Hyperspectral Spatial Frequency Domain Imaging Technique for Soluble Solids Content and Firmness Assessment of Pears

High Spectral Spatial Frequency Domain Imaging (HSFDI) combines high spectral imaging and spatial frequency domain imaging techniques, offering advantages such as wide spectral range, non-contact, and differentiated imaging depth, making it well-suited for measuring the optical properties of agricultural products. The diffuse reflectance spectra of the samples at spatial frequencies of 0 mm-1 (Rd0) and 0.2 mm-1 (Rd0) were obtained using the three-phase demodulation algorithm. The pixel-by-pixel inversion was performed to obtain the absorption coefficient (mu a) spectra and the reduced scattering coefficient (mu s ') spectra of the pears. For predicting the SSC and firmness of the pears, these optical properties and their specific combinations were used as inputs for partial least squares regression (PLSR) modeling by combining them with the wavelength selection algorithm of competitive adaptive reweighting sampling (CARS). The results showed that mu a had a stronger correlation with SSC, whereas mu s ' exhibited a stronger correlation with firmness. Taking the plane diffuse reflectance Rd0 as the comparison object, the prediction results of SSC based on both mu a and the combination of diffuse reflectance at two spatial frequencies (Rd) were superior (the best Rp2 of 0.90 and RMSEP of 0.41%). Similarly, in the prediction of firmness, the results of mu s ', mu ax mu s ', and Rd1 were better than that of Rd0 (the best Rp2 of 0.80 and RMSEP of 3.25%). The findings of this research indicate that the optical properties represented by HSFDI technology and their combinations can accurately predict the internal quality of pears, providing a novel technical approach for the non-destructive internal quality evaluation of agricultural products.