Non-Destructive Detection of Physical and Chemical Indicators of Salted Duck Eggs during Salting Using Near-Infrared Spectroscopy

The near-infrared spectral data of salted duck eggs, prepared from Gaoyou Ma duck eggs, were collected during the whole curing period and based on them, a model for nondestructive and rapid detection of the key quality indicators of salted duck eggs (yolk moisture content, yolk sodium chloride concentration and salted egg yolk index). In order to reduce the influence of other external factors on spectrum acquisition, various spectral preprocessing methods such as multiplicative scatter correction and normalization combined with three feature selection algorithms including competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and uninformative variables elimination (UVE) were used to establish partial least squares regression (PLSR) models. Furthermore, CARS or UVE combined with SPA was used to establish a more robust model. It was found that the optimal band selection method for the three quality indicators of salted duck eggs was UVE combined with SPA, which had the best overall performance. Comparative analysis showed that the optimal model structures for egg yolk moisture content, egg yolk sodium chloride concentration, and salted egg yolk index were standardization-UVE + SPA-PLSR, Savitzky-Golay-UVE + SPA-PLSR, and mean centering-UVE + SPA-PLSR, respectively. The correlation coefficients were 0.9334, 0.8978 and 0.9286 for the training set (R_c), and 0.9276, 0.9085 and 0.9163 for the prediction set (R_p), respectively. The spectral model established in this study can allow the non-destructive detection of the physical and chemical indicators of salted duck eggs during the salting period.