中文摘要：研究以去壳花生和花生仁为对象，应用18种金属氧化物气体传感器阵列建立了一个质量追溯模型。采用传统方法测定了不同贮藏时间花生的酸度、过氧化值和粗脂肪含量。结果表明，传感器信号与掺假水平相关性较好；18种金属氧化物气体传感器阵列与化学分析法相结合可以作为花生质量无损检测的有效手段。
外文摘要：Quality tracing models were set up for both unshelled peanuts and *peanut* kernels by applying an array of 18 metal-oxide (MOX) based gas sensors. Acid value, peroxide value and content of crude fat of the peanuts at different storage times were measured by traditional methods as a reference. Classification results for both unshelled peanuts and *peanut* kernels at different storage times based on Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were acceptable Storage time, acid value, peroxide value and content of crude fat of peanuts were predicted by Partial Least Squares Regression (PLSR) and SVM on the basis of different normalized datasets. Original datasets, datasets normalized in [0,1] and in [1,1] were considered. PLSR and SVM provided better prediction results when normalized in [0,1] and [-1,1], respectively. Correlations between adulterated levels (stale peanuts blended in fresh peanuts at levels of 0%, 25%, 50%, 75% and 100%) and sensor signals were researched by PLSR and SVM. It was found that the sensor signals and adulterated levels exhibited good correlation (R-2 > 0.801 for training and testing sets by both methods). Meanwhile, The R-2 for training and testing sets were 0.941 and 0.896 by applying SVM, respectively, and both of them were correspondingly higher than the R-2 for training and testing sets by PLSR (training: R-2=0.812; testing: R-2=0.802). The research indicates that the 18 MOX based gas sensors combined with appropriate chemometrics methods can be used as a non-destructive method in detecting *peanut* quality.
外文关键词：MOX sensor array； Peanuts storage； Adulteration； Quality tracing； Support Vector Machine
作者：Xu, M; Ye, LS; Wang, J; Wei, ZB; Cheng, SM
作者单位：浙江大学
期刊名称：POSTHARVEST BIOLOGY AND TECHNOLOGY
期刊影响因子：2.618
出版年份：2017
出版刊次：6
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