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.

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 (R2 = 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 (R2 = 0.8037, RMSE = 0.0090, RPD = 2.2570), valine, and histidine. This study offers an effective novel approach for intelligent quality assessment of grains.