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Mei H, Peng J, Wang T, Zhou T, Zhao H, Zhang T, Yang Z. Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array. NANO-MICRO LETTERS 2024; 16:269. [PMID: 39141168 PMCID: PMC11324646 DOI: 10.1007/s40820-024-01489-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 07/21/2024] [Indexed: 08/15/2024]
Abstract
As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.
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Affiliation(s)
- Haixia Mei
- Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, People's Republic of China
| | - Jingyi Peng
- Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, People's Republic of China
| | - Tao Wang
- Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, People's Republic of China.
| | - Tingting Zhou
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China
| | - Hongran Zhao
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China
| | - Tong Zhang
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China.
| | - Zhi Yang
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
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2
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Kumar A, Castro M, Feller JF. Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages. SENSORS (BASEL, SWITZERLAND) 2023; 23:4017. [PMID: 37112358 PMCID: PMC10141392 DOI: 10.3390/s23084017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
Food quality control is an important area to address, as it directly impacts the health of the whole population. To evaluate the food authenticity and quality, the organoleptic feature of the food aroma is very important, such that the composition of volatile organic compounds (VOC) is unique in each aroma, providing a basis to predict the food quality. Different types of analytical approaches have been used to assess the VOC biomarkers and other parameters in the food. The conventional approaches are based on targeted analyses using chromatography and spectroscopies coupled with chemometrics, which are highly sensitive, selective, and accurate to predict food authenticity, ageing, and geographical origin. However, these methods require passive sampling, are expensive, time-consuming, and lack real-time measurements. Alternately, gas sensor-based devices, such as the electronic nose (e-nose), bring a potential solution for the existing limitations of conventional methods, offering a real-time and cheaper point-of-care analysis of food quality assessment. Currently, research advancement in this field involves mainly metal oxide semiconductor-based chemiresistive gas sensors, which are highly sensitive, partially selective, have a short response time, and utilize diverse pattern recognition methods for the classification and identification of biomarkers. Further research interests are emerging in the use of organic nanomaterials in e-noses, which are cheaper and operable at room temperature.
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Ni J, Zhao Y, Zhou Z, Zhao L, Han Z. Condiment recognition using convolutional neural networks with attention mechanism. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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4
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Abu-Khalaf N, Masoud W. Electronic Nose for Differentiation and Quantification of Yeast Species in White Fresh Soft Cheese. Appl Bionics Biomech 2022; 2022:8472661. [PMID: 35082918 PMCID: PMC8786551 DOI: 10.1155/2022/8472661] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/16/2021] [Accepted: 12/27/2021] [Indexed: 11/18/2022] Open
Abstract
Detection of food spoilage with simple and fast methods is an important issue in food security and safety. The present study is mainly aimed at identifying and quantifying four yeast species in white fresh soft cheese using an electronic nose (EN). The yeast species Pichia anomala, Pichia kluyveri, Hanseniaspora uvarum, and Debaryomyces hansenii were used. Six concentrations of each yeast species (100, 200, 400, 600, 800, and 1000 cells/g cheese) were inoculated in 100 g of fresh soft cheese and incubated for 48 h at 25°C. The EN was used to identify and quantify different yeast species in cheese samples. It was found that EN was able to discriminate between four yeast species using principal component analysis (PCA). Moreover, EN was able to quantify in good precision three (Pichia anomala, Pichia kluyveri, and Debaryomyces hansenii) of the four tested yeasts presented in cheese samples using partial least squares (PLS) models. It seems that EN is a reliable tool that can be used as a fast technique to identify and quantify cheese spoilage in the cheese industry.
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Affiliation(s)
- Nawaf Abu-Khalaf
- Department of Agricultural Biotechnology, Faculty of Agricultural Sciences and Technology, Palestine Technical University-Kadoorie (PTUK), P.O. Box 7, Jaffa Street, Tulkarm, State of Palestine
| | - Wafa Masoud
- Department of Agricultural Biotechnology, Faculty of Agricultural Sciences and Technology, Palestine Technical University-Kadoorie (PTUK), P.O. Box 7, Jaffa Street, Tulkarm, State of Palestine
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Yang B, Chen C, Chen F, Chen C, Tang J, Gao R, Lv X. Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 260:119956. [PMID: 34049008 DOI: 10.1016/j.saa.2021.119956] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/17/2021] [Accepted: 05/08/2021] [Indexed: 06/12/2023]
Abstract
Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future.
