

8

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Blackbutt - Machined Finish(Thickness = 26 mm)

-8

-6

-4

-2

0

2

4

6

8

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 27: Comparative 95% CI for all the dielectric type moisture meters when used on regrowth Blackbutt (E. pilularis). The 95% CI shown are only for 25 mm thick boards.

57

Table 20: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the regrowth Blackbutt (E. pilularis) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(M

eter

R

eadi

ng)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

24 7 10 13 17 20 23 26 7 10 13 16 18 21 Electrophysics 0.5 1 2.2203 0.6067 -0.0593 -12.3588 0.7333

28 8 10 12 15 17 19 24 6 9 11 13 15 17 26 6 8 10 12 15 17 Kett 0.5 25 0.5826 -

0.2576 -0.0015 9.3578 0.7092

28 5 8 10 12 14 16 24 5 9 13 17 21 25 26 6 9 13 16 20 23 Merlin HMB Group

2 _ 2.4928 0.5757 -0.0616 -14.6779 0.7587

28 6 9 12 15 18 21 24 8 11 14 17 20 23 26 10 12 14 17 19 21 Merlin PM 1E 0.5 20 3.0076 1.5149 -0.0933 -33.0856 0.1980

28 12 13 15 16 18 20 24 5 9 12 16 19 23 26 5 9 12 15 18 22 Wagner 0.5 _ 1.4407 0.1009 -0.0237 -2.3486 0.6185

28 5 8 11 14 18 21

58

Table 21: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the regrowth Blackbutt (E. pilularis) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

24 6 9 11 13 16 18 26 8 10 12 14 16 17 Electrophysics 0.5 1 2.2045 1.4087 -0.0677 -30.7968 0.7333

28 10 12 13 14 15 17 24 6 8 10 11 13 15 26 6 7 9 11 13 14 Kett 0.5 25 0.3923 -

0.2460 0.0019 9.3713 0.7369

28 5 7 9 10 12 14 24 4 8 11 15 19 22 26 6 9 12 15 18 21 Merlin HMB Group

2 _ 2.8350 1.3998 -0.0805 -34.8596 0.7590

28 8 10 12 15 17 19 24 8 10 12 14 16 18 26 10 12 13 15 16 18 Merlin PM 1E 0.5 20 2.0916 1.5223 -0.0657 -31.8523 0.1406

28 12 13 14 15 16 17 24 3 7 10 13 17 20 26 4 7 10 13 16 20 Wagner 0.5 _ 1.6614 0.5472 -0.0335 -15.2042 0.7274

28 4 7 10 13 16 19

59

Blue Gum - Sawn Finish(Thickness = 27 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Blue Gum - Machined Finish( Thickness = 27 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 28: Comparative 95% CI for all the dielectric type moisture meters when used on plantation grown Blue Gum (E. globulus). The 95% CI shown are only for 25 mm thick boards.

60

Table 22: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the plantation grown Blue Gum (E. globulus) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(M

eter

R

eadi

ng)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

25 9 12 15 17 20 23 27 10 13 15 17 20 22 Electrophysics 0.5 1 1.1394 0.4396 -0.0194 -5.4650 0.7947

29 11 13 15 18 20 22 25 12 14 16 18 20 22 27 12 14 16 18 20 22 Kett 0.5 25 0.4973 0.0076 0.0005 8.4807 0.8519

29 12 14 16 18 20 22 25 9 12 15 18 21 24 27 9 12 15 18 21 24 Merlin HMB Group

2 _ 0.3901 -0.0863 0.0135 6.7986 0.8482

29 9 12 15 18 22 25 25 8 12 16 19 23 27 27 8 12 16 19 23 27 Merlin PM 1E 0.5 20 0.4280 -

0.3049 0.0192 10.6214 0.8608

29 8 12 16 19 23 27 25 7 11 14 18 22 25 27 7 11 14 18 22 26 Wagner 0.5 _ 0.2864 -

0.3331 0.0242 10.2016 0.8669

29 6 10 14 18 22 26

61

Table 23: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the plantation grown Blue Gum (E. globulus) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

25 9 11 12 14 15 17 27 10 11 13 14 16 17 Electrophysics 0.5 1 0.1286 0.0948 0.0099 4.5842 0.7947

29 10 11 13 15 16 18 25 11 13 15 17 18 20 27 11 13 15 17 19 20 Kett 0.5 25 0.2549 -

0.0379 0.0074 9.5812 0.8581

29 11 13 15 17 19 21 25 9 11 13 15 18 20 27 9 11 13 16 18 21 Merlin HMB Group

2 _ 0.3935 0.0518 0.0074 3.7760 0.8432

29 9 11 14 16 19 21 25 6 9 12 15 17 20 27 6 9 12 15 18 21 Merlin PM 1E 0.5 20 0.5808 0.0539 0.0053 0.3979 0.8366

