a novel method for color determination of edible oils in lab* format

Kıvanc Kılıca

Baran Onal-Ulusoyb

Ismail Hakkı Boyacıb

a Biasis Ltd. Sti.,Kosgeb-Hacettepe TechnologyResearch Center,Ankara, Turkey

b Department of FoodEngineering,Hacettepe University,Ankara, Turkey

A novel method for color determination of edibleoils in L*a*b* format

A simple method that uses visible spectrophotometer data and an artificial neural net-work (ANN) was developed to determine edible oil color based on the L*a*b* format.The 100 oil samples consisted of nine pure oils, a sesame oil blend and three heatedoils. Binary, ternary and quaternary mixtures of these 13 oils in different ratios wereprepared, and absorbance values of the samples were measured in the visible region(380–700 nm). The absorbance values at wavelengths of 416, 456, 483, 537, 611 and672 nm were used to train, validate and test the network. Strong correlations betweenthe instrumental L*a*b*DE and the estimated L*a*b*DE were found for the test samples,with correlation coefficients (R2) of 0.989, 0.984, 0.996 and 0.992 for L*, a*, b*, and DE,respectively. The effects of number and combination of the wavelengths used fortraining of the ANN on the estimation capability of the network for the test sampleswere also investigated. Although a good agreement, average R2 of 0.991– 0 993 forL*a*b*, was obtained for combinations composed of three to six wavelengths with 483and 537 nm in common, the best R2 value was obtained when all six wavelengths wereused to train the ANN. The developed method is objective, cost effective and simple,and allows the color measurement with a basic visible spectrophotometer and dis-posable cuvettes.

Keywords: Oil, color, L*a*b*, spectrophotometer, artificial neural network.

1 Introduction

Color is an important quality parameter of edible oil, bothin the refining process and in the marketplace. The edibleoil industry often analyzes the oil color, either qualitativelyor quantitatively, during the process to maintain a con-sistent quality. Oil appearance might be an indicator of aproblem having occurred during blending, storage,crushing, and extraction or the refining process [1]. Eachoil has its own characteristics color, primarily due to thepresence of carotenoids and/or chlorophyll pigments orgossypol. Therefore, the oil color is often specified in thetrade rules by various associations in different countries[2].

The American Oil Chemists’ Society (AOCS) [3] has pro-posed four official methods for the color determination offats and oils. These methods are Lovibond color (AOCSmethod Cc 13e-92), Wesson color (AOCS methodCc 13b-45), spectrophotometer color (AOCS Cc 13c-50)and chlorophyll color (AOCS method Cc 13d-55). Thelatter method is not applicable to hydrogenated anddeodorized oils because 670 nm, representing the max-

imum absorbance of chlorophyll pigments, is missing inmost processed oils [1]. The Lovibond color method is themost widely used color measurement technique in theedible oil industry, but it requires a Lovibond tintometer. ALovibond tintometer consists of red, yellow, and bluepermanently colored glass standards. The addition of theblue color field provides a greater degree of brightnessand greenness than given by the Wesson color. Thespectrophotometric method, an instrumental technique,eliminates the visual judgment of a user required in boththe Lovibond and Wesson methods. This methoddepends on the calculation of the absorbance of fourdefinite wavelengths. The results can be transformed tothe Lovibond or Wesson color values with a developedequation. The method is not an alternative for both theLovibond and Wesson color methods because of occa-sional disagreement, economics, and the firm entrench-ment of the visual procedure. However, it is still an officialmethod as well as the only available objective method [1].

Currently, CIE L*a*b*, XYZ, Hunter Lab, and RGB (Red,Green, Blue) are the alternative color models that mightbe used in objective oil color evaluation. L*a*b* is aninternational standard for color measurements, adoptedby the Commission Internationale d’Eclairage (CIE) in1976 [4]. The L*a*b* values have been widely used in foodstudies. L* is the lightness component ranging from 0 to100. a* (from green to red) and b* (from blue to yellow) are

Correspondence: Ismail Hakkı Boyacı, Hacettepe University,Department of Food Engineering, Beytepe TR 06800, Ankara,Turkey. Phone: 190 312 2977100, Fax: 190 312 2992123,e-mail: [email protected]

Eur. J. Lipid Sci. Technol. 109 (2007) 157–164 DOI 10.1002/ejlt.200600211 157

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158 K. Kılıc et al. Eur. J. Lipid Sci. Technol. 109 (2007) 157–164

two chromatic components ranging from 2120 to 120.The CIE L*a*b* color is device independent, providingconsistent color regardless of the input or output device,such as digital cameras, scanners, monitors and printers[5]. In contrast to other color models such as RGB, XYZand Hunter Lab, the color perception is uniform in theL*a*b* space, i.e. the Euclidean distance between twodifferent colors corresponds approximately to the colorperceived by the human eye [6]. Commercial and objec-tive color measurement instruments measuring the colorwith alternative color methods are readily available andhave been used in several studies [7–10], but measure-ments with such devices require large amounts of oilsamples (,10–13 mL), which is too much for food re-searches, and these devices are relatively expensive anduse expensive disposable sample cuvettes.