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Affiliation(s)
- Bo Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Fangfang Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Jun Tang
- Centre for Physical and Chemical Analysis, Xinjiang University, Urumqi 830046, China
| | - Rui Gao
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, Xinjiang, China.
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Evaluation of volatile metabolites as potential markers to predict naturally-aged seed vigour by coupling rapid analytical profiling techniques with chemometrics. Food Chem 2021; 367:130760. [PMID: 34390911 DOI: 10.1016/j.foodchem.2021.130760] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 07/08/2021] [Accepted: 08/01/2021] [Indexed: 11/21/2022]
Abstract
Rapid volatile detection methods for seed vigour rely heavily on artificial ageing (AA), however the comparability of volatile organic compounds (VOCs) to natural ageing (NA) and practicability of the detection models were not well known. In this study, VOCs between AA and NA sweet corn seeds were compared and Partial Least Squares Regression (PLS-R) modelswere constructed based on AA to predict the seed vigour of NA. A total of 33 VOCs were identified, among which aldehydes showed the highest consistency between NA and AA. Furthermore, a AS-PLS-R model with variable importance in projection (VIP > 1) and Pearson Correlation Coefficient (r > 0.9) algorithms, which was built on 3 volatile markers: benzaldehyde monomer, n-nonanal, 1-butanol monomer, achieved the best performance (R2p of 0.901 and RMSEP of 0.050). Therefore, coupling Gas Chromatography- Ion Mobility Spectrometry (GC-IMS) with chemometrics can be an effective way to monitor and predict stored seeds vigour.
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Gaião Calixto M, Alves Ramos H, Veríssimo LS, Dantas Alves V, D Medeiros AC, Alencar Fernandes FH, Veras G. Trends and Application of Chemometric Pattern Recognition Techniques in Medicinal Plants Analysis. Crit Rev Anal Chem 2021; 53:326-338. [PMID: 34314279 DOI: 10.1080/10408347.2021.1953370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Medicinal plants have been used and studied for ages, from very old registers to modern ethnopharmacology, which encompasses analytical chemistry, foods, and pharmacy. Based on international norms and governmental organizations of health, phytomedicine-for example, herbal drugs-needs to guarantee the quality control of products and identify contaminants, biomarkers, and chemical profiles, among other issues. In this sense, is necessary to develop advanced analytical methods that show interesting possibilities and obtain a great amount of data. In order to treat the data, a set of mathematical and statistical procedures named chemometrics is necessary. In terms of herbal drugs, chemometric tools may be used to identify the following in plants: parts, development stages, processing, geographic origin, authentication, and chemical markers. This review describes applications of chemometric pattern recognition tools to analyze herbal drugs in different conditions associated with analytical methods in the last six years (2015-2020).
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Affiliation(s)
- Mariana Gaião Calixto
- Laboratório de Química Analítica e Quimiometria, Universidade Estadual da Paraíba, Campina Grande, Brasil
| | - Hilthon Alves Ramos
- Laboratório de Química Analítica e Quimiometria, Universidade Estadual da Paraíba, Campina Grande, Brasil
| | - Lucas Silva Veríssimo
- Laboratório de Química Analítica e Quimiometria, Universidade Estadual da Paraíba, Campina Grande, Brasil
| | - Vitor Dantas Alves
- Laboratório de Química Analítica e Quimiometria, Universidade Estadual da Paraíba, Campina Grande, Brasil
| | - Ana Cláudia D Medeiros
- Laboratório de Desenvolvimento e Ensaios de Medicamentos, Universidade Estadual da Paraíba, Campina Grande, Brasil
| | - Felipe Hugo Alencar Fernandes
- Laboratório de Desenvolvimento e Ensaios de Medicamentos, Universidade Estadual da Paraíba, Campina Grande, Brasil.,Centro Universitário UNIFACISA, Campina Grande, Brasil
| | - Germano Veras
- Laboratório de Química Analítica e Quimiometria, Universidade Estadual da Paraíba, Campina Grande, Brasil
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John AT, Murugappan K, Nisbet DR, Tricoli A. An Outlook of Recent Advances in Chemiresistive Sensor-Based Electronic Nose Systems for Food Quality and Environmental Monitoring. SENSORS (BASEL, SWITZERLAND) 2021; 21:2271. [PMID: 33804960 PMCID: PMC8036444 DOI: 10.3390/s21072271] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 01/05/2023]
Abstract
An electronic nose (Enose) relies on the use of an array of partially selective chemical gas sensors for identification of various chemical compounds, including volatile organic compounds in gas mixtures. They have been proposed as a portable low-cost technology to analyse complex odours in the food industry and for environmental monitoring. Recent advances in nanofabrication, sensor and microcircuitry design, neural networks, and system integration have considerably improved the efficacy of Enose devices. Here, we highlight different types of semiconducting metal oxides as well as their sensing mechanism and integration into Enose systems, including different pattern recognition techniques employed for data analysis. We offer a critical perspective of state-of-the-art commercial and custom-made Enoses, identifying current challenges for the broader uptake and use of Enose systems in a variety of applications.
