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Yang H, Huang X, Yang M, Zhang X, Tang F, Gao B, Gong M, Liang Y, Liu Y, Qian X, Li H. Advanced analytical techniques for authenticity identification and quality evaluation in Essential oils: A review. Food Chem 2024; 451:139340. [PMID: 38678649 DOI: 10.1016/j.foodchem.2024.139340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/25/2024] [Accepted: 04/09/2024] [Indexed: 05/01/2024]
Abstract
Essential oils (EO), secondary metabolites of plants are fragrant oily liquids with antibacterial, antiviral, anti-inflammatory, anti-allergic, and antioxidant effects. They are widely applied in food, medicine, cosmetics, and other fields. However, the quality of EOs remain uncertain owing to their high volatility and susceptibility to oxidation, influenced by factors such as the harvesting season, extraction, and separation techniques. Additionally, the huge economic value of EOs has led to a market marked by widespread and varied adulteration, making the assessment of their quality challenging. Therefore, developing simple, quick, and effective identification techniques for EOs is essential. This review comprehensively summarizes the techniques for assessing EO quality and identifying adulteration. It covers sensory evaluation, physical and chemical property evaluation, and chemical composition analysis, which are widely used and of great significance for the quality evaluation and adulteration detection of EOs.
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Affiliation(s)
- Huda Yang
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Xiaoying Huang
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China; Jiangxi Guxiangjinyun Great Health Industry Co. Ltd, Nanchang 330096, China.
| | - Ming Yang
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Xiaofei Zhang
- Jiangxi Guxiangjinyun Great Health Industry Co. Ltd, Nanchang 330096, China; College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang 712046, China
| | - Fangrui Tang
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China; Jiangxi Guxiangjinyun Great Health Industry Co. Ltd, Nanchang 330096, China
| | - Beibei Gao
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Mengya Gong
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Yong Liang
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Yang Liu
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Xingyi Qian
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Huiting Li
- Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang 330004, China; Jiangxi Guxiangjinyun Great Health Industry Co. Ltd, Nanchang 330096, China.
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2
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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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He HJ, da Silva Ferreira MV, Wu Q, Karami H, Kamruzzaman M. Portable and miniature sensors in supply chain for food authentication: a review. Crit Rev Food Sci Nutr 2024:1-21. [PMID: 39066550 DOI: 10.1080/10408398.2024.2380837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
Abstract
Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.
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Affiliation(s)
- Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang, China
| | | | - Qianyi Wu
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hamed Karami
- Department of Petroleum Engineering, Collage of Engineering, Knowledge University, Erbil, Iraq
| | - Mohammed Kamruzzaman
- School of Food Science, Henan Institute of Science and Technology, Xinxiang, China
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Liu J, Sun R, Bao X, Yang J, Chen Y, Tang B, Liu Z. Machine Learning Driven Atom-Thin Materials for Fragrance Sensing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2401066. [PMID: 38973110 DOI: 10.1002/smll.202401066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 06/05/2024] [Indexed: 07/09/2024]
Abstract
Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.
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Affiliation(s)
- Juanjuan Liu
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Ruijia Sun
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Xuan Bao
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Jiefu Yang
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Yanling Chen
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Bijun Tang
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zheng Liu
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
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5
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Chen X, Liu H, Li C, Xu Y, Xu B. Revealing the characteristic aroma and boundary compositions of five pig breeds based on HS-SPME/GC-O-MS, aroma recombination and omission experiments. Food Res Int 2024; 178:113954. [PMID: 38309911 DOI: 10.1016/j.foodres.2024.113954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/18/2023] [Accepted: 01/02/2024] [Indexed: 02/05/2024]
Abstract
To clarify the characteristic aroma compounds and flavor discrepancies of five Chinese typical pig species, headspace-solid phase microextraction gas chromatography-olfactometry-mass spectrometry (HS-SPME/GC-O-MS), electronic nose (E-nose), aroma recombination and omission experiments were used to analyze the characteristic aroma and boundary of five boiled pork. A total of 38 volatile compounds were identified, of which 14 were identified as important odorants with odor-activity values (OAVs) greater than 1. Aroma recombination and omission experiments revealed 8 key characteristic aroma compounds, which significantly contributed to the overall aroma. Sensory evaluation of the recombination model with the 8 aroma compounds scored 3.0 to 4.0 out of 5 points. 12 potential markers were identified to distinguish by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), including (E)-2-octenal, 3-ethyl-2-methyl-1,3-hexadiene, (E)-2-heptenal, 2-pentylfuran, cyclooctanol, 1-heptanol, sec-butylamine, D-limonene, N-vinylformamide, 2,3-octanedione, 2-ethylfuran and 3-pentanamine. Alongside benzaldehyde and pentanal, the combinations and fluctuations of these 14 aroma markers were proposed to constitute the aroma boundaries of different pork breeds. The aroma-active substances were able to effectively differentiate different breeds.