Organic molecules can absorb UV/visible light. As a resultof the absorption of light, electronic transitions occur,leading to a higher energy or excited state of the mole-cule. Because of the decolorization of the electrons alongthe chromophore, the excited state of the molecule is oflow energy so that absorption of visible light is enough togive rise to the transition. The human eye sees the wavesthat are left over from the absorption of the compounds,and the oil appears colored [10].

Computerized image analysis systems has been devel-oped and used successfully to measure the color of thevarious solid food samples [8, 9, 11–13]. Fengxia et al. [2]have attempted to develop an objective system to meas-ure the color of various oils, depending on image analy-ses. The image of the oil samples was captured with ascanner and a camera, and it was processed with soft-ware. A good agreement, R2 of 0.999, between the visualLovibond red color and the image analysis red color wasreported. Since the regulations on oils produced in Chinahave limitations only for red readings, the authors report-ed that it is not necessary to correlate their system toyellow and blue color readings.

Recently, knowledge-based approaches such as statis-tical learning, fuzzy logic and artificial neural networks(ANN) have been applied successfully to the inspection offood quality, modeling and control of various biologicalprocesses. Neural networks mimic the human intelligencefor objective learning [14–16]. The combination of imageanalysis and ANN brings about a powerful tool formachine vision inspection. ANN have been utilized forclassification, prediction and segmentation in qualityevaluation of food products in the recent years [16–18].

The objective of this study was to develop a method tomeasure the color of a wide range oil samples in CIEL*a*b* format with a visible spectrophotometer, which is a

common laboratory equipment and uses low-volumedisposable cuvettes in the visible region. An ANN wasused for the estimation of L*a*b* values from the absorb-ance values obtained with the visible spectrophotometer,and the performance of the trained network was furtherinvestigated by testing the size and nature of the inputmatrix of the ANN.

2 Materials and methods

2.1 Oil samples

Refined soybean, corn, cottonseed, hazelnut, sunflower,canola and olive oils, and a sesame oil blend (40%sesame and 60% soybean oils), and riviera olive oil, ablend of virgin and refined olive oils (free fatty acid content,1.5%), and virgin olive oil were purchased from localmarkets. The sunflower, refined olive and soybean oilswere heated continuously at 180 7C for 8, 8 and 30 h,respectively, to get oil colors between the color of cot-tonseed oil and a sesame oil blend. The 100 oil samplesconsisted of nine pure oils, a sesame oil blend, threeheated oils, and binary, ternary, and quaternary mixturesof these oils (except canola oil) in different ratios, to getmore oil samples containing different types and/or levelsof color pigments, were used.

2.2 Instrumental determination of color

The CIE L*a*b* values of the oil samples were measuredusing a Minolta Spectrophotometer CM-3600d (MinoltaCamera Co., Japan). The instrument was calibrated withthe zero calibration box and the white calibration plate,respectively, and the white calibration plate was used as atarget for the calculation of total color difference (DE). Theoil samples were put into 13-mL solvent-resistant cu-vettes (10 mm path length), and the cuvette was washedwith HPLC grade hexane before each measurement. Thefinal color score of each oil sample was obtained byaveraging the scores of the duplicate measurements. DEvalues were calculated from the L*a*b* values accordingto the following equation:

DE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

L� � L�target

� �2þ a� � a�target

� �2þ b� � b�target

� �2r

2.3 Absorbance measurement with UV/visiblespectrophotometer

Absorbance values of each oil sample were measured inthe visible region (380–700 nm) with an Agilent 8453 UV-Visible Spectrophotometer (Agilent Technologies, PaloAlto, CA, USA) and a disposable polystyrene sample cu-


Eur. J. Lipid Sci. Technol. 109 (2007) 157–164A new method for color determination of edible oils in L*a*b* format 159

vette (,3 mL) with a path length of 10 mm. Zero absorb-ance was set with distilled water as a blank. Wavelengthswere scanned at room temperature for each sample induplicate to check variations.

