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Artificial Intelligence in Agriculture 2020年第1期4卷 104-115页

作者： Juzhong Tan Jie Xu Department of Food Science Rutgers UniversityNew BrunswickNJ 08901USA Department of Food Science and Technology The Ohio State UniversityColumbusOH 43210USA

Background:An e-nose or an e-tongue is a group of gas sensors or chemical sensors that simulate human nose or human *** e-nose and e-tongue have showngreat promise and utility in improving assessments of food quality characteristics compared with traditional detection *** and approach:This review summarizes the application of e-nose and e-tongue in determining the quality-related properties of *** working principles,applications,and limitations of the sensors employed by electronic noses and electronic tongueswere introduced and *** pattern recognition algorithms,including artificial neural network(ANN),convolutional neural network(CNN),principal component analysis(PCA),partial least square regression(PLS),and support vector machine(SVM),were introduced and compared in this *** findings and conclusions:Overall,e-nose or e-tongue combining pattern recognition algorithms are very powerful analytical tools,which are relatively low-cost,rapid,and accurate.e-nose and e-tongue are also suitable for both in-line and off-line measurements,which are very useful in monitoring food processing and detecting the end product *** user of e-nose and e-tongue need to strictly control sample preparation,sampling,and data processing.

关键词： e-nose e-tongue Food quality assessments

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Odor evaluation of Vehicle Interior Materials Based on Portable e-nose

第43届中国控制会议

作者： Zhen-Peng Chen Hong-Yue Li Tian-Shu Song Hui-Rang Hou Qing-Hao Meng Institute of Robotics and Autonomous Systems School of Electrical and Information EngineeringTianjin University

Detecting ignitable liquids(ILs) at the fire scene is essential for fire *** nose(e-nose) plays a crucial role in the detection of ILs due to its low cost and rapid ***,when we used different e-noses with the same sensor array(i.e.,identical model and number of gas sensors) and running the same algorithm to identify the same ILs,we found that the identification results were different because of the poor consistency of the gas sensors used in the *** address the issue of ILs identification across e-noses,we built a platform for sensor similarity detection,which is used to reject sensors with large *** on this platform,we constructed a similarity dataset using eight types of gas sensors(each containing eight sensors) to collect samples of four ILs(ethanol,gasoline,diesel and thinner).Moreover,a multi-branch residual convolutional neural network for evaluating sensor similarity was *** experiments on the dataset show that compared with the two-dimensional convolutional neural network(2D-CNN) and multilayer perceptron(MLP),the average accuracy of similarity evaluation of our method is improved by 13.1% and 3.23%,respectively.

关键词： e-nose Ignitable Liquids Deep Learning Similarity evaluation Affinity Sequence

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Odor Evaluation of Vehicle Interior Materials Based on Porta...

基于HS-SPMe-GC-MS和e-nose分析不同温湿度贮藏条件下变质毛椰子果中椰肉的挥发性物质变化

第三十九届中国控制会议

作者： Zhe-Hua Sun Ke-Xian Liu Xue-Han Xu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

The evaluation of the odor emitted by the interior materials is of great significance to the air quality control in the vehicle. This paper focuses on the multi-label classification problem of the odor type（i.e. the type of material from which the odor is released） and the grade evaluation in the vehicle. Two multi-label classifiers based on binary relevance（BR） and classification chain（CC） were designed to distinguish odor types and grades simultaneously and to distinguish odor types first and then grades, respectively. Based on the self-designed portable e-nose, six grades of odors released from six types of common interior materials were detected and classified. experimental results showed that both the multi-label classifiers achieved better recognition accuracy, but the CC had an average recognition rate of 88.17% slightly better than the BR. It is preliminarily verified that the portable e-nose based multi-label classifiers could be used to evaluate the odor of automotive interior materials.

