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Authentication of canned fish packing oils by means of Fourier transform infrared spectroscopy
Food Chemistry 190 (2016) 122–127
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Analytical Methods
Authentication of canned fish packing oils by means of Fourier transform infrared spectroscopy Ana Dominguez-Vidal a, Jaime Pantoja-de la Rosa a, Luis Cuadros-Rodríguez b, María José Ayora-Cañada a,⇑ a b
Department of Physical and Analytical Chemistry, Universidad de Jaén, Campus Las Lagunillas, E-23071 Jaén, Spain Department of Analytical Chemistry, Universidad de Granada, c/Fuentenueva s.n., E-18071 Granada, Spain
a r t i c l e
i n f o
Article history: Received 25 March 2014 Received in revised form 4 May 2015 Accepted 15 May 2015 Available online 16 May 2015 Keywords: Olive oil High-oleic sunflower oil Vegetable oil Canned fish Tuna fish Infrared spectroscopy
a b s t r a c t The authentication of packing oil from commercial canned tuna and other tuna-like fish species was examined by means of attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and chemometrics. Using partial least squares discriminant analysis (PLS-DA), it was possible to differentiate olive oil from seed oils. Discrimination of olive oil from high-oleic sunflower oil was possible, despite the latter having a degree of unsaturation more similar to olive oil than to sunflower oil. However, in the samples analyzed, sunflower oil could not be differentiated clearly from those labeled with the generic term ‘‘vegetable oil’’. Furthermore, the authentication of extra virgin olive oil, although more difficult, could be achieved using ATR-FTIR spectroscopy. The method could be applied regardless of fish type, without interference from fish lipids. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Interest in fish consumption has increased in recent years due to the wide range of health benefits associated with the high polyunsaturated fatty acids (PUFAs) content. In addition to fresh fish, canned products enable delayed consumption of this otherwise perishable food. Canned tuna is one of the most widespread and recognizable fish commodities in the world, and oil is frequently adopted as a liquid medium. In fact, oil has a preserving effect and helps make the product more palatable. Among the different types of oil, the most commonly used in canning are: olive oil (OO, made up of refined olive oil blended with virgin oil, apart from ‘lampante’ oil, in an undefined ratio) and refined seed oils (RSOs). The most expensive extra virgin olive oil (EVOO, obtained from the fruits of Olea europaea L. by mechanical or other physical means that do not lead to any chemical changes) is reserved for tuna due to the higher commercial value of this fish. In general, the price of canned fish in OO is higher than fish in RSOs, (sunflower or soybean oils), which are commonly labeled as ‘‘vegetable oil’’. Furthermore, the presence of OO is usually highlighted in the labeling by the producers because its consumption is associated with a healthy diet. European Commission Regulation No. 1536/92 (European Commission, 1992), laying down common ⇑ Corresponding author. E-mail address: [email protected] (M.J. Ayora-Cañada). http://dx.doi.org/10.1016/j.foodchem.2015.05.064 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved.
marketing standards for preserved tuna and bonito, establishes that if the covering medium used forms an integral part of the trade description, (i.e. name of the product includes the description ‘in olive oil’), only OO can be used, excluding all other oils. In addition, the introduction of new EU legislation (European Parliament, 2011) concerning food information for consumers, which took effect in 2014, brings important changes in the labeling of foodstuffs. Regarding processed foods containing vegetable oil as an ingredient, the new regulation states the type of oil employed must be indicated on the package. Up to now, it has been common practice to label the oil with the generic term ‘‘vegetable oil’’. Consequently, there is a growing need to develop methodologies for industry and regulators to verify the type of oil used (Osorio, Haughey, Elliott, & Koidis, 2014). Few studies assessing the genuineness of the liquid medium in production of in-oil canned fish have been reported. Of those that have been published, the first was designed to assess the genuineness of oil used as medium for canned tuna, mackerel and sardines by analysis of fatty acid composition (Cerma & Remoli, 1966; Remoli & Doro, 1968, 1970). Similarly, the percentage of unsaturated fatty acids trans isomers has been evaluated to test the genuineness of the oil for canned sardines, tuna and mackerel (Bizzozero & Carnelli, 1996; Cavallaro, Bizzozero, Carnelli, & Renon, 1996). Triglycerides profiles were proposed as a more reliable tool by Vittucci, Pierri, and Maffei (1999). These studies stressed the difficulty of assessing whether the oil, used as liquid medium in canned fish, was genuine
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because of lipid exchange between the fish and oil. Thus, the fact that fish releases lipids, and trans linoleic and trans linolenic acids can affect features essential in determining whether the oil is genuine (Vittucci et al., 1999). To address this problem, Rossi and co-workers reported the sterol composition could contribute when assessing oil authenticity (Rossi, Colonello, & Alamprese, 2001). In recent studies, more emphasis has been given to the evaluation of oil quality, particularly aspects such as the extent of oxidative and hydrolytic degradation (Caponio, Gomes, & Summo, 2003; Caponio, Summo, Pasqualone, & Gomes, 2011). IR spectroscopy is a rapid and non-destructive technique for the authentication of food samples. Analysis of a food sample using the MIR spectrum (4000–400 cm 1) reveals information about the molecular bonds present and can, therefore, give details of the types of molecules present in the food. This technique is suitable for use in an industrial setting due to its ease of use and the relatively low financial cost of obtaining and running the equipment. Different types of foods have been tested for adulteration using IR spectroscopy: wine samples have been differentiated on the basis of geographical and varietal origin (Roussel, Bellon-Maurel, Roger, & Grenier, 2003); MIR and chemometrics detected adulteration of apple juice with beet syrup and cane syrup (100% and 96.2% correct, respectively, Sivakesava, Irudayaraj, & Korach, 2001); and adulteration of honey samples with sugar solutions at 14% w/w was detected using MIR and PLS (Kelly, Downey, & Fouratier, 2004). Extensive research has been performed regarding virgin olive oil adulteration because of its especial characteristics of high quality