2.4 Data selection

Absorbance values of selected wavelengths (416, 456,483, 537, 611 and 672 nm) and L*a*b* values of eachsample were collected in Matlab 7.0 structure (The Math-works, Natick, MA, USA). Both input (absorbance values)and output (L*a*b* values) data were normalized using theMatlab function prestd. The means of normalized datawere zero and the standard deviations of those datawere 1. Input data were divided randomly into threegroups: training (50 samples), test (25 samples) and vali-dation (25 samples).

2.5 Training and testing the neural network

Feedforward neural network was used in this study. Thenetwork consists of an input matrix, hidden and outputlayers. The hidden and output layers consisted of six andthree neurons, respectively. Logsig and Purelin werechosen as transfer functions for the hidden and outputlayers, respectively. The Levenberg-Marquardt back-propagation (trainlm) training algorithm with early stop-ping method was used for training of the network. In thefirst part of the study, six absorbance values, measured atsix different wavelengths selected in a previous section,

were used as input variables, and the designed networkwas trained using training and validation data. The per-formance of the trained network was investigated bycomparison of the L*a*b* values determined with theMinolta instrument and those estimated with the ANN forthe test samples. However, measuring six absorbancevalues at six different wavelengths might be difficult with abasic spectrophotometer. Therefore, training of the net-work using lower numbers of input variables (absorbancevalues at selected wavelengths) were investigated in thesecond part of the study. All the possible combinations ofsix wavelengths were created and used individually as aninput matrix for training of the network.

3 Results and discussion

Absorbance spectra of nine pure oils, three heated oilsand a sesame oil blend, between 380 and 700 nm, aregiven in Fig. 1. Absorption peaks at wavelengths of 416,456, 483, 537, 611 and 672 nm were detected only for thevirgin and riviera olive oils. The absorbance values of thevirgin olive oil at these six wavelengths were higher thanthe values for the riviera olive oil. This is not surprisingbecause only the virgin olive oil part of riviera olive oil isnot bleached, so it preserves its color pigments more thanthe refined oils. No absorbance peaks were detected inthe visible region for the pure oils, including refined oliveoil. This is related mainly to the reduction of the colorpigment concentration of edible oils during the bleachingprocess with activated earth. Absorbance peaks between380 and 440 nm were detected only for the three heated

Fig. 1. Absorbance spectra of nine pure oils,three heated oils and a sesame oil blend at thevisible wavelengths. CSO, cottonseed oil;SeOB, sesame oil blend; VOO, virgin olive oil;HROO, heated refined olive oil; RivOO, rivieraolive oil; CO, corn oil; ROO, refined olive oil;HZO, hazelnut oil; SBO, soybean oil; SFO, sun-flower oil; HSFO, heated sunflower oil; HSBO,heated soybean oil; CNO, canola oil.



oils and the sesame oil blend. These peaks may be relat-ed to colored oxidation products occurring during heatingof these three oils and during roasting of the sesameseeds. The absorption peaks detected at 416, 456 and483 nm mainly belong to isomers of carotene or chloro-phyll, because isomers of carotene and some isomers ofchlorophyll have been reported to have absorption peaksat 400–480 and 430–466 nm, respectively [10, 19]. Sinceflavonoids and some isomers of chlorophyll pigmentshave been reported to have absorption peaks at 500–550and 670 nm, respectively, the absorption peaks at 537and 672 nm strongly indicate the presence of color pig-ments and phenolic compounds [1, 3, 20, 21]. Althoughthere was no information found on what kind of a com-pound might have an absorption maximum at 611 nm,Paul and Mittal [22] have reported that major degradationproducts might have absorption peaks between 550 and695 nm.

Absorbance values at six wavelengths for 50 randomlyselected oil samples were used for training the network.The performance of the trained network was investigatedby comparison of the measured L*a*b*DE values with theestimated L*a*b*DE color values. The relationship be-tween the L*a*b*DE values measured using the Minoltainstrument (instrumental L*a*b*DE) and those estimatedby ANN for the training data set is shown in Fig. 2. Thecorrelation coefficients (R2) between the two systemswere 0.995, 0.994, 0.997 and 0.996 for L*, a*, b* and DE,respectively. The plots of the instrumental L*a*b*DE vs.the L*a*b*DE estimated with the trained ANN were linear,having slopes close to unity.

The trained network was validated with the absorbancevalues of 25 oil samples, the second data group. Theresults obtained from the ANN were compared withthose measured with the instrument. Strong correlations

Fig. 2. Experimentally determined L*a*b*DE values vs. estimated L*a*b*DE values for training data.



between the instrumental L*a*b*DE and the L*a*b*DEestimated with the ANN were found, with correlationcoefficients of 0.986, 0.980, 0.995 and 0.991 for L*, a*, b*and DE, respectively (Fig. 3). The plots of the instrumentalL*a*b*DE vs. the L*a*b*DE estimated with the ANN werelinear and had slopes between 0.966 and 0.988.

