检索结果-内蒙古大学图书馆

22nd National Conference on sensors and Microsystems

作者： Capuano, Rosamaria Nicolai, Eleonora Catini, Alexandro Paolesse, Roberto Bernardini, Sergio Rizzo, Giuseppe Di Natale, Corrado Univ Roma Tor Vergata Dept Elect Engn Rome Italy Univ Roma Tor Vergata Interdept Ctr Volatil AD Amico Rome Italy Univ Roma Tor Vergata Dept Expt Med Rome Italy Univ Roma Tor Vergata Dept Chem Sci & Technol Rome Italy Univ Roma Tor Vergata Dept Biomed & Prevent Rome Italy Univ Roma Tor Vergata Dept Obstet & Gynecol Fdn Policlin Tor Vergata Rome Italy

ISBN: (纸本)9783031820786;9783031820762;9783031820755

This study investigated changes in the volatile organic compound (VOC) pattern induced by pregnancy, focusing on urine samples. Pregnancy leads to significant alterations in maternal anatomy and physiology to meet metabolic demands and support fetal development. Understanding these changes is crucial for distinguishing between normal and pathological conditions. Metabolic variations in the body result in modifications to VOC patterns in biological fluids. Urine VOCs from 30 pregnant women (at the end of the first trimester) and 21 non-pregnant women were analyzed using a porphyrinoids QMB-based gas sensor array and gas chromatography coupled with ion mobility spectrometry (GC-IMS) to characterize the volatilome of pregnancy under physiological conditions. Kruskal-Wallis rank test on GC-IMS data identified 27 compounds that differentiated pregnant from non-pregnant women (p < 0.05). Considering those data, Linear Discriminant Analysis (LDA) achieved an accuracy of over 80% in testing. The gas sensor array had an accuracy of 71% in the LDA model test, a slightly lower compared to the analytical method.

关键词： gas sensor array volatile organic compounds volatilome pregnancy gas chromatography

来源：评论

学校读者我要写书评

暂无评论

gas sensor array for blueberry fruit disease detection and classification

引用

POSTHARVEST BIOLOGY AND TECHNOLOGY 2010年第3期55卷 144-149页

作者： Li, Changying Krewer, Gerard W. Ji, Pingsheng Scherm, Harald Kays, Stanley J. Univ Georgia Dept Biol & Agr Engn Tifton GA 31794 USA Univ Georgia Dept Hort Tifton GA 31794 USA Univ Georgia Dept Plant Pathol Tifton GA 31794 USA Univ Georgia Dept Plant Pathol Athens GA 30602 USA Univ Georgia Dept Hort Athens GA 30602 USA

A conducting polymer gas sensor array (electronic nose) was evaluated for detecting and classifying three common postharvest diseases of blueberry fruit: gray mold caused by Botrytis cinerea, anthracnose caused by Colletotrichum gloeosporioides, and Alternaria rot caused by Alternaria sp. Samples of ripe rabbiteye blueberries (Vaccinium virgatum cv. Brightwell) were inoculated individually with one of the three pathogens or left non-inoculated, and volatiles emanating from the fruit were assessed using the gas sensor array 6-10d after inoculation in two separate experiments. Principal component analysis of volatile profiles revealed four distinct groups corresponding to the four inoculation treatments. MANOVA, conducted on profiles from individual assessment days or from combined data, confirmed that the four treatments were significantly different (P<0.0001). A hierarchical cluster analysis indicated two super-clusters, i.e., control cluster (non-inoculated fruit) vs. pathogen cluster (inoculated fruit). Within the pathogen cluster, fruit infected by B. cinerea and Alternaria sp. were more similar to each other than to fruit infected by C. gloeosporioides. A linear Bayesian classifier achieved 90% overall correct classification for data from experiment 1. Tenax(TM) trapping of volatiles with short-path thermal desorption and quantification by gas chromatography-mass spectrometry was used to characterize volatile compounds emanated from the four groups of berries. Six compounds [styrene, 1-methyl-2-(1-methylethyl) benzene, eucalyptol, undecane, 5-methyl-2-(1-methylethyl)-2-cyclohexen-1-one, and thujopsene] were identified as contributing most in distinguishing differences in the volatiles emanating from the fruit due to infection. A canonical discriminant analysis model using the relative concentration of each of these compounds was developed and successfully classified the four categories of berries. This study underscores the potential feasibility of using a gas s

关键词： Blueberry Fungal disease gas sensor array Electronic nose gas chromatography-mass spectrometry

来源：评论

学校读者我要写书评

暂无评论

gas sensor array system inspired on the sensory diversity and redundancy of the olfactory epithelium

Gas sensor array system inspired on the sensory diversity an...

