基于高光谱成像技术的鸡肉菌落总数快速无损检测

Rapid Non-Destructive Detection of Total Bacterial Count in Chicken Using Hyperspectral Imaging

以市售新鲜冷藏(4℃)鸡胸肉为研究对象,采集鸡胸肉表面的高光谱(400~1 100 nm)图像信息,采用偏最小二乘回归(partial least square regression,PLSR)建立菌落总数预测模型,采用不同预处理方法提高模型的预测准确性和稳健性,实现快速无损检测生鲜鸡胸菌落总数的目的。结果表明:标准变量变换(standard normalized variate,SNV)预处理后,模型性能最佳。模型的校正标准差(standard error of calibration,s_(EC))和验证标准差(standard error of prediction,s_(EP))分别为0.40和0.57,s_(EP)/s_(EC)为1.08,校正集相关系数(correlation coefficient of prediction,R_C)和验证集相关系数(correlation coeffic ient of prediction,R_P)分别为0.93和0.86;且应用最佳模型可有效预测样品菌落总数的分布地图。

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（s（EC））and standard error of prediction（S（EP））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.