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| Rapid Non-Destructive Detection of Total Bacterial Count in Chicken Using Hyperspectral Imaging |
| LI Wencai1, LIU Fei1, TIAN Hanyou1, ZOU Hao1, WANG Hui1, ZHANG Zhenqi1, ZHENG Xiaochun2, LI Yongyu2, LI Jiapeng1, QIAO Xiaoling1,* |
1. Beijing Key Laboratory of Meat Processing Technology, China Meat Research Center, Beijing 100068, China;
2.College of Engineering, China Agricultural University, Beijing 100083, China |
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Abstract In order to develop a rapid and non-destructive method to predict total bacterial count in chicken breasts by using hyperspectral imaging technology, 83 chicken breast samples refrigerated at 4 ℃ were collected from local supermarket and 63 of these were used as calibration samples. The hyperspectral scattering image of each sample was collected by using hyperspectral imaging system in the wavelength range of 400‒1 100 nm. Various algorithm combinations were used to preprocess the hyperspectral information of the samples to enhance the performance of the model developed by using partial least square regression (PLSR) algorithm. Based on the predictive accuracy and stability of the model, the efficiency of different algorithm combinations for spectral preprocessing were evaluated and discussed. The results showed that the optimal model performance was achieved by preprocessing of the hyperspectral information with standard normalized variate. The standard error of calibration (sEC) and standard error of prediction (SEP) of the model were 0.40 and 0.57, respectively. The correlation coefficients of calibration (RC) and prediction (RP) were 0.93 and 0.86, respectively. The optimal model allowed effective prediction of distribution maps of total bacterial count in chicken breasts.
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