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Wawrzyniak J. Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry. SENSORS (BASEL, SWITZERLAND) 2023; 23:9548. [PMID: 38067920 PMCID: PMC10708670 DOI: 10.3390/s23239548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/24/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023]
Abstract
Volatile compounds not only contribute to the distinct flavors and aromas found in foods and beverages, but can also serve as indicators for spoilage, contamination, or the presence of potentially harmful substances. As the odor of food raw materials and products carries valuable information about their state, gas sensors play a pivotal role in ensuring food safety and quality at various stages of its production and distribution. Among gas detection devices that are widely used in the food industry, metal oxide semiconductor (MOS) gas sensors are of the greatest importance. Ongoing research and development efforts have led to significant improvements in their performance, rendering them immensely useful tools for monitoring and ensuring food product quality; however, aspects related to their limited selectivity still remain a challenge. This review explores various strategies and technologies that have been employed to enhance the selectivity of MOS gas sensors, encompassing the innovative sensor designs, integration of advanced materials, and improvement of measurement methodology and pattern recognize algorithms. The discussed advances in MOS gas sensors, such as reducing cross-sensitivity to interfering gases, improving detection limits, and providing more accurate assessment of volatile organic compounds (VOCs) could lead to further expansion of their applications in a variety of areas, including food processing and storage, ultimately benefiting both industry and consumers.
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Affiliation(s)
- Jolanta Wawrzyniak
- Faculty of Food Science and Nutrition, Poznań University of Life Sciences, 60-624 Poznań, Poland
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2
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Abi-Rizk H, Jouan-Rimbaud Bouveresse D, Chamberland J, Cordella CBY. Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5410-5440. [PMID: 37818969 DOI: 10.1039/d3ay01132a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.
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Affiliation(s)
- Hala Abi-Rizk
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
| | | | - Julien Chamberland
- Department of Food Sciences, STELA Dairy Research Center, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada
| | - Christophe B Y Cordella
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
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3
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Wawrzyniak J. Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228959. [PMID: 36433555 PMCID: PMC9697949 DOI: 10.3390/s22228959] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/13/2022] [Accepted: 11/17/2022] [Indexed: 06/01/2023]
Abstract
Metal oxide semiconductor (MOS) gas sensors have many advantages, but the main obstacle to their widespread use is the cross-sensitivity observed when using this type of detector to analyze gas mixtures. Thermal modulation of the heater integrated with a MOS gas sensor reduced this problem and is a promising solution for applications requiring the selective detection of volatile compounds. Nevertheless, the interpretation of the sensor output signals, which take the form of complex, unique patterns, is difficult and requires advanced signal processing techniques. The study focuses on the development of a methodology to measure and process the output signal of a thermally modulated MOS gas sensor based on a B-spline curve and artificial neural networks (ANNs), which enable the quantitative analysis of volatile components (ethanol and acetone) coexisting in mixtures. B-spline approximation applied in the first stage allowed for the extraction of relevant information from the gas sensor output voltage and reduced the size of the measurement dataset while maintaining the most vital features contained in it. Then, the determined parameters of the curve were used as the input vector for the ANN model based on the multilayer perceptron structure. The results show great usefulness of the combination of B-spline and ANN modeling techniques to improve response selectivity of a thermally modulated MOS gas sensor.
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Affiliation(s)
- Jolanta Wawrzyniak
- Faculty of Food Science and Nutrition, Poznań University of Life Sciences, 60-624 Poznań, Poland
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4
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Icagic SD, Kvascev GS. A Smart Alcoholmeter Sensor Based on Deep Learning Visual Perception. SENSORS (BASEL, SWITZERLAND) 2022; 22:7394. [PMID: 36236494 PMCID: PMC9571611 DOI: 10.3390/s22197394] [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: 09/11/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Process automation, in general, enables the enhancement of productivity, product quality, and consistency alongside other production metrics. Liquor production on an industrial scale also follows the automation trend. However, small and medium producers lag with equipment modernization due to the high costs of industrial equipment. One of the important sensors in automation equipment for distilleries is the alcohol concentration sensor used for fraction separation, process automation, and supervision. This paper proposes a novel low-cost approach to alcohol concentration sensing by employing deep learning on the visual perception of traditional alcoholmeter. For purposes of the training model, dataset acquisition apparatus is developed and a large dataset of labeled images of alcoholmeter readings is acquired. The problem of reading alcohol concentration from an alcoholometer image is treated as a regression and classification problem. Performances of both regression and classification models were investigated with Resnet18 as an architecture of choice. Both models achieved satisfying performance metrics demonstrating the feasibility of the proposed approaches. The proposed system implemented on Raspberry Pi with a camera can be integrated into new distillation equipment. Additionally, it can be used for retrofitting existing equipment due to its non-invasive nature of reading. The scope of use can be further expanded to the reading of other types of analog instruments simply by retraining the model.
