Rapid and Non-Destructive Assessment of Eight Essential Amino Acids in Foxtail Millet: Development of an Efficient and Accurate Detection Model Based on Near-Infrared Hyperspectral.

Abstract

Foxtail millet is a vital grain whose amino acid content affects nutritional quality. Traditional detection methods are destructive, time-consuming, and inefficient. This work established a rapid and non-destructive method for detecting essential amino acids in the foxtail millet. To address these limitations, this study developed a rapid, non-destructive approach for quantifying eight essential amino acids-lysine, phenylalanine, methionine, threonine, isoleucine, leucine, valine, and histidine-in foxtail millet (variety: Changnong No. 47) using near-infrared hyperspectral imaging. A total of 217 samples were collected and used for model development. The spectral data were preprocessed using Savitzky-Golay, adaptive iteratively reweighted penalized least squares, and standard normal variate. The key wavelengths were extracted using the competitive adaptive reweighted sampling algorithm, and four regression models-Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM)-were constructed. The results showed that the key wavelengths selected by CARS account for only 2.03-4.73% of the full spectrum. BiLSTM was most suitable for modeling lysine (R 2 = 0.5862, RMSE = 0.0081, RPD = 1.6417). CNN demonstrated the best performance for phenylalanine, methionine, isoleucine, and leucine. SVR was most effective for predicting threonine (R 2 = 0.8037, RMSE = 0.0090, RPD = 2.2570), valine, and histidine. This study offers an effective novel approach for intelligent quality assessment of grains.

Plant phenotyping relevance

フォックステイルミレット粒のアミノ酸含量という植物器官の品質形質を、近赤外ハイパースペクトル画像と回帰モデルで非破壊推定する手法の開発が中心である。

Code and data availability

The supplied article blocks describe hyperspectral data collection and modeling for foxtail millet amino acids but contain no data availability statement, no public dataset deposit, and no author code/model release. The custom SpectralView batch program is described but not deposited. No qualifying paper-specific asset

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