real-time and online inspection of multiple pork quality parameters using dual-band visible/near-infrared spectroscopy

The real-time and online inspection of pork quality is urgently necessary in the meat industry. in the present work, an online inspection system that can detect multiple quality parameters of pork simultaneously based on dual-band visible/near-infrared spectroscopy was developed. specifically, a tungsten halogen lamp and ring light guide were used for illumination and a laser sensing unit was integrated with height adjustment and in-position recognition units, whereby stable dual-band spectral information was obtained from pork samples, resolving the inspection inaccuracy problem induced by unsuitable light sources and nonuniform sample thicknesses. then, partial least squares regression models for color (l*, a*, and b*), ph, total volatile basic nitrogen content, fat, protein, cooking loss, tenderness, and moisture content were established based on spectra after different pretreatments. to further improve the prediction accuracy and stability, an improved competitive adaptive reweighted sampling algorithm was used to identify the optimum characteristic variables of each parameter, and simplified prediction models were established with the correlation coefficients rp greater than 0.9 for all the aforementioned attributes except for moisture (rp = 0.881). the results show that the inspection system combined with the spectral processing algorithm can realize rapid, nondestructive, and simultaneous detection of multiple quality parameters and can be readily applied for practical and industrial real-time, online inspection and grading of pork quality.