Zhang Di, Chen Xu, Lin Zitao, Lu Minmin, Yang Wenhao, Sun Xiaoxia, Battino Maurizio, Shi Jiyong, Huang Xiaode, Shi Bolin, Zou Xiaobo
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; Faculty of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Food Chem. 2025 Mar 30;469:142593. doi: 10.1016/j.foodchem.2024.142593. Epub 2024 Dec 22.
The levels of capsaicin (CAP) and hydroxy-α-sanshool (α-SOH) are crucial for evaluating the spiciness and numbing sensation in spicy hotpot seasoning. Although liquid chromatography can accurately measure these compounds, the method is invasive. This study aimed to utilize hyperspectral imaging (HSI) combined with machine learning for the nondestructive detection of CAP and α-SOH in hotpot seasoning. Spectral reflectance within the range of 370-1030 nm was used to develop regression models to predict CAP and α-SOH content. The results indicated that the PSO-BPNN model was optimal for predicting CAP (R = 0.9942) and α-SOH (R = 0.9939). Feature selection algorithms and tallow model experiments identified characteristic wavelengths for CAP (740-800 nm and 850-940 nm) and α-SOH (450-550 nm, 650-700 nm, 740-800 nm, and 850-940 nm). These findings demonstrated the potential of HSI for rapid, precise, and nondestructive assessment of CAP and α-SOH levels in hotpot seasoning.
辣椒素（CAP）和羟基 -α-山嵛醇（α-SOH）的含量对于评估麻辣火锅底料的辣度和麻感至关重要。虽然液相色谱法能够准确测量这些化合物，但该方法具有侵入性。本研究旨在利用高光谱成像（HSI）结合机器学习对火锅底料中的CAP和α-SOH进行无损检测。利用370 - 1030 nm范围内的光谱反射率建立回归模型，以预测CAP和α-SOH的含量。结果表明，PSO - BPNN模型在预测CAP（R = 0.9942）和α-SOH（R = 0.9939）方面表现最佳。特征选择算法和牛油模型实验确定了CAP（740 - 800 nm和850 - 940 nm）和α-SOH（450 - 550 nm、650 - 700 nm、740 - 800 nm和850 - 940 nm）的特征波长。这些发现证明了HSI在快速、精确和无损评估火锅底料中CAP和α-SOH含量方面的潜力。
