Food Chemistry 225 (2017) 188–196

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Food Chemistry

journal homepage: www.elsevier .com/locate / foodchem

Analytical Methods

Prediction of peroxide value in omega-3 rich microalgae oil by ATR-FTIRspectroscopy combined with chemometrics

http://dx.doi.org/10.1016/j.foodchem.2017.01.0130308-8146/� 2017 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail address: mtyilmaz@yildiz.edu.tr (M.T. Yilmaz).

Nur Cebi a, Mustafa Tahsin Yilmaz a,⇑, Osman Sagdic a, Hande Yuce b, Emrah Yelboga b

aYıldız Technical University, Chemical and Metallurgical Engineering Faculty, Food Engineering Department, 34210 _Istanbul, Turkeyb _Istanbul Technical University, Technopolis, Vitatis Biotechnology R&D Industry and Trade Limited Company, 34396 _Istanbul, Turkey

a r t i c l e i n f o a b s t r a c t

Article history:Received 14 August 2016Received in revised form 26 November 2016Accepted 4 January 2017Available online 5 January 2017

Keywords:Peroxide valueOxidationAlgae oilATR-FTIRChemometrics

Our work explored, for the first time, monitoring peroxide value (PV) of omega-3 rich algae oil using ATR-FTIR spectroscopic technique. The PV of the developed method was compared by that obtained by stan-dard method of Association of Official Analytical Chemists (AOAC). In this study, peak area integration(PAI), Partial Least Squares Regression (PLSR), and Principal Component Regression (PCR) were used asthe calibration techniques. PV obtained by the AOAC method and by FTIR-ATR technique were well cor-related considering the peak area related to trans double bonds and chemometrics techniques of PLSR andPCR. Calibration model was established using the band with a peak point at 966 cm�1 (990–940 cm�1)related to CAH out of plane deformation vibration of trans double bond. Algae oil oxidation could be suc-cessfully quantified using PAI, PLSR and PCR techniques. Additionally, hierarchical cluster analysis wasperformed and significant discrimination was observed coherently with oxidation process.

� 2017 Elsevier Ltd. All rights reserved.

1. Introduction

Microalgae can be defined as primary and the most abundantproducers in marine ecosystems. Basically, they convert lightenergy and carbon dioxide into biomass which includes carbohy-drates, protein and lipids, thus algae has crucial importance forfood chain. Importantly, microalgae synthesis omega-3 fatty acidswhich are consumed by marine plants and animals (Lenihan-Geels,Bishop, & Ferguson, 2013). Omega-3 poly-unsaturated fatty acids(PUFA) increasingly gain importance commercially since they havenutritional and health-promoting effects such as preventing coro-nary heart disease, alleviating inflammation, curing hyperlipi-demia and hypertension (Barclay, Meager, & Abril, 1994;Covington, 2004). The most important omega-3 fatty acids areeicosapentaenoic acid (EPA; C20:5n-3) and docosapentaenoic acid(DHA; 22:6n-3) (Barclay et al., 1994). Obviously, omega-3 fattyacids are considerably valuable nutrients thus x-3 fatty acids areimportant for a wide range of scientific and industrial processesand find application in health supplements, food enrichment andanimal feeds (Barclay et al., 1994). Poly unsaturated fatty acidsare mainly obtained from fatty fish species such as tuna, salmonand mackerel (Wu & He, 2014). However, alternative sources arerequired for omega-3 fatty acids since fish cannot synthesized long

chain poly unsaturated fatty acids; moreover, there has beendecline in fish biodiversity and abundance. Beneficial health effectsof omega-3 fatty acids are obtained provided that fish is eaten sev-eral times in a week or fish oil supplements are consumed. Also, afrequent eating fish may pose a risk of accumulation of mercury inliver. What is more, omega-3 fatty acids from fish oil has unlikableflavor, odor and stability problems (Lenihan-Geels et al., 2013). Theaforementioned disadvantages limit usage of fish oil in dietarysupplements and food-additives. As an alternative, marinemicroalgae can be used to produce omega-3 long chain fatty acids(Barclay, Weaver, Metz, & Hansen, 2010). However, production offatty acids by microalgae biofactories is challenging since manyproblems associated with fish such as flavor, taste and stabilityshould be overcome (Barclay et al., 1994). Lipid oxidation limitsthe usage of these oils in processed foods and dietary supplementsin fortified foods; therefore, monitoring oil oxidation has a pivotalrole in food quality and safety (Frankel, Satué-Gracia, Meyer, &German, 2002). Oxidative stability can be mentioned as the resis-tance that oils show in the presence of oxygen. Oxidation of oilsis a major area of interest since oxidation has been a criterion forshelf life and product quality. In other words, oxidation of unsatu-rated fatty acids causes formation off-flavor compounds anddecreases the nutritional value of products (Guillen & Cabo,2002). Hydroperoxides are the initial oxidation products and thesecompounds are unstable thus easily break down into alcohols,aldehydes, free fatty acids and ketones (Wsowicz et al., 2004).