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Affiliation(s)
- Savo D. Icagic
- University of Belgrade, School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11120 Beograd, Serbia
- The Institute for Artificial Intelligence Research and Development of Serbia, Fruskogorska 1, 21000 Novi Sad, Serbia
| | - Goran S. Kvascev
- University of Belgrade, School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11120 Beograd, Serbia
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5
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Abstract
A lab-made electronic nose (Enose) with vacuum sampling and a sensor array, comprising nine metal oxide semiconductor Figaro gas sensors, was tested for the quantitative analysis of vapor–liquid equilibrium, described by Henry’s law, of aqueous solutions of organic compounds: three alcohols (i.e., methanol, ethanol, and propanol) or three chemical compounds with different functional groups (i.e., acetaldehyde, ethanol, and ethyl acetate). These solutions followed a fractional factorial design to guarantee orthogonal concentrations. Acceptable predictive ridge regression models were obtained for training, with RSEs lower than 7.9, R2 values greater than 0.95, slopes varying between 0.84 and 1.00, and intercept values close to the theoretical value of zero. Similar results were obtained for the test data set: RSEs lower than 8.0, R2 values greater than 0.96, slopes varying between 0.72 and 1.10, and some intercepts equal to the theoretical value of zero. In addition, the total mass of the organic compounds of each aqueous solution could be predicted, pointing out that the sensors measured mainly the global contents of the vapor phases. The satisfactory quantitative results allowed to conclude that the Enose could be a useful tool for the analysis of volatiles from aqueous solutions containing organic compounds for which Henry’s law is applicable.
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Abstract
Fermented foods and beverages have become a part of daily diets in several societies around the world. Emitted volatile organic compounds play an important role in the determination of the chemical composition and other information of fermented foods and beverages. Electronic nose (E-nose) technologies enable non-destructive measurement and fast analysis, have low operating costs and simplicity, and have been employed for this purpose over the past decades. In this work, a comprehensive review of the recent progress in E-noses is presented according to the end products of the main fermentation types, including alcohol fermentation, lactic acid fermentation, acetic acid fermentation and alkaline fermentation. The benefits, research directions, limitations and challenges of current E-nose systems are investigated and highlighted for fermented foods and beverage applications.
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7
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Gonzalez Viejo C, Fuentes S. Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling. SENSORS 2022; 22:s22062303. [PMID: 35336472 PMCID: PMC8955090 DOI: 10.3390/s22062303] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 12/14/2022]
Abstract
The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning to detect and assess wine faults. For this purpose, red and white base wines were used, and treatments consisted of spiked samples with 12 faults that are traditionally formed in wines. Results showed high accuracy in the classification models using NIR and e-nose for red wines (94–96%; 92–97%, respectively) and white wines (96–97%; 90–97%, respectively). Implementing new and emerging digital technologies could be a turning point for the winemaking industry to become more predictive in terms of decision-making and maintaining and increasing wine quality traits in a changing and challenging climate.