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Affiliation(s)
- Xueli Chen
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province, China
| | - Haoyue Liu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province, China; School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Cong Li
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province, China.
| | - Yujuan Xu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province, China
| | - Baocai Xu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province, China; Engineering Research Center of Bio-Process of Ministry of Education, School of Food & Biological Engineering, Hefei University of Technology, Hefei 230601, Anhui Province, China.
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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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Aghili NS, Rasekh M, Karami H, Edriss O, Wilson AD, Ramos J. Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil. SENSORS (BASEL, SWITZERLAND) 2023; 23:6294. [PMID: 37514589 PMCID: PMC10383484 DOI: 10.3390/s23146294] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/02/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.
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Affiliation(s)
- Nadia Sadat Aghili
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Mansour Rasekh
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Hamed Karami
- Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq
| | - Omid Edriss
- Department of Computer, Rafsanjan Branch, Islamic Azad University, Rafsanjan 77181-84483, Iran
| | - Alphus Dan Wilson
- Southern Hardwoods Laboratory, Pathology Department, Center for Forest Health & Disturbance, Forest Genetics & Ecosystems Biology, Southern Research Station, USDA Forest Service, 432 Stoneville Road, Stoneville, MS 38776-0227, USA
| | - Jose Ramos
- College of Computing and Engineering, Nova Southeastern University (NSU), 3301 College Avenue, Fort Lauderdale, FL 33314-7796, USA
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Ward RJ, Wuerger SM, Ashraf M, Marshall A. Physicochemical features partially explain olfactory crossmodal correspondences. Sci Rep 2023; 13:10590. [PMID: 37391587 PMCID: PMC10313698 DOI: 10.1038/s41598-023-37770-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 06/27/2023] [Indexed: 07/02/2023] Open
Abstract
During the olfactory perception process, our olfactory receptors are thought to recognize specific chemical features. These features may contribute towards explaining our crossmodal perception. The physicochemical features of odors can be extracted using an array of gas sensors, also known as an electronic nose. The present study investigates the role that the physicochemical features of olfactory stimuli play in explaining the nature and origin of olfactory crossmodal correspondences, which is a consistently overlooked aspect of prior work. Here, we answer the question of whether the physicochemical features of odors contribute towards explaining olfactory crossmodal correspondences and by how much. We found a similarity of 49% between the perceptual and the physicochemical spaces of our odors. All of our explored crossmodal correspondences namely, the angularity of shapes, smoothness of textures, perceived pleasantness, pitch, and colors have significant predictors for various physicochemical features, including aspects of intensity and odor quality. While it is generally recognized that olfactory perception is strongly shaped by context, experience, and learning, our findings show that a link, albeit small (6-23%), exists between olfactory crossmodal correspondences and their underlying physicochemical features.
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Affiliation(s)
- Ryan J Ward
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, L3 3AF, UK.
- Digital Innovation Facility, University of Liverpool, Liverpool, L69 3RF, UK.
| | - Sophie M Wuerger
- Department of Psychology, University of Liverpool, Liverpool, L69 7ZA, UK
| | - Maliha Ashraf
- Department of Psychology, University of Liverpool, Liverpool, L69 7ZA, UK
| | - Alan Marshall
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, UK
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Viciano-Tudela S, Parra L, Navarro-Garcia P, Sendra S, Lloret J. Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:5812. [PMID: 37447662 DOI: 10.3390/s23135812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/13/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.
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Affiliation(s)
- Sandra Viciano-Tudela
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
| | - Lorena Parra
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
| | - Paula Navarro-Garcia
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
| | - Sandra Sendra
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
| | - Jaime Lloret
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
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Khorramifar A, Karami H, Lvova L, Kolouri A, Łazuka E, Piłat-Rożek M, Łagód G, Ramos J, Lozano J, Kaveh M, Darvishi Y. Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:5716. [PMID: 37420880 PMCID: PMC10300923 DOI: 10.3390/s23125716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.
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Affiliation(s)
- Ali Khorramifar
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199, Iran; (A.K.); (A.K.)
| | - Hamed Karami
- Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq;
| | - Larisa Lvova
- Department of Chemical Science and Technologies, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Alireza Kolouri
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199, Iran; (A.K.); (A.K.)
| | - Ewa Łazuka
- Department of Applied Mathematics, Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland; (E.Ł.); (M.P.-R.)
| | - Magdalena Piłat-Rożek
- Department of Applied Mathematics, Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland; (E.Ł.); (M.P.-R.)
| | - Grzegorz Łagód
- Department of Water Supply and Wastewater Disposal, Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland;
| | - Jose Ramos
- College of Computing and Engineering, Nova Southeastern University (NSU), 3301 College Avenue, Fort Lauderdale, FL 33314-7796, USA;
| | - Jesús Lozano
- Department of Electric Technology, Electronics and Automation, University of Extremadura, Avda. De Elvas S/n, 06006 Badajoz, Spain;
| | - Mohammad Kaveh
- Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq;
| | - Yousef Darvishi
- Department of Biosystems Engineering, University of Tehran, Tehran P.O. Box 113654117, Iran;
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Putri LA, Rahman I, Puspita M, Hidayat SN, Dharmawan AB, Rianjanu A, Wibirama S, Roto R, Triyana K, Wasisto HS. Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication. NPJ Sci Food 2023; 7:31. [PMID: 37328497 DOI: 10.1038/s41538-023-00205-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 05/26/2023] [Indexed: 06/18/2023] Open
Abstract
Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.