29 6 9 12 15 18 21 25 7 10 13 15 18 21 27 7 10 13 16 19 22 Wagner 0.5 _ 0.1032 -

0.2243 0.0245 8.1981 0.8389

29 7 10 13 16 20 23

62

Jarrah - Sawn Finish(Thickness = 25 mm)

-8

-6

-4

-2

0

2

4

6

8

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Jarrah - Machined Finish(Thickness = 25 mm)

-8

-6

-4

-2

0

2

4

6

8

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 29: Comparative 95% CI for all the dielectric type moisture meters when used on regrowth Jarrah (E. marginata). The 95% CI shown are only for 25 mm thick boards.

63

Table 24: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the regrowth Jarrah (E. marginata) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

21 6 9 12 15 18 21 25 7 10 13 15 18 21 Electrophysics 0.5 1 1.3042 0.5128 -0.0253 -9.4563 0.8126

27 8 11 13 16 18 21 21 7 9 11 12 14 16 25 5 7 10 12 14 16 Kett 0.5 25 0.0393 -

0.5159 0.0211 14.6359 0.7110

27 4 7 9 12 14 17 21 4 8 12 16 20 24 25 5 9 13 16 20 24 Merlin HMB Grou

p 2 _ 1.5350 0.4423 -0.0247 -11.3294 0.8252

27 6 9 13 16 20 23 21 4 8 12 17 21 25 25 4 8 12 16 20 24 Merlin PM 1E 0.5 20 1.2181 -

0.0302 -0.0075 -1.9081 0.6376

27 3 7 12 16 20 24 21 1 6 11 16 21 26 25 2 6 11 16 20 25 Wagner 0.5 _ 1.5299 0.1610 -0.0152 -9.1523 0.7387

27 2 6 11 15 20 24

64

Table 25: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the regrowth Jarrah (E. marginata) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

21 8 10 11 13 15 17 25 9 11 12 14 15 17 Electrophysics 0.5 1 0.6569 0.3368 -0.0102 -1.9169 0.8126

27 9 11 13 14 16 17 21 12 13 13 14 15 16 25 10 11 12 13 14 16 Kett 0.5 25 -

0.3572-

0.6848 0.0261 25.0696 0.3484

27 9 10 11 13 14 16 21 5 8 11 14 17 20 25 6 9 12 15 18 20 Merlin HMB Group

2 _ 0.9318 0.2022 -0.0085 -3.3425 0.7726

27 6 9 12 15 18 20 21 5 7 9 12 14 16 25 2 5 8 11 14 17 Merlin PM 1E 0.5 20 -

0.1637-

0.8438 0.0358 18.8400 0.6248

27 1 4 7 10 14 17 21 3 7 10 13 17 20 25 2 6 9 13 17 20 Wagner 0.5 _ 0.4367 -

0.4291 0.0190 7.2882 0.7515

27 1 5 9 13 17 20

65

Lemon Scented Gum - Sawn Finish(Thickness = 26 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Lemon Scented Gum - Machined Finish( Thickness = 26 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 30: Comparative 95% CI for all the dielectric type moisture meters when used on mature Lemon Scented Gum (C. citriodora). The 95% CI shown are only for 25 mm thick boards.

66

Table 26: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the mature Lemon Scented Gum (C. citriodora) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

24 8 10 11 13 15 16 26 9 10 12 14 15 17 Electrophysics 0.5 1 0.1677 0.2341 0.0066 -2.4762 0.6145

28 9 11 13 15 16 18 24 4 7 9 11 14 16 26 4 7 9 11 14 16 Kett 0.5 25 0.3834 -

0.0815 0.0033 -0.6790 0.7264

28 4 7 9 11 14 16 24 7 10 13 16 18 21 26 8 10 13 16 19 22 Merlin HMB Grou

p 2 _ 0.7166 0.3695 -0.0060 -10.4409 0.7544

28 8 11 14 16 19 22 24 6 9 12 15 19 22 26 7 10 13 16 19 22 Merlin PM 1E 0.5 20 0.8624 0.5662 -0.0093 -17.4328 0.6063

28 7 10 14 17 20 23 24 5 9 13 16 20 24 26 6 10 13 17 20 24 Wagner 0.5 _ 0.9442 0.4481 -0.0090 -16.2244 0.7355

28 7 10 14 17 21 24

67

Table 27: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the mature Lemon Scented Gum (C. citriodora) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

24 8 9 10 11 12 13 26 8 9 10 11 12 13 Electrophysics 0.5 1 0.3202 0.3616 -0.0043 -4.3172 0.6145

