[A method for assessing the total viable count of fresh meat based on hyperspectral scattering technique].

The objective of this study is to develop a hyperspectral imaging system to predict the bacteria total viable count in fresh pork. The hyperspectral scattering data were curvefitted by different fitting methods, and correlation differences of models were compared based on the bacteria total viable count of fresh pork, thus providing modeling basis of device for future study. Total 63 fresh pork samples which was used in the experiment were stored at 4 degrees C in the refrigerator of constant temperature. Experiment was performed everyday for 15 days. 4 or 5 random samples were used each day for the experiment. Hyperspectral scattering images and spectral scattering optical data in the wavelength region of 400 to 1 100 nm were acquired from the surface of all of the pork samples. Lorentz and Gompertz function and the modified function was applied to fit the scattering profiles of pork samples. Different parameters could be obtained by Lorentz and Gompertz fitting and the modified function fitting. The different parameters could represent the optical characteristic of the scattering profiles. The standard values of the bacteria total viable count of pork were obtained by classical microbiological plating methods. Because the standard value of the bacteria total viable count was big, log10 of the bacteria total viable count obtained by classical microbiological plating was used to simplify the calculation. Both individual parameters and integrated parameters were explored to develop the models. The multi-linear regression statistical approach was used to establish the models for predicting pork the bacteria total viable count. Both Lorentz and Gompertz function and the modified function included three and four parameters formula. The results showed that correlation coefficient of the models is higher with Lorentz three parameters combination, Lorentz four parameters combination and Gompertz four parameters combination than the individual parameters and other two or three integrated parameters. The three models' correction set and prediction set correlation coefficients were 0.93, 0.96, 0.96 and 0.90, 0.90, 0.92, and the corresponding standard deviation were 0.47, 0.44, 0.39 and 0.56, 0.46, 0.42. Correlation was best with Gompertz four parameters. The device system will select the best correlation and the best stability of the model as the final model.