引用

24th Eurosensor Conference

作者： Fernandez, L. Gutierrez-Galvez, A. Marco, S. Inst Bioengn Catalonia IBEC Catalonia Spain

This paper presents a chemical sensing system that takes inspiration from the combination of sensory diversity and redundancy at the olfactory epithelium to enhance the chemical information obtained from the odorants. The system is based on commercial MOS sensors and achieves, first, diversity trough different types of MOS along with modulation of their temperatures, and second redundancy including 12 MOS sensors for each type (12x8) combined with a high-speed multiplexing system that allows connecting 16 load resistors with each and every one of the 96 sensors in about two seconds. Exposition of the system to ethanol, ammonia, and acetone at different concentrations shows how the system is able to capture a large amount of information of the identity and the concentration of the odorant. (C) 2009 Published by Elsevier Ltd.

关键词： gas sensor array biologically inspired system redundancy diversity MOX sensors temperature modulation

来源：评论

学校读者我要写书评

暂无评论

High Accuracy Real-Time Multi-gas Identification by a Batch-Uniform gas sensor array and Deep Learning Algorithm

引用

ACS sensorS 2022年第2期7卷 430-440页

作者： Kang, Mingu Cho, Incheol Park, Jaeho Jeong, Jaeseok Lee, Kichul Lee, Byeongju Henriquez, Dionisio Del Orbe Yoon, Kukjin Park, Inkyu Korea Adv Inst Sci & Technol KAIST Dept Mech Engn Daejeon 34141 South Korea

Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. In this study, these problems have been solved by fabricating uniform gas sensor arrays and applying the deep learning algorithm to the data from the sensor arrays. Nanocolumnar films of metal oxides (SnO2, In2O3, WO3, and CuO) with a high batch uniformity deposited through glancing angle deposition were used as the sensing materials. The convolutional neural network (CNN) using the input data as a matrix form was adopted as a learning algorithm, which could conduct pattern recognition of the sensor responses. Finally, real-time selective gas detection for CO, NH3, NO2, CH4, and acetone (C3H6O) gas was achieved (minimum response time of 1, 8, 5, 19, and 2 s, respectively) with an accuracy of 98% by applying preprocessed response data to the CNN.

关键词： gas sensor array electronic nose glancing angle deposition deep learning real-time gas identification

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis of SF6-Insulated Equipment by Micro gas sensor array

引用

IEEE TRANSACTIONS ON POWER DELIVERY 2023年第1期38卷 222-230页

作者： Chu, Jifeng Wang, Qiongyuan Liu, Yuyang Pan, Jianbin Yuan, Huan Yang, Aijun Wang, Xiaohua Rong, Mingzhe Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China

SF6 decomposition products could reflect the running status and inner faults of power equipment, and it's expected to realize a timely warning. In this work, six faults including spark and corona discharge were simulated, and SF6 decomposition products with various types and contents were obtained as well. Different from previous investigations employing precision instruments, such as gas chromatography and infrared spectroscopy, a micro sensor array loaded with three gas-sensitive nanomaterials was used to discriminate fault characteristic gases, performing obvious advantages in small size, high integration, and rapid detection. gas chromatography-mass spectrometry (GCMS) indicated that seven analytes had significant differences in types and contents. Meanwhile, the as-prepared micro gas sensor array also outputted significantly various signals for seven analytes, which provided a basis for gas identification. With the assistance of stacked denoising autoencoder (SDAE)-based discrimination algorithms, the recognition model between the response signals of the array and the discharge faults in power equipment could be established. In comparison with KNN (66.67 %), decision tree (70.47 %), and BPNN (73.33 %), SVM has achieved the highest average accuracy of 75.23 %. Totally, this work provides a promising novel method for rapid on-site inspection of SF6-insulated power equipment.

