A flexible visible/near-infrared (Vis/NIR) sensing system (FVNS) was developed for the non-destructive assessment of almond composition. Almonds from four distinct varieties were measured under non-contact conditions, and the acquired spectra were preprocessed using Savitzky–Golay (S–G) smoothing and standard normal variate (SNV). Based on the spectral data captured by the FVNS, random forest (RF) regression models were established to quantify protein and fat contents. The optimized RF models achieved prediction coefficients of determination (R2p) of 0.91 for protein and 0.86 for fat, with corresponding residual predictive deviation (RPD) values of 3.32 and 2.67, respectively. These results demonstrate that the FVNS possesses reliable quantitative capability and can accurately capture compositional variations in almonds while maintaining low cost, portability, and real-time wireless operation.
Sun et al. (Thu,) studied this question.
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