N. Cebi et al. / Food Chemistry 225 (2017) 188–196 189

These molecules are major factors which are triggering the rancidconditions in foods (Van de Voort, Ismail, Sedman, Dubois, &Nicodemo, 1994). As a result, monitoring of oxidation keeps crucialplace in developing new products, oils or dietary supplements.

A number of methods have been used to evaluate oxidation infood products and these methods are based on measuring primaryand secondary oxidation products (Rohman, Che Man, Ismail, &Hashim, 2011). In literature, a great number of studies were dedi-cated to evaluate oxidation in foods and oils. Among these, themost widely used method is measurement of peroxide value (PV)which is a wet chemistry and standard method of Association ofOfficial Analytical Chemists Inc (AOAC) (Mehta, Darji, &Aparnathi, 2015). This method directly measures hydroperoxideconcentration which is resulted from oxidation process as primaryoxidation products. Another widely used technique is the spec-trophotometric method utilizing from ultraviolet (UV) range andabsorbance is obtained at 232–234 nm for conjugated dienes, at268 nm for conjugated trienes and also at 268 nm for some ethyle-nic diketones and conjugated ketodienes and dienals (Guillen &Cabo, 2002; Rohman et al., 2011). Although these methods are reli-able and have been reported to be successful, they are generallytoilful, time consuming and requires toxic chemical materials. Asan alternative method, FTIR spectroscopy as rapid, inexpensiveand effective technique can be used non-destructively and rapidlyto obtain biochemical fingerprints that would provide reliableinformation on molecular structure and composition (Sivakesava& Irudayaraj, 2001). Therefore, this spectroscopic technique hasbeen used as an effective and successful tool in a wide range ofapplications in food products. There have been a number of valu-able studies evaluating oil oxidation and peroxide value by FTIRspectroscopy technique. For example, rapid method for quantita-tive analysis of peroxide value in virgin coconut oil was developed(Marina, Rosli, & Noorhidayah, 2013). In another study, a robustmethod based on FTIR transmission technique was successfullydeveloped for quantitative prediction of peroxide value in veg-etable oils (Van de Voort et al., 1994). In addition to these studies,another rapid and accurate method was developed for determina-tion of peroxide value in edible oils using FTIR spectroscopy (Ruiz,Canada, & Lendl, 2001). Also, simple and useful FTIR-based methodwas developed for quantification of peroxide value in palm olein(Setiowaty, Man, Jinap, & Moh, 2000). Previous studies reveal thatFTIR spectroscopy technique has high potential for quantificationof peroxide value in oil species; thus, can be successfully used formonitoring and evaluating of oxidation process.

The aim of this paper was to develop a new, rapid, effective,non-destructive and cost-effective method based on ATR-FTIR(attenuated total reflectance)-(Fourier transform infrared spec-troscopy) technique for monitoring and quantifying of peroxidevalue in omega-3 rich algae oil. Our research explored, for the firsttime, monitoring peroxide value of algae oil by using FTIR spectro-scopic technique combined with chemometrics of peak area inte-gration, Partial Least Squares Regression, and PrincipalComponent Regression. Also, oxidations of oils were discriminatedusing hierarchical cluster analysis. Standard method of Associationof Official Analytical Chemists Inc (AOAC) for peroxide value wasused as reference method.