关键词： Spark discharge corona discharge SF(6 )decomposition products gas sensor array fault diagnosis on-site inspection

来源：评论

学校读者我要写书评

暂无评论

Classification and Quantification of Binary Mixtures of gases/Odors Using Thick-Film gas sensor array Responses

引用

IEEE sensorS JOURNAL 2015年第2期15卷 1252-1260页

作者： Sunny Kumar, Vinod Mishra, V. N. Dwivedi, R. Das, R. R. Indian Inst Technol Dept Elect Engn Varanasi 221005 Uttar Pradesh India

This paper presents the results of experiments done for classification and quantification of two volatile organic compounds (VOCs), namely, acetone (CH3COCH3) and 2-Propanol (CH3CHOHCH3) in their single as well as in mixture forms. A sensor array consisting of four sensor elements was fabricated in our lab using thick-film fabrication technology. The steady-state responses of the sensor array were collected for the mentioned VOCs in their single as well as in mixture form. A hierarchical system consisting of gating network and three quantification networks was designed to classify and subsequently quantify the individual and mixture of VOCs. The classification accuracy results of gating network have been ensured using back-propagation neural network (BPNN) and support vector machine (SVM). For quantification multioutput support vector regression method was used in slave networks for single as well as binary mixture data. k-fold cross validation scheme was adopted for all the experiments. The average classification accuracy of gating network for the mixture data in raw form was 89.5% using BPNN and 94.7% using nu-SVM. With principal component analysis preprocessed data, the average accuracy was 95.6% with BPNN and 100% using nu-SVM, respectively. For quantification, good 0.9828 and 0.9764 correlation coefficients for the predicted versus real concentration of acetone and 2-Propanol, respectively, in mixture form were obtained. Thus, we report a promising approach for binary mixture of gases/odors analysis using thick-film gas sensor array responses.

关键词： Binary gas mixture gas sensor array neural classifier support vector machine quantification thick film

来源：评论

学校读者我要写书评

暂无评论

Combination of metal oxide semiconductor gas sensor array and solid-phase microextraction gas chromatography-mass spectrometry for odour classification of brewed coffee

引用

FLAVOUR AND FRAGRANCE JOURNAL 2023年第6期38卷 451-463页

作者： Hardoyono, Fajar Windhani, Kikin Univ Islam Negeri Prof Kiai Haji Saifuddin Zuhri Fac Islamic Educ & Teaching Sci Lab Phys Purwokerto Jawa Tengah Indonesia Univ Jenderal Soedirman Fac Econ & Business Dept Econ & Dev Studies Agro Econ Res Grp Purwokerto Jawa Tengah Indonesia Univ Islam Negeri Prof Kiai Haji Saifuddin Zuhri Fac Islamic Educ & Teaching Sci Lab Phys Jalan A Yani 40 Purwokerto Jawa Tengah Indonesia

Odour analysis of coffee using low-cost and portable instruments in coffee shops, restaurants and bars is essential to keep the loyalty of coffee consumers. This paper aimed to analyse the performance of a gas sensor array for odour classification of brewed coffee. Five grams of ground coffee sample from five different brands was brewed in 80 mL of hot water at a temperature of 90 & DEG;C. The gas sensor array then measured the sensor's response to the brewed coffee odour. The recorded data were analysed using a principal component analysis (PCA), a hierarchical cluster analysis (HCA) and a support vector machine (SVM). Solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS) was used to identify the five coffee samples' volatile organic compounds (VOCs). The visualisation of the PCA score plot shows that the gas sensor array efficiently classifies the brewed coffee based on different odours. The SVM classification using a polynomial kernel obtained an accuracy of 95.21% using training data sets and an accuracy of 96.94% using testing data sets. Meanwhile, for SVM classification using radial basis function kernel, the SVM obtained an accuracy of 100% for training data sets and 93.06% for testing data sets. The SPME-GC-MS analysis showed that the abundance of 2-furanmethanol;2-methoxy-4-vinyl phenol;phenol, 4-ethyl-2-methoxy- and acetic acid contributed to the separation of the first and the second clusters in the principal components coordinate. Based on data analysis, the gas sensor showed high performance as a low-cost and portable instrument for odour analysis of coffee based on sensory technique.

