FINDIK YAĞININ FT-NIR, FT-MIR VE RAMAN SPEKTROMETRELERİ İLE BİRLİKTE ÇOK BİLEŞENLİ VERİ ANALİZLERİ KULLANILARAK DOĞRULANMASI

Bu araştırma fındık yağının taşınabilir FT-NIR, FT-MIR ve Raman spektrometreleri ile tağşişlerinin belirlenmesi üzerinedir. Fındık yağları değişik konsantrasyonlardaki (0-25%) (w/w) bitkisel yağlar ile karıştırılmıştır. Toplanan spektralarin Temel Bileşen Analizi (PCA) ve Sınıf Analojisinin Yumuşak Bağımsız Modellenmesi (SIMCA) ile saf fındık yağı sınıflandırma modelleri oluşturulmuştur. Yağ asitleri ve tağşiş seviyesi Kısmi En Küçük Kareler Regresyonu (PLSR) kullanılarak belirlenmiştir. Sonuçların doğrulanması için gaz kromatografisi kullanılarak yağların yağ asidi profilleri belirlenmiştir. Her üç cihazda da SIMCA, saf ve tağşiş edilmiş örneklerin gruplarının sınıflar arası mesafesi (ICD) üçten büyük olarak bulunmuştur. Tüm cihazlar, yağ asidi ve tağşiş miktarlarının belirlenmesinde yüksek performans göstermiştir, rval>0.93 ve standart hata tahmini (SEP)<1.75%. Özellikle, FT-MIR cihazı en iyi performansı göstermiştir. Yine de, tüm cihazlar geleneksel yöntemlere alternatif olarak kullanılabilir. Bu cihazlar, fındık yağı tağşişinin yerinde belirlenmesi icin yüksek bir potansiyel göstermiştir.

Anahtar Kelimeler:

taşınabilir cihazlar, FT-NIR, FT-MIR, Raman, Tağşiş, Fındık Yağı

AUTHENTICATION OF HAZELNUT OIL BY PORTABLE FT-NIR, FT-MIR AND RAMAN SPECTROMETERS COMBINED WITH MULTIVARIATE DATA ANALYSIS

This research studied the authentication of hazelnut oil by portable FT-NIR, FT-MIR, and Raman spectrometers. Hazelnut oils were adulterated with vegetable oils at various concentrations (0-25%) (w/w). Collected spectra were analyzed using Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) to generate classification models to authenticate pure hazelnut oil and Partial Least Squares Regression (PLSR) to predict the fatty acids and adulterant levels. For confirmation, oil’s fatty acid profile was determined by gas chromatography. In all three instruments, SIMCA provided distinct clusters for pure and adulterated samples with interclass distance (ICD)3. All instruments showed excellent performance in predicting fatty acids and adulteration levels with rval>0.93 and standard error prediction (SEP)<1.75%. Specifically, the FT-MIR unit provided the best performances. Still, all the units can be used as an alternative to traditional methods. These units showed great potential for in-situ surveillance to detect hazelnut oil adulterations.

Keywords:

