Comparing the Sensitivity of Spot Test Method and a NovelComputer Vision System for Iron Detectingin Fortificated Flours

Iraj Khodadadi & Seid Mehdi Seyedain Ardebili & Orang Eyvazzadeh &

Kaveh Zargari & Mohammadreza Moradi

Received: 14 August 2013 /Accepted: 30 September 2013# Springer Science+Business Media New York 2013

Abstract Controlling of added iron to fortified flours is veryimportant, and the common method is spot test inaccuratemethod. In this study, we invented a new method based on acomputer vision system. We also compared the accuracy ofthis method and the spot test with atomic absorption spectros-copy. In new method, ferrous sulfate particles in the sampleswere oxidized, and some red spots were formed on the surfaceof samples. The captured images from samples were changedto binary images and analyzed using the Clemex Vision 3.5software. After processing of image, the number of coloredspots and the area of spots were determined. The calibrationcurves were drawn, and in order to compare the sensitivity ofthe new method with spot test, 33 samples were selectedrandomly, and the amounts of added iron were detected usingnew method, spot test, and atomic absorption. We used t testand linear regression tests with a confidence interval of 95 %to compare the results. Results showed that there was a higher

correlation (R2=0.988, p <0.001) between new method andatomic absorption method in comparison with spot test (R2=0.501, p <0.001). Therefore, spot test and atomic absorptioncan be replaced by an accurate but inexpensive method.

Keywords Flour . Iron . Fortified flour . Computer vision .

Spot test

Introduction

Food fortification is obligatory in many countries. For example,flour has been fortified in Iran with iron since 2001, and thisiron is added to flour in a form of powder named premix.Premix is a mixture added to flour in the final stage of produc-tion line and contains 42 % iron sulfate, 0.75 % folic acid, and57.25 % corn starch as a filler (Sheykhol Eslami and Abdollahi2004). The reference method of iron detection in fortified flouris atomic absorption spectroscopy. This method is very expen-sive and time consuming. Moreover, the procedure needs to becarried out twice to determine the amount of added iron bysubtracting the natural wheat iron from iron in fortified flour.On the other hand, the common method in flour mills is spottest which is very inaccurate because it is strongly affected bythe laboratory and personal errors. Therefore, introducing anovel method which is chip and accurate is very necessary. Ifthe novel method is accurate compared with spot test, this willbecome a common method for production and administration.Computer vision is a relatively young technique originatedsince 1960s (Abdullah et al. 2004; Tillet 1990; Timmermans1998). The usage of digital image processing with computer infood quality control tests has been developed in the past fewdecades (Abdullah et al. 2004). The food industry ranks amongthe top 10 industries using computer vision technology. Theusage of automated assessment is now increasing rather than

I. KhodadadiDepartment of Biochemistry and Nutrition, Hamadan University ofMedical Sciences, Hamadan, Islamic Republic of Iran

S. M. S. ArdebiliDepartment of Food Science, Science and Research Branch, IslamicAzad University, Tehran, Iran

O. EyvazzadehDepartment of Food Technology, Faculty of Agriculture,Varamin – Pishva Campus, Islamic Azad University, Varamin, Iran

K. ZargariDepartment of Agronomy and Plant Breeding, Varamin-PishvaBranch, Islamic Azad University, Varamin, Iran

M. Moradi (*)Department of Food and Drug, Hamadan University of MedicalSciences, Hamadan, Islamic Republic of Irane-mail: moradiava@gmail.com

Food Anal. MethodsDOI 10.1007/s12161-013-9735-0

sensory evaluation as it is very accurate, consistent, and eco-nomic (Baxes 1994). During the last decade, considerableresearch effort has been directed to develop techniques for foodquality evaluation (Chen et al. 2007; Sun 2008). Computervision provides a mechanism in which human thinking processis simulated artificially and can help make complicated judg-ments accurately, quickly, and consistently over a long period(Goyache et al. 2001; Timmermans 1998; Sun 2000).Computer vision is based on image processing and analysiswith numerous algorithms andmethods available to achieve therequired classification and measurements (Horwitz 2006). Thisgives us the possibility to evaluate different parts of the land-scape including color, size, shape, and texture very intelligentlyand close to the threshold limit of human vision, but with a largescale and without fatigue (Krutz et al. 2000). This method isbased on a nonexperimental vision evaluation with no limita-tion for the type of food products (Matas and MaK 2005). Ifquality evaluation is achieved automatically, speed and efficien-cy of production can be improved in addition to evaluationaccuracy increment, which also leads to cost reduction (Mohd

Jusoh et al. 2009). This technique has also been proven to be anondestructive quality evaluation on food products (Simal et al.2003). As a rapid, economic, consistent, and even more accu-rate and objective inspection tool, computer vision systemshave been used increasingly in the food industry for qualityevaluation purposes (Mohd Jusoh et al. 2009). Computer visionhas long been recognized as a potential technique for theguidance or control of agricultural and food processes (Sunand Brosnan 2003; Sun 2004). Because of the importance ofiron fortification plane and supplies needed for vulnerablegroups, especially women and girls, ensuring the accuracy ofthis program is very important in manufacturing.

