Multivariate Analysis of Spectroscopic Data for Agriculture Applications
In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24
 Millam S (2007). Potato (Solanum tuberosum L.). Methods in Molecular Biology 344, 25–35.
 Potatopro, 2019. https://www.potatopro.com/world/potato-statistics
 Messiha NAS, van Bruggen AHC, van Diepeningen AD, de Vos OJ, Termorshuizen AJ, Tjou-Tam-Sin NNA, Janse JD (2007). Potato brown rot incidence and severity under different management and amendment regimes in different soil types. Eur J Plant Pathol 119:367–381
 Kabeil SS, Lashin SM, El-Masry MH, El-Saadani MA, Abd Elgawad, MM and Aboul-Einean AM (2008). Potato brown rot disease in Egypt: current status and prospects. American–Eurasian J Agric Environ Sci, 4 (1) : 44-54
 Magwaza LS, Opara UL, Nieuwoudt H, Cronje PJR, Saeys W and Nicolaï B, NIR spectroscopy applications for internal and external quality analysis of citrus fruit – A review. Food Bioprocess Technol 5: 425– 444 (2012).
 Gunasekaran S and Irudayaraj J, Nondestructive Food Evaluation. Techniques to Analyse Properties and Quality, Optical Methods: Visible NIR and FTIR Spectroscopy. Marcel Dekker, New York (2000).
 Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI et al., Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biol Technol 46: 99– 118 (2007).
 Lister SJ, Dhanoa MS, Stewart JL and Gill M, Classification and comparison of Gliricidia provenances using near infrared reflectance spectroscopy. Anim Feed Sci Technol 86: 221– 238 (2000).
 Berardinelli, A., Cevoli, C., Silaghi, F. A., Fabbri, A., Ragni, L., Giunchi, A.; and Bassi, D. (2010). FT-NIR spectroscopy for the quality characterization of apricots (Prunus armeniaca L.). Journal of Food Science, 75(7), 462-468.
 Jie, D., Xie, L., Rao, X.; and Ying, Y. (2014). Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system. Postharvest Biology and Technology, 90, 1-6.
 EPPO (2009). PM 7/97 (1): Indirect immunofluorescence test for plant pathogenic bacteria. Bulletin OEPP/EPPO Bulletin, 39: 413–416.
 EPPO (2018). PM 7/21 (2): Ralstonia solanacearum, R. pseudosolanacearum and R. syzygii (Ralstonia solanacearum species complex). Bulletin OEPP/EPPO Bulletin, 48: 32–63.
 R. Davis and L.J. Mauer. Fourier tansform infrared (FT-IR) spectroscopy: A rapid tool for detection and analysis of foodborne pathogenic bacteria. In book: Current research, technology and education topics in Applied Microbiology and Microbial Biotechnology Volume II. Publisher: Formatex Research Center. Editors: A. Mendez-Vilas
 Wo-Ruo Chen, Yong-Huan Yun, Ming Wen, Hong-Mei Lu, Zhi-Min Zhang* and Yi-Zeng Liang*., Representative subset selection and outlier detection via isolation forest. DOI: 10.1039/C6AY01574C (Paper) Anal. Methods, 2016, 8, 7225-7231
 (Reich, 2005) Reich, G., 2005. Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications. Advanced Drug Delivery Reviews 57, 1109e1143
 (Cen and He, 2007; Wu et al., 1995) Cen, H., He, Y., 2007. Theory & application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology 18, 72e83
 Dhanoa MS, Sister SJ and Barnes RJ, On the scales associated with near infrared reflectance difference spectra. Appl Spectros 49:765–772 (1995)
 S. Gopal Krishna Patro, Kishore Kumar sahu, March 2015, Normalization: A Preprocessing Stage, DOI: 10.17148/IARJSET.2015.2305
 D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, “A learning algorithm for Boltzmann machines,” Cognitive Science, vol. 9, 1985
 Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.
 Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000.
 Marc Rebillat, Nazih Mechba, (2019). Principal Least Squares Canonical Correlation Analysis for damage quantification in aeronautic composite structures. Processes and Engineering in Mechanics and Materials Laboratory (PIMM, UMR CNRS 8006, Arts et Métiers ParisTech (ENSAM)), 151, Boulevard de l'Hôpital, Paris, F-75013, France.
 Mitchell TM (1997) Machine Learning. 1st edition. New York: McGraw-Hill
 Qi, X., Silvestrov, S., & Nazir, T. (2017). Data classification with support vector machine and generalized support vector machine. doi:10.1063/1.4972718
 Breiman, 2001 L. Breiman, Random forests, Mach. Learn., 45 (2001), pp. 5-32