LOCAL regression applied to a citrus multispecies library to assess chemical quality parameters using near infrared spectroscopy.

Abstract

The non-destructive on-tree measurement of the chemical quality attributes of fruits belonging to the Citrus genus using rapid spectral sensors is of vital interest to citrus growers, allowing them to carry out a selective harvest of any species of Citrus fruit. With this objective, the viability of using of a handheld portable near infrared spectroscopy (NIRS) instrument to predict soluble solid content (SSC), pH, titratable acidity (TA), maturity index and BrimA, in order to measure the optimum harvest time in a group made up of 608 samples belonging to the Citrus genus (378 oranges and 230 mandarins) was evaluated. For each of the parameters analysed, both non-linear regression (LOCAL algorithm) and linear regression (Modified Partial Least Squares, MPLS) strategies were designed and compared. The use of the LOCAL algorithm in the sample group of oranges and mandarins for all the parameters analysed allowed to obtain more robust models than those obtained with MPLS regression, and it could also be extended more easily when routinely applied. The results confirm that NIRS technology combined with non-linear regression strategies such as the LOCAL algorithm can indeed respond to the needs of the Citrus growers and help them to set the optimum harvest time, in this case of oranges and mandarins, by predicting the chemical quality parameters in situ.

Plant phenotyping relevance

携帯型NIRSによる果実品質形質の非破壊・樹上推定を評価し、LOCAL回帰とMPLSを比較しているため、植物フェノタイピング手法が中心である。

Code and data availability

The article describes NIRS spectral and chemical quality data for 608 citrus samples analyzed with LOCAL and MPLS regression, but no public dataset, spectral library, code, or model deposit is mentioned. The only URL present (postharvest.ucdavis.edu) is a cited background reference on postharvest recommendations, not a

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