2. Materials and methods

2.1. Materials

All chemicals and reagents used in this study were analyticalgrade. Algae oil samples were produced and supplied by VitatisBiotechnology R&D Co., _Istanbul, Turkey. The materials used forthe iodometric peroxide value (PV) analysis were comprised ofchloroform, glacial acetic acid, saturated solution of potassium

iodide (KI), starch solution (1%), adjusted sodium thiosulphate(Na2S2O3) 0.002 M and distilled water. All chemicals were obtainedfrom Sigma Aldrich Germany.

2.2. Oven test

In order to monitor changes in oxidation of algae oil samples,they were held in an oven (Binder, Germany) for 216 h at60 ± 1 �C. Oil samples were rapidly and periodically analyzed atthe end of each 24 h period. Three samples (20 ml) of each algaeoil treatment were placed in separate 50 ml beakers (40 mm indiameter and 60 mm in length). Samples were covered with alu-minium foil to prevent light exposure.

2.3. Determination of peroxide value by wet chemistry analysis

Peroxide values (PV) of algae oils were determined according tothe method, as outlined AOAC (2000) with some modifications.Two grams of each oil sample were placed in a flask and dissolvedwith 10 ml of chloroform. 15 ml of glacial acetic acid and 1 ml ofsaturated solution of KI were added into the flask. After the shakenwith hand for 1 min, the flask was closed and placed in a dark placefor 5 min. 15 ml of distilled water was added and the mixture wastitrated against 0.002 M sodium thiosulphate solution with starchsolution as the indicator. A blank was also titrated under the sameconditions. PV was determined using the following equation:

PV ¼ 1000� ðV � V0Þ � cm

ð1Þwhere V represented the volume of sodium thiosulphate used bysample and V0 was the volume (mL) of sodium thiosulphate usedby blank, m was the mass of algae oil (g) and c was the concentra-tion (M) of sodium thiosulphate. Three samples were separatelyanalyzed in each day. Totally, 24 samples were analyzed and aver-age peroxide values were obtained.

2.4. FTIR measurements

Bruker Tensor 27 spectrometer equipped with a DLa TGS detec-tor (Bremen-Germany) with a KBr beam splitter was used in thisstudy. All measurements were performed with a diamond single-bounce ATR accessory. Instrument control and data acquisitionwere accomplished by using OPUS program Version 7.2 for Win-dows from Bruker Gmbh. ATR-FTIR spectra of all samples wererecorded with a resolution of 4 cm�1, accumulating 16 scans perspectra. The spectra were recorded in 4000–600 cm�1 spectralrange. All samples were dripped on ATR crystal and measureddirectly. Three samples were analyzed for each day. Spectrumacquisition of each sample was repeated four times and an averagespectrum was obtained. Three samples were separately analyzedin each day. Totally, 24 samples were analyzed using FTIR tech-niques and average spectra were obtained. Air spectrum was col-lected as background spectrum before each measurement.Cleanliness of the ATR crystal was assured by ethanol and hexanesolvents.

2.5. Quantitative analysis and chemometrics

Quantitative analysis of samples was performed using the soft-ware OPUS Version 7.2 (Bruker, Germany). Principal ComponentRegression (PCR) and Partial Least Squares Regression (PLSR) anal-yses were performed by using Grams IQ (Galactic Industries Corp,Salem, N.H., USA). PCR and PLSR techniques were used successfullyfor quantitative analysis and these chemometric techniques pro-vided opportunity to develop powerful correlations between FTIRspectral range and observed chemical properties. In this study per-oxide values of samples were determined using chemical method

190 N. Cebi et al. / Food Chemistry 225 (2017) 188–196

and these values correlated with FTIR spectral data using PAI, PLSRand PCR techniques. Peroxide values which were obtained bychemical method and predicted by FTIR were presented in Table 2in great detail. PCR and PLSR are widely used in scientific and tech-nological problems due to their simplicity and strong predictiveproperties (Liang et al., 2013; Rohman & Che Man, 2012;Setiowaty et al., 2000). Generally, PLSR and PCR analysis are builton two processes: (a) the calibration and (b) validation of themethod (Tewarii & Irudayaraj, 2004).