and high price commodity (Aparicio, Morales, Aparicio-Ruiz, Tena, & Garcia-Gonzalez, 2013; Nunes, 2014). Thus, several models of principal component analysis (PCA) (Lai, Katherine Kemsley, & Wilson, 1994), stepwise linear discriminant analysis (SLDA) (Baeten et al., 2005) and partial least squares discriminant analysis, PLS-DA (Obeidat, Khanfar, & Obeidat, 2009) have been proposed to identify the presence of different vegetable oils in OO, mainly EVOO. Quantification of different adulterants in OO have also been approached – using FTIR and PLS (Gurdeniz & Ozen, 2009; Lerma-Garcia, Ramis-Ramos, Herrero-Martinez, & Simon-Alfonso, 2010; Maggio, Cerretani, Chiavaro, Kaufman, & Bendini, 2010; Tay, Singh, Krishnan, & Gore, 2002). Furthermore, recently, we have proposed a methodology for quantification of OO in blends that meet specific EU legislation concerning commercialization and labeling of OO products (de la Mata et al., 2012). Here, we will evaluate for the first time the potential use of FTIR in combination with chemometrics to assess the authenticity of the oil added to canned fish. 2. Experimental 2.1. Samples Samples of in-oil canned fish (90) were purchased from different retailers (10) in Spain. They included different brands of canned tuna and other tuna-like species in a variety of olive (48) and seed (42) oils. A more detailed description of the samples is provided in Table 1. According to EU Regulation 1536/92 (European Commission, 1992), the light tuna label refers to Thunnus albacares, and the label generically denominated as ‘‘tuna’’ may include any Thunnus or similar species (e.g., Katsuwonus pelamis). The bonito products can contain species of the genera Sarda, Euthynnus and Auxis. 2.2. Acquisition of infrared spectra Infrared spectra were collected with a Varian 660 FT spectrometer, equipped with a MCT detector. Spectra were recorded in the
Table 1 Summary of canned fish samples with indication of the type of covering oil. Canned fish
Type of oil
Quantity
Tuna Light tuna Light tuna Bullet tuna Bonito Bonito Light tuna Light tuna Bullet tuna Mackerel Tuna Light tuna
Olive Olive Extra virgin olive Olive Olive Extra virgin olive Sunflower High oleic sunflower Sunflower Sunflower Vegetable Vegetable
5 15 3 8 10 7 10 9 6 4 4 9
range 450–4000 cm 1. A MIRacle ATR (Pike Technologies) integrated sampling accessory equipped with a three-reflection diamond crystal was used. 1 mL of oil was taken from each can placed in a glass vial and dried by adding Na2SO4. Samples were kept in the freezer until analysis. For measurement, a drop of oil was placed on the ATR element (diamond IRE). The diamond IRE was cleaned with methanol and dried with a clean tissue after every measurement. Each sample was measured five times and the spectra were averaged. A spectrum of the clean and dry ATR crystal against air was used as background before each measurement. All the spectra were recorded at a resolution of 2 cm 1 and were the average of 64 scans. Sample measurement order was randomized to avoid any biased results due to instrumental effects. To record the FTIR spectra of the fish flesh, the packing medium was drained and fish samples were ground, spread onto the ATR crystal and pressed using a tool that ensured contact between the sample and crystal. In all cases, the spectral region from 1900 to 2350 cm 1 was not considered because of the absence of useful bands and the strong absorbance of the diamond IRE.
2.3. Data analysis PCA and PLS-DA were performed with the PLS-ToolBox (Eigenvector Research Inc., Wenatchee, WA, USA) for MATLAB R2010a 7.6 (The Mathworks Inc., Natick, MA, USA). PCA was used to explore relationships between variables and observations. This unsupervised technique reduces the dimensionality of the original data matrix by constructing Principal Components (PCs) that are linear combinations of the original variables. The first few PCs capture most of the variability in the original data. Data were mean-centered prior to applying PCA. PLS-DA consists of a classic PLS regression in which the response variable (Y) is a categorical variable expressing the class membership. PLS-DA performs a PCA-like reduction of the independent variables X (in this case, the absorbance values at individual wavenumbers) to obtain a maximum correlation between them and the class membership, (i.e. type of oil). Therefore, PLS-DA is a supervised classification technique that requires a training and validation sets of spectra. The performance of the fitted model was evaluated using random subsets: five different test sets were constructed through random selection of 18 objects in the dataset, avoiding the inclusion of any single object in more than one test set. The remaining data were used to fit a model, which was applied subsequently to predict class membership for the removed samples. This procedure was repeated 10 times to ensure that a prediction was available for all samples.
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3. Results and discussion 3.1. Infrared spectra of fish flesh and packing oils The ATR-FTIR spectra of different canned tuna-like fishes and different oils are shown in Fig. 1. The infrared spectrum of fish flesh was dominated by the absorption bands characteristics of proteins at 3270 cm 1 (NAH stretching, amide A), 1621 (C@O stretching, amide I) and 1539 cm 1 (NAH bending coupled with CAN stretching, amide II). The absorption bands of water, which usually accounts for about 60% of the weight of fresh fish muscle and is tightly bound to the proteins in the structure (Rasmussen & Morrissey, 2007), are located at 3370, 1640 and 692 cm 1 and overlap strongly with the bands of the proteins. Band characteristics of lipids were also present in the spectra of the fish flesh at 2960, 2923, 2854, 1743, 1456 and 1160 cm 1. As can be seen in Fig. 1a, absorption bands corresponding to lipids were far less intense in the spectrum of tuna canned in water, which was included for comparison, than in the rest of samples packed in oil media. This reflects the increase in fat content that take place after canning in oil. A parallel decrease in water content has been reported to occur during processing (Garcia-Arias, Sanchez-Muniz, Castrillon, & Pilar Navarro, 1994; Rasmussen & Morrissey, 2007). Fig. 1b shows the typical spectra of several packing oils including OO and RSOs. The main differences can be observed in the bands related to unsaturation, namely 3006 cm 1 (CAH stretching in cis olefinic double bonds) and 710 cm 1 (CH wag in cis double bonds), and in the weak bands of the region between 1000 and 800 cm 1 where band assignment is more complicated. The broad band, centered about 3370 cm 1, is
Fig. 1. Typical ATR-FTIR spectra of (a) canned tuna-like fishes (including one sample canned in water, black and dotted line) and (b) covering oils.