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8
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Wang S, Hu XZ, Liu YY, Tao NP, Lu Y, Wang XC, Lam W, Lin L, Xu CH. Direct authentication and composition quantitation of red wines based on Tri-step infrared spectroscopy and multivariate data fusion. Food Chem 2022; 372:131259. [PMID: 34627087 DOI: 10.1016/j.foodchem.2021.131259] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 12/21/2022]
Abstract
A robust data fusion strategy integrating Tri-step infrared spectroscopy (IR) with electronic nose (E-nose) was established for rapid qualitative authentication and quantitative evaluation of red wines using Cabernet Sauvignon as an example. The chemical fingerprints of four types of wines were thoroughly interpreted by Tri-step IR, and the defined spectral fingerprint region of alcohol and sugar was 1200-950 cm-1. The wine types were authenticated by IR-based principal component analysis (PCA). Furthermore, ten quantitative models by partial least squares (PLS) were built to evaluate alcohol and total sugar contents. In particular, the model based on the fusion datasets of spectral fingerprint region and E-nose was superior to the others, in which RMSEP reduced by 47.95% (alcohol) and 79.90% (total sugar), rp increased by 11.95% and 43.47%, and RPD >3.0. The developed methodology would be applicable for mass screening and rapid multi-chemical-component quantification of wines in a more comprehensive and efficient manner.
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Affiliation(s)
- Song Wang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Qinpu Biotechnology Pte Ltd, Shanghai 201306, China
| | - Xiao-Zhen Hu
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, PR China
| | - Yan-Yan Liu
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Qinpu Biotechnology Pte Ltd, Shanghai 201306, China
| | - Ning-Ping Tao
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, PR China
| | - Ying Lu
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, PR China
| | - Xi-Chang Wang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, PR China
| | - Wing Lam
- Department of Pharmacology, Yale University, New Haven, CT 06520, US
| | - Ling Lin
- Comprehensive Technology Service Center of Quanzhou Customs, Quanzhou 362018, PR China.
| | - Chang-Hua Xu
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Department of Pharmacology, Yale University, New Haven, CT 06520, US; Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai 201306, PR China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai 201306, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Shanghai 201306, China.
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9
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WANG A, ZHU Y, ZOU L, ZHU H, CAO R, ZHAO G. Combination of machine learning and intelligent sensors in real-time quality control of alcoholic beverages. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.54622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
| | | | | | - Hong ZHU
- Ministry of Agriculture and Rural Affairs, China
| | - Ruge CAO
- Tianjin University of Science and Technology, China
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10
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Capris T, Takagi Y, Figueiredo D, Henriques J, Pires IM. A Convolutional Neural Network-enabled IoT framework to verify COVID-19 hygiene conditions and authorize access to facilities. PROCEDIA COMPUTER SCIENCE 2022; 203:727-732. [PMID: 35974969 PMCID: PMC9374321 DOI: 10.1016/j.procs.2022.07.108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | | | | | - João Henriques
- Polytechnic of Viseu, Viseu, Portugal
- University of Coimbra, Coimbra, Portugal
- CISeD - Research Centre in Digital Services, Polytechnic of Viseu, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, Covilha˜, Portugal
- Escola de Ciências e Tecnologia, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
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11
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Determination of Alcohol Content in Beers of Different Styles Based on Portable Near-Infrared Spectroscopy and Multivariate Calibration. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02126-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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12
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Sierra-Padilla A, García-Guzmán JJ, López-Iglesias D, Palacios-Santander JM, Cubillana-Aguilera L. E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing. SENSORS (BASEL, SWITZERLAND) 2021; 21:4976. [PMID: 34372213 PMCID: PMC8347095 DOI: 10.3390/s21154976] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/17/2021] [Accepted: 07/18/2021] [Indexed: 01/14/2023]
Abstract
Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.
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Affiliation(s)
- Alfonso Sierra-Padilla
- Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Polígono del Río San Pedro S/N, 11510 Puerto Real, Cadiz, Spain; (A.S.-P.); (L.C.-A.)
| | - Juan José García-Guzmán
- Instituto de Investigación e Innovación Biomédica de Cadiz (INiBICA), Hospital Universitario ‘Puerta del Mar’, Universidad de Cadiz, 11009 Cadiz, Cadiz, Spain;
| | - David López-Iglesias
- Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Polígono del Río San Pedro S/N, 11510 Puerto Real, Cadiz, Spain; (A.S.-P.); (L.C.-A.)
| | - José María Palacios-Santander
- Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Polígono del Río San Pedro S/N, 11510 Puerto Real, Cadiz, Spain; (A.S.-P.); (L.C.-A.)
| | - Laura Cubillana-Aguilera
- Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Polígono del Río San Pedro S/N, 11510 Puerto Real, Cadiz, Spain; (A.S.-P.); (L.C.-A.)