In this research, calibration samples were composed of algaeoils that were subjected to oxidation process for 216 h. The ATRFTIR spectra of eight calibration standards were used to developcalibration methods. The eight calibration standards belonged to0th, 1st, 2nd, 3rd, 4th, 7th, 8th and 9th days. This study was per-formed in weekdays; thus 5th and 6th days were not included.Generally two types of validation are used to confirm establishedmethods; cross validation and test set validation. In this study,cross validation (leave-one-out) was preferred since numbers ofthe samples were limited. Calibration curves and cross validationscurves were created on the basis of the relation between peroxidevalues obtained using chemical method and predicted by FTIR.Also, in this study, calibration and cross validation procedureswere performed using three different types of methods. The firstone was peak area integration (PAI) in which the peak area of planedeformation vibration of trans double bonds (990–940 cm�1) wasintegrated and calibration equation related to oxidation wasobtained by a simple linear regression. The second and third tech-niques were the aforementioned multivariate calibration tech-niques; namely, PLSR and PCR.

In substance, PLSR and PCR were used to model the relationshipbetween a set of predictor variables (X) and a set of response vari-ables (Y) in this study in which the response was the peroxidevalue predicted by FTIR technique. Original (Normal), 1st deriva-tive and 2nd derivative spectra of calibration and cross validationsamples were used to develop PAI, PLSR and PCR models. R2, stan-dard error of calibration (SEC), standard error of cross validation(SECV) and bias values were taken as criteria to judge accuracyof the developed methods.

Discrimination and classification of algae oil samples wereensured by using the OPUS Version 7.2 software. In HCA, firstderivative versions of all spectra were included in classificationmodel and Ward’s algorithm was employed. In this work, 990–940 cm�1 spectral range was determined to perform hierarchicalcluster analysis (HCA) analysis.

Fig. 1. FTIR spectra of algae oil at mid

3. Results and discussion

3.1. Characterization of algae oil based on FTIR spectra

FTIR spectrum of algae oil in the 4000–600 cm�1 spectral rangeis presented in Fig. 1. The ATR-FTIR spectrum of algae oil is quitesimilar to those of other oil species reported in the literature. Asshown in Fig. 1, infrared spectrum of algae oil had absorbtionbands at 3012, 2922, 2853, 1744, 1464, 1377, 1150, 1114, 966and 721 cm�1. These bands were related with the functionalgroups present in chemical structure of the algae oil, whichrevealed the unique characteristic properties specific to the com-ponents. The band observed at 3012 cm�1 was related to thestretching vibration of cis olefinic @CAH double bonds (Rohman& Che Man, 2012). Strong band intensity was observed at 2922and 2853 cm�1. These vibrations arose from CAH stretching vibra-tions; in other words, asymmetric and symmetric stretching vibra-tions of methylene (�CH2) and methyl (�CH3) could be observed at2922 and 2853 cm�1, respectively (Maurer, Hatta-Sakoda, Pascual-Chagman, & Rodriguez-Saona, 2012). The band at 1744 cm�1 wasrelated to the stretching vibrations of triglyceride ester linkage(–C@O) (Henna Lu & Tan, 2009; Setiowaty et al., 2000). CAH bend-ing vibrations arising from methylene and methyl groups wereobserved at 1646 cm�1 and 1377 cm�1 (Coates, 2000). The bandobserved at 1150 cm�1 could be attributed to CAO, CH2 stretching,bending vibrations (Henna Lu & Tan, 2009; Innawong,Mallikarjunan, Irudayaraj, & Marcy, 2004). The band at1114 cm�1 resulted from the stretching vibrations of –CAO estergroups (Guillén & Cabo, 2000). The region between 900 and1200 cm�1 was the fingerprint region, so properties or spectral fea-tures specific to the molecules can be observed especially in thisrange. In this range, the band at 966 cm�1 can be attributed toCAH out of plane deformation vibration of trans double bonds(Birkel & Rodriguez-Saona, 2011). Lastly, absorbtion band at721 cm�1 arouse from bending (rocking) vibrations of –(CH2) n–,HC@CH– (cis) groups (Henna Lu & Tan, 2009).

3.2. Determination of spectral range for tracking oxidation

In this study, oxidation process was tracked and evaluatedusing the absorbtion band at 966 cm�1 in the spectral range of990–940 cm�1. As mentioned in the previous section, the band at966 cm�1 was associated with the trans double bonds. This bandis used for quantification of total trans fatty acid and uniquely

infrared region (4000–650 cm�1).