due to the OH stretching of water and reflects the high variability in water content of the packing media due to the release of water from the fish flesh during canning. Poor reproducibility was observed both in this region and around 1640 cm 1 when measuring replicates of the same oil sample due to the heterogeneous distribution of water in the oil. For this reason, oil samples were dried with Na2SO4 prior to the ATR-FTIR measurements. 3.2. Exploratory data analysis A preliminary exploration of the data using PCA was carried out to evaluate the influence of fish and oil types on the natural grouping of the samples according to their spectral characteristics. In the case of infrared spectra for fish flesh, three PCs captured 98.0% of the variance. However, no grouping of the samples based on fish or oil type was observed. In fact, inspection of the loading vectors for these PCs revealed the main sources of variance were just differences in water, fat and protein content among the samples. For this reason, only packing oils spectra were considered for further analysis. In the case of the oil spectra, three PCs captured 98.1% of the variance. As can be seen in Fig. 2, PC1 establishes a clear separation
Fig. 2. Results of principal component analysis of ATR-FTIR spectra of covering oils (a) scores plot in the space defined by PC1 and PC2, (b) loadings vector of PC1 together with ATR-FTIR spectra of triglycerides triolein (OOO) and trilinolein (LLL).
A. Dominguez-Vidal et al. / Food Chemistry 190 (2016) 122–127 Table 2 Results of PLS-DA classification for a binary model: olive/non-olive. Class
Non olive
Olive
Sensitivity (Cal) Specificity (Cal) Sensitivity (CV) Specificity (CV)
1.000 1.000 1.000 0.988
1.000 1.000 0.988 1.000
Table 3 Results of PLS-DA classification for a four-classes model: OO, olive oil; EVOO, extra virgin olive oil, (SO + VO), class formed by all samples labeled as sunflower oil and vegetable oil; HOSO, high oleic sunflower oil. Class
SO + VO
HOSO
OO
EVOO
Sensitivity (Cal) Specificity (Cal) Sensitivity (CV) Specificity (CV)
1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000
0.985 0.970 0.948 0.932
1.000 0.986 0.930 0.968
between OO samples and the rest of the oils except high-oleic sunflower oil (HOSO) samples. The strongest contributions to the loadings vector of PC1 were in the region 2800–3030 cm–1 and, specifically, 3010 cm 1 (characteristic region of CH stretching in cis-olefins [negative]), and 2920 and 2852 cm–1 (typical of CH2
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and CH3 stretching [positive]). Thus, this component is clearly related to differences in unsaturation. Assignment of other features, for example at 914 cm 1, was not so straightforward (Guillén & Cabo, 1999), but they represented the main differences in FTIR spectra for triolein and trilinolein, the major triglycerides in OO and RSOs, respectively (see Fig. 2b). The higher total unsaturation in RSOs due to their high content of polyunsaturated fatty acids (mainly linoleic acid) was reflected in their positive score for this component. In contrast, OO samples showed negative values. In the case of HOSO, its high content of monounsaturated oleic acid (usually more than 80% (Chiavaro et al., 2009)) made its composition more similar to OO. PC2 does not contribute to any useful group separation, as can be seen in Fig. 2a; samples appear scattered along the axis defined by this component. Looking at its loading vector (not shown), this component was related to water content in oil samples. Thus, despite drying with Na2SO4, differences in water content were still a significant source of spectral variability. PC3 axis also did not contribute to any further separation among types of oil. In order to check the influence of the fish type, the oil samples were labeled with this information (see Figs. S1a and S1b in supporting information). As can be clearly seen in the scatter plot, the major variability sources in the oil spectra were not related to fish type since samples of the same fish did not group together.
Fig. 3. Prediction results (cross validation) of PLS-DA model for the four considered classes: OO, olive oil; EVOO, extra virgin olive oil, (SO + VO), class formed by sunflower oil and vegetable oil samples; HOSO, high oleic sunflower oil.