28 9 10 11 12 13 14 24 3 5 7 9 11 13 26 3 5 7 9 11 13 Kett 0.5 25 0.5148 0.0520 -0.0054 -3.5266 0.7817

28 3 5 7 9 11 12 24 7 9 11 14 16 18 26 7 9 12 14 16 18 Merlin HMB Group

2 _ 0.5786 0.2228 -0.0048 -5.5854 0.7785

28 7 10 12 14 16 18 24 5 7 9 12 14 16 26 5 7 9 12 14 16 Merlin PM 1E 0.5 20 0.4344 -

0.0278 0.0008 -1.3377 0.7535

28 5 7 9 12 14 16 24 5 8 11 14 18 21 26 5 8 11 14 18 21 Wagner 0.5 _ 0.6727 0.0561 -0.0011 -6.3570 0.8152

28 5 8 11 14 18 21

68

Messmate - Sawn Finish(Thickness = 25 mm)

-6

-4

-2

0

2

4

6

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Messmate - Machined Finish( Thickness = 25 mm)

-6

-4

-2

0

2

4

6

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 31: Comparative 95% CI for all the dielectric type moisture meters when used on regrowth Messmate (E. obliqua). The 95% CI shown are only for 25 mm thick boards.

69

Table 28: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the regrowth Messmate (E. obliqua) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

23 6 9 12 15 18 21 25 6 9 12 15 18 21 Electrophysics 0.5 1 0.9302 0.1638 -0.0076 -2.3515 0.8428

27 6 9 12 15 18 21 23 6 8 11 14 16 19 25 5 8 11 14 16 19 Kett 0.5 25 0.5053 -

0.2030 0.0073 6.2124 0.7663

27 5 8 11 13 16 19 23 4 8 12 16 20 24 25 5 9 13 16 20 24 Merlin HMB Grou

p 2 _ 1.6207 0.4921 -0.0274 -12.8212 0.8511

27 6 9 13 16 20 23 23 3 7 12 16 20 24 25 3 7 12 16 20 24 Merlin PM 1E 0.5 20 0.7758 -

0.2176 0.0121 1.8597 0.6838

27 3 7 11 16 20 25 23 2 7 11 16 21 26 25 2 7 11 16 21 26 Wagner 0.5 _ 1.2793 0.0731 -0.0032 -7.1069 0.8091

27 2 7 12 16 21 26

70

Table 29: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the regrowth Messmate (E. obliqua) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

23 7 9 11 13 15 17 25 8 10 12 13 15 17 Electrophysics 0.5 1 1.4222 0.9336 -0.0388 -18.0584 0.8428

27 9 11 12 14 15 17 23 4 6 9 11 14 16 25 4 6 9 11 14 16 Kett 0.5 25 0.4776 -

0.1468 0.0056 3.6388 0.8220

27 3 6 8 11 14 16 23 5 8 11 15 18 22 25 6 9 12 15 18 21 Merlin HMB Group

2 _ 1.5464 0.6005 -0.0306 -14.1703 0.8374

27 6 9 12 15 18 21 23 1 4 8 11 15 18 25 1 4 8 11 15 18 Merlin PM 1E 0.5 20 0.7926 -

0.0591 0.0028 -2.7936 0.7658

27 1 4 8 11 15 18 23 2 6 10 14 18 22 25 2 6 10 14 18 22 Wagner 0.5 _ 1.1372 0.1320 -0.0060 -7.1734 0.8208

27 2 6 10 14 18 22

71

Mountain Ash - Sawn Finish(Thickness = 28 mm)

-6

-4

-2

0

2

4

6

0 5 10 15 20 25 30

Meter Reading (MC%)d

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Mountain Ash - Machined Finish( Thickness = 28 mm)

-6

-4

-2

0

2

4

6

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 32: Comparative 95% CI for all the dielectric type moisture meters when used on regrowth Mountain Ash (E. regnans). The 95% CI shown are only for 25 mm thick boards.

72

Table 30: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the regrowth Mountain Ash (E. regnans) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

26 9 12 16 20 23 27 28 9 12 16 20 24 27 Electrophysics 0.5 1 0.8993 0.1184 0.0008 -0.0515 0.7989

30 9 13 16 20 24 28 26 8 11 13 16 19 22 28 9 11 14 17 20 23 Kett 0.5 25 -

0.1658 0.0381 0.0323 3.0325 0.7663

30 9 12 15 19 22 25 26 8 12 16 20 24 28 28 8 12 17 21 26 30 Merlin HMB Grou

p 2 _ -0.3606

-0.3684 0.0523 11.6885 0.8039

30 8 13 18 22 27 32 26 7 11 16 20 24 29 28 7 12 17 21 26 31 Merlin PM 1E 0.5 20 -