关键词： e-nose vehicle interior materials multi-label classifier Odor evaluation

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现代食品科技 2024年

作者：王媛媛沈晓君陈华阚金涛宋菲张建国张玉锋龚胜华中国热带农业科学院椰子研究所海南省椰子深加工工程技术研究中心椰子产业技术创新战略联盟海南航旅饮品股份有限公司

该研究利用顶空固相微萃取技术结合气相色谱-质谱联用（Headspace Solid-phase Microextraction-gas Ghromatography-mass Spectrometry,HS-SPMe-GC-MS）和电子鼻（electronic nose,e-nose）对未贮藏正常新鲜毛椰子果椰肉和三种不同温湿度（25℃/70%、35℃/80%和45℃/90%）贮藏条件下的变质毛椰子果椰肉中挥发性物质进行测定，并借助主成分分析（Principal Component Analysis,PCA）和正交偏最小二乘判别分析（Orthogonal Partial Least Squares-Discriminant Analysis,OPLS-DA）进行差异和特征挥发性物质分析。结果显示，利用HS-SPMe-GC-MS分析变质后椰肉的挥发性物质种类和含量都有所增加（种类从30种分别增加到45、40和39种，总含量从50.51分别增加到53.32、50.21和90.16μg/g），而酯类物质含量却降低（从15.30μg/g分别降到7.71、7.94和13.92μg/g）。PCA和OPLS-DA均能将四种椰肉进行有效区分，并分别筛选出5、9、6和9种特征挥发性物质。e-nose结果表明变质果椰肉在W2W（对芳香成分和有机硫化物敏感）的响应值（13.19、5.16和5.39）显著高于FCM(4.80)。通过利用e-nose对芳香成分和有机硫化物的分析有望进行变质毛椰子果椰肉的筛选。该研究为探索变质毛椰子果快速筛选的方法提供了一定的理论基础，对提高后续产品品质有重要的指导意义。

关键词：变质毛椰子果椰肉挥发性物质 HS-SPMe-GC-MS e-nose OPLS-DA

Identification of the flavor profiles of Chinese pancakes from various areas using smart instruments combined with e-noses and e-tongues

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International Journal of Agricultural and Biological engineering 2023年第1期16卷 283-290页

作者： Wei Liu Yue Fan Qiannan Liu Fen Xu Liang Zhang Honghai Hu Institute of Food Science and Technology Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-Products ProcessingMinistry of Agriculture and Rural AffairsBeijing 100193China

In order to reveal the flavor characteristics of Chinese pancakes,the aroma and taste compounds of seven traditional Chinese pancakes were *** results showed that electronic nose(e-nose)analysis with PCA could successfully distinguish the aroma profiles of seven Chinese pancakes;the principal components PC1 and PC2 represented 75.74%and 23.2%of the total variance(98.94%)***,the discrimination index of taste profiles of seven Chinese pancakes based on electronic tongue(e-tongue)analysis with LDA was 99.32%;the discriminant factors DF1 and DF2 represented 94.99%and 4.33%of the total variance ***,GC-MS results demonstrated that thirty-threeﬂavor compounds were identiﬁed in seven Chinese pancakes,including aldehydes,alcohols,alkanes,acids,and *** the flavor components,aldehydes with ROAVs higher than 1 contributed most significantly to the overall aroma,such as(e,e)-2,4-nonadienal present the largest contribution in Qingzhou pancake,Jinan and Yishui pancake;hexanal present the largest contribution in Shenxian and Gaomi pancake;nonanal and benzeneacetaldehyde present the largest contribution in Linqu and Tai’an pancake,*** umami and sweet taste amino acids were the most abundant in all the Chinese pancake samples,and Qingzhou pancake had relatively high amino acid *** content of glucose was higher than maltose in Gaomi,Shenxian,and Tai’an pancakes,whereas the content of maltose was higher than glucose in Linqu,Qingzhou,Jinan,and Yishui *** results indicated that the aroma and taste proﬁles of Chinese pancakes differed significantly in terms of their flavor compound *** presented results could be beneficial for providing a comprehensive method for flavor profile identification of traditional whole-grain-based staples such as Chinese pancakes.

关键词： Chinese pancake flavor profile e-nose e-tongue smart instrument identification

electronic noses Using Quantitative Artificial Neural Network

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Chemical Research in Chinese Universities 2001年第4期17卷 380-386页

作者： WeN Li-jing BIAN Li-ping LU Yu ZHANG Mei-zhuo YU Li-ping YANG Peng-yuan Department of Chemistry Fudan UniversityShanghai 200433P.R.China

The present paper covers a new type of electronic nose (e-nose) with a four-sensor array, which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error (Re) in changing quantitative analysis into the artificial neural network (ANN). Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, iiquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.