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Affiliation(s)
- Linda Ardita Putri
- PT Nanosense Instrument Indonesia, Yogyakarta, 55167, Indonesia
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia
| | - Iman Rahman
- PT Nanosense Instrument Indonesia, Yogyakarta, 55167, Indonesia
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia
| | - Mayumi Puspita
- PT Nanosense Instrument Indonesia, Yogyakarta, 55167, Indonesia
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia
- Indonesian Oil Palm Research Institute, Jalan Taman Kencana No 1, Bogor, 16128, Indonesia
| | | | - Agus Budi Dharmawan
- PT Nanosense Instrument Indonesia, Yogyakarta, 55167, Indonesia
- Faculty of Information Technology, Universitas Tarumanagara, Jl. Letjen S. Parman No. 1, Jakarta, 11440, Indonesia
| | - Aditya Rianjanu
- Department of Materials Engineering, Institut Teknologi Sumatera, Terusan Ryacudu, Way Hui, Jati Agung, Lampung, 35365, Indonesia
| | - Sunu Wibirama
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Jl. Grafika 2, Yogyakarta, 55281, Indonesia
| | - Roto Roto
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia
| | - Kuwat Triyana
- Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia.
- Institute of Halal Industry and System (IHIS), Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281, Indonesia.
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12
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Elghandour MMMY, Maggiolino A, García EIC, Sánchez-Aparicio P, De Palo P, Ponce-Covarrubias JL, Pliego AB, Salem AZM. Effects of Microencapsulated Essential Oils on Equine Health: Nutrition, Metabolism and Methane Emission. Life (Basel) 2023; 13:life13020455. [PMID: 36836812 PMCID: PMC9963397 DOI: 10.3390/life13020455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 01/29/2023] [Accepted: 02/03/2023] [Indexed: 02/09/2023] Open
Abstract
This review examines the available data regarding the positive effects of microencapsulated essential oils (EOs) on the nutrition, metabolism, and possibly the methane emission of horses. A literature review was conducted on the effect of microencapsulated (EOs) on the health of horses. The information comprises articles published in recent years in indexed journals. The results indicate that mixtures of microencapsulated EOs may be beneficial to equine health due to their antimicrobial and antioxidant activity, as well as their effects on enteric methane production, nutrient absorption, and immune system enhancement. Moreover, encapsulation stabilizes substances such as EOs in small doses, primarily by combining them with other ingredients.
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Affiliation(s)
- Mona M. M. Y. Elghandour
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Autónoma del Estado de México, Toluca 50000, Estado de México, Mexico
| | - Aristide Maggiolino
- Department of Veterinary Medicine, University of Bari Aldo Moro, 70010 Valenzano, Italy
- Correspondence: (A.M.); (A.Z.M.S.)
| | | | - Pedro Sánchez-Aparicio
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Autónoma del Estado de México, Toluca 50000, Estado de México, Mexico
| | - Pasquale De Palo
- Department of Veterinary Medicine, University of Bari Aldo Moro, 70010 Valenzano, Italy
| | - José Luis Ponce-Covarrubias
- Escuela Superior de Medicina Veterinaria y Zootecnia No. 3, Universidad Autónoma de Guerrero (UAGro), Técpan de Galeana 40900, Guerrero, Mexico
| | - Alberto Barbabosa Pliego
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Autónoma del Estado de México, Toluca 50000, Estado de México, Mexico
| | - Abdelfattah Z. M. Salem
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Autónoma del Estado de México, Toluca 50000, Estado de México, Mexico
- Correspondence: (A.M.); (A.Z.M.S.)
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13
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Caceres-Hernandez D, Gutierrez R, Kung K, Rodriguez J, Lao O, Contreras K, Jo KH, Sanchez-Galan JE. Recent Advances in Automatic Feature Detection and Classification of Fruits including with a special emphasis on Watermelon (Citrillus lanatus): a Review. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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14
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Khorramifar A, Rasekh M, Karami H, Lozano J, Gancarz M, Łazuka E, Łagód G. Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning. PLoS One 2023; 18:e0284612. [PMID: 37115737 PMCID: PMC10146475 DOI: 10.1371/journal.pone.0284612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and effective methods of storage to maintain the quality of potatoes after their harvest, while preserving their freshness and sweetness. Smart monitoring and evaluation of the quality of potatoes during the storage period could be an effective approach to improve their freshness. This study is aimed at assessing the changes in the potato quality by an electronic nose (e-nose) in terms of the sugar and carbohydrate contents. Three potato cultivars (Agria, Santé, and Sprite) were analyzed and their quality variations were separately assessed. Quality parameters (i.e. sugar and carbohydrate contents) were evaluated in six 15-day periods. The e-nose data were analyzed by means of chemometric methods, including principal component analysis (PCA), linear data analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). Quadratic discriminant analysis (QDA) and multivariate discrimination analysis (MDA) offer the highest accuracy and sensitivity in the classification of data. The accuracy of all methods was higher than 90%. These results could be applied to present a new approach for the assessment of the quality of stored potatoes.