0.2844-

0.2939 0.0527 8.2373 0.7299

30 7 12 18 23 28 33 26 6 11 15 20 25 30 28 6 11 16 22 27 32 Wagner 0.5 _ 0.5143 0.0337 0.0280 -2.7702 0.7730

30 6 12 17 23 28 33

73

Table 31: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the regrowth Mountain Ash (E. regnans) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

26 8 11 13 16 19 22 28 8 11 14 17 20 23 Electrophysics 0.5 1 0.6010 0.2448 0.0050 -3.1052 0.7989

30 9 12 15 18 21 24 26 7 10 12 15 17 19 28 8 11 13 16 18 21 Kett 0.5 25 0.1916 0.3373 0.0158 -5.0889 0.7441

30 9 12 14 17 20 22 26 7 11 15 18 22 25 28 8 12 15 19 23 26 Merlin HMB Group

2 _ 0.7378 0.3358 0.0063 -6.8310 0.8120

30 9 13 16 20 24 27 26 5 9 12 15 19 22 28 6 9 13 17 21 24 Merlin PM 1E 0.5 20 -

0.1021 0.0404 0.0373 -1.2242 0.7583

30 6 10 14 18 22 26 26 5 9 13 18 22 26 28 6 10 15 19 23 28 Wagner 0.5 _ 0.2293 0.1079 0.0308 -3.7784 0.7717

30 6 11 16 20 25 29

74

River Red Gum - Sawn Finish(Thickness = 28 mm)

-8

-6

-4

-2

0

2

4

6

8

10 15 20 25 30 35 40

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

River Red Gum - Machined Finish( Thickness = 28 mm)

-4

-3

-2

-1

0

1

2

3

4

10 15 20 25 30 35 40

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 33: Comparative 95% CI for all the dielectric type moisture meters when used on regrowth River Red Gum (E. camaldulensis). The 95% CI shown are only for 25 mm thick boards.

75

Table 32: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the regrowth River Red Gum (E. camaldulensis) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

14 18 22 26 30 34

26 14 17 20 23 26 29 28 13 16 20 23 26 29 Electrophysics 0.5 1 0.2561 -

0.5366 0.0202 16.5503 0.7917

30 13 16 19 23 26 30 26 9 11 14 16 19 21 28 8 10 13 16 19 21 Kett 0.5 25 -

0.0356-

0.8872 0.0261 22.6908 0.8811

30 7 10 13 16 19 21 26 13 17 21 25 30 34 28 13 17 21 26 30 34 Merlin HMB Grou

p 2 _ 0.4761 -0.5073 0.0217 11.5613 0.8464

30 12 17 21 26 30 35 26 14 17 21 25 28 32 28 12 16 21 25 29 33 Merlin PM 1E 0.5 20 -

0.9777-

1.8638 0.0732 49.1546 0.5977

30 10 15 20 25 30 35 26 12 16 21 26 31 36 28 11 16 21 26 31 37 Wagner 0.5 _ -

0.1233-

1.1984 0.0510 25.8371 0.7612

30 10 15 21 26 32 38

76

Table 33: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the regrowth River Red Gum (E. camaldulensis) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

14 18 22 26 30 34

26 12 14 16 18 20 22 28 12 14 16 18 20 22 Electrophysics 0.5 1 0.7749 0.3346 -0.0096 -4.5029 0.7917

30 12 14 16 18 20 22 26 6 9 11 13 15 17 28 6 8 10 12 15 17 Kett 0.5 25 0.2184 -

0.5440 0.0124 12.9874 0.9460

30 5 7 10 12 14 17 26 11 15 18 22 25 29 28 11 15 18 22 25 29 Merlin HMB Group

2 _ 1.1108 0.2270 -0.0084 -7.3770 0.8849

30 11 15 18 22 25 29 26 6 10 13 17 20 24 28 6 9 13 16 20 24 Merlin PM 1E 0.5 20 0.2935 -

0.7218 0.0221 12.9191 0.8938

30 5 9 12 16 20 24 26 8 12 17 21 25 30 28 7 12 17 21 26 30 Wagner 0.5 _ 0.6637 -

0.4291 0.0166 3.7033 0.9446

30 7 12 16 21 26 30

77

Shining Gum - Sawn Finish(Thickness = 25 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Shining Gum - Machined face( Thickness = 25 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 5 10 15 20 25 30

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 34: Comparative 95% CI for all the dielectric type moisture meters when used on plantation grown Shining Gum (E. nitens). The 95% CI shown are only for 25 mm thick boards.

