Relationship between optical properties and internal quality of potatoes during storage.

The optical properties [absorption coefficient (µa) and reduced scattering coefficient (µs')] and internal quality [firmness (FI), moisture content (MC), and soluble solids content (SSC)] of stored potatoes at 25 °C were determined, along with ultrastructure observation. Potato tissue ultrastructure changed significantly with storage time, exhibiting enhanced scattering properties and a monotonic increase in µs'. The µa spectra showed significant correlations with MC and SSC, while the µs' spectra were more strongly correlated with FI. The competitive adaptive reweighted sampling (CARS) algorithm improved the prediction accuracy for partial least squares regression (PLSR) and support vector regression (SVR) models. The best predictions were 1st-Derivative-µs'-FI-PLSR (RP = 0.897, RMSEP = 0.036 N, RPD = 2.262), SG-µa -MC-SVR (RP = 0.886, RMSEP = 0.438 %, RPD = 2.157), and Raw-µa -SSC-SVR (RP = 0.873, RMSEP = 0.137 %, RPD = 2.050). These results demonstrate the potential for predicting internal quality using potato's optical properties.

Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization.

The quantitative determination of the soluble solid content (SSC) of potatoes using NIR spectroscopy is useful for predicting the internal and external quality of potato products, especially fried products. In this study, the effect of peel on the partial least squares regression (PLSR) quantitative prediction of potato SSC was investigated by transmission and reflection. The results show that the variable sorting for normalization (VSN) pre-processing method improved model accuracy. Additive multiplicative scattering effects and intensity drift interference of the peels were reduced. The model accuracy reached a correlation coefficient of prediction (RP) of 0.85. The selection algorithm using variable combination population analysis and iterative retention of information variables (VCPA-IRIV) demonstrated that peel increases unnecessary information. When the effect of irrelevant variables was reduced, the results reached RP = 0.88 and the root mean square error of prediction (RMSEP) = 0.25 in the transmission mode was close to that of the full-wavelength peeled PLSR model (RP = 0.89 and RMSEP = 0.25). This indicates that the use of the combined algorithm (VSN-VCPA-IRIV) reduces the effect of the peel and enables samples with a peel to still be predicted accurately in the full-wavelength model. It also improves detection efficiency through the extraction of the necessary variables and optimizes the stability and accuracy of the model.