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Affiliation(s)
- Ali Khorramifar
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Mansour Rasekh
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Hamed Karami
- Department of Petroleum Engineering, College of Engineering, Knowledge University, Erbil, Iraq
| | - Jesús Lozano
- Escuela de Ingenierías Industriales, Universidad de Extremadura, Badajoz, Spain
| | - Marek Gancarz
- Institute of Agrophysics, Polish Academy of Sciences, Lublin, Poland
- Faculty of Production and Power Engineering, University of Agriculture in Kraków, Kraków, Poland
| | - Ewa Łazuka
- Faculty of Technology Fundamentals, Lublin University of Technology, Lublin, Poland
| | - Grzegorz Łagód
- Faculty of Environmental Engineering, Lublin University of Technology, Lublin, Poland
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15
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Khorramifar A, Sharabiani VR, Karami H, Kisalaei A, Lozano J, Rusinek R, Gancarz M. Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy. Foods 2022; 11:foods11244077. [PMID: 36553819 PMCID: PMC9778509 DOI: 10.3390/foods11244077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.
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Affiliation(s)
- Ali Khorramifar
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Vali Rasooli Sharabiani
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Hamed Karami
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
- Correspondence: (H.K.); or (M.G.)
| | - Asma Kisalaei
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Jesús Lozano
- Department of Electric Technology, Electronics and Automation, University of Extremadura, Avda. de Elvas S/n, 06006 Badajoz, Spain
| | - Robert Rusinek
- Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
| | - Marek Gancarz
- Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
- Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Krakow, Poland
- Correspondence: (H.K.); or (M.G.)
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16
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Momeny M, Neshat AA, Jahanbakhshi A, Mahmoudi M, Ampatzidis Y, Radeva P. Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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17
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Detection of fraud in sesame oil with the help of artificial intelligence combined with chemometrics methods and chemical compounds characterization by gas chromatography–mass spectrometry. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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18
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Truzzi E, Durante C, Bertelli D, Catellani B, Pellacani S, Benvenuti S. Rapid Classification and Recognition Method of the Species and Chemotypes of Essential Oils by ATR-FTIR Spectroscopy Coupled with Chemometrics. Molecules 2022; 27:molecules27175618. [PMID: 36080384 PMCID: PMC9458032 DOI: 10.3390/molecules27175618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/28/2022] [Accepted: 08/29/2022] [Indexed: 11/16/2022] Open
Abstract
In the present work, the applicability of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric tools in recognizing essential oils (EOs) for routine control, was evaluated. EOs belonging to Mentha, Cymbopogon, and Lavandula families and to S. rosmarinus and T. vulgaris species were analyzed, and the performance of several untargeted approaches, based on the synergistic combination of ATR-FTIR and Partial Least Squares Discriminant Analysis (PLS-DA), was tested to classify the species and chemotypes. Different spectra pre-processing methods were employed, and the robustness of the built models was tested by means of a Receiver Operating Characteristic (ROC) curve and random permutations test. The application of these approaches revealed fruitful results in terms of sensitivity and specificity, highlighting the potentiality of ATR-FTIR and chemometrics techniques to be used as a sensitive, cost-effective, and rapid tool to differentiate EO samples according to their species and chemotype.
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Affiliation(s)
- Eleonora Truzzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125 Modena, Italy
| | - Caterina Durante
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125 Modena, Italy
| | - Davide Bertelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125 Modena, Italy
- Correspondence:
| | - Benedetta Catellani
- Department of Life Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125 Modena, Italy
| | - Samuele Pellacani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125 Modena, Italy
| | - Stefania Benvenuti
- Department of Life Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125 Modena, Italy
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Zhang K, Wang J, Fan X, Zhu G, Lu T, Xue R. Discrimination between raw and ginger juice processed Magnoliae officinalis cortex based on HPLC and Heracles NEO ultra-fast gas phase electronic nose. PHYTOCHEMICAL ANALYSIS : PCA 2022; 33:722-734. [PMID: 35318753 DOI: 10.1002/pca.3123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/27/2022] [Accepted: 03/07/2022] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Magnoliae officinalis cortex (MOC), a traditional Chinese medicine, has been used in treating gastrointestinal diseases since ancient time. According to the Chinese Pharmacopoeia, it includes two kinds of decoction pieces, raw and ginger juice processed Magnoliae officinalis cortex (RMOC and GMOC). OBJECTIVE The aim of this paper was to study the differences between non-volatile and volatile components in RMOC and GMOC. METHODS The non-volatile components were detected by HPLC fingerprinting coupled with content determination (syringin, magnoflorine, honokiol and magnolol). Meanwhile, their odor information was obtained using a Heracles NEO ultra-fast gas phase electronic nose to conduct radar fingerprint analysis, principal component analysis and discriminant factor analysis, and the volatile components were analyzed qualitatively by the Kovats retention index and the AroChemBase database. RESULTS The HPLC fingerprints were established and 20 common peaks were found in all chromatograms with similarity values of more than 0.900. The content determination results showed that the contents of syringin and magnoflorine decreased, while the contents of honokiol and magnolol increased in GMOC. By the gas phase electronic nose, the two decoction pieces could be distinguished obviously and 16 possible compounds were identified. Among them, the relative contents of (-)-α-pinene and β-pinene increased, while β-phellandrene and (+)-limonene levels decreased. CONCLUSION The results suggested that honokiol, magnolol, (-)-α-pinene and β-pinene might be the main substances which could enhance the harmonizing effect on the stomach. Moreover, this paper could lay a foundation for exploring the processing mechanism of MOC and provide a novel method for the research of other traditional Chinese medicine with strong aroma.
