Co-Authors:
Ignat, T., Department of Physics-Control, Corvinus University of Budapest, Somlói 14-16, Budapest 1118, Hungary, Institute of Agricultural Engineering ARO, The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Schmilovitch, Z., Institute of Agricultural Engineering ARO, The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Feföldi, J., Department of Physics-Control, Corvinus University of Budapest, Somlói 14-16, Budapest 1118, Hungary
Bernstein, N., Institute of Soil, Water and Environmental Sciences, ARO, The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Steiner, B., Department of Physics-Control, Corvinus University of Budapest, Somlói 14-16, Budapest 1118, Hungary
Egozi, H., Institute of Agricultural Engineering ARO, The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Hoffman, A., Institute of Agricultural Engineering ARO, The Volcani Center, P.O. Box 6, Bet Dagan 50250, Israel
Abstract:
The objective of the present study was to develop a fast, non-destructive method to measure the bell pepper chlorophyll content, which is one of the major maturity indices for determining harvesting time. The research is based on visibleenear-infrared (VISeNIR) and short-wave infrared (SWIR) spectrometry. Red, green and yellow varieties were examined: 'Celica', 'Ever Green' and 'No.117', respectively. Peppers were marked at the flowering stage, and 20 samples of each variety were collected weekly during nine weeks until full growth. Disc samples of the fruit flesh were analysed destructively, the spectrometry data were analysed chemometrically, and a nonlinear-kernel algorithm was developed for spectral data analysis. Comparisons were made between the linear and nonlinear regression analyses of the raw reflectance spectra (R), on one hand, and the preprocessed spectra such as the first derivative of R (D1R), log(1/R), D1(log(1/R)) and D2(log(1/R)), on the other hand. For further evaluation of the regression models a standardised weighted sum (SWS) index was developed, based on criterion weighting. The developed kernel algorithm, partial least squares (PLSR), and support vector machine (SVM) regression models were able to predict total chlorophyll and carotenoid contents for all three tested bell pepper cultivars, with average cross-validation errors of 0.007 and 0.01 mg g-1, respectively. The kernel nonlinear analysis of the spectral data yielded the most promising regression models for all three cultivars. © 2012 IAgrE.
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