Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network

Mohammadigol, Reza; Azadshahraki, Farzad; Lotfi, Valiollah

doi:10.22101/JRIFST2017.11.18.638

Quantification of Total Phenol in Grape by Near Infrared Spectroscopy and Artificial Neural Network

Document Type : Original Paper

Authors

¹ Assistant Professor, Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran

² Assistant Professor, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran

³ MSc, Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran

10.22101/JRIFST2017.11.18.638

Abstract

Grape is one of the most important fruits in the world. Phenolic compounds are antioxidants are important compositions of grape. Phenolic compounds phrase includes all the aromatic molecules consisting amino acids to complex molecules like tannins and lignin’s. Near infrared spectroscopy is one of the most common nondestructive methods for fruits and vegetables qualification analysis. This research is conducted to evaluate the possibility of the quantification of total phenol in grape by near infrared spectroscopy and artificial neural network (perceptron). The number of 444 samples (107 Asgari, 106 Bidane, 111 shahroodi and 120 khoshnav varieties) were selected to model calibrating and test as well. Developed ANNs were compared on phenol prediction by residual prediction deviation (RPD) index in the test sample dataset (101 samples).The maximum RPD was 1.66 by 8-5-1 topology with correlation coefficient and root mean square (RMSE) equal to 0.79 and 48.66 respectively. It was concluded that NIR spectroscopy and back propagation perceptron ANN could be used to discriminate low and high amounts of grape total phenol as a nondestructive method.

Keywords

References

Ahmadi, A., Eehsanzadeh, P., & Jabbari, F. 2009. Introduction to plant physiology. University of Tehran press. Vol. 1, 681pp. [In Persian]

Cen, H., & He, Y. 2007. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food science and Technology, 18(2): 72-83.

Cozzolino, D., Kwiatkowski, M.J., Parker, M., Cynkar, W.U., Dambergs, R.G., Gishen, M., & Herderich, M.J. 2004. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytica Chimica Acta, 513(1): 73-80.

Feng, S., Chen, R., Lin, J. Pan, J., Chen, G., Li, Y., Cheng, M., Huang, Z., Chen, J., & Zeng. H. 2010. Nasopharyngeal cancer detection based on blood plasma surface-enhanced Raman spectroscopy and multivariate analysis. Biosensors and Bioelectronics 25(11): 2414-2419.

Ferrari, E., Focab, G., Vignalic, M., Tassia, L., & Ulrici, A. 2011. Adulteration of the anthocyanin content of red wines: Perspectives for authentication by Fourier Transform-Near InfraRed and 1H NMR spectroscopies. Analytica Chimica Acta, 701(2):139- 151.

Han, J., Kamber, M., & Pei, J. 2012. Data mining: concepts and techniques. Third edition., Morgan kaufmann Publishers (An imprint of Elsevier.), USA.

Hemingway, R.W., & Laks, P.E. (eds). 1992. Plant polyphenols synthesis, Properties, Significance. A division of plenum press, New York. N. Y. 1013-1053pp.

Hertog, M., Lammertyn, J., De Ketelaere, B., Scheerlinck, N., & Nicola, B.M. 2007. Managing quality variance in the postharvest food chain. Trends in Food Science & Technology,18(6): 320-32.

Ishikawa, S., & Gulick, V. 2013. An automated mineral classifier using Raman spectra. Computers & Geosciences, 54: 259-268.

Jiang, Q., Chen, Y., Guo, L., Fei. T., & Qi, K. 2016. Estimating soil oganic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy. Remote Sensing, 8(9): 755-771.

Mireei, A., Mohtasebi, S.S., & Sadeghi, M. 2013. Comparison of linear and non-linear calibration models for non-destructive firmness determining of ‘Mazafati’ date fruit by NIR spectroscopy. International Journal of Food Properties. International Journal of Food Properties, 17(6): 1199-1210.

Mohammadigol, R., Khoshtaghaza, M., Malekfar, R., Mirabolfathi, M., & Nikbakht, A. 2014. Detection of pistachio aflatoxin using raman spectroscopy and artificial neural networks. Journal Of Agricultural Machinery, 5(1), 1-9. [In Persian]

Mouazen, A.M., Kuang, B., De Baerdemaeker, J., & Ramon, H. 2010. Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma, 158(1): 23-31.

Nicolai, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., Karen, I.T., & Lammertyn, J. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46(2): 99-118.

Raja, H.N., Dara, N.E., Hobaika, Z., Boussetta, N., Vorobiev, E., Maroun, R.G., & Louka, N. 2014. Extraction of total phenolic compounds, flavonoids, anthocyanins and tannins from grape byproducts by response surface methodology. Influence of solid-liquid ratio, particle size, time, temperature and solvent mixtures on the optimization process. Journal of Food and Nutrition Sciences, 5(04): 397-409.

Rolle, L., Torchio, F., Lorrain, B., Giacosa, S., Rio, S., Cagnasso, E., Gerbi, V., & Teissedre, P.L. 2012. Rapid methods for the evaluation of total phenol content and extract ability in intact grape seeds of cabernet-sauvignon: Instrumental mechanical properties and FT-NIR spectrum. Journal International des Sciences de la Vigne et du Vin, 46(1):29-40.

Viscarra Rossel, R.V., McGlyn, R.N., & McBratney, A.B. 2006. Determining the composition of mineral–organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma, 137(10):70-82.