###### Abstract  
A nondestructive method was developed for assessing total viable count (TVC) in pork during refrigerated storage by using hyperspectral imaging technique in this study. The hyperspectral images in the visible/near-infrared (VIS/NIR) region of 400-1100 nm were acquired for fifty pork samples, and their VIS/NIR diffuse reflectance spectra were extracted from the images. The reference values of TVC in pork samples were determined by classical microbiological plating method. Both partial least square regression (PLSR) model and support vector machine regression model (SVR) of TVC were built for comparative analysis to achieve better results. Different transformation methods and filtering methods were applied to improve the models. The results show that both the optimized PLSR model and SVR model can predict the TVC very well, while the SVR model based on second derivation was better, which achieved with R\({}_{\text{P}}\) (correlation coefficient of prediction set) = 0.94 and SEP (standard error of prediction set) = 0.4570 log CFU/g in the prediction set. An image processing algorithm was then developed to transfer the prediction model to every pixel of the image of the entire sample; the visualizing map of TVC would be displayed in real-time during the detection process due to the simplicity of the model. The results demonstrated that hyperspectral imaging is a potential reliable approach for non-destructive and real-time prediction of TVC in pork. p 2017 2017 213  
much longer and sensitive to temperature. Some biological methods, such as polymerase chain reaction (PCR) and enzyme linked immunosorbent assay (ELISA) have also been developed and applied for microbial identification and determination [5,6]. Although the results are relatively accurate and reliable, they are also accompanied with strong technicality and high cost. As a new technology that has developed swiftly, near-infrared (NIR) spectroscopy technology has been used widely in the quality and safety detection of various types of agricultural products. Because it has the advantages of being rapid, nondestructive, and inexpensive, NIR spectroscopy has also been applied to detect safety and quality parameters of pork meat by many researchers from all over the world. However, NIR cannot reflect the detailed information of the pork samples, and only gives the average results of the quality parameters. Hyperspectral technology is newly developed from the 1980s, which combines optics, electronics, information processing, and computer science and technology. The advantage of hyperspectral technology lies in its ability to obtain spatial information and spectral information of the object simultaneously. In recent years, the spatially scattering feasibility of hyperspectral imaging for total viable count (TVC) has been investigated [7, 8, 9, 10, 11, 12, 13, 14]. [PERSON] et al (2011) [8] applied hyperspectral scattering imaging for the determination of TVC of beef, and a very good result with R\({}^{2}\) = 0.95 and SEP(standard error of prediction set) = 0.30 log CFU/g was obtained by the established partial least square regression (PLSR) model based on three combinations of scattering parameters (i.e., a, b, and a \(\times\) b). [PERSON], [PERSON], [PERSON], and [PERSON] [10] applied a hyperspectral imaging system working in the 430-960 nm region combined with a back-propagation artificial neural network (BP-ANN) model for TVC evaluation in pork meat, obtaining good results with R\({}^{2}\)p of 0.83 and SEP of 0.24 log CFU/g. However, there are some problems with the stability and efficiency of real-time detection. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [11] adopted NIR (900-1700 nm) reflectance hyperspectral imaging combined with PLS algorithm to evaluate the TVC degree of chilled pork meat. The PLS model developed with the full NIR spectra gave the result of R\({}^{2}\)p of 0.93 and SEP of 0.70 log CFU/g, which was better than with selected wavelengths. However, the cost of the detection device is higher than the former.