Beyond the NIR Tradition

04/12/2018

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Beyond the NIR Tradition

What are the benefits of NIR?

• Ease of use

• Fast

• Non-contact

• High precision

• Non-destructive

• Multi-component analysis

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Commercialization of NIR

NIR spectrometers

Full range

(Scanning)

Dispersive

Foss Unity

FT

Bruker Buchi Thermo

Limited range

Diode Array

Perten

NIR Systems

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UV NIR IR

800 nm 2500 nm

What is NIR light?

Good Vibrations

Symmetric stretch

Asymmetric stretch

Rocking

WaggingTwisting

Scissoring

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400050006000700080009000

Wavenumber cm-1

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Ab

sorb

ance

Un

its

C-H

O-H

N-H• N-H, C-H, O-H and C=O vibrations (protein, fat, and moisture)

• Contain information about physical and chemical properties

• NIR spectra are fingerprints

NIR spectraWhat is NIR light?

Cost of QC

USD 58,000

USD 7,100

USD 39,000

USD 9,200

USD 50,900

USD 29,800

USD 0

USD 20,000

USD 40,000

USD 60,000

USD 80,000

USD 100,000

USD 120,000

Lab NIR

100,000 tonnes/year feed mill

Savings on AdditionalAnalysis**

Savings on Proximate*

Cost of AdditionalAnalysis** (300samples)

Cost of Proximate*(1000 samples)

*Moisture, fat, protein, fibre & ash.**Starch, sugar, structural carbohydrates, amino acids, minerals.

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Analyte Analytical Variation % Sample Result +/- Uncertainty

Moisture 12 11.55 1.39

Protein (20/Protein) +2 46 1.12

Fat 10 4.5 0.45

Crude Fibre (30/Fibre)+6 4.94 0.60

Ash (45/ash)+3 7.95 0.69

Total sugars 12 5 0.60

Calcium 10 1.5 0.15

Phosphorus (3/Phos)+8 0.66 0.08

Salt (7/Salt)+5 0.25 0.08

Vitamin A 30 400 120.00

Association of American Control Officials 2011, Official publication 2011, page 298-299

Analyte Sample Result +/- Uncertainty Min Max

Moisture 11.55 1.39 10.16 12.94

Protein 46 1.12 44.88 47.12

Fat 4.5 0.45 4.05 4.95

Crude Fibre 4.94 0.60 4.34 5.54

Ash 7.95 0.69 7.26 8.64

Total sugars 5 0.60 4.4 5.6

Calcium 1.5 0.15 1.35 1.65

Phosphorus 0.66 0.08 0.58 0.74

Salt 0.25 0.08 0.17 0.33

Vitamin A 400 120.00 280 520

Analytical Variation

NIR Equation �� = �(�� , �� , … , �� )

ChemometricianReference lab

(wet chemistry)

Hundreds of samples

Training NIR

spectra

Labresults

What is calibration?