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3.3. Discrimination of oil type using PLS-DA
4. Conclusions
Preliminary exploratory analysis using PCA revealed its potential use for classification of oils by means of their infrared spectra. A PLS-DA algorithm was used, subsequently, to build a model able to discriminate the different types of oil used for canning on the basis of their infrared spectra. First, the suitability of the method to discriminate between fish canned in OO (either OO or EVOO) and RSOs of any type was evaluated. Four latent variables were necessary to build the optimal model. These explained 98.6% of the variance in X (spectral variance) and 91.3% of the variance in Y (class membership). As can be seen in Table 2, successful calibration was achieved with sensitivity (rate of true positives) and specificity (rate of true negatives) of 100% for both classes. Validation results using cross-validation were also satisfactory because the classification performance did not decreased significantly. It is interesting to note that, although the major variance sources revealed in the exploratory PCA grouped HOSO with OO samples together, using PLS-DA it was possible to discriminate between these; HOSO samples being assigned to the non-OO class. In fact, PLS-DA performed a rotation of PCA components and a selection of the variables that carried the class separating information to achieve a maximum separation between OO and non-OO classes resulting in successful classification. In order to investigate the chemical basis of the discrimination, we inspected the loading vectors and the latent variables scores. Although only two classes (OO and non-OO) were considered in this model, the samples were labeled with all the information from the sub-classes (EVOO, OO, HOSO, SO, VO) in order to get better insight into the possibilities for discrimination. The loading vector LV1, which is very similar to the one of PC1, contained information related to total unsaturation. Looking at the scores for this, LV1 established a clear separation between oils rich in monounsaturated fatty acids (EVOO, OO and HOSO) and oils rich in polyunsaturated fatty acids (SO and VO). LV2 was influenced by differences in water content. LV3 and LV4 were the most important to distinguish between HOSO and other oils. However, clear assignment of spectral contributions to these LVs was not possible, probably because they correspond to differences in the content of several minor triglycerides as reflected in spectral contributions from C-H stretching, C@O stretching and, especially, from the fingerprint region (700–1470 cm 1). We also tried to develop a classification model able to discriminate among these five sub-classes. However, the prediction performance of this model was not satisfactory. Five LVs explained 98.7% of the spectral variance (variance in X), but only 56.8% of the variance in Y (class membership). Although EVOO, OO, HOSO classes produced reasonable predictions, the main problem with the model was distinction between classes SO and VO, with poor specificity for both (82% and 78%, respectively, using cross-validation). Inspection of sample scores for the different LVs revealed strong over-lapping between these two classes. This may be due to the fact that HOSO is the most common vegetable oil used in Spain and most of the packing oils declared as vegetable oils were probably just sunflower oil or mixtures containing a large proportion of this oil. If these two classes were considered together, as only one class (SO + VO), a model with successful prediction results could be obtained as shown in Table 3 and Fig. 3. Using six LVs, it was possible to discriminate between the four classes with sensitivity and specificity greater than 90% in all cases. In fact, for HOSO and (SO + VO), the prediction performance was 100%, whereas it was more difficult to distinguish between EVOO and OO. This is logical because, in this case, both oils have the same botanical origin and the differences are limited just to the refining process and the superior sensorial quality attributes of EVOO.
Information from the ATR-FTIR spectra of oil media in canned fish can be used for authentication of the botanical origin of the oil regardless of the type of fish. A clear distinction between oils rich in monounsaturated fatty acids and oils rich in polyunsaturated fatty acids was achieved easily using PCA. In addition, using PLS-DA, it was also possible to differentiate accurately HOSO from the rest and, even, to distinguish EVOO from OO with reasonable prediction performance. On the contrary, it was virtually impossible to distinguish between samples packed in sunflower oil from those in ‘vegetable oil’, probably because most of the oils labeled as ‘vegetable’ are, indeed, sunflower oil. Interestingly, highly unsaturated fatty acids (typical of fish lipids), which have been reported to occur in the oil of canned fish (Caponio, Gomes, & Summo, 2002; Caponio et al., 2003), did not interfere with the assessment of packing oil. The proposed method provides a rapid tool for assessing the authenticity of packing oil in diverse fish products. This could be of great interest in the current global market where international regulations have increased the numbers of samples that must be analyzed with the immediate consequence of increased demand for rapid methods to ensure food traceability and safety. Acknowledgments The authors acknowledge the Andalusia Regional Government (Consejería de Innovación, Ciencia y Empresa) for financial support through project P07-FQM-02667 and PAIDI Research Group FQM363. J.P. De la Rosa is also grateful to the University of Jaén for a Beginning Research grant. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foodchem.2015. 05.064. References Aparicio, R., Morales, M. T., Aparicio-Ruiz, R., Tena, N., & Garcia-Gonzalez, D. L. (2013). Authenticity of olive oil: Mapping and comparing official methods and promising alternatives. Food Research International, 54, 2025–2038. Baeten, V., Pierna, J. A. F., Dardenne, P., Meurens, M., Garcia-Gonzalez, D. L., & Aparicio-Ruiz, R. (2005). Detection of the presence of hazelnut oil in olive oil by FT-Raman and FT-MIR spectroscopy. Journal of Agricultural and Food Chemistry, 53, 6201–6206. Bizzozero, N., & Carnelli, L. (1996). Fatty acid composition and trans unsaturation of the covering oil of canned mackerels and tunas. Industrie Alimentari, 35, 680–683. Caponio, F., Gomes, T., & Summo, C. (2002). Use of HPSEC analysis of polar compounds in the ascertainment of the degradation level of oils utilised as liquid medium in canned tuna. Rivista Italiana delle Sostanze Grasse, 79, 285–288. Caponio, F., Gomes, T., & Summo, C. (2003). Quality assessment of edible vegetable oils used as liquid medium in canned tuna. European Food Research and Technology, 216, 104–108. Caponio, F., Summo, C., Pasqualone, A., & Gomes, T. (2011). Fatty acid composition and degradation level of the oils used in canned fish as a function of the different types of fish. Journal of Food Composition and Analysis, 24, 1117–1122. Cavallaro, A., Bizzozero, N., Carnelli, L., & Renon, P. (1996). Fatty acid composition and trans unsaturation of the covering oil of canned sardines. Industrie Alimentari, 35, 801–805. Cerma, E., & Remoli, S. (1966). Analisi dell’olio di copertura degli tonno in scatola e possibilitá di rilevarne la genuinitá. Bollettino dei laboratori chimici provinciali, 17, 236–251. Chiavaro, E., Vittadini, E., Rodriguez-Estrada, M. T., Cerretani, L., Capelli, L., & Bendini, A. (2009). Differential scanning calorimetry detection of high oleic sunflower oil as an adulterant in extra-virgin olive oil. Journal of Food Lipids, 16, 227–244. de la Mata, P., Dominguez-Vidal, A., Bosque-Sendra, J. M., Ruiz-Medina, A., CuadrosRodriguez, L., & Ayora-Cañada, M. J. (2012). Olive oil assessment in edible oil blends by means of ATR-FTIR and chemometrics. Food Control, 23, 449–455. European Commission (1992). Regulation No. 1536/92 laying down common marketing standards for preserved tuna and bonito. Council Regulation (EEC).