The performance of the trained and validated network wasfurther tested with the last group of data collected from25 oil samples having different color intensities. The resultsof the ANN were compared with those obtained from theinstrument (Tab. 1). R2 values between the instrumentalL*a*b*DE and the L*a*b*DE estimated with the ANN weredetermined as 0.989, 0.984, 0.996 and 0.992 for L*, a*, b*

and DE, respectively. These results indicate that the ANNsuccessfully estimated the L*a*b* values using absorbancevalues measured at six wavelengths.

Absorbance measurements at six wavelengths for onesample may be time consuming, unless a spectro-photometer with a diode array detector is used. Chem-ical compounds may give more than one absorptionpeak at different wavelengths. Measurement of absorb-ance at one wavelength may give information about theabsorbance values at the other wavelengths. In thesecond part of the study, the possibility to train thesame network using a lower number of absorbancevalues was investigated. The effect of the number of the

Fig. 3. Experimentally determined L*a*b*DE values vs. estimated L*a*b*DE values for validation data.



Tab. 1. Comparison of instrumental and ANN-estimated color measurements for various oil samples§.

Pure oil/oil mixtures (wt/wt) Instrumental ANN

L* a* b* ˜E L* a* b* ˜E

CO 40.2 21.8 10.7 59.8 40.1 21.7 10.5 59.8SFO 41.2 21.2 4.2 57.9 40.9 21.2 4.5 58.2CSO1CO (1 : 1) 40.0 21.8 10.7 60.0 40.1 21.8 10.6 59.9CSO1SFO (1 : 1) 40.5 21.8 8.6 59.1 40.5 21.7 8.2 59.1VOO1ROO (1 : 1) 36.5 0.9 21.2 66.0 36.8 1.1 21.4 65.8VOO1HSFO (1 : 1) 36.0 2.3 20.2 66.3 36.0 2.1 20.8 66.4FROO1RivOO (1 : 1) 38.5 20.2 17.7 63.1 39.1 22.0 18.2 62.7FROO1SBO (1 : 1) 40.0 22.8 15.5 61.1 39.6 22.6 16.3 61.7RivOO1HZO (1 : 1) 39.9 21.5 10.7 60.1 39.9 21.6 11.2 60.2RivOO1HSBO (1 : 1) 36.8 1.9 20.7 65.6 37.8 0.7 20.4 64.5CO1SFO (1 : 1) 40.7 21.4 7.1 58.7 40.5 21.4 7.2 58.9HZO1SBO (1 : 1) 41.1 21.2 3.8 58.0 41.0 20.9 4.0 58.1SBO1SFO (1 : 1) 40.9 21.4 4.7 58.3 40.9 21.1 4.5 58.2SeOB1SBO (3 : 2) 30.3 6.6 9.8 69.7 29.9 7.1 9.3 70.1VOO1CO1HROO (1 : 1 : 1) 37.4 0.5 21.5 65.3 37.3 0.5 21.0 65.2VOO1SBO1HSFO (1 : 1 : 1) 37.2 0.8 20.9 65.1 36.9 1.1 21.1 65.7VOO1SFO1HSBO (1 : 1 : 1) 36.6 2.4 20.6 65.7 36.9 1.4 20.6 65.5RivOO1CO1HROO (1 : 1 : 1) 39.6 22.1 16.3 61.7 39.4 22.1 16.1 61.8RivOO1SBO1HSFO (1 : 1 : 1) 38.8 21.7 17.4 62.7 39.0 21.8 17.0 62.4RivOO1SFO1HSFO (1 : 1 : 1) 39.2 21.7 17.3 62.3 39.0 21.8 17.0 62.4CSO1SeOB1SBO (5 : 6 : 4) 33.7 4.3 14.3 66.8 33.7 3.8 14.3 66.9SeOB1SBO1HZO1HROO (6 : 4 : 5 : 5) 33.5 4.5 14.6 67.2 33.5 4.3 14.7 67.3SeOB1SBO1HZO1HSBO (6 : 4 : 5 : 5) 32.7 5.5 14.2 68.1 32.5 5.6 14.1 68.2SeOB1SBO1CSO1HSBO (6 : 4 : 5 : 5) 33.0 5.6 14.3 67.7 32.6 5.6 14.1 68.1SeOB1SBO1SFO1HROO (6 : 4 : 5 : 5) 33.0 4.4 14.6 67.5 33.6 4.2 14.5 67.1

R2 0.989 0.984 0.996 0.992

§ Abbreviations of oils/oil mixtures are defined in the legend of Fig. 1.