Fig. 2. Overlaid FTIR spectra of algae oil at selected region (1000–850 cm�1) obtained for different days.

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characteristic of isolated trans double bands (Mossoba, Milosevic,Milosevic, Kramer, & Azizian, 2007). In this study, algae oil wassubjected to thermal treatment for 216 h at 60 ± 1 �C in an oven.During thermal treatment, lipid oxidation and trans fat formationphenomena occur simultaneously. Trans fat formation actualizewith two mechanisms in thermal treatment: one of them is singletoxygen reaction and other one is free radical induced izomeriza-tion. Singlet oxygen reacts with cis double bond and alters cis dou-ble bond of fatty acids into trans configuration. In other way, a freeradical can react reversibly to a double bond to form a radicaladduct. In other words, free radical and singlet oxygen are knownas key initiators in lipid oxidation (Vu & Boonyarattanakalin, 2013).In this study, trans fat formation was resulted from free radicalinduced isomerization. Fig. 2 represents the overlaid FTIR spectraof algae oil during thermal treatment at 0th, 1st, 2nd, 3rd, 4th,7th, 8th, 9th days, respectively. Also, calibration range (990–940 cm�1) is marked and illustrated in Fig. 2. When all spectrawere evaluated, it was clearly seen that all spectra had similarabsorbtion bands and characteristic properties. On the other hand,when we compared all spectra with each other, it was seen thatpeak intensity changes were in association with the thermal treat-ment period. The intensity of CAH out of plane deformation vibra-tion of trans double bonds at 966 cm�1 increased directlyproportional to the exposure time. While oxidation is taking place,double bonds of unsaturated fatty acids undergo isomerizationfrom cis to trans (Beltran, Ramos, Grane, Martin, & Garrigos,2011); thus an increase in trans fatty acid content of the algae oilcould be expected, just like a phenomenon in our work in whichwe observed in the ATR-FTIR spectral data. Also, results from ear-lier studies demonstrated a strong and consistent associationbetween trans double bonds at 966 cm�1 and peroxide value. Forexample, peroxide value was successfully predicted by FTIR spec-troscopy technique in virgin coconut oil using the 988–900 cm�1

spectral range (Marina et al., 2013). In addition, trans fat contentcouldbe successfullyquantifiedusing thebandat966 cm�1 in edibleoils by portable hand held IR spectrometer combinedwith a chemo-metric method, Partial Least Squares Regression (PLSR) (Birkel &Rodriguez-Saona, 2011). Generally, the band at 967 cm�1 is usedfor tracing oxidation process (Guillén & Cabo, 2000).

3.3. Calibration and validation models for quantitative analysis

In this study, normal, 1st derivative and 2nd derivative spectraof all samples were used in order to develop calibration model for

peroxide value (PV) quantification. Fig. 3 displays the normal, 1stderivative and 2nd derivative FTIR spectra of all algae oil samplesin the spectral region of 1800–600 cm�1. Calibration model forquantification of PV in algae oil samples was established andvalidated with cross-validation technique. Numerical valueswhich were used to build calibration curves were presented inTable 2. In other words, Table 2 represents peroxide valuesobtained by chemical method and predicted by PAI, PLSR andPCR techniques.

There were eight samples in calibration set and cross-validationwas performed with same samples for all calibration techniques. Inthis study, mainly three different techniques were applied toobtain calibration equation. These techniques were integration ofpeak area integration (PAI), PLS (Partial Least Squares) and PCR(Principal Component Regression) (Table 1). In the first technique,ATR FTIR spectra of 8 calibration standards were used to obtain cal-ibration equation relating peroxide value to the integrated peakarea of the trans double bonds absorbtion (990–940 cm�1).Obtained equation was employed for prediction of peroxide valueof algae oil samples. Also, PLSR and PCR multivariate techniqueswere used to predict peroxide value and similarly these twochemometrics techniques were performed in the 990–940 cm�1