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Affiliation(s)
- Alishba T. John
- Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra 2601, Australia;
| | - Krishnan Murugappan
- Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra 2601, Australia;
| | - David R. Nisbet
- Laboratory of Advanced Biomaterials, Research School of Chemistry and the John Curtin School of Medical Research, The Australian National University, Canberra 2601, Australia;
| | - Antonio Tricoli
- Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra 2601, Australia;
- Nanotechnology Research Laboratory, Faculty of Engineering, The University of Sydney, Camperdown 2006, Australia
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9
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Kim S, Brady J, Al-Badani F, Yu S, Hart J, Jung S, Tran TT, Myung NV. Nanoengineering Approaches Toward Artificial Nose. Front Chem 2021; 9:629329. [PMID: 33681147 PMCID: PMC7935515 DOI: 10.3389/fchem.2021.629329] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 01/05/2021] [Indexed: 12/16/2022] Open
Abstract
Significant scientific efforts have been made to mimic and potentially supersede the mammalian nose using artificial noses based on arrays of individual cross-sensitive gas sensors over the past couple decades. To this end, thousands of research articles have been published regarding the design of gas sensor arrays to function as artificial noses. Nanoengineered materials possessing high surface area for enhanced reaction kinetics and uniquely tunable optical, electronic, and optoelectronic properties have been extensively used as gas sensing materials in single gas sensors and sensor arrays. Therefore, nanoengineered materials address some of the shortcomings in sensitivity and selectivity inherent in microscale and macroscale materials for chemical sensors. In this article, the fundamental gas sensing mechanisms are briefly reviewed for each material class and sensing modality (electrical, optical, optoelectronic), followed by a survey and review of the various strategies for engineering or functionalizing these nanomaterials to improve their gas sensing selectivity, sensitivity and other measures of gas sensing performance. Specifically, one major focus of this review is on nanoscale materials and nanoengineering approaches for semiconducting metal oxides, transition metal dichalcogenides, carbonaceous nanomaterials, conducting polymers, and others as used in single gas sensors or sensor arrays for electrical sensing modality. Additionally, this review discusses the various nano-enabled techniques and materials of optical gas detection modality, including photonic crystals, surface plasmonic sensing, and nanoscale waveguides. Strategies for improving or tuning the sensitivity and selectivity of materials toward different gases are given priority due to the importance of having cross-sensitivity and selectivity toward various analytes in designing an effective artificial nose. Furthermore, optoelectrical sensing, which has to date not served as a common sensing modality, is also reviewed to highlight potential research directions. We close with some perspective on the future development of artificial noses which utilize optical and electrical sensing modalities, with additional focus on the less researched optoelectronic sensing modality.
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Affiliation(s)
- Sanggon Kim
- Department of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States
| | - Jacob Brady
- Department of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States
| | - Faraj Al-Badani
- Department of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States
| | - Sooyoun Yu
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Joseph Hart
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Sungyong Jung
- Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, United States
| | - Thien-Toan Tran
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Nosang V. Myung
- Department of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States
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Jian Y, Hu W, Zhao Z, Cheng P, Haick H, Yao M, Wu W. Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials. NANO-MICRO LETTERS 2020; 12:71. [PMID: 34138318 PMCID: PMC7770957 DOI: 10.1007/s40820-020-0407-5] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 02/02/2020] [Indexed: 05/12/2023]
Abstract
Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.
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Affiliation(s)
- Yingying Jian
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 710071, People's Republic of China
| | - Wenwen Hu
- School of Aerospace Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
| | - Zhenhuan Zhao
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 710071, People's Republic of China
| | - Pengfei Cheng
- School of Aerospace Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
| | - Hossam Haick
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 710071, People's Republic of China.
