A feasibility quantification study of capsaicin content in chili powder for rapid evaluation using near-infrared reflectance spectroscopy

Capsaicin, a unique alkaloid mainly found in pepper (Capsicum spp.), is in high demand in the food, chemical, and medical fields. Near-infrared (NIR) reflectance spectroscopy at 940–1660 nm was implemented in this study to determine the capsaicin content in dried chili pepper powder. The quantitative calibration models between the spectral data and the measured capsaicin content were built by partial least square regression (PLSR) and extreme learning machine regression (ELM). Different preprocessing methods were used to process the spectral data, and successive projection algorithm (SPA) and uninformative variables elimination (UVE) were used to select characteristic wavelengths. The PLSR model with the full wavelengths pretreated by the first derivative yielded optimum results with a root mean squared error for the prediction set (RMSEP) of 1.0338 g/kg and a correlation coefficient (Rp) of 0.9003 and residual prediction deviation of 2.25. It demonstrated that NIR spectroscopy could be used as an objective tool for the rapid and accurate quantitative determination of capsaicin content.