4 4 Vol. 4 No. 4 2013 8 Journal of Food Safety and Quality Aug., 2013 * 王妍稳, 张瑶, 丁武 (, 712100) 摘要 : 目的 方法, G, (linear discriminant analysis, LDA) (multilayer perceptron neural network, MLPN) 结果 LDA, ; MLPN 94.9%, 结论 LDA MLPN G 关键词 : ; ; ; Rapid detection of three kinds of antibiotic residues in goat milk by electronic nose WANG Yan-Wen, ZHANG Yao, DING Wu * (College of Food Science and Engineering, Northwest Agriculture and Forest University, Yangling 712100, China) ABSTRACT: Objective To develop a rapid detection method of usual antibiotics resedues in goat milk by electronic nose. Methods The goat milk with four concentrations (0, 50, 100, and 200 μg/l) antibiotics, including penicillin, gentamycin and streptomycin was detected by electronic nose as a rapid non-destructive detecting equipment, to collect the volatiles emitted from the goat milk. Then the linear discriminant analysis (LDA) was applied to analyze the results and the response signals were chosen for the pattern recognitions by multilayer perceptron neural network (MLPN). Results LDA results showed that the response signals of the goat milk could distinguish the concentrations of antibiotics. The correct rates of the training group and the testing group of MLPNs were both more than 94.9%. Conclusion MLPN can be used as an effective forecasting tool for predicting the concentration of three kinds of antibiotic in goat milk using the signals of electronic nose. KEY WORDS: electronic nose; goat milk; antibiotic; rapid detection 1 引言,,,,,, 基金项目 : (31172236) Fund: Supported by National Natural Science Foundation of China (31172236) * 通讯作者 :,,, E-mail: dingwu10142000@hotmail.com *Corresponding author: DING Wu, Professor, College of Food Science and Engineering, Northwest Agriculture and Forest University, No.28, Xinong Road, Yangling 712100, China. E-mail: dingwu10142000@hotmail.com
4, : 1135 [1-3], [4], [5-6], [7-10] ;, (PD ) [11] ;, (ELISA ) [12] [13],, ;,, ; (electronic nose, EN), [14-16] [17-19], [20-22] [23-24], G, 2 材料与方法 2.1 材料与药品 ; G Solarbio ; Sanland 2.2 仪器与设备 PEN3 ( Airsense ) 2.3 试验方法 2.3.1 样品制备 G, 10 mg/l G, 0 50 100 200 μl, 10 ml, 0 50 100 200 μg/l, 40, 160 160 2.3.2 电子鼻响应信号采集 PEN3 10 ml, 30 min, PEN3 400 ml/min G G 0, 1 [19], : 5 s, 5 s, 60 s, 1 s, 120 s 2.3.3 数据分析 60 s, 56 s,, (LDA), ; (MLPN), 3 结果与分析 3.1 对含有不同浓度青霉素 G 的羊奶进行分析 3.1.1 LDA 处理 1 G LDA, LDA 70.86% 11.77%, 82.63% LDA,,,, G,,, G 200 μg/l 100 μg/l, LDA G 3.1.2 MLPN 分析 160 5:3,, 101, 59, 1, 10, 4, SPSS18.0 5, 10 5 4 1 1, 97.0%, 94.9%, G
1136 4 Fig. 1 1 G LDA Linear discriminant analysis plots for the goat milk samples with different concentrations of Penicillin G Table 1 表 1 对青霉素 G 浓度多层感知器神经网络判别结果 Results of multilayer perceptron neural network for concentrations of Penicillin G (μg/l) 0 50 100 200 (%) (%) Training group Predicted group 0 24 0 0 0 100.0 50 0 24 0 0 100.0 100 0 0 26 1 96.3 200 0 0 2 24 92.3 0 16 0 0 0 100.0 50 0 15 0 1 93.8 100 0 0 13 0 100.0 200 0 0 2 12 85.7 97.0 94.9 3.2 对含有不同浓度庆大霉素的羊奶进行分析 3.2.1 LDA 处理 2 LDA,, LDA 63.97% 17.79%, 81.756%,,,, LDA 3.2.2 MLPN 分析 160 5:3,, 99, 61, 2 10, 8, 4, 10 8 4 2, 98.0%, 98.4%,
4, : 1137 Fig. 2 2 LDA Linear discriminant analysis plots for the goat milk samples with different concentrations of Gentamicin Table 2 表 2 对庆大霉素浓度多层感知器神经网络判别结果 Results of multilayer perceptron neural network for concentrations of Gentamicin (μg/l) 0 50 100 200 (%) (%) Training group Predicted group 0 23 0 0 0 100.0 50 0 27 0 0 100.0 100 0 0 22 1 95.7 200 0 0 1 25 96.2 0 17 0 0 0 100.0 50 0 13 0 0 100.0 100 0 0 17 0 100.0 200 0 0 1 13 92.9 98.0 98.4 3.3 对含有不同浓度链霉素的羊奶进行分析 3.3.1 LDA 处理 3 LDA, LDA 87.72% 4.37%, 92.09% 3, 50 μg/l 100 μg/l,, 3.3.2 MLPN 分析 160 5:3,, 93, 67, 3 10, 6, 4, 10 6 4 3, 100.0%, 95.2%,
1138 4 Fig. 3 3 LDA Linear discriminant analysis plots for goat milk samples with different concentrations of streptomycin Table 3 表 3 对链霉素浓度多层感知器神经网络判别结果 Results of multilayer perceptron neural network for concentrations of streptomycin (μg/l) 0 50 100 200 (%) (%) Training group Predicted group 0 23 0 0 0 100.0 50 0 28 0 0 100.0 100 0 0 22 0 100.0 200 0 0 0 20 100.0 0 17 0 0 0 100.0 50 0 12 0 0 100.0 100 0 1 17 0 94.4 200 0 0 0 20 100.0 100.0 98.5 4 讨论, MLPN,,,,,,, 参考文献 [1],,,. -
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