霉变玉米气体传感器阵列快速检测
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摘要
玉米易受霉菌感染发生霉变,影响食用安全。快速测定玉米霉变程度是控制其危害的前提。本研究拟利用基于气体传感器阵列的电子鼻技术,获取不同霉变程度玉米的特征气味信息,建立玉米霉变程度快速检测方法。辐照灭菌玉米分别接种5种谷物中常见有害霉菌,并于28℃和85%相对湿度环境中储藏15 d直至严重霉变。在第0、6、9、12和15 d,采集样品的气味信息的电子鼻特征响应信号,建立了玉米霉变程度的定性定量模型。结果表明,主成分分析(PCA)法可成功区分不同霉变程度的玉米样品。线性判别分析(LDA)对受单一霉菌侵染的不同霉变程度玉米样品的平均识别率达93.3%以上,全部样品达76.7%。样品中菌落总数的偏最小二乘回归分析(PLSR)模型的预测决定系数(R~2_p)达0.777,预测均方根误差和相对分析偏差(RPD)分别为0.981 log CFU/g和2.12。结果表明,应用电子鼻技术快速检测玉米霉变具有一定可行性,下一步需要不断扩大样本量以提高方法的精度和可靠性。
Maize is susceptible to mildew infection,which affects food safety. Rapid determination of moldy maize is the prerequisite for controlling its hazards. This study intends to use the electronic nose (E-nose) technology based on gas sensor array to obtain the characteristic odor information of maize with different mildew degrees,and to establish a rapid detection method for mildew degree detection of maize. Irradiated sterilized maize was inoculated with 5 common harmful fungal strains of grains,and stored in 28 ℃ and 85% relative humidity for 15 days to serious mildew. On day 0,6,9,12 and 15,E-nose characteristic response signal of the sample's odor information was collected,and qualitative and quantitative models towards maize mildew degree were established. The results showed that principal component analysis (PCA) can successfully distinguish maize samples with different mildew degrees. Linear discriminant analysis (LDA) has an average recognition rate of 93.3% for maize samples with different mildew degree infestation by single fungal,and 76.7% for all samples. The prediction coefficient (R~2_p) of the partial least squares regression analysis (PLSR) model of the colony count in maize samples reached 0.777,the root mean square error of prediction set and the residual predictive deviation (RPD) was 0.981 log CFU/g and 2.12,respectively. The results showed that it is feasible to use the E-nose to quickly detect moldy maize. The next step is to expand the sample set size to improve the accuracy and reliability of the method.
Maize is susceptible to mildew infection,which affects food safety. Rapid determination of moldy maize is the prerequisite for controlling its hazards. This study intends to use the electronic nose (E-nose) technology based on gas sensor array to obtain the characteristic odor information of maize with different mildew degrees,and to establish a rapid detection method for mildew degree detection of maize. Irradiated sterilized maize was inoculated with 5 common harmful fungal strains of grains,and stored in 28 ℃ and 85% relative humidity for 15 days to serious mildew. On day 0,6,9,12 and 15,E-nose characteristic response signal of the sample's odor information was collected,and qualitative and quantitative models towards maize mildew degree were established. The results showed that principal component analysis (PCA) can successfully distinguish maize samples with different mildew degrees. Linear discriminant analysis (LDA) has an average recognition rate of 93.3% for maize samples with different mildew degree infestation by single fungal,and 76.7% for all samples. The prediction coefficient (R~2_p) of the partial least squares regression analysis (PLSR) model of the colony count in maize samples reached 0.777,the root mean square error of prediction set and the residual predictive deviation (RPD) was 0.981 log CFU/g and 2.12,respectively. The results showed that it is feasible to use the E-nose to quickly detect moldy maize. The next step is to expand the sample set size to improve the accuracy and reliability of the method.
引文
[1]崔丽静,周显青,林家永,等.电子鼻快速判别玉米霉变技术研究[J].中国粮油学报,2011,26(10):103-107.
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[8]Saevels S,Lammertyn J,Berna A Z,et al.An electronic nose and a mass spectrometry-based electronic nose for assessing apple quality during shelf life[J].Postharvest Biology and Technology,2004,31(1):9-19.