spectral range. Partial least square fit method was successfullyused in software for quantitative analysis using Partial LeastSquares Regression (PLSR). PLSR is a recently developed generaliza-tion of multiple linear regression (MLR) and PLSR provide quantita-tive multivariate modeling methods (Wold, Sjöström, & Eriksson,2001). PLSR mathematically correlates the spectral data with amatrix of the property of interest. Basically, the purpose of thistechnique is to measure quantities like the absorption of infraredradiation with properties of the system, for example, the concen-tration of one component in a multi component system. Generally,PLS regression technique involves two steps: the calibration of themethod and validation of the method to determine a value of anunknown sample (Tewarii & Irudayaraj, 2004). In addition to PLSanalysis, PCR (Principal Component Regression) analysis was per-formed. Principal component analysis can gather highly correlatedindependent variables into a principal component and all principalcomponents are independent of each other. PCR transforms a set ofcorrelated variables to a set of uncorrelated principal components.Then regression equations are built with a set of uncorrelated prin-cipal components and the best equation with maximum R2 andminimum standard error is chosen. Lastly, best equation is trans-formed into the general linear regression equation (Liu, Kuang,

Fig. 3. Overlaid normal, first derivative and second derivative of FTIR spectra of oxidized algae oil samples in the spectral region of 1800–600 cm�1.

192 N. Cebi et al. / Food Chemistry 225 (2017) 188–196

Gong, & Hou, 2003) In other words, the aim of PCA technique is tofind a new group of dimensions which is catching data variety bet-ter (Berkhin, 2006).

Fig. 2 represents the overlaid ATR-FTIR spectra of algae oil sam-ples and calibration range that was chosen for peak integration,PLSR and PCR analysis. Main spectral variations were observed in

Table 1Calibration and validation results of peak area integration and multivariate analysis of PLS and PCR in spectral range of 990–940 cm�1.

Accuracy parametersCalibration methods Equations R2

Spectra Calibration Validation Calibration Validation SEC SECV Bias SDC

PLSRy Normal y = 1.0037x + 0.6951 y = 1.006x � 1.3047 0.999 0.994 2.488 5.966 4.664 2.661st derivative y = 0.9991x + 0.9984 y = 0.9868x + 0.0982 0.998 0.990 2.690 5.437 3.572 2.872nd derivative y = 1.0002x + 0.422 y = 0.9513x � 0.9789 0.995 0.979 5.299 10.13 5.288 5.65

PCR� Normal y = 1.0033x + 0.6628 y = 1.0044x � 0.9526 0.998 0.993 2.891 6.715 4.689 3.091st derivative y = 0.9966x + 0.2627 y = 0.9762x + 1.2607 0.996 0.986 4.481 8.631 4.972 4.682nd derivative y = 0.9893x + 0.4228 y = 0.9543x + 1.2561 0.984 0.950 7.495 15.25 8.209 8.01

PAI§ Normal y = 1.0182x � 1.1454 y = 1.0185x � 0.9700 0.999 0.999 1.990 2.288 0.286 2.12

y PLSR, Partial Least Squares Regression.� PCR, Principal Component Regression.§ PAI, Peak area integration.

Table 2Peroxide values obtained by chemical method and predicted by PAI, PLSR and PCR techniques.

Caibration Methods PAI§ PLSRy (Normal) PLSRy (1st derv.) PLSRy (2nd derv.) PCR� (Normal) PCR� (1st derv.) PCR� (2nd derv.)

Cal Cross-Val

Cal Cross-Val

Cal Cross-Val

Cal Cross-Val

Cal Cross-Val

Cal Cross-Val

Cal Cross-Val

PV(Chemical)(meq/kg)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

PV(FTIR)

1.3 1.50 1.70 3.41 5.12 4.19 0.205 5.83 3.39 3.87 5.26 0.89 1.67 8.16 26.4110.20 10.33 11.05 11.96 14.76 4.42 10.93 10.67 18.57 12.44 14.48 5.54 10.71 9.82 8.8417.54 17.11 17.02 12.73 12.52 17.74 14.31 21.55 20.29 11.86 13.24 14.05 17.54 18.42 3.6430.00 28.02 27.65 26.99 16.00 30.23 29.84 25.21 16.56 26.61 15.93 33.71 32.91 21.27 20.30352.00 50.96 50.82 53.25 56.63 49.39 46.72 43.66 38.83 53.83 57.80 51.54 55.83 46.41 49.14130.00 132.00 132.36 129.46 127.59 134.69 140.72 128.12 114.14 129.07 125.81 137.10 140.76 113.49 107.74160.60 159.80 159.57 159.21 162.10 158.64 159.59 161.67 167.19 159.30 165.33 159.79 162.76 166.22 185.23220.00 224.60 225.22 221.38 220.22 219.07 210.37 221.66 206.29 221.41 218.88 215.75 205.08 224.58 201.99

y PLSR, Partial Least Squares Regression.� PCR, Principal Component Regression.§ PAI, Peak area integration.