Portable devices, FT-NIR, FT-MIR, Raman, Adulteration, Hazelnut oil,

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Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdisciplinary Reviews: Computational Statistics, 2(1), 97–106. https://doi.org/10.1002/wics.51
Akkaya, M. R. (2018). Prediction of fatty acid composition of sunflower seeds by near-infrared reflectance spectroscopy. Journal of Food Science and Technology, 55(6), 2318–2325. https://doi.org/10.1007/s13197-018-3150-x
Aykas, D. P., Ball, C., Sia, A., Zhu, K., Shotts, M. L., Schmenk, A., & Rodriguez-Saona, L. (2020a). In-situ screening of soybean quality with a novel handheld near-infrared sensor. Sensors (Switzerland), 20(21), 1–19. https://doi.org/10.3390/s20216283
Aykas, D. P., Karaman, A. D., Keser, B., & Rodriguez-Saona, L. (2020b). Non-targeted authentication approach for extra virgin olive oil. Foods, 9(2), 1–17. https://doi.org/10.3390/foods9020221
Aykas, D. P., & Menevseoglu, A. (2021). A rapid method to detect green pea and peanut adulteration in pistachio by using portable FT-MIR and FT-NIR spectroscopy combined with chemometrics. Food Control, 121. https://doi.org/10.1016/j.foodcont.2020.107670
Aykas, D. P., Shotts, M.-L., & Rodriguez-Saona, L. E. (2020c). Authentication of commercial honeys based on Raman fingerprinting and pattern recognition analysis. Food Control, 117(May), 107346. https://doi.org/10.1016/j.foodcont.2020.107346
Ayvaz, H., & Rodriguez-Saona, L. E. (2015). Application of handheld and portable spectrometers for screening acrylamide content in commercial potato chips. Food Chemistry, 174, 154–162. https://doi.org/10.1016/j.foodchem.2014.11.001
Ayvaz, H., Sierra-Cadavid, A., Aykas, D. P., Mulqueeney, B., Sullivan, S., & Rodriguez-Saona, L. E. (2016). Monitoring multicomponent quality traits in tomato juice using portable mid-infrared (MIR) spectroscopy and multivariate analysis. Food Control, 66, 79–86. https://doi.org/10.1016/j.foodcont.2016.01.031
Ballabio, D., & Todeschini, R. (2009). Infrared Spectroscopy for Food Quality Analysis and Control Multivariate Classifi cation for Qualitative Analysis. In D.-W. Sun (Ed.), Infrared Spectroscopy for Food Quality Analysis and Control (1st ed., pp. 83–104). Burlington, MA: Elsevier.
Basri, K. N., Hussain, M. N., Bakar, J., Sharif, Z., Khir, M. F. A., & Zoolfakar, A. S. (2017). Classification and quantification of palm oil adulteration via portable NIR spectroscopy. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 173, 335–342. https://doi.org/10.1016/j.saa.2016.09.028
Benitez-Sánchez, P. L., León-Camacho, M., & Aparicio, R. (2003). A comprehensive study of hazelnut oil composition with comparisons to other vegetable oils, particularly olive oil. European Food Research and Technology, 218(1), 13–19. https://doi.org/10.1007/s00217-003-0766-4
Brereton, R. G. (2000). Introduction to multivariate calibration in analytical chemistry. Analyst, 125(11), 2125–2154. https://doi.org/10.1039/b003805i
Celenk, V. U., Argon, Z. U., & Gumus, Z. P. (2020). Cold pressed hazelnut (Corylus avellana) oil. In M. F. Ramadan (Ed.), Cold Pressed Oils. https://doi.org/10.1016/b978-0-12-818188-1.00020-7
Cercaci, L., Rodriguez-Estrada, M. T., & Lercker, G. (2003). Solid-phase extraction-thin-layer chromatography-gas chromatography method for the detection of hazelnut oil in olive oils by determination of esterified sterols. Journal of Chromatography A, 985(1–2), 211–220. https://doi.org/10.1016/S0021-9673(02)01397-3
Christy, A. A., Kasemsumran, S., Du, Y., & Ozaki, Y. (2004). The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics. Analytical Sciences, 20(6), 935–940. https://doi.org/10.2116/analsci.20.935