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13
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Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence. FERMENTATION 2021. [DOI: 10.3390/fermentation7030117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification ma-chine learning (ML) modelling. Six different ML models were developed; Model 1 (M1) and M2 were developed using the NIR absorbance values (100 inputs from 1596–2396 nm) and e-nose (nine sensor readings) as inputs, respectively, to classify the samples into control, low and high concentration of faults. Model 3 (M3) and M4 were based on NIR and M5 and M6 based on the e-nose readings as inputs with 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neu-ron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 95.6%, M2 = 95.3%, M3 = 98.9%, M4 = 98.3%, M5 = 96.8%, and M6 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies.
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14
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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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15
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Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning. FERMENTATION 2020. [DOI: 10.3390/fermentation6040104] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.
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16
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Rapid Non-Destructive Quantification of Eugenol in Curdlan Biofilms by Electronic Nose Combined with Gas Chromatography-Mass Spectrometry. SENSORS 2020; 20:s20164441. [PMID: 32784818 PMCID: PMC7472399 DOI: 10.3390/s20164441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/20/2020] [Accepted: 08/06/2020] [Indexed: 02/02/2023]
Abstract
Eugenol is hepatotoxic and potentially hazardous to human health. This paper reports on a rapid non-destructive quantitative method for the determination of eugenol concentration in curdlan (CD) biofilms by electronic nose (E-nose) combined with gas chromatography-mass spectrometry (GC-MS). Different concentrations of eugenol were added to the film-forming solution to form a series of biofilms by casting method, and the actual eugenol concentration in the biofilm was determined. Analysis of the odor collected on the biofilms was carried out by GC-MS and an E-nose. The E-nose data was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) in order to establish a discriminant model for determining eugenol concentrations in the biofilms. Further analyses involving the application of all sensors and featured sensors, the prediction model-based partial least squares (PLS) and support vector machines (SVM) were carried out to determine eugenol concentration in the CD biofilms. The results showed that the optimal prediction model for eugenol concentration was obtained by PLS at R2p of 0.952 using 10 sensors. The study described a rapid, non-destructive detection and quantitative method for determining eugenol concentration in bio-based packaging materials.
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Wu Z, Zhang H, Sun W, Lu N, Yan M, Wu Y, Hua Z, Fan S. Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification. SENSORS 2020; 20:s20154239. [PMID: 32751427 PMCID: PMC7435456 DOI: 10.3390/s20154239] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/24/2020] [Accepted: 07/28/2020] [Indexed: 12/25/2022]
Abstract
In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.
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Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modeling. BEVERAGES 2020. [DOI: 10.3390/beverages6020028] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Foam-related parameters are associated with beer quality and dependent, among others, on the protein content. This study aimed to develop a machine learning (ML) model to predict the pattern and presence of 54 proteins. Triplicates of 24 beer samples were analyzed through proteomics. Furthermore, samples were analyzed using the RoboBEER to evaluate 15 physical parameters (color, foam, and bubbles), and a portable near-infrared (NIR) device. Proteins were grouped according to their molecular weight (MW), and a matrix was developed to assess only the significant correlations (p < 0.05) with the physical parameters. Two ML models were developed using the NIR (Model 1), and RoboBEER (Model 2) data as inputs to predict the relative quantification of 54 proteins. Proteins in the 0–20 kDa group were negatively correlated with the maximum volume of foam (MaxVol; r = −0.57) and total lifetime of foam (TLTF; r = −0.58), while those within 20–40 kDa had a positive correlation with MaxVol (r = 0.47) and TLTF (r = 0.47). Model 1 was not as accurate (testing r = 0.68; overall r = 0.89) as Model 2 (testing r = 0.90; overall r = 0.93), which may serve as a reliable and affordable method to incorporate the relative quantification of important proteins to explain beer quality.
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Jiang W, Gao D. Five Typical Stenches Detection Using an Electronic Nose. SENSORS 2020; 20:s20092514. [PMID: 32365549 PMCID: PMC7248900 DOI: 10.3390/s20092514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/03/2020] [Accepted: 04/07/2020] [Indexed: 02/06/2023]
Abstract
This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.
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Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages. BEVERAGES 2019. [DOI: 10.3390/beverages5040062] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.
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