基于GA和CARS的真空包装冷却羊肉细菌菌落总数高光谱检测

在光谱建模过程中，采用不同的变量筛选算法进行光谱特征波段的提取已成为提高模型效果的重要方法。以真空包装的冷却羊肉细菌菌落总数作为研究指标，比较了两种变量筛选算法对其高光谱偏最小二乘(partial least squares, PLS)模型效果的影响。研究提取了样品肌肉感兴趣区域(ROIs)的羊肉光谱并进行预处理，进而采用遗传算法(genetic algorithm, GA)和竞争性自适应重加权法(competitive adaptive reweighted sampling, CARS)分别对预处理后的473～1 000 nm范围光谱进行特征波段的提取，对比分析了不同波段下羊肉细菌菌落总数的GA-PLS, CARS-PLS和全波段PLS(W-PLS)模型效果。结果表明，GA-PLS和CARS-PLS的模型效果均优于W-PLS，且CARS-PLS模型效果最好，其校正集的决定系数(R2_c)和均方根误差(root mean square error, RMSEC)分别为0.96和0.29，交互验证的决定系数(R2_cv)和均方根误差(root mean square errorof cross validation, RMSECV)分别为0.92和0.46，预测集的决定系数(R2_p)和均方根误差(root mean square error of prediction, RMSEP)分别为0.92和0.47，预测相对分析误差(relative prediction deviation, RPD)为3.58。因此利用高光谱图像技术结合CARS-PLS可以实现羊肉细菌菌落总数快速无损准确检测。

Hyperspectral Imaging Detection of Total Viable Count from Vacuum Packing Cooling Mutton Based on GA and CARS Algorithms

In the process of spectral modeling,spectral extraction of characteristic bands with different variable screening algorithms is an important step for improving the model effects .Total viable count of cooling mutton under vacuum packing condition was chosen as the research index in this paper,while the influence of 2 variable screening algorithms on its hyperspectral PLS model effects was compared .Mutton muscle spectra of Regions of interest (ROIs) were extracted and preprocessed .Subsequently,Genetic Algorithm (GA) and Competitive Adaptive Reweighted Sampling (CARS) were applied to extract characteristic bands from preprocessed spectra at full band range of 473～1 000 nm .Model effects of GA-PLS,CARS-PLS and W-P LS with corresponding bands selection were contrasted and analyzed .The results indicated that both model effects of GA-PLS,CARS-PLS were better than that of W-PLS,and CARS-PLS model effect was optimal .As for the CARS-PLS model,the determination coefficient (R2c) and root mean square error (RMSEC) o f calibration set was 096 and 029,and the determination coefficient (R2cv) and root mean square error (RMSECV) of leave-one-out cross validation was 092 and 046,respectively .Meanwhile,the determination coefficient (R2p),root mean square error of prediction (RMSEP) and the ratio of standard deviation to standard error of prediction (RPD) of prediction set was 0 .92 and 047 and 358,respectively .Therefore,hyperspectral imaging (HSI) technology combined with CARS-PLS can achieve quick,non-destructive and accurate detection of mutton total viable count .