De Maesschalck, R., Candolfi, A., Massart, D. L., & Heuerding, S. (1999). Decision criteria for soft independent modelling of class analogy applied to near infrared data. Chemometrics and Intelligent Laboratory Systems, 47(1), 65–77. https://doi.org/10.1016/S0169-7439(98)00159-2
FAO. (2020). FAOSTAT Crops. Retrieved from http://www.fao.org/faostat/en/#data/QC
Gelpí, E., Posada de la Paz, M., Terracini, B., Abaitua, I., Gómez de la Cámara, A., Kilbourne, E. M., … Tarkowski, S. (2002). The spanish toxic oil syndrome 20 years after its onset: A multidisciplinary review of scientific knowledge. Environmental Health Perspectives, 110(5), 457–464. https://doi.org/10.1289/ehp.02110457
Guiné, R. P. F., & Correia, P. M. R. (2020). Hazelnut: A Valuable Resource. ETP International Journal of Food Engineering, (December), 67–72. https://doi.org/10.18178/ijfe.6.2.67-72
Haaland, D. M., & Thomas, E. V. (1988). Partial Least-Squares Methods for Spectral Analyses. 1. Relation to Other Quantitative Calibration Methods and the Extraction of Qualitative Information. Analytical Chemistry, 60(11), 1193–1202. https://doi.org/10.1021/ac00162a020
Hourant, P., Baeten, V., Morales, M. T., Meurens, M., & Aparicio, R. (2000). Oil and fat classification by selected bands of near-infrared spectroscopy. Applied Spectroscopy, 54(8), 1168–1174. https://doi.org/10.1366/0003702001950733
Ichihara, K., Shibahara, A., Yamamoto, K., & Nakayama, T. (1996). An improved method for rapid analysis of the fatty acids of glycerolipids. Lipids, 31(5), 535–539. https://doi.org/10.1007/BF02522648
Jong, S. (1993). PLS Fits Closer Than PCR. Journal of Chemometrics, 7, 551–557. https://doi.org/10.1515/jpme.1998.26.4.325
Karabulut, I., Topcu, A., Yorulmaz, A., Tekin, A., & Ozay, D. S. (2005). Effects of the industrial refining process on some properties of hazelnut oil. European Journal of Lipid Science and Technology, 107(7–8), 476–480. https://doi.org/10.1002/ejlt.200501147
Lavine, B. K. (2000). Clustering and Classification of Analytical Data. Encyclopedia of Analytical Chemistry, 1–21. https://doi.org/10.1002/9780470027318.a5204
MAF – Republic of Turkey Ministry of Agriculture And Forestry. (2020). Gida Olarak Findigin Yeri. Retrieved from https://arastirma.tarimorman.gov.tr/findik/Sayfalar/Detay.aspx?SayfaId=29
Mariani, C., Bellan, G., Lestini, E., & Aparicio, R. (2006). The detection of the presence of hazelnut oil in olive oil by free and esterified sterols. European Food Research and Technology, 223(5), 655–661. https://doi.org/10.1007/s00217-005-0249-x
Massart, D. L., Vandeginste, B. G. M., Deming, S. N., Michotte, Y., & Kaufman, L. (2003). Chemometrics: a textbook (fifth). Amsterdam, Netherlands: Elsevier B.V.
Mcgrath, T. F., Haughey, S. A., Islam, M., & Elliott, C. T. (2020). The Potential of Handheld Near Infrared Spectroscopy to detect food adulteration: Results of a global, multi-instrument inter-laboratory study. Food Chemistry, 128718. https://doi.org/10.1016/j.foodchem.2020.128718
Menevseoglu, A. (2021) Non-destructive detection of sesame oil adulteration by portable FT-NIR, FT-MIR, and Raman spectrometers combined with chemometrics. JOTCSA, 8(3),775-786. https://doi.org/10.18596/jotcsa.940424
Menevseoglu, A., Aykas, D. P., & Adal, E. (2020). Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV–Vis spectroscopy. Journal of Food Measurement and Characterization. https://doi.org/10.1007/s11694-020-00710-y
Miaw, C. S. W., Sena, M. M., Souza, S. V. C. de, Ruisanchez, I., & Callao, M. P. (2018). Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars. Talanta, 190(July), 55–61. https://doi.org/10.1016/j.talanta.2018.07.078
Mossoba, M. M., Srigley, C. T., Farris, S., Kramer, J. K. G., Chirtel, S., & Rader, J. (2014). Evaluation of the Performance of a Portable Mid-Infrared Analyzer for the Rapid Determination of Total Trans Fat in Fast Food. JAOCS, Journal of the American Oil Chemists’ Society, 91(10), 1651–1663. https://doi.org/10.1007/s11746-014-2521-3