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the spectral range 990–940 cm�1. This spectral range was also usedin HCA (Hierarchical Cluster Analysis). Table 1 represents the cali-bration and cross validation results of PAI, PLSR and PCR analysisfor normal, first derivative and second derivative spectra. The suc-cess of these techniques was evaluated on the basis of linear equa-tions, R2, SEC (standard error of calibration), SECV (standard errorof cross-validation),bias and SDC (standard deviation of calibra-tion) values. In this study, the highest R2 values and the lowestSEC, SECV and bias values were observed in the normal spectrafor PLS and PCR analysis. Relationship between peroxide valuesdetermined by FTIR technique and chemical method revealed bet-ter correlation with R2 (0.99) using normal spectra than using thefirst and the second derivative spectra. Similarly, SEC values werelower for the normal spectra when PLS and PCR were employed.In this study, cross validation (leave one out) technique was pre-ferred to validate the developed calibration methods of PLSR andPCR since numbers of samples were limited. In cross validation,before starting the calibration, one sample is excluded from theentity of samples. This sample is used for the validation. Theremaining samples are used to calibrate the system. After predic-tion of all the observations by the cross-validation technique, com-puted R2 and SECV values enlighten the relationship betweenperoxide values by the FTIR and those by the chemical method inthe selected spectral range (990–940 cm�1). Favorable R2 and SECVvalues were obtained by both of the PLSR and PCR analysis. PLSRanalysis was well performed using normal spectra, revealing thattrans double bonds at 966 cm�1 spectral band gave R2 value of0.99 and SECV value of 5.966 (Table 1). Similarly, PCR analysiswas performed quite well, giving R2 value of 0,99 and SECV valueof 6.715 (Table 1). Basically, the coefficient of determination, R2

is the measure of fitness of the proposed model to the observed

data and gives the percentage of variance present in the true com-ponent values, which is reproduced in the regression. R2

approaches 1 as the fitted concentration values approach the truevalues. Considering this, R2 value of 0.99 was quite favorable forquantification model that we developed.

In addition to PLSR and PCR, PAI was obtained by a simple lin-ear regression. In PAI, the peak area of plane deformation vibra-tion of trans double bonds (990–940 cm�1) was integrated andcalibration equation, R2, SEC and SECV values were related todeveloped method. Table 1 compares the results of three differenttypes of calibration techniques and it is clearly seen that PAI tech-nique provides quite favorable, even the best prediction results. Inthis technique, simple linear regression analysis was used to pre-dict peroxide value of algae oil samples. Peroxide values predictedby FTIR technique and those by chemical method were correlatedwith a quite high (0,999) R2 value in both calibration and cross-validation methods. Furthermore, the lowest SEC, SECV and biasvalues as 1.99, 2.28 and 0.286, respectively were obtained bythe PAI technique. Overall, the three calibration techniques;namely, PAI, PLSR, PCR were performed and the calibration andcross validation regression plots of peroxide value by chemicalanalysis versus peroxide value by FTIR using normal spectra arepresented in Fig. 4. In this Figure, peroxide values which wereobtained using chemical method are presented on x-axis, andthe ones predicted by FTIR combined PLSR, PCR and PAI tech-niques are presented on y-axis. Calibration and cross-validationplots are presented for normal spectra and quite high R2 valueof 0.99 was obtained for all techniques. Linear regression modelscalculate an equation that minimizes the distance between thefitted line and all data points. R2 value is a statistical measureof how close the data are to the fitted regression line and it varies

Fig. 4. Regression plots of peroxide values determined by chemical analysis and peroxide values determined by FTIR. (A) Calibration set of PLSR, (B) Cross-validation set ofPLSR, (C) Calibration set of PCR, (D) Cross-validation set of PCR, (E) Calibration set of PAI, (F) Cross-validation set of PAI. PLSR, Partial Least Squares Regression; PCR, PrincipalComponent Regression; PAI, peak area integration.