[9]Reinhard H,Sager F,Zoller O.Citrus juice classification by SPME-GC-MS and electronic nose measurements[J].LWT-Food Science and Technology,2008,41(10):1906-1912.
[10]Hong X,Wang J,Hai Z.Discrimination and prediction of multiple beef freshness indexes based on electronic nose[J].Sensors and Actuators B:Chemical,2012,161(1):381-389.
[11]Han F,Huang X,Teye E,et al.Nondestructive detection of fish freshness during its preservation by combining electronic nose and electronic tongue techniques in conjunction with chemometric analysis[J].Analytical Methods,2014,6(2):529-536.
[12]Xu L,Yu X,Liu L,et al.A novel method for qualitative analysis of edible oil oxidation using an electronic nose[J].Food Chemistry,2016,202:229-235.
[13]Upadhyay R,Sehwag S,Mishra H N.Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis[J].Food Chemistry,2017,221:379-385.
[14]Di Natale C,Davide F A M,D’Amico A,et al.An electronic nose for the recognition of the vineyard of a red wine[J].Sensors and Actuators B:Chemical,1996,33(1-3):83-88.
[15]Jing Y,Meng Q,Qi P,et al.Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification[J].Review of Scientific Instruments,2014,85(5):055004.
[16]Paolesse R,Alimelli A,Martinelli E,et al.Detection of fungal contamination of cereal grain samples by an electronic nose[J].Sensors and Actuators B:Chemical,2006,119(2):425-430.
[17]Yin Y,Hao Y,Yu H,et al.Detection potential of multi-features representation of E-nose data in classification of moldy maize samples[J].Food and Bioprocess Technology,2017,10(12):2226-2239.
[18]Cheli F,Campagnoli A,Pinotti L,et al.Electronic nose for determination of aflatoxins in maize[J].Biotechnologie,Agronomie,Société et Environnement,2009,13(s):39-43.
[19]中华人民共和国国家卫生和计划生育委员会国家食品药品监督管理总局.GB/T 4789.2-2016 食品安全国家标准食品微生物学检验菌落总数测定平板计数法[S].2016.
[20]Yao Y,Pan S,Fan G,et al.Evaluation of volatile profile of Sichuan dongcai,a traditional salted vegetable,by SPME-GC-MS and E-nose[J].LWT-Food Science and Technology,2015,64(2):528-535.
[21]Berrueta L A,Alonso-Salces R M,Héberger K.Supervised pattern recognition in food analysis[J].Journal of Chromatography A,2007,1158,196-214.
[22]Cui C,Fearn T.Comparison of partial least squares regression,least squares support vector machines,and Gaussian process regression for a near infrared calibration[J].Journal of Near Infrared Spectroscopy,2017,25(1):5-14.
[23]Shen F,Wu Q,Shao X,Zhang Q.Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques[J].Journal of Food Science and Technology,2018,55(3):1175-1184.
[24]赵炎.基于计算机图像处理技术的霉变玉米检测研究[D].郑州:河南工业大学,2015.
[25]熊洋,司民真,高飞,等.基于NIR-SERS 光谱技术分析宫颈癌氧合血红蛋白[J].中国激光,2015,42(1):0115001.
[26]Genisheva Z,Quintelas C,Mesquita D P,et al.New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy(NIR)[J].Food Chemistry,2018,46:172-178.
[2]周显青,暴占彪,崔丽静,等.霉变玉米电子鼻识别及其传感器阵列优化[J].河南工业大学学报:自然科学版,2011,32(4):16-20.
[3]王若兰,赵炎,张令,等.玉米霉变与其图像颜色特征参数之间的相关性研究[J].粮食与饲料工业,2015(2):13-16.
[4]Lino C M,Silva L J G,Pena A L S,et al.Determination of fumonisins B1 and B2 in Portuguese maize and maize-based samples by HPLC with fluorescence detection[J].Analytical and Bioanalytical Chemistry,2006,384(5):1214-1220.