The high cost of providing and maintaining the atomicabsorption method and low accuracy spot test method is aproblem we face in manufacturing. Therefore, in this study,we introduce a new method based on computer vision systemwith a special focus on the sensitivity of this method comparedwith spot test. The correlations of obtained data from bothmethods were determined. Although the first stages of newmethod contains appearance of red spots similar to spot test,in this study, we introduce two quantitative variables includingthe number of colored spots and the area of color spots forcomputer analysis.

Material and Methods

Concentrated hydrochloric acid, hydrogen peroxide, and potas-sium thiocyanate powder were obtained from Merck (MerckCo., Darmstadt Germany), and premix powder was purchasedfrom Qazvin_Iran Food Industry Company. Also, image anal-ysis software (Clemex Image Analysis Software) was pur-chased from Meyer (Meyer Instruments, Inc., Houston,USA), and white flour was obtained from Sina Flour Factory(Hamadan, Iran).

Designed Image Acquisition and Analyzer Units

Image Acquisition Unit

At first, there was a designed aluminum dark chamber with30×40×40 cm dimensions on which a digital camera wasplaced (Canon Model X200 12.1 megapixels zoom) in roof

Table 1 Control sample preparation

Sample number

1 2 3 4 5 6 7 8 9 10 11

Premix (g) 0 0.003 0.006 0.01 0.013 0.016 0.02 0.023 0.026 0.03 0.033

Flour (g) 100 100 100 100 100 100 100 100 100 100 100

Final amount of iron (ppm) 0 10 20 30 40 50 60 70 80 90 100

Fig. 1 A simple scheme of the constituent components for computervision techniques

Food Anal. Methods

Table 2 Statistical analysis results for count of points and area of spots

Sample number

1 2 3 4 5 6 7 8 9 10 11

Numbers of replications 15 15 15 15 15 15 15 15 15 15 15

Highest number of spots 0 21 31 42 54 61 72 82 92 102 109

Lowest number of spots 0 14 25 35 43 52 63 73 84 95 103

Mode of number of spots 0 17 28 39 48 56 76 78 88 99 105

Standard deviation of number of spots ±0 ±2.64 ±1.99 ±2.09 ±3.17 ±2.49 ±2.72 ±2.49 ±2.61 ±1.92 ±2.08

Highest total area of spots μ)m2) 0 15,450 29,219 35,334 49,561 58,849 69,503 76,317 88,989 98,268 109,247

Lowest total area of spots μ)m2) 0 11,700 22,360 31,862 40,007 50,263 60,024 70,631 82,110 91,235 103,569

Mode of area of spots μ)m2) 0 13,107 26,165 33,168 43,713 53,264 65,422 72,343 85,796 94,618 104,495

Standard deviation of total area of spots ±0 ±1,492 ±2,360 ±1,220 ±3,196 ±3,265 ±3,092 ±1,844 ±2,073 ±2,359 ±1,800

Fig. 2 The relationship betweeniron concentration and thenumber of colored spots (a) andthe area of spots relative to thetotal area (b) of the flour samples.After image processing, colorstains from 0 to 100 ppm samplepreparation parameters and thenumber of spots, with coloredsurface area, weremeasured in therange. Correlation between theamounts of iron in the sampleswas determined. Results arereported in terms of mean ± SD

Food Anal. Methods

and four 60-w halogen lamps in its inner corners. The sampleswere placed at the floor of chamber. The images captured bythis camera were transferred to a computer via a wire andstored in JPEG format 1,721×1,721 pixels. Figure 1 showsthe simple schema of this set.