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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem
Analytical Methods
Authentication of canned fish packing oils by means of Fourier transform infrared spectroscopy Ana Dominguez-Vidal a, Jaime Pantoja-de la Rosa a, Luis Cuadros-Rodríguez b, María José Ayora-Cañada a,⇑ a b
Department of Physical and Analytical Chemistry, Universidad de Jaén, Campus Las Lagunillas, E-23071 Jaén, Spain Department of Analytical Chemistry, Universidad de Granada, c/Fuentenueva s.n., E-18071 Granada, Spain
a r t i c l e
i n f o
Article history: Received 25 March 2014 Received in revised form 4 May 2015 Accepted 15 May 2015 Available online 16 May 2015 Keywords: Olive oil High-oleic sunflower oil Vegetable oil Canned fish Tuna fish Infrared spectroscopy
a b s t r a c t The authentication of packing oil from commercial canned tuna and other tuna-like fish species was examined by means of attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and chemometrics. Using partial least squares discriminant analysis (PLS-DA), it was possible to differentiate olive oil from seed oils. Discrimination of olive oil from high-oleic sunflower oil was possible, despite the latter having a degree of unsaturation more similar to olive oil than to sunflower oil. However, in the samples analyzed, sunflower oil could not be differentiated clearly from those labeled with the generic term ‘‘vegetable oil’’. Furthermore, the authentication of extra virgin olive oil, although more difficult, could be achieved using ATR-FTIR spectroscopy. The method could be applied regardless of fish type, without interference from fish lipids. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Interest in fish consumption has increased in recent years due to the wide range of health benefits associated with the high polyunsaturated fatty acids (PUFAs) content. In addition to fresh fish, canned products enable delayed consumption of this otherwise perishable food. Canned tuna is one of the most widespread and recognizable fish commodities in the world, and oil is frequently adopted as a liquid medium. In fact, oil has a preserving effect and helps make the product more palatable. Among the different types of oil, the most commonly used in canning are: olive oil (OO, made up of refined olive oil blended with virgin oil, apart from ‘lampante’ oil, in an undefined ratio) and refined seed oils (RSOs). The most expensive extra virgin olive oil (EVOO, obtained from the fruits of Olea europaea L. by mechanical or other physical means that do not lead to any chemical changes) is reserved for tuna due to the higher commercial value of this fish. In general, the price of canned fish in OO is higher than fish in RSOs, (sunflower or soybean oils), which are commonly labeled as ‘‘vegetable oil’’. Furthermore, the presence of OO is usually highlighted in the labeling by the producers because its consumption is associated with a healthy diet. European Commission Regulation No. 1536/92 (European Commission, 1992), laying down common ⇑ Corresponding author. E-mail address: [email protected] (M.J. Ayora-Cañada). http://dx.doi.org/10.1016/j.foodchem.2015.05.064 0308-8146/Ó 2015 Elsevier Ltd. All rights reserved.
marketing standards for preserved tuna and bonito, establishes that if the covering medium used forms an integral part of the trade description, (i.e. name of the product includes the description ‘in olive oil’), only OO can be used, excluding all other oils. In addition, the introduction of new EU legislation (European Parliament, 2011) concerning food information for consumers, which took effect in 2014, brings important changes in the labeling of foodstuffs. Regarding processed foods containing vegetable oil as an ingredient, the new regulation states the type of oil employed must be indicated on the package. Up to now, it has been common practice to label the oil with the generic term ‘‘vegetable oil’’. Consequently, there is a growing need to develop methodologies for industry and regulators to verify the type of oil used (Osorio, Haughey, Elliott, & Koidis, 2014). Few studies assessing the genuineness of the liquid medium in production of in-oil canned fish have been reported. Of those that have been published, the first was designed to assess the genuineness of oil used as medium for canned tuna, mackerel and sardines by analysis of fatty acid composition (Cerma & Remoli, 1966; Remoli & Doro, 1968, 1970). Similarly, the percentage of unsaturated fatty acids trans isomers has been evaluated to test the genuineness of the oil for canned sardines, tuna and mackerel (Bizzozero & Carnelli, 1996; Cavallaro, Bizzozero, Carnelli, & Renon, 1996). Triglycerides profiles were proposed as a more reliable tool by Vittucci, Pierri, and Maffei (1999). These studies stressed the difficulty of assessing whether the oil, used as liquid medium in canned fish, was genuine
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because of lipid exchange between the fish and oil. Thus, the fact that fish releases lipids, and trans linoleic and trans linolenic acids can affect features essential in determining whether the oil is genuine (Vittucci et al., 1999). To address this problem, Rossi and co-workers reported the sterol composition could contribute when assessing oil authenticity (Rossi, Colonello, & Alamprese, 2001). In recent studies, more emphasis has been given to the evaluation of oil quality, particularly aspects such as the extent of oxidative and hydrolytic degradation (Caponio, Gomes, & Summo, 2003; Caponio, Summo, Pasqualone, & Gomes, 2011). IR spectroscopy is a rapid and non-destructive technique for the authentication of food samples. Analysis of a food sample using the MIR spectrum (4000–400 cm 1) reveals information about the molecular bonds present and can, therefore, give details of the types of molecules present in the food. This technique is suitable for use in an industrial setting due to its ease of use and the relatively low financial cost of obtaining and running the equipment. Different types of foods have been tested for adulteration using IR spectroscopy: wine samples have been differentiated on the basis of geographical and varietal origin (Roussel, Bellon-Maurel, Roger, & Grenier, 2003); MIR and chemometrics detected adulteration of apple juice with beet syrup and cane syrup (100% and 96.2% correct, respectively, Sivakesava, Irudayaraj, & Korach, 2001); and adulteration of honey samples with sugar solutions at 14% w/w was detected using MIR and PLS (Kelly, Downey, & Fouratier, 2004). Extensive research has been performed regarding virgin olive oil adulteration because of its especial characteristics of high quality and high price commodity (Aparicio, Morales, Aparicio-Ruiz, Tena, & Garcia-Gonzalez, 