Ozen, B. F., & Mauer, L. J. (2002). Detection of hazelnut oil adulteration using FT-IR spectroscopy. Journal of Agricultural and Food Chemistry, 50, 3898–3901. https://doi.org/10.1021/jf0201834
Pei, X., Tandon, A., Alldrick, A., Giorgi, L., Huang, W., & Yang, R. (2011). The China melamine milk scandal and its implications for food safety regulation. Food Policy, 36(3), 412–420. https://doi.org/10.1016/j.foodpol.2011.03.008
Platteau, C., De Loose, M., De Meulenaer, B., & Taverniers, I. (2011). Quantitative detection of hazelnut (Corylus avellana) in cookies: ELISA versus real-time PCR. Journal of Agricultural and Food Chemistry, 59(21), 11395–11402. https://doi.org/10.1021/jf202167b
Quintanilla-Casas, B., Strocchi, G., Bustamante, J., Torres-Cobos, B., Guardiola, F., Moreda, W., … Vichi, S. (2021). Large-scale evaluation of shotgun triacylglycerol profiling for the fast detection of olive oil adulteration. Food Control, 123. https://doi.org/10.1016/j.foodcont.2020.107851
Rodriguez-Saona, L., Aykas, D. P., Borba, K. R., & Urtubia, A. (2020). Miniaturization of optical sensors and their potential for high-throughput screening of foods. Current Opinion in Food Science, 31, 136–150. https://doi.org/10.1016/j.cofs.2020.04.008
Rodriguez-Saona, L. E., Giusti, M. M., & Shotts, M. (2016a). Advances in infrared spectroscopy for food authenticity testing. In Advances in Food Authenticity Testing. https://doi.org/10.1016/B978-0-08-100220-9.00004-7
Shenk, J. S., & Westerhaus, M. O. (1996). Calibration the ISI way. Near infrared spectroscopy: The future waves. Chichester, UK: NIR Publications.
Shotts, M. L., Plans Pujolras, M., Rossell, C., & Rodriguez-Saona, L. (2018). Authentication of indigenous flours (Quinoa, Amaranth and kañiwa) from the Andean region using a portable ATR-Infrared device in combination with pattern recognition analysis. Journal of Cereal Science, 82, 65–72. https://doi.org/10.1016/j.jcs.2018.04.005
Şisik Ogras, Ş., Kaban, G., & Kaya, M. (2018). Ham ve Rafine Fındık Yağlarının Uçucu Bileşikleri ve Yağ Asidi Kompozisyonu. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 9(2), 104–110. https://doi.org/10.17097/ataunizfd.392547
Sun, Y., Yuan, M., Liu, X., Su, M., Wang, L., Zeng, Y., … Nie, L. (2020). Comparative analysis of rapid quality evaluation of Salvia miltiorrhiza (Danshen) with Fourier transform near-infrared spectrometer and portable near-infrared spectrometer. Microchemical Journal, 159(August), 105492. https://doi.org/10.1016/j.microc.2020.105492
Turan, A. (2018). Effect of drying methods on fatty acid profile and oil oxidation of hazelnut oil during storage. European Food Research and Technology, 244(12), 2181–2190. https://doi.org/10.1007/s00217-018-3128-y
Urbano Cuadrado, M., Luque De Castro, M. D., Pérez Juan, P. M., & Gómez-Nieto, M. A. (2005). Comparison and joint use of near infrared spectroscopy and Fourier transform mid infrared spectroscopy for the determination of wine parameters. Talanta, 66(1), 218–224. https://doi.org/10.1016/j.talanta.2004.11.011
Wold, S. (1976). Pattern recognition by means of disjoint principal components models. Pattern Recognition, 8(3), 127–139. https://doi.org/10.1016/0031-3203(76)90014-5
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1
Zabaras, D., & Gordon, M. H. (2004). Detection of pressed hazelnut oil in virgin olive oil by analysis of polar components: Improvement and validation of the method. Food Chemistry, 84(3), 475–483. https://doi.org/10.1016/j.foodchem.2003.07.029
Zambiazi, R. U. I. C., Przybylski, R., Zambiazi, M. W., & Mendonça, C. B. (2007). Fatty Acid Composition of Vegetable Oils and Fats. Boletim Do Centro de Pesquisa de Processamento de Alimentos, 25(1), 111–120.