中文摘要：

驴肉具有极高的食用价值,资源的缺乏使其价格持续走高,由此引发的欺骗和掺假亟待解决。选取了不同部位(脖子、肋板、后墩和腱子)的驴肉样品(n=167)及牛肉(n=47)、猪肉(n=51)和羊肉(n=32)样品在4 000~12 500cm-1光谱范围上建立了驴肉的近红外光谱鉴别模型。比较了马氏距离判别分析、簇类独立软模式分类法、最小二乘-支持向量机方法分别结合平滑(5点、15点及25点)、一阶和二阶微分、多元散射校正和标准归一化的光谱预处理方法对肉块样品及大中小三个不同粉碎粒径(7,5,3mm)肉糜样品的分类模型结果发现,原始光谱前11个主成分得分作为输入的马氏距离判别及前6个主成分作为输入的最小二乘-支持向量机肉块样品分类模型较优,校正集和预测集正确率分别为100%和98.96%;原始光谱前5个主成分作为输入的LS-SVM大粒径肉糜样品分类模型结果较优,校正集和预测集判别正确率为100%和97.53%;原始光谱前8个主成分得分作为输入的簇类独立软模式分类法中粒径肉糜样品分类模型结果较优,校正集和预测集的判别正确率均为100%;而对于小粒径肉糜样品,原始光谱前7主成分输入的马氏距离判别和前9主成分输入的簇类独立软模式分类法模型均得到了校正集和预测集100%的判别正确率。以上模型中的驴肉样品均得到了100%的判别正确率。研究结果表明,使用近红外光谱分析技术结合化学计量学方法鉴别驴肉是可行的。

英文摘要：

Donkey meat samples (n=167) from different parts of donkey body (neck ,costalia ,rump ,and tendon) ,beef (n=47) ,pork (n=51) and mutton (n=32) samples were used to establish near-infrared reflectance spectroscopy (NIR) classifica-tion models in the spectra range of 4 000~12 500 cm-1 .The accuracies of classification models constructed by Mahalanobis dis-tances analysis ,soft independent modeling of class analogy (SIMCA) and least squares-support vector machine (LS-SVM) ,re-spectively combined with pretreatment of Savitzky-Golay smooth (5 ,15 and 25 points) and derivative (first and second) ,multi-plicative scatter correction and standard normal variate ,were compared .The optimal models for intact samples were obtained by Mahalanobis distances analysis with the first 11 principal components (PCs) from original spectra as inputs and by LS-SVM with the first 6 PCs as inputs ,and correctly classified 100% of calibration set and 98.96% of prediction set .For minced samples of 7 mm diameter the optimal result was attained by LS-SVM with the first 5 PCs from original spectra as inputs ,which gained an ac-curacy of 100% for calibration and 97.53% for prediction .For minced diameter of 5 mm SIMCA model with the first 8 PCs from original spectra as inputs correctly classified 100% of calibration and prediction .And for minced diameter of 3 mm Mahalanobis distances analysis and SIMCA models both achieved 100% accuracy for calibration and prediction respectively with the first 7 and 9 PCs from original spectra as inputs .And in these models ,donkey meat samples were all correctly classified with 100% either in calibration or prediction .The results show that it is feasible that NIR with chemometrics methods is used to discriminate don-key meat from the else meat .