Control Sample Preparation

Then, a certain amount of premix powder (Hashtgerd FoodIndustries, Qazvin, Iran) containing ferrous sulfate was weighedusing a digital model Mettler AE-160 Analytical Lab Scale, and100 g of wheat flour (Sina Flour Factory, Hamadan, Iran) was

mixed by horizontal–vertical mixer model Rotabit (Saman en-vironment, Tehran, Iran) for 30 min and 100 rpm. Three series(triplicate) of fortified flour were achieved with known concen-trations of iron. The details of this section and the amount offlour and premixe are given in Table 1. Some amount of sampleswas spread on a flat surface in 5×5×5 cm dimensions and 4mmin height. Acidic reagent contained 20ml potassium thiocyanatesolution (10 % w /w) and 17 ml hydrochloric acid (2 M) in atotal volume of 100 ml. Reagent 2 contained 9 ml hydrogenperoxide (30 %v /v) and 91 ml distillated water. The conse-quence of adding reagent was that at first, the acidic reagentwas added, and after 60 s, hydrogen was added.

Drawing the Calibration Curve

If the iron sulfate particles are oxidized, it will create some redspots in flour. These red spots specifically will be visible. Inorder to make the red spots in samples, each sample wasspread in a flat surface in the range of 5×5 cm and a heightof 4 mm. Then, the first reagent containing potassium thiocy-anate acid solution (10 wt%) and 17 ml of 2 M hydrochloricacid to a volume of 100 ml were added to the samples' surface.After 60 s, the second agent containing 2 ml of 30% hydrogenperoxide was added to each surface of each sample. Aftercompletion of the reaction, the red spots which are indicativeof the iron sulfate particles appeared. Then, the samples weretransferred into a dark room, and after photos were taken, thepictures were analyzed using Clemex Vision 3.5 software.After segmentation and processing of image, the number ofcolored spots and the area of spots were determined.

In order to achieve this aim, the images were changed into abinary image using the difference between the color intense ofred spots and background of pictures. The final results wererecorded in Excel 2007 software. In order to enhance theaccuracy, five pictures were prepared from each sample.According to the triplicate, 165 images were captured totally.The obtained data from the images processing are shown inTable 2. The calibration curves were drawn based on thesedata. Figure 2a, b shows the relationship between the aboveparameters and iron concentration in samples (Fig. 2). Tocompare between computer vision and spot test method pre-cision atomic absorption method, at first, the amount of iron inall samples (33 samples) was detected according to no. 2000(999/11) American Association of Cereal Chemists as thereference method by using BRAIC WFX-210 AtomicAbsorption Model (Thermo Electron Spectroscopy Ltd.,Cambridge, UK) (Horwitz 2006). Then, in order to reducethe human error and increase the accuracy of measurements,99 samples from three concentration sets were given to threepersons to report the amount of iron according to spot testmethod. Also, five samples from each concentration range(collectively 165 samples) were used for imaging techniques

Table 3 Validation theaccuracy of computervision and spot testmethods

AA atomic absorption,CV computer vision, STspot test

Sample no. Method

AA CV ST

1 0 0 0

2 14.26 17.22 30

3 25.26 30.13 60

4 33.36 31.45 70

5 49.22 54.51 60

6 55.35 62.52 80

7 59.32 65.51 40

8 78.22 71.24 80

9 82.95 91.14 70

10 98.49 101.82 60

11 112.23 116.85 80

12 0 0 0

13 20.32 18.67 50

14 30.26 32.51 50

15 31.35 32.82 60

16 58.39 60.24 80

17 49.23 52.53 70

18 65.99 69.65 60

19 70.39 74.22 70

20 88.96 90.86 60

21 110.63 112.91 70

22 140.28 142.24 80

23 0 0 0

24 9.62 15.02 30

25 23.01 28.23 60

26 38.7 32.51 70

27 44.92 54.52 60

28 67.05 65.29 80

29 58.57 71.23 40

30 85.23 94.16 80

31 85.23 90.82 70

32 96.2 101.17 60

33 95.68 101.54 80

Food Anal. Methods

in computer visual analysis. Finally, the correlation betweenthe obtained result from spot test and computer vision systemwas compared with atomic absorption spectroscopy. Since thewheat naturally has some amount of iron and can enter flourduring milling process, we should detect this iron, in order tomeasure the actual amount of iron which is added to flour.Thus, three samples of unfortified flour (control samples)were selected, and the amount of iron in these samples wasdetected

Statistical Analysis

Results were analyzed by SPSS 16 and Excel 2007 softwares,and the results were presented as mean ± SD. In order tocompare the amount of iron in different concentrations ofthe test samples, t test was used. The linear regression corre-lation was used for the determination of correlation betweenthe results obtained from spot test and newmethod and atomicabsorption spectroscopy as a reference method.