78

Table 34: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the plantation grown Shining Gum (E. nitens) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

23 9 13 16 19 22 25 25 10 13 16 18 21 24 Electrophysics 0.5 1 2.3692 0.8914 -0.0683 -15.8982 0.8729

27 11 13 16 18 20 22 23 10 14 19 23 27 32 25 10 14 18 21 25 28 Kett 0.5 25 3.2886 0.8345 -0.0960 -15.7273 0.8890

27 11 14 17 19 22 25 23 9 13 17 21 25 29 25 10 13 17 20 23 27 Merlin HMB Grou

p 2 _ 2.8579 1.0027 -0.0808 -20.2004 0.8717

27 11 14 16 19 22 24 23 7 12 18 23 28 33 25 9 13 17 21 25 29 Merlin PM 1E 0.5 20 4.6839 1.6606 -0.1470 -38.7472 0.8329

27 10 13 16 19 22 25 23 7 12 18 23 28 33 25 9 13 17 21 25 30 Wagner 0.5 _ 4.0301 1.3377 -0.1189 -31.2193 0.8857

27 10 13 16 20 23 26

79

Table 35: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the plantation grown Shining Gum (E. nitens) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

6 10 14 18 22 26

23 8 11 14 17 20 23 25 10 12 14 17 19 21 Electrophysics 0.5 1 3.0233 1.4212 -0.0995 -28.6734 0.8729

27 12 13 14 16 17 18 23 10 13 17 21 24 28 25 10 14 17 20 23 26 Kett 0.5 25 2.1945 0.7196 -0.0555 -12.4797 0.7600

27 11 14 17 19 22 25 23 8 12 16 20 24 28 25 10 13 16 19 22 26 Merlin HMB Group

2 _ 3.2498 1.2055 -0.0979 -25.3341 0.8735

27 11 13 16 18 21 23 23 7 11 15 20 24 29 25 8 12 15 19 22 26 Merlin PM 1E 0.5 20 3.6917 1.4315 -0.1118 -33.1165 0.7666

27 10 12 15 18 20 23 23 7 12 17 22 27 32 25 8 12 16 20 25 29 Wagner 0.5 _ 3.9660 1.4250 -0.1179 -33.4763 0.8757

27 10 13 16 19 22 25

80

Spotted Gum - Sawn Finish(Thickness = 26 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

5 10 15 20 25 30 35

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Spotted Gum - Machined Finish( Thickness = 26 mm)

-5

-4

-3

-2

-1

0

1

2

3

4

5

5 10 15 20 25 30 35

Meter Reading (MC%)

95%

Con

fiden

ce In

terv

al fo

r Pr

edic

ted

Ove

n D

ry M

C%

ElectrophysicsKettMerlin HMBMerlin PM1EWagner

Figure 35: Comparative 95% CI for all the dielectric type moisture meters when used on regrowth Spotted Gum (C. maculata). The 95% CI shown are only for 25 mm thick boards.

81

Table 36: Multiple regression coefficients, for the various dielectric type moisture meters when used on a sawn finish, for the regrowth Spotted Gum (C. maculata) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

10 14 18 22 26 30

24 10 12 13 15 16 18 26 10 12 13 15 16 18 Electrophysics 0.5 1 0.4499 0.0684 -0.0020 4.2575 0.7995

28 10 12 13 15 16 18 24 8 10 12 14 17 19 26 8 10 12 15 17 19 Kett 0.5 25 0.8686 0.4092 -0.0127 -7.8458 0.7397

28 9 11 13 15 17 19 24 9 11 14 16 18 20 26 9 11 13 16 18 20 Merlin HMB Grou

p 2 _ 0.4588 -0.1401 0.0041 6.8687 0.8648

28 9 11 13 16 18 20 24 8 11 14 17 20 23 26 9 12 14 17 20 23 Merlin PM 1E 0.5 20 1.0243 0.3165 -0.0121 -6.5673 0.7403

28 9 12 15 17 20 23 24 8 10 13 16 19 22 26 8 10 13 16 19 22 Wagner 0.5 _ 0.6832 -

0.0215 0.0008 1.1664 0.7908

28 8 10 13 16 19 22

82

Table 37: Multiple regression coefficients, for the various dielectric type moisture meters when used on a machined finish, for the regrowth Spotted Gum (C. maculata) species corrections. Example corrections are provided in the useful meter reading and thickness range for the multiple regression corrections.

