Feature extraction method of hyperspectral scattering images for prediction of total viable count in pork meat

This study aimed to investigate the capabilities of hyperspectral scattering imaging in tandem with Gaussian function,Exponential function and Lorentzian function for rapid and nondestructive determination of total viable count(TVC)in pork meat.Two batches of fresh pork meat was purchased from a local market and stored at 10°C for 1-9 d.Totally 60 samples were used,and several samples were taken out randomly for hyperspectral scattering imaging and conventional microbiological tests on each day of the experiments.The functions of Gaussian,Exponential and Lorentzian were employed to model the hyperspectral scattering profiles of pork meat,and good fitting results were obtained by all three functions between 455 nm and 1000 nm.The Lorentzian function performed best for fitting the hyperspectral scattering profiles of pork meat compared with other functions.Both principal component regression(PCR)and partial least squares regression(PLSR)methods were performed to establish the prediction models.Among all the developed models,the models developed using parameters CE(scattering width parameter of Exponential function)and CL(scattering width parameter of Lorentzian function)by PLSR method gave superior results for predicting pork meat TVC,with RV and RMSEV of 0.92,0.59 log CFU/g,and 0.91,0.61 log CFU/g,respectively.In addition,based on the improved hyperspectral scattering system,parameter c which represented the scattering widths in all three functions gave more accurate prediction results,regardless of the modeling methods(PCR or PLSR).The obtained results demonstrated that hyperspectral scattering imaging combined with the presented data analysis algorithm can be a powerful tool for evaluating the microbial safety of meat in the future.

Prediction of Fresh Pork Quality using Hyperspectral Scattering Imaging(HSI) Technique

In this study, fresh pork tenderness, drip-loss, pH value and color parameters （ CIE, a ＊ , b ＊ and L ＊ values） were simultaneously predicted using hyperspectral scattering imaging （HSI） technique. The hyperspectral scattering images of dO fresh pork samples were collected at the wavelength of 400 -I 100 nm, and the scattering profiles were fitted via Lorontzian distribution （ LD ） function to give three parameters a （ asymptotic value ）, b （peak value ） and c （ full width at b/2）. Stepwise discrimination was performed to determine the optimal wavelengths combinations. The LD parameters combinations （a, b and c） of optimal wavelengths were used to establish multi-linear regression （MLR） models to predict the pork attributes. The models were able to predict pork with high correlation coefficients of 0.92 for drip-loss, 0.94, 0.92 and 0.98 respectively for color parameters （ a ＊ , b＊ and L ＊ ）, and for tenderness and pH value the models gave the correlation coefficients of 0.69 and 0.76, respectively. These results showed that the hyperspectral scattering technique was capable of predicting quality parameters of perk. The study provides an efficient means for rapid and nondestructive determination of pork quality simultaneously.