2013; Nunes, 2014). Thus, several models of principal component analysis (PCA) (Lai, Katherine Kemsley, & Wilson, 1994), stepwise linear discriminant analysis (SLDA) (Baeten et al., 2005) and partial least squares discriminant analysis, PLS-DA (Obeidat, Khanfar, & Obeidat, 2009) have been proposed to identify the presence of different vegetable oils in OO, mainly EVOO. Quantification of different adulterants in OO have also been approached – using FTIR and PLS (Gurdeniz & Ozen, 2009; Lerma-Garcia, Ramis-Ramos, Herrero-Martinez, & Simon-Alfonso, 2010; Maggio, Cerretani, Chiavaro, Kaufman, & Bendini, 2010; Tay, Singh, Krishnan, & Gore, 2002). Furthermore, recently, we have proposed a methodology for quantification of OO in blends that meet specific EU legislation concerning commercialization and labeling of OO products (de la Mata et al., 2012). Here, we will evaluate for the first time the potential use of FTIR in combination with chemometrics to assess the authenticity of the oil added to canned fish. 2. Experimental 2.1. Samples Samples of in-oil canned fish (90) were purchased from different retailers (10) in Spain. They included different brands of canned tuna and other tuna-like species in a variety of olive (48) and seed (42) oils. A more detailed description of the samples is provided in Table 1. According to EU Regulation 1536/92 (European Commission, 1992), the light tuna label refers to Thunnus albacares, and the label generically denominated as ‘‘tuna’’ may include any Thunnus or similar species (e.g., Katsuwonus pelamis). The bonito products can contain species of the genera Sarda, Euthynnus and Auxis. 2.2. Acquisition of infrared spectra Infrared spectra were collected with a Varian 660 FT spectrometer, equipped with a MCT detector. Spectra were recorded in the
Table 1 Summary of canned fish samples with indication of the type of covering oil. Canned fish
Type of oil
Quantity
Tuna Light tuna Light tuna Bullet tuna Bonito Bonito Light tuna Light tuna Bullet tuna Mackerel Tuna Light tuna
Olive Olive Extra virgin olive Olive Olive Extra virgin olive Sunflower High oleic sunflower Sunflower Sunflower Vegetable Vegetable
5 15 3 8 10 7 10 9 6 4 4 9
range 450–4000 cm 1. A MIRacle ATR (Pike Technologies) integrated sampling accessory equipped with a three-reflection diamond crystal was used. 1 mL of oil was taken from each can placed in a glass vial and dried by adding Na2SO4. Samples were kept in the freezer until analysis. For measurement, a drop of oil was placed on the ATR element (diamond IRE). The diamond IRE was cleaned with methanol and dried with a clean tissue after every measurement. Each sample was measured five times and the spectra were averaged. A spectrum of the clean and dry ATR crystal against air was used as background before each measurement. All the spectra were recorded at a resolution of 2 cm 1 and were the average of 64 scans. Sample measurement order was randomized to avoid any biased results due to instrumental effects. To record the FTIR spectra of the fish flesh, the packing medium was drained and fish samples were ground, spread onto the ATR crystal and pressed using a tool that ensured contact between the sample and crystal. In all cases, the spectral region from 1900 to 2350 cm 1 was not considered because of the absence of useful bands and the strong absorbance of the diamond IRE.
2.3. Data analysis PCA and PLS-DA were performed with the PLS-ToolBox (Eigenvector Research Inc., Wenatchee, WA, USA) for MATLAB R2010a 7.6 (The Mathworks Inc., Natick, MA, USA). PCA was used to explore relationships between variables and observations. This unsupervised technique reduces the dimensionality of the original data matrix by constructing Principal Components (PCs) that are linear combinations of the original variables. The first few PCs capture most of the variability in the original data. Data were mean-centered prior to applying PCA. PLS-DA consists of a classic PLS regression in which the response variable (Y) is a categorical variable expressing the class membership. PLS-DA performs a PCA-like reduction of the independent variables X (in this case, the absorbance values at individual wavenumbers) to obtain a maximum correlation between them and the class membership, (i.e. type of oil). Therefore, PLS-DA is a supervised classification technique that requires a training and validation sets of spectra. The performance of the fitted model was evaluated using random subsets: five different test sets were constructed through random selection of 18 objects in the dataset, avoiding the inclusion of any single object in more than one test set. The remaining data were used to fit a model, which was applied subsequently to predict class membership for the removed samples. This procedure was repeated 10 times to ensure that a prediction was available for all samples.
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3. Results and discussion 3.1. Infrared spectra of fish flesh and packing oils The ATR-FTIR spectra of different canned tuna-like fishes and different oils are shown in Fig. 1. The infrared spectrum of fish flesh was dominated by the absorption bands characteristics of proteins at 3270 cm 1 (NAH stretching, amide A), 1621 (C@O stretching, amide I) and 1539 cm 1 (NAH bending coupled with CAN stretching, amide II). The absorption bands of water, which usually accounts for about 60% of the weight of fresh fish muscle and is tightly bound to the proteins in the structure (Rasmussen & Morrissey, 2007), are located at 3370, 1640 and 692 cm 1 and overlap strongly with the bands of the proteins. Band characteristics of lipids were also present in the spectra of the fish flesh at 2960, 2923, 2854, 1743, 1456 and 1160 cm 1. As can be seen in Fig. 1a, absorption bands corresponding to lipids were far less intense in the spectrum of tuna canned in water, which was included for comparison, than in the rest of samples packed in oil media. This reflects the increase in fat content that take place after canning in oil. A parallel decrease in water content has been reported to occur during processing (Garcia-Arias, Sanchez-Muniz, Castrillon, & Pilar Navarro, 1994; Rasmussen & Morrissey, 2007). Fig. 1b shows the typical spectra of several packing oils including OO and RSOs. The main differences can be observed in the bands related to unsaturation, namely 3006 cm 1 (CAH stretching in cis olefinic double bonds) and 710 cm 1 (CH wag in cis double bonds), and in the weak bands of the region between 1000 and 800 cm 1 where band assignment is more complicated. The broad band, centered about 3370 cm 1, is
Fig. 1. Typical ATR-FTIR spectra of (a) canned tuna-like fishes (including one sample canned in water, black and dotted line) and (b) covering oils.
