基于金属氧化物传感器阵列的小麦霉变程度检测
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摘要
研制了一套由8个金属氧化物传感器组成、用于检测小麦霉变的电子鼻系统。使用该电子鼻对不同霉变程度和掺入不同百分比含量霉麦的小麦样品进行检测。通过方差分析和主成分分析优化传感器阵列并去掉冗余传感器,对优化后的数据进行主成分分析(PCA)和线性判别分析(LDA),其中PCA的前两个主成分对两类实验结果分析的总贡献率为98.30%和99.27%,LDA前两个判别因子对两类实验结果分析的总贡献率为99.68%和93.30%,且由得分图可知两种方法均能很好地区分不同的小麦样品。利用BP神经网络建立预测模型,对样品菌落总数和掺入样品中霉麦的百分比进行预测。两种预测模型的预测值和测量值之间的相关系数分别为0.91和0.94,表明预测模型具有较好预测性能。
An electronic nose was developed to detect the moldy wheat. Different wheat samples which had different degree of mildew and were mixed with different percentage of moldy wheat were detected by this electronic nose system. Analysis of variance and principal component analysis( PCA) were applied to optimize the sensor array and 2 redundant sensors were removed. PCA and linear discriminant analysis( LDA) methods were used to qualitatively analysis the optimized data of electronic nose. The total contributions of first two principal components in PCA were98.30% and 99.27%,and the total contributions of first two discriminant functions in LDA were 99.68% and 93.30%,respectively. The analysis of PCA and LDA indicated that both of the two methods had good classification performance. BP neural network were applied to build the predictive models to predict the aerobic bacterial count in the samples and the percentage of moldy wheat mixed into the samples. The correlation coefficient between the predictive values and virtual valuesof the two predictive models were 0.91 and 0.94 which indicated that the predictive models had good prediction performance.
An electronic nose was developed to detect the moldy wheat. Different wheat samples which had different degree of mildew and were mixed with different percentage of moldy wheat were detected by this electronic nose system. Analysis of variance and principal component analysis( PCA) were applied to optimize the sensor array and 2 redundant sensors were removed. PCA and linear discriminant analysis( LDA) methods were used to qualitatively analysis the optimized data of electronic nose. The total contributions of first two principal components in PCA were98.30% and 99.27%,and the total contributions of first two discriminant functions in LDA were 99.68% and 93.30%,respectively. The analysis of PCA and LDA indicated that both of the two methods had good classification performance. BP neural network were applied to build the predictive models to predict the aerobic bacterial count in the samples and the percentage of moldy wheat mixed into the samples. The correlation coefficient between the predictive values and virtual valuesof the two predictive models were 0.91 and 0.94 which indicated that the predictive models had good prediction performance.
引文
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[26]Liu Y D,Sun X D,Ouyang A.Nondestructive Measurement of Soluble Solid Content of Navel Orange Fruit by Visible-NIR Spectrometric Technique With PLSR and PCA-BPNN[J].LWT Food Science and Technology,2010,43:602-607.
[2]何建宁,于振文,石玉,等.长期耕作方式对小麦光合特性和产量的影响[J].应用生态学报,2017,28(4):135-141.
[3]许高峰.我国粮食储藏技术现状、问题及对策研究[J].粮食储藏,2015,44:1-5.
[4]张珊珊,刘伟.粮仓内霉变粮食颗粒检测装置[J].山东理工大学学报(自然科学版),2016,30(4):37-40.
[5]单晓雪,胡建蓉,熊升伟.我国粮食作物中的黄曲霉毒素的研究进展[J].食品安全导刊,2015(18):47-48.
[6]张学娣,杨晨晓.浅析新技术在粮食检测中的应用[J].粮油加工(电子版),2014(2):66-68.
[7]王海龙.基于光谱和光谱成像技术的转基因大豆品种鉴别与品质检测研究[D].浙江大学,2016.
[8]成芳.稻种质量的机器视觉无损检测研究[D].浙江大学,2004.
[9]苏忆楠.基于机器视觉和高光谱图像技术的粮食水分检测及杂质与不完善粒识别方法研究[D].浙江大学,2011.
[10]张燕燕,蔡静平,蒋澎,等.气体分析法监测粮食储藏安全性的研究与应用进展[J].中国粮油学报,2014(10):122-128.
[11]周显青,崔丽静,林家永,等.电子鼻用于粮食储藏的研究进展[J].粮油食品科技,2010(5):63-66.
[12]Hui G H,Jin J J,Deng S G,et al.Winter Jujube(Zizyphusjujuba Mill.)Quality Forecasting Method Based on Electronic Nose[J].Food Chemistry,2015,170:484-491.
[13]傅均,黄灿钦,章铁飞.便携式智能电子鼻系统及其葡萄货架期评价研究[J].传感器技术学报,2017,30(5):782-788.
[14]周慧敏,李莎怡,陈婷婷,等.用于绍兴黄酒总糖含量预测的电子鼻系统构建即其实验研究[J].传感器技术学报,2017,30(11):1776-1780.
[15]Jiang S,Wang J.Internal Quality Detection of Chinese Pecans(Caryacathayensis)during Storage Using Electronic Nose Responses Combined with Physicochemical Methods[J].Postharvest Biology and Technology,2016,118:17-25.
[16]Tian X J,Wang J,Cui S Q.Analysis of Pork Adulteration in Minced Mutton Using Electronic Nose of Metal Oxide Sensors[J].Journal of Food Engineering,2013,119:744-749.
[17]Wang B,Xu S Y,Sun D W.Application of the Electronic Nose to the Identification of Different Milk Flavorings[J].Food Research International,2010,43:255-262.
[18]Haddi Z,Alami H,El Bari N,et al.Electronic Nose and Tongue Combination for Improved Classification of Moroccan Virgin Olive Oil Profiles[J].Food Research International,2013,54:1488-1498.
[19]张红梅,何玉静.电子鼻技术在粮食质量检测中的应用[J].农机化研究,2009(3):180-182.
[20]沈飞,刘鹏,蒋雪松,等.基于电子鼻的花生有害霉菌种类识别及侵染程度定量检测[J].农业工程学报,2016(24):297-302.
[21]赵丹.小麦储藏过程中挥发性物质与品质变化关系研究[D].河南工业大学,2012.
[22]张蓝月.小麦储藏期间指标、气味成分及谷蠹培养气味成分变化的研究[D].南京:南京财经大学,2016.
[23]沈飞,吴启芳,魏颖琪,等.谷物霉菌挥发性物质的电子鼻与GC-MS检测研究[J].中国粮油学报,2016(7):148-152.
[24]袁志发,宙静芋.多元统计分析[M].科学出版社,2002:46-74.
[25]边肇祺,张学工,阎平凡,等.模式识别[M].北京:清华大学出版社,1999,12:256.
[26]Liu Y D,Sun X D,Ouyang A.Nondestructive Measurement of Soluble Solid Content of Navel Orange Fruit by Visible-NIR Spectrometric Technique With PLSR and PCA-BPNN[J].LWT Food Science and Technology,2010,43:602-607.