用于山核桃陈化时间检测的电子鼻传感器阵列优化

为更好地进行山核桃陈化时间检测,论文拟通过传感器阵列优化来有效提高电子鼻对其区分预测能力。该文依据响应曲线保留响应明显的传感器,并在提取传感器特征值构成初始特征矩阵的基础上,结合均值分析、变异系数分析、聚类分析、相关性分析和多重共线性分析进行逐步优化以获取最终优化传感器阵列。对优化前后的数据采用主成分分析法（principal component analysis,PCA）和偏最小二乘回归（partial least squares regression,PLSR）进行样品区分和预测能力的对比。结果表明：通过优化,经不同人工陈化时间（0、5、10、15d）处理的山核桃能有效区分开,且在PCA得...

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Bibliographic Details
Published in农业工程学报 Vol. 33; no. 3; pp. 281 - 287
Main Author 徐克明 王俊 邓凡霏 韦真博 程绍明
Format Journal Article
LanguageChinese
Published 浙江大学生物系统工程与食品科学学院,杭州,310058 2017
Subjects
主成分分析
优化
传感器
偏最小二乘回归
特征值矩阵
电子鼻
主成分分析
电子鼻
sensors
partial least squares regression
optimization
特征值矩阵
优化
electronic nose
传感器
偏最小二乘回归
feature matrix
principal component analysis
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ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2017.03.038

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Summary:为更好地进行山核桃陈化时间检测,论文拟通过传感器阵列优化来有效提高电子鼻对其区分预测能力。该文依据响应曲线保留响应明显的传感器,并在提取传感器特征值构成初始特征矩阵的基础上,结合均值分析、变异系数分析、聚类分析、相关性分析和多重共线性分析进行逐步优化以获取最终优化传感器阵列。对优化前后的数据采用主成分分析法（principal component analysis,PCA）和偏最小二乘回归（partial least squares regression,PLSR）进行样品区分和预测能力的对比。结果表明：通过优化,经不同人工陈化时间（0、5、10、15d）处理的山核桃能有效区分开,且在PCA得分图中更为聚集;优化后的陈化时间回归模型（R2=0.933 4）较优化前（R2=0.888 7）具有更好的预测能力。说明所给出的阵列优化方法有效可行,为电子鼻针对性检测提供了一种思路。
Bibliography:Xu keming, Wangjun, Deng fanfei, Wei zhenbo, Cheng Shaoming （ College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China）
11-2047/S
sensors; optimization; principal component analysis; electronic nose; feature matrix; partial least squares regression
As one of the most popular nuts produced in China, pecan contains large amounts of protein and a variety ofunsaturated fatty acids required for human body. However, pecans are prone to rancidity because of the influence ofenvironmental factors such as light, oxygen, and moisture. Therefore, the detection of pecan＇s quality has a certain practicalsignificance. As a bionic electronic system, electronic nose （E-nose） detects the quality of pecan qualitatively andquantitatively through the analysis of sample volatile gas＇s fingerprint information, and is pretty suitable for pecan qualitydetection. However, pecan odor is comprised of complicated compositions and small differences exist among pecans withdifferent qualities, which makes
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2017.03.038