Tab. 2. Effect of number and combination of the wavelengths used for training of the ANN on the estimation capability ofthe network for the test samples.

Numberof inputvariables

Number ofwavelengthcombinations

Wavelength combination forwhich the highest R2 value wasdetermined [nm]

Average of correlation coefficients (R2)for test samples

L* a* b* Average§

16!

6! 6� 6ð Þ!¼ 1 416, 456, 483, 537, 611 and 672 0.993 0.990 0.996 0.993

26!

5! 6� 5ð Þ!¼ 6 456, 483, 537, 611 and 672 0.993 0.988 0.993 0.991

36!

4! 6� 4ð Þ!¼ 15 456, 537, 611 and 672 0.994 0.988 0.992 0.991

46!

3! 6� 3ð Þ!¼ 20 483, 537 and 611 0.994 0.989 0.992 0.992

56!

2! 6� 2ð Þ!¼ 15 483 and 537 0.993 0.980 0.992 0.988

66!

1! 6� 1ð Þ!¼ 6 537 0.976 0.959 0.986 0.974

§ Average of correlation coefficients of L*a*b* used for the selection of the best wavelength combinations.



wavelengths used for training of the ANN on the estima-tion capability of the network for the test samples is givenin Tab. 2. Although the ANN trained with one wavelengthhad a good average R2 (0.974) for L*a*b* and could suc-cessfully estimate L*a*b*, increasing the number ofwavelengths also increases the correlation coefficientsfor the test samples, and the best average R2 (0.993) wasobtained when the ANN was trained with the data of sixwavelengths. The wavelength of 537 nm was included inall six wavelength combinations, and 483 nm was inclu-ded in the first five wavelength combinations (Tab. 2). Theabsorption peak at 483 nm may be attributed to b-caro-tene because Melendez-Martinez et al. [10] have reportedthat b-carotene has absorption peaks at around 450 and480 nm. It is apparent that the absorbance peak at537 nm strongly affects the L*a*b*DE values, and the colorof the oils as well. Since Merzlyak et al. [21] have reportedthat anthocyanins have absorption maxima near 550 nm,the absorption peak detected at 537 nm may be attribut-ed to these flavonoids.

Instrumental measurement of oil color is a very useful toolfor quality control of oil in the edible oil industry because itis an objective and rapid technique that enables the ana-lyst to obtain L*a*b* values in a few seconds. Besideshaving all the advantages of an instrumental color meas-urement, the method developed in this study is costeffective because it uses a simple UV/visible spectro-photometer readily available in many laboratories anddisposable cuvettes allowing easy color measurement.The method makes it possible to avoid operator variabilityassociated with visual comparison. Because of using alarge number of training and validation data sets, resultsfrom the ANN agree closely with those obtained with theMinolta spectrophotometer over a wide color range of oilsamples. The oils to be measured in color with the devel-oped method should be completely liquid and clear,otherwise the impurities in the oil will affect the absorb-ance and also the L*a*b*DE values. The oil samples usedin this study have wide ranges of a* and b* values, butclose L* values. Therefore, the ANN should be recali-brated for oils having L* values much lower or higher thanthe ones used in this study.

Refined edible oils do not contain fluorescent compoundssuch as mycotoxins, because most of such compounds,extracted from oil seeds during oil extraction with hexane,are removed during deodorization [23]. However, myco-toxins can be easily extracted into the oil phase duringpressing or centrifugation of the oil from olive fruits; sothey might be found in olive oils that are not subjected torefining processes. Therefore, the developed methodshould be calibrated in the presence of fluorescent com-pounds if the color of non-refined oils is to be measured.

4 Conclusions

Edible oils have colors ranging from light yellow to darkgreen or brown, depending on the type and concentrationof the color pigments. The concentration of these pig-ments not only depends on the type of plant, but also onthe type of process. Therefore, color determination is anindispensable kind of analysis for the edible oil industrybefore marketing. Currently, the available official meth-ods, except the spectrophotometric one, require visualjudgment and/or more oil samples. This study shows thatANN can be used effectively to determine the color ofmost edible oils, a sesame oil blend, and of heated oils aswell. Further research needs to be conducted to examinethe performance of ANN for the color determination ofliquid food samples, such as clear fruit juice, and alsonon-food samples, such as inks which are non-transpar-ent and more viscous than edible oils and widely used inthe textile or paint industries.

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[Received: September 29, 2006; accepted: December 5, 2006]


a novel method for color determination of edible oils in l*a*b* format

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a novel method for color determination of edible oils in lab* format