关键词： e-nose ANN Relative error Quantitative analysis

High Pressure Treatment Changes Spoilage Characteristics and Shelf Life of Pacific Oysters(Crassostrea gigas)During Refrigerated Storage

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Journal of Ocean University of China 2017年第2期16卷 351-355页

作者： CAO Rong ZHAO Ling LIU Qi Department of Food engineering and Nutrition Yellow Sea Fisheries Research InstituteChinese Academy of Fishery SciencesQingdao 266071P.R.China

The effects of high pressure(HP) treatment on spoilage characteristic and shelf life extension of Pacific oysters(Crassostrea gigas) during refrigerated storage were *** showed that HP treatment of 275 MPa for 3 min or 300 MPa for 2 min could achieve 100% full release of oyster adductor muscle,pressures higher than 350 MPa caused excessive release as the shells of oysters were broken,thus use of higher pressures should be cautious in oyster processing industry because of its adverse impact on the appearance of *** treatment(300 MPa,2 min) was proper for the shucking of Pacific oyster(Crassostrea gigas) in *** treatment caused no organoleptic ***,HP treatment resulted in obvious differences in biochemical spoilage indicators(p H,TVB-N and TBARS) changes and volatile compounds profile determined by electronic nose during *** treatment(300 MPa,2 min) also led to a reduction of aerobic bacterial count(APC) by 1.27 log ***,the APC values of oysters treated by HP were always lower than those of the control samples during *** on the organoleptic,biochemical and microbiological indicators,shelf life of 6-8 d for control and 12 d for HP-treated oysters could be *** treatment showed great potential in oyster processing and preservation.

关键词： high pressure processing oyster spoilage e-nose shelf life

Projective Mapping and Product Positioning of Deep Fat Fried Snack

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Food and Nutrition Sciences 2011年第6期2卷 674-683页

作者： Ramasamy Ravi Vivek Kumar Singh Maya Prakash Department of Sensory Science Central Food Technological Research Institute

A study was carried out to examine the quality parameters of commercially available popular deep-fried snack-Mu- rukku. eight samples (four spicy and four salty) of most commonly consumed deep-fried snacks in India, viz., murukku (an extruded strands-like product made from the mixture of rice flour and black gram dhal flour) were analyzed for moisture, oil content, CIe instrumental color (L*a*b*), instrumental texture measurement (breaking strength of murukku strands), aroma finger printing by electronic nose and sensory quality. Quantitative descriptive analysis (QDA) and results showed that sample ‘A’ had highest acceptance and sample D lowest acceptance scores. The results indicated that significant variations were observed in moisture content (2.21 - 3.35%), oil content (30.10 - 34.61%) and textural parameters. electronic nose technique was found useful in fingerprinting the aroma pattern of market samples in a short time. Descriptive sensory profiling coupled with principal component analysis showed the interrelationship among and between sensory, instrumental, chemical parameters.

关键词： Deep-Fried Snacks Texture e-nose Sensory Profiling Product Positioning PCA QDA CIe

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

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Journal of Biomedical Science and engineering 2019年第1期12卷 40-52页

作者： Lu Kou David Zhang Jane You Yingbin Jiang Biometrics Research Center Department of Computing The Hong Kong Polytechnic University Hong Kong China Department of Computer Science Harbin Institute of Technology Shenzhen Graduate School Shenzhen China

Recently, biological technology and computer science are of great importance in medical applications. Since one’s breath biomarkers have been proved to be related with diseases, it is possible to detect diseases by analysis of breath samples captured by e-noses. In this paper, a novel medical e-nose system specific to disease diagnosis was used to collect a large-scale breath dataset. Methods for signal processing, feature extracting as well as feature & sensor selection were discussed for detecting diseases on respiratory, metabolic and digestive system. Sequential forward selection is used to select the best combination of sensors and features. The experimental results showed that the proposed system was able to well distinguish healthy samples and samples with different diseases. The results also showed the most significant sensors and features for different tasks, which meets the relationship between diseases and breath biomarkers. By selecting best combination of different sensors and features for different tasks, the e-nose system is shown to be helpful and effective for diseases diagnosis on respiratory, metabolic and digestive system.

关键词： e-nose Chemical Sensors Breath Analysis

模拟不同运输条件下大豆的霉变规律及霉变程度的识别

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模拟不同运输条件下大豆的霉变规律及霉变程度的识别

作者：符丽雪黑龙江八一农垦大学

学位级别：硕士

受种植地域以及产量等因素的影响,我国南北地区的大豆供需量差异较大,为维持大豆市场的供应稳定,需要将北方主产区的大豆跨区域运输至南方销售区—“北粮南运”。集装箱运输作为一种经济高效的多模式联运方式被广泛应用。但由于南北气... 详细信息

