Nondestructive Discrimination of Grape Seed Varieties Using UV-VIS-NIR Spectroscopy and Chemometrics

Abstract:

Classification of grape seed species is a useful tool to obtain seeds with desired quality traits. This study aimed at rapidly and nondestructively discriminating four varieties of grape seeds using ultra violet, visible and near infrared (UV-VIS-NIR) spectroscopy with wavelength range of 210­1100 nm. A hundred twenty grape seed samples were divided for calibration (n=80) and validation (n=40). The spectra were subjected to a principal component analysis (PCA) with the leading 10 principal components (PCs) used to build calibration models. The obtained PCs were treated by linear discriminant analysis (LDA), artificial neural network (ANN) and support vector machine (SVM) to build various discrimination models. Validation results showed that the PC-LDA model developed for the full range of UV-VIS-NIR achieved better performance than those developed for partial wavelengths, i.e. UV, VIS, NIR, UV-VIS, and VIS-NIR. The PC-LDA model with 8 PCs achieved best performance with 100% discrimination accuracy. This experiment suggests that the UV-VIS-NIR spectroscopy coupled with PC-LDA calibration method is promising for the nondestructive discrimination of grape seed varieties.

• Periodical: Applied Mechanics and Materials (Volumes 236-237)
• Pages: 89-94
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.236-237.89
• Online since: November 2012
• Authors: Hai Qing Yang, Wei Qiang Luo, Wen Jing Wang
• Keywords: Artificial Neural Network (ANN), Grape Seed, Linear Discriminant Analysis (LDA), Near Infrared Spectroscopy (NIRS), Principal Component Analysis (PCA), Support Vector Machine (SVM), Ultra Violet, Variety Discrimination, Visible Infrared Spectroscopy

References

[1] V.S. Chedea, C. Echim, C. Braicu, M. Andjelkovic, R. Verhe, C. Socaciu, Composition in polyphenols and stability of the aqueous grape seed extract from the Romanian variety Merlot Recas,. J. Food Biochem. 35 (2011) 92-108.

DOI: 10.1111/j.1745-4514.2010.00368.x

[2] C.P. Passos, R.M. Silva, F.A.D. Silva, M.A. Coimbra, C.M. Silva, Supercritical fluid extraction of grape seed (Vitis vinifera L. ) oil: Effect of the operating conditions upon oil composition and antioxidant capacity. Chem. Eng. J. 160 (2010).

DOI: 10.1016/j.cej.2010.03.087

[3] A.V.S. Perumalla, N.S. Hettiarachchy, Green tea and grape seed extracts: Potential applications in food safety and quality. Food Res. Int. 44 (2011) 827-839.

DOI: 10.1016/j.foodres.2011.01.022

[4] S.G. Alonso, E.G. Romero, I.H. Gutierrez, HPLC analysis of diverse grape and wine phenolics using direct injection and multidetection by DAD and fluorescence. J. Food Compos. Anal. 20 (2007) 618-626.

DOI: 10.1016/j.jfca.2007.03.002

[5] I.I. Rockenbach, L.V. Gonzaga, V.M. Rizelio, S.S. Goncalves, M.I. Genovese, R. Fett, Phenolic compounds and antioxidant activity of seed and skin extracts of red grape (Vitis vinifera and Vitis labrusca) pomace from Brazilian winemaking. Food Res. Int. 44 (2011).

DOI: 10.1016/j.foodres.2011.01.049

[6] H. Yang, B. Kuang, A.M. Mouazen, Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction. Eur. J. Soil Sci. 63(2012)410-420.

DOI: 10.1111/j.1365-2389.2012.01443.x

[7] L. Velasco, J. Fernandez-Martinez, A.D. Haro, A. De Haro, Application of near infrared spectroscopy to estimate the bulk density of Ethiopian mustard seeds. J. Sci. Food Agric. 77 (1998) 312-318.

DOI: 10.1002/(sici)1097-0010(199807)77:3<312::aid-jsfa39>3.0.co;2-z

[8] A. Fassio, D. Cozzolino, Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy. Ind. Crop. Prod. 20 (2004) 321-329.

DOI: 10.1016/j.indcrop.2003.11.004

[9] C.R. Moschner, B. Biskupek-Korell, Estimating the content of free fatty acids in high-oleic sunflower seeds by near-infrared spectroscopy. Eur. J. Lipid Sci. Technol. 108 (2006) 606-613.

DOI: 10.1002/ejlt.200600032

[10] A.G. Patil, M.D. Oak, S.P. Taware, S.A. Tamhankar, V.S. Rao, Nondestructive estimation of fatty acid composition in soybean [Glycine max (L. ) Merrill] seeds using Near-infrared Transmittance Spectroscopy. Food Chem. 120 (2010) 1210-1217.

DOI: 10.1016/j.foodchem.2009.11.066

[11] M.A. Cantarelli, I.G. Funes, E.J. Marchevsky, J.M. Camiña, Determination of oleic acid in sunflower seeds by infrared spectroscopy and multivariate calibration method. Talanta. 80 (2009) 489-492.

DOI: 10.1016/j.talanta.2009.07.004

[12] R. Ferrer-Gallego, M. García-Marino, J.M. Hernández-Hierro, J.C. Rivas-Gonzalo, M.T. Escribano-Bailón, Statistical correlation between flavanolic composition, colour and sensorial parameters in grape seed during ripening. Anal. Chim. Acta. 660 (2010).

DOI: 10.1016/j.aca.2009.09.039

[13] Y. Vaknin, M. Ghanim, S. Samra, L. Dvash, E. Hendelsman, D. Eisikowitch, Y. Samocha, Predicting Jatropha curcas seed-oil content, oil composition and protein content using near-infrared spectroscopy: A quick and non-destructive method. Ind. Crop. Prod. 34 (2011).

DOI: 10.1016/j.indcrop.2011.03.011

[14] J.H. Lee, M.G. Choung, Nondestructive determination of herbicide-resistant genetically modified soybean seeds using near-infrared reflectance spectroscopy. Food Chem. 126 (2011) 368 - 373.

DOI: 10.1016/j.foodchem.2010.10.106

[15] L.E. Agelet, D.D. Ellis, S. Duvick, A.S. Goggi, C.R. Hurburgh, C.A. Gardner, Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels. J. Cereal Sci. 55 (2012) 160-165.

DOI: 10.1016/j.jcs.2011.11.002

[16] G. Lv, H. Yang, Discrimination of different brands of Nescafe coffee using VIS-NIR spectroscopy and comparative study. Advances in Biomedical Engineering, 1-2(2011) 163-166.

[17] H. Yang, B. Kuang, A.M. Mouazen, In situ determination of growing stages and harvest time of tomato (Lycopersicon esculentum) fruits using fiber-optic visible-near-infrared (Vis-NIR) spectroscopy. Appl. Spectrosc. 65(2011)931-938.

DOI: 10.1366/11-06270