Non-destructive assessment of apple internal quality using rotational hyperspectral imaging.

Abstract

This work aims to predict the starch, vitamin C, soluble solids, and titratable acid contents of apple fruits using hyperspectral imaging combined with machine learning approaches. First, a hyperspectral camera by rotating samples was used to obtain hyperspectral images of the apple fruit surface in the spectral range of 380~1018 nm, and its region of interest (ROI) was extracted; then, the optimal preprocessing method was preferred through experimental comparisons; on this basis, genetic algorithms (GA), successive projection algorithms (SPA), and competitive adaptive reweighting adoption algorithms (CARS) were used to extract feature variables; subsequently, multiple machine learning models (support vector regression SVR, principal component regression PCR, partial least squares regression PLSR, and multiple linear regression MLR) were used to model the inversion between hyperspectral images and internal nutrient quality physicochemical indexes of fruits, respectively. Through the comparative analysis of all the model prediction results, it was found that among them, for starch, vitamin C, soluble solids, and titratable acid content, 2 nd Der-CARS-MLR were the optimal prediction models with superior performance (the prediction coefficients of determination R p 2 exceeded 90% in all of them). In addition, potential relationships among four nutritional qualities were explored based on t-values and p-values, and a significant conclusion was drew that starch and vitamin C was highly correlated.

Plant phenotyping relevance

回転ハイパースペクトル画像と機械学習により、リンゴ果実の内部品質形質を非破壊推定する手法の開発・比較が中心である。

Code and data availability

The article describes hyperspectral imaging of 60 apples (240 images) and chemometric modeling, but no public dataset, image repository, or author code is deposited. The data availability statement only offers inquiries via the corresponding author, and no public URL or repository identifier appears in the suppliedtext

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