关键词： coffee gas sensor array odour classification SPME-GC-MS support vector machine volatile organic compounds

来源：评论

学校读者我要写书评

暂无评论

A Programmable Interface Circuit for MOS gas sensor array

引用

IEEE sensorS JOURNAL 2024年第16期24卷 25869-25878页

作者： Chen, Dongliang Xie, Dongcheng Geng, Liang Yang, Yujie Wu, Qiuju Rong, Qian Xu, Lei Wu, Feng Univ Sci & Technol China Sch Microelect Hefei 230027 Peoples R China Micro Nano Sensing Hefei Technol Co Ltd Hefei 230031 Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Hefei 230027 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China

In this article, a programmable interface circuit for metal-oxide-semiconductors (MOS) gas sensor array is proposed. Integrated with analog and digital circuits, the interface circuit can realize sensor configuration, signal acquisition, data processing, and recognition results output by embedded with timing and algorithm code. A fully integrated and programmable analog front-end (AFE) circuit is introduced for sensor configuration, such as heating voltage generator, measuring voltage generator, and load resistor selector. A 12-bit SAR ADC supporting multiple channels is used for signal acquisition. Data processing and analysis are completed in a Cortex-M0 processor and recognition results can be output via common digital interfaces. The programmable interface circuit is realized in Nexchip 110 nm embedded flash process platform with an area of $2.53 \times 2.73$ mm. By introducing this interface circuit, an e-nose system with virtualized 16 channels is built up based on four MOS gas sensors and applied in safety monitoring of automotive power lithium batteries. With k-nearest neighbor classification algorithm embedded in the processor, the e-nose system identified gas types of thermal runaway process and output the current state of lithium battery pack with high accuracy.

关键词： gas detectors sensors Temperature sensors Generators Voltage measurement sensor arrays Resistors Analog front-end (AFE) gas recognition gas sensor array programmable

来源：评论

学校读者我要写书评

暂无评论

Screening of volatile compounds composition of white truffle during storage by GCxGC-(FID/MS) and gas sensor array analyses

引用

LWT-FOOD SCIENCE AND TECHNOLOGY 2015年第2期60卷 905-913页

作者： Costa, R. Fanali, C. Pennazza, G. Tedone, L. Dugo, L. Santonico, M. Sciarrone, D. Cacciola, F. Cucchiarini, L. Dacha, M. Mondello, L. Univ Messina Dipartimento Sci Farm & Prod Salute SCIFAR I-98168 Messina Italy CIR I-00128 Rome Italy Univ Messina Chromaleont Srl Dipartimento SCIFAR I-98168 Messina Italy Univ Messina Dipartimento Sci Ambiente Sicurezza Territorio Al I-98166 Messina Italy Univ Urbino Dipartimento Sci Biomol I-61029 Urbino Italy

White truffle (Tuber magnatum Pico) is one of the most valuable delicacies of the Italian and French cuisine. Previous works on truffle aroma report sulfur compounds and short-chain aldehydes as typical constituents which abundance is influenced by storage conditions. In this study, the head space of truffle aroma has been evaluated by two different (innovative) approaches: HS-SPME extraction and comprehensive two-dimensional gas chromatography (GCxGC), exploiting a dual-stage loop-type modulator within a GC system with simultaneous FID and MS double detection, analysis and gas sensors array system analysis. gas chromatographic peak identification was carried out by means of dedicated laboratory-made GC MS libraries and comparison of Retention Indices with those retrieved from literature, while peak quantification by means of a specifically designed software for comprehensive GC. The truffle samples were stored at +8 degrees C for four days in three different ways: wrapped in blotting paper or covered by rice or under vacuum. Eighty compounds were identified with high confidence (>90% of mass spectra similarity). PCA of compounds relative percentages showed differences among the fresh product and truffles stored in rice and under vacuum;on the contrary no significant volatiles composition modification was observed for truffles stored in paper. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： GCxGC SPME gas sensor array Truffle flavor Preservation

来源：评论

学校读者我要写书评

暂无评论

Drift compensation of gas sensor array data by Orthogonal Signal Correction

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2010年第1期100卷 28-35页

作者： Padilla, M. Perera, A. Montoliu, I. Chaudry, A. Persaud, K. Marco, S. Univ Barcelona Dept Elect E-08028 Barcelona Spain Inst Bioengn Catalonia IBEC Barcelona 08028 Spain Protea Ltd Crewe CW1 6ZQ Cheshire England Univ Manchester Sch Chem Engn & Analyt Sci Manchester M60 1QD Lancs England

Drift is an important issue that impairs the reliability of gas sensing systems. sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period. (C) 2009 Elsevier B.V. All rights reserved.

关键词： gas sensor array Drift Orthogonal Signal Correction Component Correction Cross-validation Electronic nose Data shift

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：