Meter Readings (MC%)

Den

sity

se

tting

on

met

er

Thic

knes

s Se

tting

on

met

er

m1

(Met

er

Rea

ding

)

m2

(Thi

ckne

ss)

m3

(Inte

ract

ion)

C r2

Boa

rd

Thic

knes

s (m

m)

10 14 18 22 26 30

24 10 11 12 12 13 14 26 10 11 12 12 13 14 Electrophysics 0.5 1 0.1812 -

0.0882 0.0003 10.4153 0.7995

28 10 11 11 12 13 14 24 7 9 10 12 14 15 26 7 8 10 12 14 15 Kett 0.5 25 0.3450 -

0.1459 0.0035 6.0880 0.7661

28 6 8 10 12 14 15 24 9 10 12 14 15 17 26 8 10 12 14 15 17 Merlin HMB Group

2 _ 0.3764 -0.1348 0.0019 7.7216 0.8915

28 8 10 12 13 15 17 24 6 8 10 12 14 16 26 6 8 10 12 14 16 Merlin PM 1E 0.5 20 0.4398 -

0.1729 0.0030 4.8541 0.8541

28 5 7 9 12 14 16 24 6 9 11 14 16 19 26 6 9 11 14 16 19 Wagner 0.5 _ 0.4209 -

0.2624 0.0085 6.5000 0.8325

28 6 8 11 14 16 19

83

Hand pressure In generating the species corrections in this study no hand pressure was applied to the dielectric meters. This is in keeping with previously published species correction figures and is done to minimise the effect of variable hand pressure in adding to the variation in meter readings. However, most of the dielectric meter manufacturers recommend light, but firm hand pressure. To explore what effect hand pressure has on meter readings, 1.5 kg and 3.0 kg of hand force4 was applied to a sub-sample of 5 boards from 3 different species (to represent a range of basic densities) when the boards were equalized to approximately 12% MC. Table 38 shows the mean increase in the meter readings relative to the readings with no hand pressure applied. It can be seen that the increases in meter readings are greater with boards of higher density. The increases in meter readings were also greater on the sawn finish. This again was expected as hand pressure partly overcomes the air gap that forms between the meter and the rough board surface, which is the main reason meter readings are lower on the sawn finish surface compared with the machined finish surface where contact is more intimate. The difference between 1.5 kg and 3 kg of hand force is not great and supports the recommendation that if hand pressure is to be applied it only needs to be light. While the data is not shown here, changing the density setting on most of the meters had a similar effect to actual board density in that increasing the density setting generally increased the rise in meter readings.

Table 38: Average difference in meter readings with approximately 1.5 and 3 kg of force compared with no hand pressure on a sub-sample of 5 boards of 3 different species. (All meters set to a density setting of 0.5 except the Merlin HMB WS25 which was set to group 1)

Kett

MOCO 2 HM530

Merlin PM1-E

Merlin HMB WS25

Electro-physics CT808

Wagner MM1 1100

Basic Density Finish 1.5

(kg)3

(kg)1.5 (kg)

3 (kg)

1.5 (kg)

3 (kg)

1.5 (kg)

3 (kg)

1.5 (kg)

3 (kg)

Sawn 1.3 1.4 0.3 0.4 0.8 1.1 0.0 0.0 0.4 0.4Shining Gum 421 Machine

d 1 1 0.4 0.5 0.6 0.7 0.0 0.2 0.2 0.3

Sawn 1.5 1.7 0.6 0.9 0.3 0.4 0.6 0.6 0.3 0.5Alpine Ash 550 Machine

d 1.5 1.7 0.6 0.8 0.4 0.5 0.8 0.8 0.4 0.6

Sawn 2.2 2.3 1.5 2.0 0.6 0.7 0.4 0.4 0.9 1.1River Red Gum

689 Machined 1.5 1.6 0.8 1.0 0.5 0.6 1.0 1.4 0.6 0.8

4 Hand pressure was measured by setting up sample boards on a set of electronic scales, zeroing the scales and then the approximate amount of hand force was applied before a reading was taken.

84

CONCLUSIONS AND RECOMMENDATIONS This project has generated new species corrections for a range of moisture meters for 10 of the most commercially important eucalypt species in Australia. When used on timber with a MC between 7 and 22%, the 95% confidence intervals, or expected levels of accuracy, of the corrected meter readings are generally between ±1.5% and ±3.0% for resistance type moisture meters and between ±3.0% and ±6.0% for dielectric or capacitance type moisture meters. The main reason for the poor confidence intervals, with the dielectric type meters is the effect of within species density variation on meter readings. For most species and most meters, with boards equalized under the same or similar conditions, the effect of density on meter readings is as great if not greater than that of OD MC%. The corrections provided here for both types of meters are only for timber with a moisture content between 7 and 22% as that is the range used to generate the regression equations. The corrections can probably be reasonably safely extrapolated out to about 24-26%. However, the wide spread of data points shown in Appendix C (for the resistance type meters) with measurements taken on boards with MCs above 30%, clearly shows the danger and inaccuracy of extrapolating the corrections much beyond the 24-26% range. Perhaps the most important question that arises from the observations on the accuracy of moisture meters with eucalypt species is: how do these relate to the ability of industry to use electrical moisture meters to meet the requirements of AS 2796 (1999). For example, AS 2796 - Part 1 specifies a range of 9 to 14 % for the MC% of strip flooring or joinery or moulding products, while Part 3 specifies a range of 8 to 13% for the MC% of furniture components. These ranges are for the MC% determined using the OD method. The difficulties arise from the fact that the OD determined MC% of a board that has a meter reading of 13%, given 95% confidence interval of ±1.5%, could be as low as 12.0% or as high as 15%. For the worst of the dielectric meters the actual OD MC% could be as low 7% or as high as 19%. Depending on the confidence limits, a certain percentage of boards with an acceptable OD MC% would be rejected, and alternatively a certain percentage of boards with an unacceptable OD MC% would be accepted. The only way of ensuring that the OD MC% of boards was within the acceptable range, as measured with a moisture meter with say a given level of accuracy of ±1.5%, would be to only accept boards with readings in the range from 11-12. This conservative approach could result in a high percentage of boards with an acceptable OD MC% being rejected.

