Beyond Conventional Methods: Rapid and Precise Quantification of Polyphenols in Vigna umbellata via Hyperspectral Imaging Enhanced by Multi-Scale Residual CNN.

Abstract

Vigna umbellate , a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the demands of high-throughput rapid detection. Although hyperspectral imaging technology offers the potential for non-destructive and rapid detection, existing analytical methods are often limited by issues such as high spectral band redundancy, insufficient feature extraction, and inadequate model stability, which constrain prediction accuracy and practical application potential. To address this, this study proposes a multi-scale residual convolutional neural network (MS-RCNN) based on competitive adaptive reweighted sampling (CARS) for feature band selection, combined with near-infrared hyperspectral imaging technology, to construct a rapid and non-destructive prediction model for the polyphenol content of Vigna umbellata . The model employs a parallel multi-scale convolutional module to extract spectral features with different receptive fields, and incorporates residual connections and adaptive pooling mechanisms to enhance feature reuse and robustness. Experiments compared the performance of partial least squares regression (PLSR), least squares support vector machine (LS-SVM), multi-scale convolutional neural network (MS-CNN), and MS-RCNN models. The results indicate that the MS-RCNN model based on CARS screening achieved the best prediction performance, with a coefficient of determination (R 2 ) of 0.9467, a root mean square error of prediction (RMSEP) of 0.0448, and a residual predictive deviation (RPD) of 4.33. Compared with the optimal PLSR and LSSVM models, its R 2 values were improved by 0.2078 and 0.1119, respectively. In summary, the MS-RCNN model proposed in this study enables rapid, non-destructive, and accurate prediction of polyphenol content in Vigna umbellata , providing an efficient technical approach for quality detection of edible and medicinal crops.

Plant phenotyping relevance

ハイパースペクトル画像とCNNを用いて植物試料のポリフェノール含量を非破壊・迅速推定する手法を開発・比較しており、植物形質（化学的状態）の取得・推定が中心です。

Code and data availability

The supplied blocks describe hyperspectral imaging of Vigna umbellata and the MS-RCNN model, but contain no data availability statement, no public dataset, no author code repository, and no public URL for any paper-specific asset.

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