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Affiliation(s)
- Kewei Zhang
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - Jing Wang
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - Xingchen Fan
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - Guangfei Zhu
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - Tulin Lu
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
| | - Rong Xue
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
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20
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Rasekh M, Karami H, Fuentes S, Kaveh M, Rusinek R, Gancarz M. Preliminary study non-destructive sorting techniques for pepper (Capsicum annuum L.) using odor parameter. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113667] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Neural Network Robustness Analysis Using Sensor Simulations for a Graphene-Based Semiconductor Gas Sensor. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10050152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Despite their advantages regarding production costs and flexibility, chemiresistive gas sensors often show drawbacks in reproducibility, signal drift and ageing. As pattern recognition algorithms, such as neural networks, are operating on top of raw sensor signals, assessing the impact of these technological drawbacks on the prediction performance is essential for ensuring a suitable measuring accuracy. In this work, we propose a characterization scheme to analyze the robustness of different machine learning models for a chemiresistive gas sensor based on a sensor simulation model. Our investigations are structured into four separate studies: in three studies, the impact of different sensor instabilities on the concentration prediction performance of the algorithms is investigated, including sensor-to-sensor variations, sensor drift and sensor ageing. In a further study, the explainability of the machine learning models is analyzed by applying a state-of-the-art feature ranking method called SHAP. Our results show the feasibility of model-based algorithm testing and substantiate the need for the thorough characterization of chemiresistive sensor algorithms before sensor deployment in order to ensure robust measurement performance.
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22
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Zou X, Wang C, Luo M, Ren Q, Liu Y, Zhang S, Bai Y, Meng J, Zhang W, Su SW. Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine. SENSORS 2022; 22:s22082997. [PMID: 35458982 PMCID: PMC9025600 DOI: 10.3390/s22082997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 12/04/2022]
Abstract
Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.
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Affiliation(s)
- Xiuguo Zou
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
- Correspondence: (X.Z.); (S.W.S.); Tel.: +86-25-5860-6585 (X.Z.)
| | - Chenyang Wang
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Manman Luo
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Qiaomu Ren
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Yingying Liu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Shikai Zhang
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China; (C.W.); (M.L.); (Q.R.); (Y.L.); (S.Z.)
| | - Yungang Bai
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China;
| | - Jiawei Meng
- Department of Mechanical Engineering, University College London, London WC1E 7JE, UK;
| | - Wentian Zhang
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Steven W. Su
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Correspondence: (X.Z.); (S.W.S.); Tel.: +86-25-5860-6585 (X.Z.)
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23
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Grape Cultivar Identification and Classification by Machine Olfaction Analysis of Leaf Volatiles. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10040125] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Development of electronic technologies for precise identification of fruit crop cultivars in agricultural production provides an effective means for assuring product quality and authentication. The capabilities of discriminating between grape (Vitis vinifera L.) cultivars is essential for assuring certification of varieties sold in world markets. Machine olfaction, based on electronic-nose (e-nose) technologies, is readily available for rapid identification of fruit and vegetative agricultural products. This technology relies on detection of and discrimination between volatile organic compound (VOC) emissions from plant parts. It may be used in all stages of agricultural production to facilitate crop maintenance, cultivation, and harvesting decisions prior to marketing. An experimental e-nose device was constructed and tested in combination with five chemometric methods, including PCA, LDA, QDA, SVM, and ANN, as rapid, non-destructive tools for identification and classification of grape cultivars. An e-nose instrument equipped with nine metal oxide semiconductor (MOS) sensors was utilized to identify and classify five grape cultivars based on leaf VOC emissions using supervised and non-supervised methods. Grape leaf samples were first identified as belonging to specific cultivar types using PCA analyses, which are non-supervised classification methods, with the first two principal components (PC-1 and PC-2) accounting for 89% of the total variance. Four supervised statistical methods were further tested, including DA, QDA, SVM, and ANN, and provided effective discrimination accuracies of 98%, 99%, 92%, and 99%, respectively. These findings confirmed the suitable applicability of an MOS e-nose sensor array with supervised methods for accurate identification of grape cultivars, which is useful for authentication of vine cultivar types for commercial markets.