85

Table 39: Allowable range for 90% of all moisture content (MC1/3) readings around the target moisture content (MCt) (AS/NZS 4787: 2001)

Examples of ranges Target moisture contents (MCt), percent Quality

Class Allowable range

for 90% of all MC1/3 readings 8 10 12 14 18

A MCt - 0.1 MCt MCt + 0.2 MCt

7-10 9-12 11-14 13-17 16-22

B MCt - 0.2 MCt MCt + 0.3 MCt

6-10 8-13 10-16 11-18 14-23

C MCt - 0.3 MCt MCt + 0.4 MCt

6-11 7-14 8-17 10-20 13-25

D MCt - 0.4 MCt MCt + 0.5 MCt

5-12 6-15 7-18 8-21 11-27

E MCt - 0.5 MCt MCt + 0.6 MCt

4-13 5-16 6-19 7-22 9-29

Subsequent to the publishing of AS 2796 (1999), AS/NZS 4787 (2001) was published. This standard specifies allowable ranges for meter readings (MC1/3)5 as shown in Table 39. This approach of setting 90% allowable ranges for meter readings appears far more practical than the MC% limits specified in AS 2796. To illustrate this, Figure 36 shows a theoretical (spreadsheet generated) distribution of the OD MC% of 1000 boards based on a Weibull distribution. The drying quality in this instance could be considered as reasonably good or high, but should be achievable by industry. The mean or intended MC in this case was 10% and it can be seen that most of the MC% are between 9 and 12%. Figure 37 then shows a random distribution of meter readings on the OD MC% shown in Figure 36. The meter readings were randomly generated based on a normal distribution such that 95% of the readings were within ±1.5% MC of the OD MC%. In this case exactly 100 boards (10%) of boards had readings outside the range of 9-12%, which is exactly borderline for Class A. Figure 38 shows a random distribution of meter readings, such that 95% of the readings are within ±3.0% MC of the OD MC%. In this case, for the same actual drying quality, the decrease in meter accuracy means the boards could only be classed as Class C. The discussion above suggests the approach taken in AS/NZS 4787 is much more practical than that in AS 2796. However, it does suggest that Drying Class A is not achievable with some species depending on the levels of accuracy of the meter being used. Drying Class C is probably the highest class achievable if the mean MC% of the cross section is assessed using a dielectric type meter.

5 MC1/3 -the moisture content measured measured by a resistance moisture meter, with insulated pins at

one third of the thickness of the piece.

86

Care needs to be taken in applying the thickness corrections generated for the dielectric type moisture meters. These thickness corrections are based on a narrow range of thicknesses (generally in the range of 23-27 mm) and should not be extrapolated more than 2-3 mm beyond the range of thicknesses used to generate them. If the inbuilt meter thickness corrections are largely mathematical like the corrections generated here, rather than the meter actually altering the field depth or other field characteristics, it may be possible for meter manufacturers to relate their thickness corrections to the species corrections generated here based on the default thickness setting the meter was set on.

0

20

40

60

80

100

120

9 9.2 9.4 9.6 9.8 10 10.2

10.4

10.6

10.8 11 11

.211

.411

.611

.8 12 12.2

12.4

Oven Dry MC%

Freq

uenc

y

Weibull distribution(α=2, β=0.98)Total of 1000 boardsMean MC = 9.9%

Figure 36: An attempt at generating a ‘typical’ distribution of the OD MC% computer of 1000 boards based, the distribution was generated using a Weibull distribution.