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NIR equation

moisture (%) = 10.4544.x1 + 4.8248.x2 + 10.2398.x3 + 3.4289.x4 + 10.6572.x5 + 4.9966.x6 + 13.8412.x7 + 7.2681.x8 + -18.4113.x9 + -6.9992.x10 + 0.07190.x11 + 11.4301.x12 + 19.7301.x13 + 18.1935.x14 + 6.3937.x15 + 10.1244.x16 +2.6978.x17 + -1.5525.x18 + -7.4878.x19 + 0.0020.x20 + -5.9071.x21 + -11.6062.x22 + -5.5122.x23 + -15.8103.x24 + -19.1395.x25 + -45.2948.x26 + -58.9312.x27 + -67.1674.x28 + -34.4751.x29 + -42.2903.x30 + -8.6744.x31 + 23.3041.x32 +51.8231.x33 + 17.4475.x34 + -8.0626.x35 + 14.4537.x36 + -24.5411.x37 + -39.456.x38 + 16.8252.x39 + 7.7889.x40 + -6.5946.x41 + -5.7126.x42 + -18.7074.x43 + -15.8717.x44 + -15.8436.x45 + -7.7624.x46 + 2.629.x47 + 0.3723.x48 + -20.1246.x49 + -7.6553.x50 + 6.0799.x51 + 53.6871.x52 + 38.564.x53 + -7.682.x54 + -40.8992.x55 + -24.716.x56 + -13.0406.x57 + 9.3819.x58 + 8.0529.x59 + -20.4184.x60 + -24.006.x61 + 23.3997.x62 + 3.6117.x63 + -4.6881.x64 + -8.3268.x65 + 0.3421.x66 + -29.2271.x67 + -46.4831.x68 + -27.7013.x69 + -13.9413.x70 + -14.7596.x71 + 0.0566.x72 +9.9726.x73 + 0.4449.x74 + 1.4599.x75 + 0.25.x76 + -9.324.x77 + 1.8878.x78 + 5.7252.x79 + -10.199.x80 + -19.47.x81 + -3.3375.x82 + 4.7035.x83 + -2.1816.x84 + -26.5314.x85 + 19.6397.x86 + -24.048.x87 + -33.528.x88 + -41.3089.x89 + -24.9269.x90 + 56.2965.x91 + 18.5276.x92 + 57.8791.x93 + -39.0386.x94 + 25.6654.x95 + -28.3871.x96 + -12.786.x97 + -17.413.x98 + -5.7172.x99 + 5.9277.x100 + 4.071.x101 + 4.2445.x102 + 0.8553.x103 + -6.6182.x104 + -4.5078.x105 + -9.1746.x106 + -0.0763.x107 + 0.0762.x108 + 3.1419.x109 + -1.7808.x110 + -0.9396.x111 + 1.0645.x112 + -10.4238.x113 +7.4247.x114 + 11.231.x115 + 10.8354.x116 + 19.4178.x117 + 3.8102.x118 + -1.891.x119 + -23.9129.x120 + -8.0344.x121+ 2.2564.x122 + 6.2177.x123 + 5.0084.x124 + 14.234.x125 + -12.9869.x126 + -12.4583.x127 + -7.9584.x128 + -17.236.x129 + -8.8097.x130 + 12.3384.x131 + 10.4873.x132 + -3.8109.x133 + 22.7724.x134 + 8.7357.x135 + 1.9828.x136+ 6.5352.x137 + 34.8116.x138 + 41.254.x139 + 11.1641.x140 + 15.5144.x141 + 6.1496.x142 + 4.3405.x143 + 0.6304.x144+ -3.7888.x145 + -0.1487.x146 + 4.0313.x147 + 6.7094.x148 + -16.5636.x149 + -1.0031.x150 + -5.637.x151 + 9.6862.x152+ -0.8292.x153 + 27.0892.x154 + 0.7705.x155 + -15.0711.x156 + -45.161.x157 + -19.915.x158 + -28.6316.x159 + -8.1241.x160 + -3.1792.x161 + -21.3205.x162 + 18.0101.x163 + 5.6946.x164 + 7.6277.x165 + -6.6415.x166 + -5.9593.x167+ -4.4604.x168 + -0.7773.x169 + -5.1057.x170 + -9.6945.x171 + 6.8875.x172 + 9.3544.x173 + 27.4956.x174 …

What is calibration?

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Our database

4 millionspectra + ref values

What is Ingot Lab?

43%

6%4%

7%

17%16%

2%2%

1%2%

Feed and Ingredients Flour and MillingWet Forage Dried ForagePet Food Aqua FeedAnimal Protein Plant BreederMolasses Bespoke Projects

Ingot Hierarchy Levels for Feed & Feed Ingredients

Moisture Fat EE Fat AHProtein FibreAsh

Starch SugarNCGDNDFADF

What is Ingot Lab?

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Ingot vs Customer samplesR2 0.969, SEP 0.488

What is Ingot Lab?

Customer Calibration vs Unknown SamplesR2 0.757, SEP 1.910

What is Ingot Lab?

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CASE STUDY

• Integrated Poultry Business

• Customer had no on-site QC all testing was retrospective via a 3rd party wet chemistry laboratory

• Customer used ABV calibrations on a rented NIR

• Sampled every incoming delivery & outgoing load at the weighbridge

Case Study

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Raw Materials - Soya• All samples tested and

saved according to supplier. Average protein result for each supplier calculated

• By putting suppliers 2 and 3 in one silo, supplier 5 in the other and supplier 1 & 4 in either they aimed to make a more consistent finished product.