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24
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Gancarz M, Dobrzański B, Malaga-Toboła U, Tabor S, Combrzyński M, Ćwikła D, Strobel WR, Oniszczuk A, Karami H, Darvishi Y, Żytek A, Rusinek R. Impact of Coffee Bean Roasting on the Content of Pyridines Determined by Analysis of Volatile Organic Compounds. Molecules 2022; 27:1559. [PMID: 35268660 PMCID: PMC8911706 DOI: 10.3390/molecules27051559] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 01/27/2023] Open
Abstract
The aim of the study was to analyze the process of roasting coffee beans in a convection-conduction roaster (CC) without a heat exchanger and a convection-conduction-radiation roaster (CCR) with a heat exchanger for determination of the aroma profile. The aroma profile was analyzed using the SPME/GC-MS technique, and an Agrinose electronic nose was used to determine the aroma profile intensity. Arabica coffee beans from five regions of the world, namely, Peru, Costa Rica, Ethiopia, Guatemala, and Brazil, were the research material. The chemometric analyses revealed the dominance of azines, alcohols, aldehydes, hydrazides, and acids in the coffee aroma profile. Their share distinguished the aroma profiles depending on the country of origin of the coffee beans. The high content of pyridine from the azine group was characteristic for the coffee roasting process in the convection-conduction roaster without a heat exchanger, which was shown by the PCA analysis. The increased content of pyridine resulted from the appearance of coal tar, especially in the CC roaster. Pyridine has an unpleasant and bitter plant-like odor, and its excess is detrimental to the human organism. The dominant and elevated content of pyridine is a defect of the coffee roasting process in the CC roaster compared to the process carried out in the CCR machine. The results obtained with the Agrinose showed that the CC roasting method had a significant effect on the sensor responses. The effect of coal tar on the coffee beans resulted in an undesirable aroma profile characterized by increased amounts of aromatic volatile compounds and higher responses of Agrinose sensors.
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Affiliation(s)
- Marek Gancarz
- Institute of Agrophysics Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland; (M.G.); (A.Ż.)
- Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116B, 30-149 Krakow, Poland; (U.M.-T.); (S.T.)
| | - Bohdan Dobrzański
- Pomology, Nursery and Enology Department, University of Life Sciences in Lublin, Głęboka 28, 20-400 Lublin, Poland;
| | - Urszula Malaga-Toboła
- Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116B, 30-149 Krakow, Poland; (U.M.-T.); (S.T.)
| | - Sylwester Tabor
- Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116B, 30-149 Krakow, Poland; (U.M.-T.); (S.T.)
| | - Maciej Combrzyński
- Department of Thermal Technology and Food Process Engineering, University of Life Sciences in Lublin, Głęboka 31, 20-612 Lublin, Poland;
| | - Daniel Ćwikła
- Rodzinna Palarnia Coffee and Sons Roastery, Boczna Lubomelskiej 4, 20-070 Lublin, Poland;
| | - Wacław Roman Strobel
- Institute of Technology and Life Sciences—National Research Institute, Falenty, Al. Hrabska 3, 05-090 Raszyn, Poland;
| | - Anna Oniszczuk
- Department of Inorganic Chemistry, Medical University of Lublin, Chodźki 4a, 20-093 Lublin, Poland;
| | - Hamed Karami
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
| | - Yousef Darvishi
- Department of Biosystems Engineering, University of Tehran, Tehran P.O. Box 113654117, Iran;
| | - Alaksandra Żytek
- Institute of Agrophysics Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland; (M.G.); (A.Ż.)
| | - Robert Rusinek
- Institute of Agrophysics Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland; (M.G.); (A.Ż.)
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25
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Recording the Fragrance of 15 Types of Medicinal Herbs and Comparing Them by Similarity Using the Electronic Nose FF-2A. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Medical herbs have been recognized till now as having different constituents that act on the human body. However, the fragrance of herbs is a complex mixture of odors, which makes it difficult to qualify or quantify the scent objectively on the human sense of smell. In this study, aromas of 15 medicinal herbs were recorded using an electronic nose FF-2A, and their characteristics were compared with aroma samples of wine such as Le Nez du Vin, to determine which wine aromas are similar to each medicinal herb. Thereafter, the aromas of the 15 herbs were standardized to create a reference axis for the aroma of each herb, and the similarity of tea herbs to the reference axis was examined. Additionally, the results were compared with those obtained by gas chromatography-mass spectrometry (GC-MS). In FF-2A, the measured scent is recorded as an absolute value. We succeeded in calculating the similarity of the scents of other herbs with the axes of the scent of each herb by standardizing their scents and creating new axis data. Conversely, although GC-MS is able to identify the components and concentrations of fragrances, an electronic nose can analyze fragrances in a way that is uncommon with GC-MS, such as comparison of similarities between fragrances.