due to the OH stretching of water and reflects the high variability in water content of the packing media due to the release of water from the fish flesh during canning. Poor reproducibility was observed both in this region and around 1640 cm 1 when measuring replicates of the same oil sample due to the heterogeneous distribution of water in the oil. For this reason, oil samples were dried with Na2SO4 prior to the ATR-FTIR measurements. 3.2. Exploratory data analysis A preliminary exploration of the data using PCA was carried out to evaluate the influence of fish and oil types on the natural grouping of the samples according to their spectral characteristics. In the case of infrared spectra for fish flesh, three PCs captured 98.0% of the variance. However, no grouping of the samples based on fish or oil type was observed. In fact, inspection of the loading vectors for these PCs revealed the main sources of variance were just differences in water, fat and protein content among the samples. For this reason, only packing oils spectra were considered for further analysis. In the case of the oil spectra, three PCs captured 98.1% of the variance. As can be seen in Fig. 2, PC1 establishes a clear separation
Fig. 2. Results of principal component analysis of ATR-FTIR spectra of covering oils (a) scores plot in the space defined by PC1 and PC2, (b) loadings vector of PC1 together with ATR-FTIR spectra of triglycerides triolein (OOO) and trilinolein (LLL).
A. Dominguez-Vidal et al. / Food Chemistry 190 (2016) 122–127 Table 2 Results of PLS-DA classification for a binary model: olive/non-olive. Class
Non olive
Olive
Sensitivity (Cal) Specificity (Cal) Sensitivity (CV) Specificity (CV)
1.000 1.000 1.000 0.988
1.000 1.000 0.988 1.000
Table 3 Results of PLS-DA classification for a four-classes model: OO, olive oil; EVOO, extra virgin olive oil, (SO + VO), class formed by all samples labeled as sunflower oil and vegetable oil; HOSO, high oleic sunflower oil. Class
SO + VO
HOSO
OO
EVOO
Sensitivity (Cal) Specificity (Cal) Sensitivity (CV) Specificity (CV)
1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000
0.985 0.970 0.948 0.932
1.000 0.986 0.930 0.968
between OO samples and the rest of the oils except high-oleic sunflower oil (HOSO) samples. The strongest contributions to the loadings vector of PC1 were in the region 2800–3030 cm–1 and, specifically, 3010 cm 1 (characteristic region of CH stretching in cis-olefins [negative]), and 2920 and 2852 cm–1 (typical of CH2
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and CH3 stretching [positive]). Thus, this component is clearly related to differences in unsaturation. Assignment of other features, for example at 914 cm 1, was not so straightforward (Guillén & Cabo, 1999), but they represented the main differences in FTIR spectra for triolein and trilinolein, the major triglycerides in OO and RSOs, respectively (see Fig. 2b). The higher total unsaturation in RSOs due to their high content of polyunsaturated fatty acids (mainly linoleic acid) was reflected in their positive score for this component. In contrast, OO samples showed negative values. In the case of HOSO, its high content of monounsaturated oleic acid (usually more than 80% (Chiavaro et al., 2009)) made its composition more similar to OO. PC2 does not contribute to any useful group separation, as can be seen in Fig. 2a; samples appear scattered along the axis defined by this component. Looking at its loading vector (not shown), this component was related to water content in oil samples. Thus, despite drying with Na2SO4, differences in water content were still a significant source of spectral variability. PC3 axis also did not contribute to any further separation among types of oil. In order to check the influence of the fish type, the oil samples were labeled with this information (see Figs. S1a and S1b in supporting information). As can be clearly seen in the scatter plot, the major variability sources in the oil spectra were not related to fish type since samples of the same fish did not group together.
Fig. 3. Prediction results (cross validation) of PLS-DA model for the four considered classes: OO, olive oil; EVOO, extra virgin olive oil, (SO + VO), class formed by sunflower oil and vegetable oil samples; HOSO, high oleic sunflower oil.