45.6

45.8

46

46.2

46.4

46.6

46.8

47

47.2

47.4

47.6

Supplier 1 Supplier 2 Supplier 3 Supplier 4 Supplier 5

average protein

average

1 bin average

Case Study

Raw Materials - Wheat• Similar idea with wheat:

– The wheat is split into a ‘good’ and a ‘poor’ silo. By looking at the results since they started and finding the average they find that a cut-off at 10% protein should give a 50/50 split between good and poor.

• Each wheat sample tested before being tipped;-– 10% or above goes in the good bin– below 10% goes in the poor bin

Case Study

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Is it working?

• By splitting the wheat and soya they hoped to get a more consistent finished product.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

sc gp fp wd

stan

da

rd d

evi

atio

n

Protein standard deviation

pre splitting

post splitting

sc – Starter, gp – Grower, fp - Finisher, wd - Withdrawal

Case Study

What does this mean?

Case Study

• Potential saving from being able to formulate to a lower protein level, relaxing the ‘safety margin’

• Estimates are that 1 % protein difference per tonne in broiler feed is worth approximately £7.50

Starter Grower Finisher Withdrawl

Protein Protein Protein Protein

% Protein in feed 24% 22% 20% 19%

Pre-split standard deviation 1.171 0.960 0.820 1.008

Post-Split standard deviation 1.022 0.805 0.705 0.606

Reduction in Safety Margin 0.15 0.15 0.12 0.40

Cost of Protein per tonne £7.50 £7.50 £7.50 £7.50

Production per year 23400 63700 15600 87750

Savings £26,325 £71,663 £14,040 £263,250

Total Saving per year £375,278

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Feed Quality Service

• No NIR machine?

We can scan samples on your behalf and upload spectra to

your account

www.feedqualityservice.com


CENTRALISEDCALIBRATION

DATABASE

NIR Machine Results displayed immediatelyon your computer

FEEDING VALUE

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Ground sample

FQS CALIBRATIONS ARE BUILT ON GROUND SAMPLES ENABLING MORE ACCURATE ANALYSIS

Ground sample

• The NIR scans the whole cell so for unground samples this includes the gaps between grains

• The NIR has a maximum penetration of 2mm so may not be able to analyse the whole sample if the sample is unground

• Homogeneity improves when samples are ground, which translates to a higher repeatability of the results, therefore a more reliable NIR prediction

Unground sample


Moisture Protein Starch

Mean 10.6 14.5 46.2

WholeSD 0.36 0.41 1.58

CV % 3.4% 2.8% 3.4%

Coffee GrinderSD 0.1 0.09 0.78

CV % 1.0% 0.7% 1.7%

Laboratory MillSD 0.05 0.06 0.24

CV % 0.5% 0.4% 0.5%

ENERGYPROXIMATES

PHYTATE-P NSPsREACTIVE

LYSINESID AAs

PROTEIN SOLUBILITY

VITREOUSNESS

Urease

Corn

Sorghum

Wheat

Barley

Soybean meal

Canola meal

Other rawmaterials

Feeds

QUALITY MEASURES ACROSS A WIDE RANGE OF MATERIALS

• The Feed Quality Service covers a broad spectrum of raw materials and feeds, to help you monitor variability and control quality


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REACTIVE LYSINEFeed Quality Service

KOH solubility

• Detecting overcooked SBM by analysing N solubility in 0.2% KOH solution.

– Raw SBM 100% soluble in 0.2% KOH

– Recommended optimal for animal performance : 78-84%

• Overcooking

– Decrease total concentration of Lys, Cyc, Arg

– Decrease true ileal digestibility of Lys, Cys, His, Asp, (lesser extent: Thr, Ser, Ala, Leu).


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Is KOH solubility sensitive enough?

Parson et al., 1991. J Anim Sci 69, 2918-2924.

Recommended range: 78-84%


Is KOH solubility sensitive enough?

Parson et al., 1991. J Anim Sci 69, 2918-2924.


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Sensitivity of KOH solubility and UA for performance of broilers

Batal et al., 2000. Poult Sci 79, 1592-1596.