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Wang L, Yang K, Liu L. Comparative flavor analysis of four kinds of sweet fermented grains by sensory analysis combined with GC-MS. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2022. [DOI: 10.1515/ijfe-2021-0185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Four types of cereals (glutinous rice, purple rice, red rice, yellow millet) were selected to produce sweet fermented grains. Flavor profiles of sweet fermented grains are comparatively studied to distinguish various flavor types by using GC-MS, electronic nose (E-nose), and sensory analysis, and the amino acid composition and physicochemical properties of sweet fermented grains were analyzed. The results showed that the volatile compounds of sweet fermented grains were significantly different. Esters and alcohols were the major volatile compounds in sweet fermented grains. The electronic nose, electronic tongue and sensory analysis jointly verified that the volatile components of sweet fermented grains had differences between them. The sweet fermented grains could be classified based on differences in volatile compounds. In the amino acids analysis, Glu, Pro, Asp and Leu were the most abundant. The difference in physicochemical properties is more helpful to distinguish different types of sweet fermented grains. Correlation analysis between antioxidant active substances and color value showed a positive correlation between with a* value, and a negative correlation with L*, b* value. Our results suggested that there were differences in the flavor characteristics of sweet fermented grains fermented from different types of cereals. The results of the study will provide valuable information for the selection of raw materials for sweet fermented grains.
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Affiliation(s)
- Lei Wang
- College of Food Engineering and Nutrition Science , Shaanxi Normal University , Xi’an , Shaanxi , 710119 , China
| | - Ke Yang
- College of Food Science and Engineering , Northwest Agriculture and Forestry University , Yangling , Shaanxi , 712100 , China
| | - Liu Liu
- College of Food Engineering and Nutrition Science , Shaanxi Normal University , Xi’an , Shaanxi , 710119 , China
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Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods. SENSORS 2021; 21:s21227620. [PMID: 34833693 PMCID: PMC8619411 DOI: 10.3390/s21227620] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 02/07/2023]
Abstract
Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.
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Characteristic-Aroma-Component-Based Evaluation and Classification of Strawberry Varieties by Aroma Type. Molecules 2021; 26:molecules26206219. [PMID: 34684796 PMCID: PMC8540309 DOI: 10.3390/molecules26206219] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 11/21/2022] Open
Abstract
The unique fruity aroma of strawberries, a popular fruit of high economic value, is closely related to all the volatile organic compounds (VOCs) contained within them. Despite extensive studies on the identification of VOCs in strawberries, systematic studies on fruit-aroma-related VOCs are few, resulting in a lack of effective standards for accurately distinguishing aroma types. In the present study, solid-phase micro extraction and gas chromatography–mass spectrometry were used to analyze and identify VOCs in the ripe fruit of each of the 16 strawberry varieties at home and abroad and to explore their characteristic aroma components and the classification of such varieties by aroma type. The results suggested remarkable variations in the types and contents of VOCs in different strawberry varieties, of which esters were dominant. The principal volatile components, consisting of four esters, three alcohols, one aldehyde, and one ketone, in 16 strawberry varieties were detected based on the absolute and relative contents of VOCs in the fruit. The characteristic aroma components in strawberries, containing nine esters, six aldehydes, and one alcohol, were determined based on the aroma values of different VOCs, and the characteristic aroma components were divided into five types further based on aroma descriptions. Sixteen strawberry varieties were finally divided into four aroma types, namely, peachy, pineapple, fruity, and floral, based on the contributions of different types. The results provided a basis and standard for classifying strawberries by aroma type, studying the hereditary regularity of the fruity aroma of strawberries, and improving aroma quality.
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Liu H, Hui T, Fang F, Ma Q, Li S, Zhang D, Wang Z. Characterization and Discrimination of Key Aroma Compounds in Pre- and Postrigor Roasted Mutton by GC-O-MS, GC E-Nose and Aroma Recombination Experiments. Foods 2021; 10:foods10102387. [PMID: 34681435 PMCID: PMC8535600 DOI: 10.3390/foods10102387] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/25/2021] [Accepted: 09/29/2021] [Indexed: 11/16/2022] Open
Abstract
The key aroma compounds in the pre- and postrigor roasted mutton were studied in this study. The results showed that 33 and 30 odorants were detected in the pre- and postrigor roasted mutton, respectively. Eight aroma compounds, including 3-methylbutanal, pentanal, hexanal, heptanal, octanal, nonanal, 1-octen-3-ol, and 2-pentylfuran, were confirmed as key odorants by aroma recombination and omission experiments. The aroma profiles of pre- and postrigor roasted mutton both presented great fatty, roasty, meaty, grassy, and sweet odors. Particularly, the concentrations of hexanal, heptanal, octanal, nonanal, 1-octen-3-ol, and 2-pentylfuran in postrigor roasted mutton were significantly higher (p < 0.05) than those in the prerigor roasted mutton. The postrigor back strap was more suitable for roasting than the prerigor back strap. The pre- and postrigor roasted mutton could be obviously discriminated based on the aroma compounds by orthogonal partial least squares discrimination analysis (OPLS-DA) and principal component analysis (PCA). Hexanal and 1-octen-3-ol might potential markers for the discrimination of the pre- and postrigor roasted mutton.