87

1 1 116

79

157

184

220

164

111

47

172

0

50

100

150

200

250

6 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5

Meter Reading (%)

Freq

uenc

y

Figure 37: Distribution of meter readings rounded off to the nearest 0.5% in accordance with AS/NZS 1080.1 (1997). The meter readings were normally distributed around each OD MCs shown in Figure 36 so that 95% of the meter readings are within ±1.5% of the actual OD MC%.

1 1 4 312

29

47

66

9499

117

90

144

87 83

54

32

199 5 2 2

0

20

40

60

80

100

120

140

160

3.55

5.56

6.57

7.58

8.59

9.5 10 10.5 11 11

.5 12 12.5 13 13

.5 14 14.5 15

Meter Reading (%)

Freq

uenc

y

Figure 38: Distribution of meter readings rounded off to the nearest 0.5% in accordance with AS/NZS 1080.1 (1997). The meter readings were normally distributed around each OD moisture contents shown in Figure 36 so that 95% of the meter readings are within ±3.0% of the actual OD MC%.

88

ACKNOWLEDGMENTS Funding for this project was provided by both the Forest and Wood Products Research and Development Corporation and CSIRO FFP. The assistance of all the sawmills (shown in Table 4) in collecting and supplying the sample boards, and the companies (shown in Table 6) that loaned moisture meters for the duration of the project, is gratefully acknowledged. At various stages a range of CSIRO staff helped undertake measurements for this project and the assistance of the following people is gratefully acknowledged: Andrew Morrow, Mary Reilly, Jenny Carr, Richard Northway, Jugo Ilic, Sam Zoobi, Allyson Pereira and Stewart Terrill. I would also like to thank Richard Northway, Jugo Ilic and Silvia Pongracic for their helpful discussions on this project and the comments they provided on the manuscript.

REFERENCES AS/NZS 1080.1. (1997) Timber-Methods of Test. Method 1: Moisture Content AS 2796 (1999) Timber-Hardwood-Sawn and milled products AS/NZS 4787 (2001) Timber-Assessment of drying quality CSIRO (1974) Testing Timber for Moisture Content. CSIRO Division of Building

Research Special Report Edwards N.C. (1974) Procedure for the determination of species correction data

for electrical resistance type moisture meters. CSIRO Div. For.Prod. (unpublished divisional report) 10 pp

Gills C.M., Stephens W.C., and Peralta P.N. (2000) Moisture Meter Correction Factors for Four Brazilian Wood Species. Forest Prod. J. 51(4): 83-86

James W.L. (1988) Electrical Moisture meters for Wood. USDA Forest Products Laboratory, General Technical Report FPL-GTR-6. 17 pp

James W.L. (1994) Fundamentals of hand held moisture meters: An outline. In ASTM Hand-Held Moisture Meter Workshop May 5, 1993 Madison, Wisconsin. Compiled by Warren S.R. pp 13-16.

Jamroz W. (1994) Accuracy of moisture content measurements. In ASTM Hand-Held Moisture Meter Workshop May 5, 1993 Madison, Wisconsin. Compiled by Warren S.R. pp 37-43.

Keylwerth R. and Noack D. (1956) Uber den einflus hohrer temperature auf die electrische holzfeuchtigkeitsmessung nach dem widerstandsprinzip. [On the influence of higher temperatures on the electric measurement of wood moisture content using the resistance principle.] Holz als Roh- und Werkstoff (14)5: 162.

Mackay J.F.G. (1976) Effect of Moisture Gradients on the accuracy of power-loss moisture meters. Forest Products Journal (26) 3: 49-52

Milota M.R. (1994) Specific gravity as a predictor of species correction factors for a capacitance-type moisture meter. Forest Prod. J. 44(3):63-68

Milota M.R. (1996) Calibration of moisture meters for western hardwood species. Forest Prod. J. 46(1):39-42

89

Milota M.R. and Gupta R. (1996) Moisture meter correction factors for Dahurian Larch from the Russian far east. Forest Prod. J. 46(6):91-93

Quarles S.L. (1991) Sensitivity of handheld dielectric moisture meters to a wet core.

Quarles S.L. and Breiner T.A. (1989) Effect of density on in-line and handheld moisture meters. Forest Prod. J. 39(5):51-54.

Shupe T.F., Wu, Q. and Hartley I.D. (2002) Calibration of moisture meters for southern hardwoods.For. Prod. J. 52 (7/8):59-62.

Simpson W.T. (1994) Resistance Moisture Meter Correction Factors for Four Tropical Wood Species. USDA Forest Products Laboratory Research Note FPL-RN-0260

Skaar C. (1988) Wood-water relations. Springer series in wood science. Springer-Verlag, Germany.

USDA FPL (1974) Wood Handbook: Wood as an engineering material. Agricultural Handbook No. 72

Zar J.H. (1984) Biostatistical analysis: second edition. Prentice-Hall.