Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression

Pseudomonas spp.and Enterobacteriaceae are dominant spoilage bacteria in chicken during cold storage(0°C-4°C).In this study,high resolution spectra in the range of 900-1700 nm were acquired and preprocessed using Savitzky-Golay convolution smoothing(SGCS),standard normal variate(SNV)and multiplicative scatter correction(MSC),respectively,and then mined using partial least squares(PLS)algorithm to relate to the total counts of Pseudomonas spp.and Enterobacteriaceae(PEC)of fresh chicken breasts to predict PEC rapidly.The results showed that with full 900-1700 nm range wavelength,MSC-PLS model built with MSC spectra performed better than PLS models with other spectra(RAW-PLS,SGCS-PLS,SNV-PLS),with correlation coefficient(RP)of 0.954,root mean square error of prediction(RMSEP)of 0.396 log10 CFU/g and residual predictive deviation(RPD)of 3.33 in prediction set.Based on the 12 optimal wavelengths(902.2 nm,905.5 nm,923.6 nm,938.4 nm,946.7 nm,1025.7 nm,1124.4 nm,1211.6 nm,1269.2 nm,1653.7 nm,1691.8 nm and 1693.4 nm)selected from MSC spectra by successive projections algorithm(SPA),SPA-MSC-PLS model had RP of 0.954,RMSEP of 0.397 log10 CFU/g and RPD of 3.32,similar to MSC-PLS model.The overall study indicated that NIR spectra combined with PLS algorithm could be used to detect the PEC of chicken flesh in a rapid and non-destructive way.