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Yi Z, Xie J. Prediction in the Dynamics and Spoilage of Shewanella putrefaciens in Bigeye Tuna ( Thunnus obesus) by Gas Sensors Stored at Different Refrigeration Temperatures. Foods 2021; 10:foods10092132. [PMID: 34574241 PMCID: PMC8472656 DOI: 10.3390/foods10092132] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/31/2021] [Accepted: 09/07/2021] [Indexed: 12/02/2022] Open
Abstract
Shewanella putrefaciens have a faster growth rate and strong spoilage potential at low temperatures for aquatic products. This study developed a nondestructive method for predicting the kinetic growth and spoilage of S. putrefaciens in bigeye tuna during cold storage at 4, 7 and 10 °C by electronic nose. According to the responses of electronic nose sensor P30/2, the fitted primary kinetic models (Gompertz and logistic models) and secondary model (square root function model) were able to better simulate the dynamic growth of S. putrefaciens, with high R2 and low RMSE values in the range of 0.96–0.99 and 0.021–0.061, respectively. A partial least squares (PLS) regression model based on both electronic nose sensor response values and electrical conductivity (EC) values predicted spoilage of S. putrefaciens in bigeye tuna more accurately than the PLS model based on sensor signal values only. In addition, SPME/GC-MS analysis suggested that 1-octen-3-ol, 2-nonanone, 2-heptanone, dimethyl disulfide and methylamine, N, N-dimethyl- are the key VOCs of tuna inoculated with S. putrefaciens.
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Affiliation(s)
- Zhengkai Yi
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China;
| | - Jing Xie
- College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China;
- Shanghai Professional Technology Service Platform on Cold Chain Equipment Performance and Energy Saving Evaluation, Shanghai 201306, China
- National Experimental Teaching Demonstration Center for Food Science and Engineering, Shanghai 201306, China
- Shanghai Engineering Research Center of Aquatic Product Processing & Preservation, Shanghai 201306, China
- Correspondence: ; Tel.: +86-021-6190-0391
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Karami H, Rasekh M, Mirzaee‐Ghaleh E. Identification of olfactory characteristics of edible oil during storage period using metal oxide semiconductor sensor signals and ANN methods. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.15749] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Hamed Karami
- Department of Biosystems Engineering University of Mohaghegh Ardabili Ardabil Iran
| | - Mansour Rasekh
- Department of Biosystems Engineering University of Mohaghegh Ardabili Ardabil Iran
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Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9090243] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.
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Khorramifar A, Rasekh M, Karami H, Malaga-Toboła U, Gancarz M. A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array. SENSORS 2021; 21:s21175836. [PMID: 34502725 PMCID: PMC8434104 DOI: 10.3390/s21175836] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 01/03/2023]
Abstract
In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people’s diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples’ dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.
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Affiliation(s)
- Ali Khorramifar
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; (A.K.); (H.K.)
| | - Mansour Rasekh
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; (A.K.); (H.K.)
- Correspondence: (M.R.); (M.G.); Tel.: +98-451-551-2081-9 (M.R.); +48-81-744-50-61 (M.G.); Fax: +48-81-744-50-67 (M.G.)
| | - Hamed Karami
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; (A.K.); (H.K.)
| | - Urszula Malaga-Toboła
- Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, Poland;
| | - Marek Gancarz
- Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, Poland;
- Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
- Correspondence: (M.R.); (M.G.); Tel.: +98-451-551-2081-9 (M.R.); +48-81-744-50-61 (M.G.); Fax: +48-81-744-50-67 (M.G.)
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Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose. Expert Rev Mol Diagn 2021; 21:1223-1233. [PMID: 34415806 DOI: 10.1080/14737159.2021.1971079] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung cancer are detected with MOS sensor array-based e-noses. The e-nose device with the sensor array, data acquisition system, and pattern recognition can detect the variations of volatile organic compounds (VOC) present in the expelled breath of patients and healthy controls. MATERIALS AND METHODS This work presents the e-nose equipment design, study subjects selection, breath sampling procedures, and various data analysis tools. The developed e-nose system is tested in 40 patients with lung cancer, 48 patients with COPD, and 90 healthy controls. RESULTS In differentiating lung cancer and COPD from controls, support vector machine (SVM) with 3-fold cross-validation outperformed all other classifiers with an accuracy of 92.3% in cross-validation. In external validation, the same discrimination was achieved by k-nearest neighbors (k-NN) with 75.0% accuracy. CONCLUSION The reported results show that the VOC analysis with an e-nose system holds exceptional possibilities in noninvasive disease diagnosis applications.
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Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9070171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade.
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