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3.3. Discrimination of oil type using PLS-DA
4. Conclusions
Preliminary exploratory analysis using PCA revealed its potential use for classification of oils by means of their infrared spectra. A PLS-DA algorithm was used, subsequently, to build a model able to discriminate the different types of oil used for canning on the basis of their infrared spectra. First, the suitability of the method to discriminate between fish canned in OO (either OO or EVOO) and RSOs of any type was evaluated. Four latent variables were necessary to build the optimal model. These explained 98.6% of the variance in X (spectral variance) and 91.3% of the variance in Y (class membership). As can be seen in Table 2, successful calibration was achieved with sensitivity (rate of true positives) and specificity (rate of true negatives) of 100% for both classes. Validation results using cross-validation were also satisfactory because the classification performance did not decreased significantly. It is interesting to note that, although the major variance sources revealed in the exploratory PCA grouped HOSO with OO samples together, using PLS-DA it was possible to discriminate between these; HOSO samples being assigned to the non-OO class. In fact, PLS-DA performed a rotation of PCA components and a selection of the variables that carried the class separating information to achieve a maximum separation between OO and non-OO classes resulting in successful classification. In order to investigate the chemical basis of the discrimination, we inspected the loading vectors and the latent variables scores. Although only two classes (OO and non-OO) were considered in this model, the samples were labeled with all the information from the sub-classes (EVOO, OO, HOSO, SO, VO) in order to get better insight into the possibilities for discrimination. The loading vector LV1, which is very similar to the one of PC1, contained information related to total unsaturation. Looking at the scores for this, LV1 established a clear separation between oils rich in monounsaturated fatty acids (EVOO, OO and HOSO) and oils rich in polyunsaturated fatty acids (SO and VO). LV2 was influenced by differences in water content. LV3 and LV4 were the most important to distinguish between HOSO and other oils. However, clear assignment of spectral contributions to these LVs was not possible, probably because they correspond to differences in the content of several minor triglycerides as reflected in spectral contributions from C-H stretching, C@O stretching and, especially, from the fingerprint region (700–1470 cm 1). We also tried to develop a classification model able to discriminate among these five sub-classes. However, the prediction performance of this model was not satisfactory. Five LVs explained 98.7% of the spectral variance (variance in X), but only 56.8% of the variance in Y (class membership). Although EVOO, OO, HOSO classes produced reasonable predictions, the main problem with the model was distinction between classes SO and VO, with poor specificity for both (82% and 78%, respectively, using cross-validation). Inspection of sample scores for the different LVs revealed strong over-lapping between these two classes. This may be due to the fact that HOSO is the most common vegetable oil used in Spain and most of the packing oils declared as vegetable oils were probably just sunflower oil or mixtures containing a large proportion of this oil. If these two classes were considered together, as only one class (SO + VO), a model with successful prediction results could be obtained as shown in Table 3 and Fig. 3. Using six LVs, it was possible to discriminate between the four classes with sensitivity and specificity greater than 90% in all cases. In fact, for HOSO and (SO + VO), the prediction performance was 100%, whereas it was more difficult to distinguish between EVOO and OO. This is logical because, in this case, both oils have the same botanical origin and the differences are limited just to the refining process and the superior sensorial quality attributes of EVOO.
Information from the ATR-FTIR spectra of oil media in canned fish can be used for authentication of the botanical origin of the oil regardless of the type of fish. A clear distinction between oils rich in monounsaturated fatty acids and oils rich in polyunsaturated fatty acids was achieved easily using PCA. In addition, using PLS-DA, it was also possible to differentiate accurately HOSO from the rest and, even, to distinguish EVOO from OO with reasonable prediction performance. On the contrary, it was virtually impossible to distinguish between samples packed in sunflower oil from those in ‘vegetable oil’, probably because most of the oils labeled as ‘vegetable’ are, indeed, sunflower oil. Interestingly, highly unsaturated fatty acids (typical of fish lipids), which have been reported to occur in the oil of canned fish (Caponio, Gomes, & Summo, 2002; Caponio et al., 2003), did not interfere with the assessment of packing oil. The proposed method provides a rapid tool for assessing the authenticity of packing oil in diverse fish products. This could be of great interest in the current global market where international regulations have increased the numbers of samples that must be analyzed with the immediate consequence of increased demand for rapid methods to ensure food traceability and safety. Acknowledgments The authors acknowledge the Andalusia Regional Government (Consejería de Innovación, Ciencia y Empresa) for financial support through project P07-FQM-02667 and PAIDI Research Group FQM363. J.P. De la Rosa is also grateful to the University of Jaén for a Beginning Research grant. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foodchem.2015. 05.064. References Aparicio, R., Morales, M. T., Aparicio-Ruiz, R., Tena, N., & Garcia-Gonzalez, D. L. (2013). Authenticity of olive oil: Mapping and comparing official methods and promising alternatives. Food Research International, 54, 2025–2038. Baeten, V., Pierna, J. A. F., Dardenne, P., Meurens, M., Garcia-Gonzalez, D. L., & Aparicio-Ruiz, R. (2005). Detection of the presence of hazelnut oil in olive oil by FT-Raman and FT-MIR spectroscopy. Journal of Agricultural and Food Chemistry, 53, 6201–6206. Bizzozero, N., & Carnelli, L. (1996). Fatty acid composition and trans unsaturation of the covering oil of canned mackerels and tunas. Industrie Alimentari, 35, 680–683. Caponio, F., Gomes, T., & Summo, C. (2002). Use of HPSEC analysis of polar compounds in the ascertainment of the degradation level of oils utilised as liquid medium in canned tuna. Rivista Italiana delle Sostanze Grasse, 79, 285–288. Caponio, F., Gomes, T., & Summo, C. (2003). Quality assessment of edible vegetable oils used as liquid medium in canned tuna. European Food Research and Technology, 216, 104–108. Caponio, F., Summo, C., Pasqualone, A., & Gomes, T. (2011). Fatty acid composition and degradation level of the oils used in canned fish as a function of the different types of fish. Journal of Food Composition and Analysis, 24, 1117–1122. Cavallaro, A., Bizzozero, N., Carnelli, L., & Renon, P. (1996). Fatty acid composition and trans unsaturation of the covering oil of canned sardines. Industrie Alimentari, 35, 801–805. Cerma, E., & Remoli, S. (1966). Analisi dell’olio di copertura degli tonno in scatola e possibilitá di rilevarne la genuinitá. Bollettino dei laboratori chimici provinciali, 17, 236–251. Chiavaro, E., Vittadini, E., Rodriguez-Estrada, M. T., Cerretani, L., Capelli, L., & Bendini, A. (2009). Differential scanning calorimetry detection of high oleic sunflower oil as an adulterant in extra-virgin olive oil. Journal of Food Lipids, 16, 227–244. de la Mata, P., Dominguez-Vidal, A., Bosque-Sendra, J. M., Ruiz-Medina, A., CuadrosRodriguez, L., & Ayora-Cañada, M. J. (2012). Olive oil assessment in edible oil blends by means of ATR-FTIR and chemometrics. Food Control, 23, 449–455. European Commission (1992). Regulation No. 1536/92 laying down common marketing standards for preserved tuna and bonito. Council Regulation (EEC).
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