Spectral difference-based adjustment of stem near-infrared spectra for indirect prediction of durian pulp dry matter and maturity

Abstract

Indirect prediction of pulp dry matter content (DM) was investigated using near-infrared spectra acquired from fruit stems. The spectral difference between stem and pulp spectra, together with pulp DM, was used to adjust the stem spectra, thereby integrating pulp-related information into the stem spectral data. Partial least squares regression models were developed using unprocessed, preprocessed, and adjusted stem spectra for comparison. The adjusted spectra model achieved a prediction correlation coefficient of 0.828 and a root mean square error of prediction of 1.630%, marginally outperforming the unprocessed and preprocessed models. The best preprocessed model and the adjusted spectra model were subsequently used to build support vector machine classifiers. In binary classification, the adjusted spectra model showed slightly improved performance on the test set, with average accuracy, precision, and recall values of 0.882, 0.833, and 0.923, respectively. This study should be viewed as a proof of concept. Future validation using independent samples from different seasons, orchards, production lots, and environmental conditions is necessary to confirm the robustness and generalizability of the approach.

Keywords

durian / maturity / adjustment / near-infrared spectroscopy / stem

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