194 N. Cebi et al. / Food Chemistry 225 (2017) 188–196

between 0 and 1. When R2 equals 1.0, all points lie exactly on astraight line with no scatter. This means that X values let one pre-dict Y values perfectly in the developed regression model. As seenin Fig. 4, quite favorable R2 values of 0,99 were obtained. Theequation presented in Fig. 4 also provides information aboutcloseness of the predicted and actual values. Equations of modelsare presented on the plots. As seen from the equations, slopes ofthe corresponding equations were found between �0.99 and 1.00and the intercept were �0, indicating that the slope was veryclose to 1 and intercept was close to 0, which also supported suc-

cessfulness of the model. If the equation was determined as y = x,it has %100 prediction ability. The obtained equations were verysimilar to y = x line since slope and intercepts of the equationfound in this study were determined as 0.99 (close to 1) and 0(close to 0), respectively.

3.4. Hierarchical cluster analysis (HCA)

Hierarchical cluster analysis (HCA) is an algorithmic approachthat aims to construct a hierarchy of clusters. In HCA, clusters and

Fig. 5. HCA dendrogram of algae oil samples in the spectral range 990–940 cm�1.

N. Cebi et al. / Food Chemistry 225 (2017) 188–196 195

sub-clusters are visualized definitely in dendrogram graphs. Knownas the minimum variance method, the Ward’s method joins at eachstage of the cluster pairwhosemergerminimizes the increase in thetotal within-group error sum of squares. Homogeneous clusters anda symmetric hierarchy are tended to be producedwith its definitionof a cluster center of gravity which provide a useful way of repre-senting a cluster (Lorr, 1983). HCA dendrogram of algae oil samplesin the spectral range 990–940 cm�1 is presented in Fig. 5. One canconclude from the dendrogram that oxidation of algae oil samplescan be evaluated and investigated using FTIR spectral data com-bined with HCA. In accordance with the HCA dendrogram, twowell-separated clusters were observed with quite high heterogene-ity value of 140. What is striking about the HCA dendrogram washow oxidation level could be evaluated using FTIR spectra in 990–940 cm�1 range. In other words, observed classification and dis-criminationof algae oil sampleswas directly related to the oxidationtime. As in Fig. 5, Algae oil samples of 0th, 1st, 2nd, 3rd and 4th dayswere heaped together in one cluster at the left side of the HCA den-drogramwhile 7th, 8th and 9th dayswere clustered in one cluster atthe right side. When we evaluate the left arm of the dendrogram atlength, samediscrimination-oxidation time relation could be clearlyobserved. The left arm of the dendrogramwere split in two clustersin which the samples of 0th, 1st, 2nd days were grouped together inone cluster while the samples of 3rd and 4th days were heapedtogether in the other cluster. Similar phenomenon was observedin the right arm of the dendrogram. In other words, when the oxida-tion time progressed, the distance between algae oil samples pro-portionally increased, as seen in the HCA dendrogram.Accordingly, the algae oil sample of 0th daywas observed in the left-most side of the HCA dendrogram while sample of 9th day wasobserved in the rightmost side in Fig. 5. In conclusion, the obtainedHCA dendrogram revealed that the oxidation process could be read-ily predicted using the ATR-FTIR spectra combinedwith chemomet-rics of HCA.

4. Conclusions

Our work revealed that the used ATR-FTIR spectroscopy tech-nique combined with chemometrics of PLS and PCR can be usedto rapidly, easily and accurately predict the peroxide value (PV)in algae oil. In the present work, peak are integration (PAI), PLSand PCR calibration models were established using the band at966 cm�1, which related to CAH out of plane deformation vibra-tion of trans double bonds. The PV predicted by the establishedFTIR methods were successfully correlated with those determined

by a wet chemistry iodometric method. Given that the developedFTIR-ATR technique is cost-effective, rapid, easy to operate, non-destructive and can be mentioned as ‘‘green analytical technique”since no solvents and reagents were used during the study, theproposed ATR-FTIR spectroscopy technique combined with chemo-metrics of PLS and PCR can be a very good tool to predict peroxideoxidation in oils.

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