Nonlinear methods for estimation of maturity stage, total chlorophyll, and carotenoid content in intact bell peppers

Timea Ignat, Zeev Schmilovitch, József Feföldi, Nirit Bernstein, Bracha Steiner, Haim Egozi, Aharon Hoffman

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

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.

Original languageEnglish
Pages (from-to)414-425
Number of pages12
JournalBiosystems Engineering
Volume114
Issue number4
DOIs
StatePublished - 1 Jan 2013
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Food Science
  • Animal Science and Zoology
  • Agronomy and Crop Science
  • Soil Science

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