Sensitivity of KOH solubility and UA for performance of broilers

Batal et al., 2000. Poult Sci 79, 1592-1596.


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Rutherfurd and Morghan, 2007

Reactive lysine is the portion of lysine which is chemically intact following heat treatment (i.e. has not undergone the Maillard reaction) and thus can be metabolised by the animalTo identify the lysine that can be both digested and metabolised by the animal, standard ileal digestible (SID) reactive lysine must be determined

MAILLARD REACTIONFeed Quality Service

22

22.5

23

23.5

24

24.5

25

25.5

26

26.5

27

1

10

19

28

37

46

55

64

73

82

91

100

109

118

127

136

145

154

163

172

181

190

Lysin

e (

g/K

g)

Total and Reactive Lysine Content of Soybean Meal

Total Lysine

Reactive Lysine


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NIR calibration for SBM reactive lysineFeed Quality Service

PHYTATEFeed Quality Service

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0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

1.600

1.800

2.000

Mung b

ean

Casa

vaSoyb

ean h

ulls

Wheat

Glu

ten

Ric

eFre

e fat

Corn

Germ

Bis

cuit M

eal

Gre

en M

ung b

ean

Bake

ry M

eal

Corn

Ric

e w

ith h

ulls

Sorg

hum

Pota

to p

rote

in

Barley

Pea

Mill

et

Wheat

DD

GS W

heat

Oats

Ach

iote

Lupin

DD

GS c

orn

Canola

Soyb

ean

Sem

i defa

tted S

oyb

ean

Copra

meal

Soyb

ean m

eal

Corn

glu

ten m

eal

Palm

kern

el m

eal

Peanut

Bio

pro

Corn

Glu

ten F

eed

Ferm

ente

d S

oya

Soy

pro

tein

Wheatf

eed

Corn

Germ

Hom

iny

Soyc

om

ilCam

elin

a m

eal

Cott

onse

ed m

eal

Sesa

me m

eal

Canola

Meal

Wheat

bra

n

Sunflow

er

meal

Wheat

mid

ds

Ric

e B

ran

Defa

tted r

ice b

ran

Ph

yti

c P

, %

Phytate is present in all feed ingredients

Source: AB Vista


0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

0.1 0.120.140.160.18 0.2 0.220.240.260.28 0.3 0.320.340.360.38 0.4 0.420.440.460.48 0.5 0.520.54

Sa

mp

les (

%)

Corn Soybean Meal

Phytate is present in all feed ingredients

Source: AB Vista


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Global variation IN phytate-P content of BROILER feeds

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Broiler Starter Broiler Grower Broiler FinisherP

hyta

te P

(a

s i

s,

%)

Phytate P content of broiler complete feed


Phytate levels vary in diets, so understanding levels in ingredients or complete feeds allows for optimum phytaseuse, to deliver reliable cost savings

AB Vista Feed Quality Service provides you with the phytate-P level in raw materials and finished feeds. This allows you to use the optimal amount of phytase, depending on the phytate-P level present

SHED LIGHT ON YOUR PHYTATE CONTENT USING NIR

* Based on average from AB Vista global database** Amino acid recommendations vary based on FTU/kg fed


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PHYTATE SERVICE COVERAGE General

Feed Raw Materials

Poultry Swine Aqua Starch / Cereal Protein Bran DDGS

Breeder Starter Piglet Creep Aqua Feed Barley Canola Rice Bran DDGS Corn

Breeder grower Piglet Nursery Oats Corn Gluten Meal Wheat Bran / Feed

DDGS Wheat

Breeder Production

Piglet Starter Corn Cotton Meal

Breeder Rooster Pig Grower Sorghum Soya Bean –Expeller

Broiler Starter Pig Finisher Wheat Soya Bean – Full Fat

Broiler Grower Gilt Developer Rice Soya Bean – Meal

Broiler Finisher Gestation Peas Sunflower Meal

Broiler Withdrawal

Lactation Biscuit Meal

Layer Starter Boar

Layer Grower Other Feed

Layer Production

Other Feed

Making Light Work.Alejandro Criado

[email protected]

Documents

Beyond the NIR Tradition