Results and Discussion

As mentioned for determining the accuracy of the new methodand comparing the sensitivity of this method with spot test

method and atomic absorption, 33 samples of fortified floursamples were selected randomly, and the amounts of addediron were measured. Table 3 shows the results obtained frommeasurements. The results shown are as mean ± SD. Thestatistical analysis of the data showed that the correlation ofthe results obtained by computer vision method to those ofreference atomic absorption method was much greater thanthat obtained for the spot test (R2=0.988 and R2=0.501,respectively) (Fig. 3). It can be concluded that the computervision method is more accurate than traditional spot test, andtherefore, results are more precise measurements. The contin-uous control of iron concentration in fortified flour is veryimportant in factories, and conventional method in factoriesis a semiqualitative, and in an accurate method, its resultdepends very much on the human error. Also, the ability toobtain atomic absorption analysis is not possible for the facto-ries due to the high cost of providing equipment and chemicals.The government and regulatory agencies simultaneously con-trol the amount of iron in the products of factories, and themethod used for detecting iron in government laboratories isatomic absorption spectroscopy. Expensive and nonuniformatomic absorption analysis methods used in flour productioncompanies and regulatory agencies have always created prob-lems in monitoring the production of enriched flour. Our studyis the first to measure added iron in fortified flours by computer

Fig. 3 At first, the amounts of iron in three series of samples from 0 to100 ppm were measured by using atomic absorption spectroscopy as thereference method. Then, three samples from each series for spot test(collectively, 99 samples) and five samples for computer vision system

from each series for spot test (collectively, 165 samples) were selected,and the amount of iron was measured as described in “Material andMethods.” Results are reported in terms of mean ± SD

Food Anal. Methods

vision system. We could introduce an alternative method in-stead of semiquantitative and conventional method by usingtwo qualities of the abovementioned parameter and a newtechnique in food science scope as computer vision system.This is a low-price method needless to provide expensive equip-ment while having a high correlation coefficient with the atomicabsorption spectroscopy. This study also showed that theamount of added iron in fortified flour depends on the numberof colored spots and the area of spots relative to the total;therefore, twomentioned parameters can be used as a new indexto measure the iron in fortified flour. On the other hand, theextremely high correlation between the results obtained from theinvented method and atomic absorption spectroscopy empha-sizes its accuracy. There is no available any similar study tocompare the results. Therefore, the results of this study provedthat the invented method can be used for iron determination infortified flour with a high reliability, and it seems that the newmethod can be a proper alternative to spot test method.

Acknowledgments This study was supported by Hamadan Universityof Medical Sciences and Health Service.

Conflict of Interest Iraj Khodadadi has no conflict of interest. SeidMehdi Seyedain Ardebili has no conflict of interest. Orang Eyvazzadehhas no conflict of interest. Kaveh Zargari has no conflict of interest.Mohammadreza Moradi has no conflict of interest. This article does notcontain any studies with human or animal subjects.

References

Abdullah MZ, Guan LC, Lim KC, Karim AA (2004) J Food Eng 61(1):125–135

Baxes G (1994) Digital Image Processing: Principles and Applications.John Wiley & Sons, New York, pp 100–115

Chen CR, Zhu SM, Ramaswamy HS, Marcotte M, Bail L (2007) J FoodEng 79(2):401–409

Goyache F, Bahamonde A, Alonso J, Lopez S, del Coz J, Quevedo J(2001) J Food Eng 72:39–55

Horwitz W (2006) Official method of analysis of AOCO International, 3edn. AOAC International, p. 1204–8

Krutz GW, Gibson HG, Cassens DL, Zhang M (2000) Colour vision inforest and wood engineering. Lanwards 55:2–9

Matas JR, MaK J (2005) Image Vis Comput 13(9):663–669Mohd Jusoh YM, Chin NL, Yusof YA, Abdul Rahman R (2009) J Food

Eng 94(3–4):366–371Sheykhol Eslami R, Abdollahi Z (2004) Office of Community Nutrition

Ministry of Health, Treatment and Medical Education, Tehran, pp54–101 (in Persian)

Simal S, Benedito G, Clemente A, Roselló F (2003) J Food Eng 58(3):253–257

Sun DW (2000) J Food Eng 44:245–249Sun DW (2004) J Food Eng 61:1–142Sun DW (2008) Computer vision system for food quality evaluation.

Apple, Dublin, pp 100–180Sun DW, Brosnan T (2003) J Food Eng 57(1):81–89Tillett RD (1990) Image analysis for agricultural processes. Silsoe Re-

search Institute; Division Note DN 1585Timmermans AJ (1998) In Acta Horticulturae (ISHS) 421: II International

Symposium on Sensors in Horticulture. p. 91–8

Food Anal. Methods