[5]Warner R,Ram B P,Hart L P,et al.Screening for zearalenone in corn by competitive direct enzyme-linked immunosorbent assay[J].Journal of Agricultural and Food Chemistry,1986,34(4):714-717.
[6]Nicol R W.Analysis of Fusarium toxins in maize and wheat using thin layer chromatography[J].Mycopathologia,1998,142(2):107-113.
[7]Shen F,Wu Q,Liu P,et al.Detection of Aspergillus spp.contamination levels in peanuts by near infrared spectroscopy and electronic nose[J].Food Control,2018.
[8]Saevels S,Lammertyn J,Berna A Z,et al.An electronic nose and a mass spectrometry-based electronic nose for assessing apple quality during shelf life[J].Postharvest Biology and Technology,2004,31(1):9-19.
[9]Reinhard H,Sager F,Zoller O.Citrus juice classification by SPME-GC-MS and electronic nose measurements[J].LWT-Food Science and Technology,2008,41(10):1906-1912.
[10]Hong X,Wang J,Hai Z.Discrimination and prediction of multiple beef freshness indexes based on electronic nose[J].Sensors and Actuators B:Chemical,2012,161(1):381-389.
[11]Han F,Huang X,Teye E,et al.Nondestructive detection of fish freshness during its preservation by combining electronic nose and electronic tongue techniques in conjunction with chemometric analysis[J].Analytical Methods,2014,6(2):529-536.
[12]Xu L,Yu X,Liu L,et al.A novel method for qualitative analysis of edible oil oxidation using an electronic nose[J].Food Chemistry,2016,202:229-235.
[13]Upadhyay R,Sehwag S,Mishra H N.Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis[J].Food Chemistry,2017,221:379-385.
[14]Di Natale C,Davide F A M,D’Amico A,et al.An electronic nose for the recognition of the vineyard of a red wine[J].Sensors and Actuators B:Chemical,1996,33(1-3):83-88.
[15]Jing Y,Meng Q,Qi P,et al.Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification[J].Review of Scientific Instruments,2014,85(5):055004.
[16]Paolesse R,Alimelli A,Martinelli E,et al.Detection of fungal contamination of cereal grain samples by an electronic nose[J].Sensors and Actuators B:Chemical,2006,119(2):425-430.
[17]Yin Y,Hao Y,Yu H,et al.Detection potential of multi-features representation of E-nose data in classification of moldy maize samples[J].Food and Bioprocess Technology,2017,10(12):2226-2239.
[18]Cheli F,Campagnoli A,Pinotti L,et al.Electronic nose for determination of aflatoxins in maize[J].Biotechnologie,Agronomie,Société et Environnement,2009,13(s):39-43.
[19]中华人民共和国国家卫生和计划生育委员会国家食品药品监督管理总局.GB/T 4789.2-2016 食品安全国家标准食品微生物学检验菌落总数测定平板计数法[S].2016.
[20]Yao Y,Pan S,Fan G,et al.Evaluation of volatile profile of Sichuan dongcai,a traditional salted vegetable,by SPME-GC-MS and E-nose[J].LWT-Food Science and Technology,2015,64(2):528-535.
[21]Berrueta L A,Alonso-Salces R M,Héberger K.Supervised pattern recognition in food analysis[J].Journal of Chromatography A,2007,1158,196-214.
[22]Cui C,Fearn T.Comparison of partial least squares regression,least squares support vector machines,and Gaussian process regression for a near infrared calibration[J].Journal of Near Infrared Spectroscopy,2017,25(1):5-14.
[23]Shen F,Wu Q,Shao X,Zhang Q.Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques[J].Journal of Food Science and Technology,2018,55(3):1175-1184.
[24]赵炎.基于计算机图像处理技术的霉变玉米检测研究[D].郑州:河南工业大学,2015.
[25]熊洋,司民真,高飞,等.基于NIR-SERS 光谱技术分析宫颈癌氧合血红蛋白[J].中国激光,2015,42(1):0115001.
[26]Genisheva Z,Quintelas C,Mesquita D P,et al.New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy(NIR)[J].Food Chemistry,2018,46:172-178.