电子鼻和电子舌单独与联合检测掺大豆蛋白或淀粉的鸡肉糜

为实现掺杂掺假鸡肉的快速、客观评价，该研究利用电子鼻和电子舌联合检测技术对掺杂鸡肉糜进行快速检测，通过对采集的数据进行主成分分析和偏最小二乘法分析，所得结果表明，采用主成分分析，电子鼻和电子舌联合检测掺大豆蛋白鸡肉糜和掺淀粉鸡肉糜的主成分总贡献率分别为99.8%和99.1%；采用偏最小二乘法分析，电子鼻和电子舌联合检测鸡肉糜中掺杂大豆蛋白含量的预测值与真实值之间的决定系数为0.992，均方根误差为2.8%；联合检测鸡肉糜中掺杂淀粉含量的预测值与真实值之间的决定系数为0.996，均方根误差为2.4%。表明电子鼻和电子舌联合检测对鸡肉糜的掺杂情况具有良好的区分和预测能力，并且是一种有效、高精度的肉类掺假检测方法。

Separate and combined detection of minced chicken meat adulterated with soy protein or starch using electronic nose and electronic tongue

The electronic nose and electronic tongue were used to detect adulterated chicken rapidly. In this research, electronic nose and electronic tongue were used to detect the content of soybean protein and starch in chicken meat. Soy protein (0, 2.5%, 5.0%, 7.5%, and 10.0%) was mixed into minced chicken meat to prepare adulterated chicken samples, the total sample weight was 10 g. The 2 mL of distilled water and 0.006 g of neutral protease were added in sequence in a 50 ℃ water bath. After 15 min, the above samples were raised to 90 ℃ to inactivate the enzyme. And incorporated with 0.1 g of D-ribose, Maillard reaction was carried out for 20 min. Then the reaction was terminated at 20 ℃. The processed samples were put into the measuring bottle for electronic nose detection. The 50 mL of 0.1 mol/L potassium chloride extract was added to each group of prepared electronic nose samples. After 30 min, the filtrate was taken for electronic tongue detection. Twenty-four samples in each group were made in parallel, including 18 modeling sets and 6 detection sets. Starch (0, 2.5%, 5.0%, 7.5%, 10.0% and 15.0%) was mixed into minced chicken meat to prepare adulterated chicken samples, the total sample weight was 10 g. The 2 mL of distilled water and 0.006 g of α-amylase were added to each group of samples in a 45 ℃ water bath. After 15 min, the above samples were raised to 90 ℃ to inactivate the enzyme. When incorporating with 0.1 g of glycine, the Maillard reaction was carried out for 20 min. Then the reaction was terminated at 20 ℃. The processed samples were put into the measuring bottle for electronic nose detection. The 50 mL of 0.1 mol/L potassium chloride extract was added to each group of prepared electronic nose samples. After 30 min, the filtrate was taken for electronic tongue detection. Twenty-four samples in each group were made in parallel, including 18 modeling sets and 6 detection sets. The data was statistically analyzed by Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). By PCA, the results showed that the contribution rates of first principal component and second principal component combinedly detected by electronic nose and electronic tongue for minced chicken meat adulterated with soy protein were 99.2% and 0.6%, respectively, and the total contribution rate was 99.8%. By PLSR, the coefficient of determination detected by electronic nose or electronic tongue was 0.989 and 0.972, the root mean square error was 3.9% and 5.4%, respectively. The coefficient of determination and the root mean square error of combinedly detected by electronic nose and electronic tongue were 0.992 and 2.8%. By PCA, the results showed that the contribution rates of first principal component and second principal component combinedly detected by electronic nose and electronic tongue for minced chicken meat adulterated with starch were 97.0% and 2.1%, respectively, and the total contribution rate was 99.1%. By PLSR, the coefficient of determination detected by electronic nose or electronic tongue was 0.977 and 0.976, the root mean square error was 5.0% and 5.2%, respectively. The coefficient of determination and the root mean square error of combinedly detected by electronic nose and electronic tongue were 0.996 and 2.4%. The performance of combined detection by electronic nose and electronic tongue for soy protein was better than for starch. The combined detection using electronic nose and electronic tongue sensors has a potential ability to distinguish and predict soy protein-based or starch-based adulteration in minced chicken meat and has also been proved to be a useful authentication method for meat adulteration detection with high efficiency and accuracy.