Nondestructive detection of pungent and numbing compounds in spicy hotpot seasoning with hyperspectral imaging and machine learning.

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.