受种植地域以及产量等因素的影响,我国南北地区的大豆供需量差异较大,为维持大豆市场的供应稳定,需要将北方主产区的大豆跨区域运输至南方销售区—“北粮南运”。集装箱运输作为一种经济高效的多模式联运方式被广泛应用。但由于南北气候差异明显,运输过程中的环境条件复杂难控,因此集装箱内的大豆存在霉变的风险,易导致大豆质量损失,引起经济纠纷。因此,探究大豆在不同运输条件下的霉变规律及大豆霉变的主要影响因素,对大豆的霉变情况进行快速检测和早期预警具有重要意义。本研究以东北大豆为研究对象,探究模拟不同运输条件下集装箱内大豆的霉变规律及影响大豆霉变的主要因素,分离纯化出引起大豆霉变的优势霉菌菌株。利用e-nose和GC-IMS技术分析大豆霉变过程中挥发性有机化合物的组成和含量变化,筛选出用于霉变识别的特征挥发性有机化合物,对大豆的霉变程度进行区分识别。其具体研究结论如下:1.模拟不同运输条件下影响大豆霉变的主要因素为大豆的水分含量、环境温湿度、运输时间以及结露情况。未结露情况下,当大豆的水分含量在安全值以下时,大豆中的霉菌总数受环境温湿度和运输时间的影响较小,不易发生霉变;当大豆的水分含量高于安全值时,大豆中的霉菌总数受环境温湿度和运输时间的影响较大,可能发生霉变。结露情况下,大豆中的霉菌总数与大豆的水分含量显著相关,易发生霉变;水分含量为12%、13%、14%的大豆分别于4、12、24 d发生霉变。运输过程中引起集装箱内大豆霉变的菌群以曲霉属、青霉属和根霉属为主。筛选出的优势霉菌菌株分别为Aspergillus niger（黑曲霉）、Penicillium rubens（产红青霉）、Rhizopus microsporus（小孢根霉）、Penicillium oxalicum（草酸青霉）和Aspergillus versicolor（杂色曲霉）。2.利用电子鼻（e-nose）技术,对不同霉变程度的大豆进行定性和定量分析。以每种优势霉菌接种大豆后放入人工气候箱中培养（30℃、RH=90%）,分别于0、8、16、24 d取样进行e-nose检测,结合主成分分析（PCA）和线性判别分析（LDA）对霉变大豆的气味特征进行分类判别。结果显示,不同霉变程度大豆中的挥发性有机化合物含量不同,主要体现在硫类、氮氧类、甲烷等短链烷烃类以及醇醚醛酮类物质上;PCA和LDA可以区分不同霉菌污染的大豆,起主要贡献作用的传感器为W2W（对有机硫类物质敏感）,其次为W1W（对无机硫类物质响应敏感）、W5S（对小分子氮氧化合物类物质敏感）、W1S（对甲烷等短链烷烃类物质敏感）和W2S（对醇醚醛酮类物质灵敏）;PCA和LDA对不同霉变程度大豆的分类效果较好,LDA建模组和预测组判别准确率分别为97.88%和90.28%。研究结果表明,e-nose技术在不同霉菌污染的不同霉变程度大豆的区分识别上具有可行性。3.用GC-IMS技术对5种优势霉菌的m VOCs和霉变大豆中的挥发性有机化合物进行分析鉴定。从优势霉菌中筛选出了16个特征m VOCs;在霉变大豆中,共检测出54种挥发性有机化合物,包括24种醛类、15种酮类、10种醇类、2种酯类、1种吡嗪和1种芳香类化合物,其中醛类、酮类和醇类物质物质占比较大。随着霉变程度的加深,壬醛、辛醛、癸醛、庚醛、戊醛、甲基-5-庚烯-2-酮、（e）-2-己烯醛、（e）-2-戊烯醛的含量逐渐降低,而苯乙醛、3-甲基-2-丁烯醛、3-甲基丁醛、3-甲硫基丙醛、3-辛酮、1-辛烯-3-醇、2-甲基-1-丙醇、2-甲基-1-丁醇、2-乙基-3,5-二甲基吡嗪的含量逐渐升高,结合优势霉菌特征m VOCs分析,可以此作为大豆霉变程度识别的特征挥发性有机化合物。PCA和聚类结果显示,不同霉变程度的大豆样本挥发性有机化合物容易被区分,霉变程度对挥发性有机化合物的含量变化有明显的影响,此结果与e-nose检测结果一致。实验结果表明,GC-IMS结合化学计量学方法可以有效区分不同霉变程度的大豆样品,在大豆霉变程度的识别上具有可行性。

关键词：大豆霉变规律优势霉菌 e-nose GC-IMS