- Department of Chemical Engineering, Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, 3200003, Haifa, Israel.
| | - Mingshui Yao
- Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University Institute for Advanced Study, Kyoto University, Yoshida Ushinomiya-cho, Sakyo-ku, Kyoto, 606-8501, Japan.
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 710071, People's Republic of China.
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11
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Rapid detection of grape syrup adulteration with an array of metal oxide sensors and chemometrics. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.eaef.2019.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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On the feasibility of metal oxide gas sensor based electronic nose software modification to characterize rice ageing during storage. J FOOD ENG 2019. [DOI: 10.1016/j.jfoodeng.2018.10.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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Hu W, Wan L, Jian Y, Ren C, Jin K, Su X, Bai X, Haick H, Yao M, Wu W. Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing. ADVANCED MATERIALS TECHNOLOGIES 2018:1800488. [DOI: 10.1002/admt.201800488] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Affiliation(s)
- Wenwen Hu
- School of Aerospace Science and TechnologyXidian University Shaanxi 710126 P. R. China
| | - Liangtian Wan
- The Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceSchool of SoftwareDalian University of Technology Dalian 116620 China
| | - Yingying Jian
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Cong Ren
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Ke Jin
- School of Aerospace Science and TechnologyXidian University Shaanxi 710126 P. R. China
| | - Xinghua Su
- School of Materials Science and EngineeringChang'an University Xi'an 710061 China
| | - Xiaoxia Bai
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
| | - Hossam Haick
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
- Department of Chemical Engineering and Russell Berrie Nanotechnology InstituteTechnion‐Israel Institute of Technology Haifa 3200003 Israel
| | - Mingshui Yao
- Fujian Institute of Research on the Structure of MatterChinese Academy of Sciences Fuzhou 350002 P. R. China
| | - Weiwei Wu
- School of Advanced Materials and NanotechnologyXidian University Shaanxi 710126 P. R. China
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14
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Giungato P, Renna M, Rana R, Licen S, Barbieri P. Characterization of dried and freeze-dried sea fennel (Crithmum maritimum L.) samples with headspace gas-chromatography/mass spectrometry and evaluation of an electronic nose discrimination potential. Food Res Int 2018; 115:65-72. [PMID: 30599983 DOI: 10.1016/j.foodres.2018.07.067] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 07/10/2018] [Accepted: 07/31/2018] [Indexed: 02/06/2023]
Abstract
Processed samples (air-dried @ 40 and @ 60 °C and freeze-dried) of sea fennel (Crithmum maritimum L.), an autochthonous spice with interesting market potential, were analyzed by headspace gas-chromatography/mass spectrometry and classification capabilities of an electronic nose in discriminating between samples with stepwise forward statistics were evaluated as well. Freeze-drying process was the most preservative in terms of limiting darkening without compromising appearance of the final product, providing weight loss of about 85% and water activity below the limit for mold growth issues. Headspace analysis of samples highlighted the presence of 35 volatiles grouped as terpene hydrocarbons, oxygenated terpenes, sesquiterpen hydrocarbons, phenyl propanoids, not-terpenic aldehydes and not-terpenic ketones. Correlations emerged between selected sensors and some detected volatile organic compounds. Stepwise linear discriminant analysis and simple K-nearest neighbors obtained a 100% overall correct classification rate in cross-validation of the electronic nose in classifying samples, whereas stepwise quadratic discriminant analysis and Naive-Bayes gave 93.3%. The sea fennel could be a new interesting spice to launch in the food market and the electronic nose showed the potential to be used in monitoring the industrial process aimed at extending its shelf-life.
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Affiliation(s)
- Pasquale Giungato
- Department of Chemistry, University of Bari Aldo Moro, Taranto branch, Via Alcide de Gasperi, 74123 Taranto, Italy.
| | - Massimiliano Renna
- Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola, 165/A, 70126 Bari, Italy
| | - Roberto Rana
- Department of Economics, University of Foggia, Via Caggese, 1, 71121 Foggia, Italy
| | - Sabina Licen
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, via Giorgieri 1, 34127 Trieste, Italy
| | - Pierluigi Barbieri
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, via Giorgieri 1, 34127 Trieste, Italy
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