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Wanniarachchi PC, Upul Kumarasinghe KG, Jayathilake C. Recent advancements in chemosensors for the detection of food spoilage. Food Chem 2024; 436:137733. [PMID: 37862988 DOI: 10.1016/j.foodchem.2023.137733] [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: 09/18/2022] [Revised: 07/10/2023] [Accepted: 10/09/2023] [Indexed: 10/22/2023]
Abstract
The need for reliable sensors has become a major requirement to confirm the quality and safety of food commodities. Chemosensors are promising sensing tools to identify contaminants and food spoilage to ensure food safety. Chemosensing materials are evolving and becoming potential mechanisms to enable onsite and real-time monitoring of food safety. This review summarizes the information about the basic four types of chemosensors (colorimetric, optical, electrochemical, and piezoelectric) employed in the food sector, the latest advancements in the development of chemo-sensing mechanisms, and their food applications, with special emphasis on the future outlook of them. In this review, we discuss the novel chemosensors developed from the year 2018 to 2022 to detect spoilage in some common types of food like fish, meat, milk, cheese and soy sauce. This work will provide a fundamental step toward further development and innovations of chemosensors targeting different arenas in the food industry.
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Affiliation(s)
| | - K G Upul Kumarasinghe
- Department of Chemistry, Faculty of Applied Sciences, University of Sri Jayewardenepura, Gangodawila, Nugegoda 10250, Sri Lanka
| | - Chathuni Jayathilake
- School of Medicine, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH 44106, USA.
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2
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Sharma A, Eadi SB, Noothalapati H, Otyepka M, Lee HD, Jayaramulu K. Porous materials as effective chemiresistive gas sensors. Chem Soc Rev 2024; 53:2530-2577. [PMID: 38299314 DOI: 10.1039/d2cs00761d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
Chemiresistive gas sensors (CGSs) have revolutionized the field of gas sensing by providing a low-power, low-cost, and highly sensitive means of detecting harmful gases. This technology works by measuring changes in the conductivity of materials when they interact with a testing gas. While semiconducting metal oxides and two-dimensional (2D) materials have been used for CGSs, they suffer from poor selectivity to specific analytes in the presence of interfering gases and require high operating temperatures, resulting in high signal-to-noise ratios. However, nanoporous materials have emerged as a promising alternative for CGSs due to their high specific surface area, unsaturated metal actives, and density of three-dimensional inter-connected conductive and pendant functional groups. Porous materials have demonstrated excellent response and recovery times, remarkable selectivity, and the ability to detect gases at extremely low concentrations. Herein, our central emphasis is on all aspects of CGSs, with a primary focus on the use of porous materials. Further, we discuss the basic sensing mechanisms and parameters, different types of popular sensing materials, and the critical explanations of various mechanisms involved throughout the sensing process. We have provided examples of remarkable performance demonstrated by sensors using these materials. In addition to this, we compare the performance of porous materials with traditional metal-oxide semiconductors (MOSs) and 2D materials. Finally, we discussed future aspects, shortcomings, and scope for improvement in sensing performance, including the use of metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and porous organic polymers (POPs), as well as their hybrid counterparts. Overall, CGSs using porous materials have the potential to address a wide range of applications, including monitoring water quality, detecting harmful chemicals, improving surveillance, preventing natural disasters, and improving healthcare.
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Affiliation(s)
- Akashdeep Sharma
- Hybrid Porous Materials Laboratory, Department of Chemistry, Indian Institute of Technology Jammu, Jammu & Kashmir, 181221, India.
| | - Sunil Babu Eadi
- Department of Electronics Engineering, Chungnam National University, Daejeon, South Korea.
| | - Hemanth Noothalapati
- Faculty of Life and Environmental Sciences, Shimane University, Matsue, 690-8504, Japan
| | - Michal Otyepka
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
- IT4Innovations, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Hi-Deok Lee
- Department of Electronics Engineering, Chungnam National University, Daejeon, South Korea.
- Korea Sensor Lab, Department of Electronics Engineering, Chungnam National University, Daejeon, South Korea
| | - Kolleboyina Jayaramulu
- Hybrid Porous Materials Laboratory, Department of Chemistry, Indian Institute of Technology Jammu, Jammu & Kashmir, 181221, India.
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Yue Y, Yin J, Xie J, Wu S, Ding H, Han L, Bie S, Song W, Zhang Y, Song X, Yu H, Li Z. Comparative Analysis of Volatile Compounds in the Flower Buds of Three Panax Species Using Fast Gas Chromatography Electronic Nose, Headspace-Gas Chromatography-Ion Mobility Spectrometry, and Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis. Molecules 2024; 29:602. [PMID: 38338347 PMCID: PMC10856343 DOI: 10.3390/molecules29030602] [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: 11/27/2023] [Revised: 12/09/2023] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources.
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Affiliation(s)
- Yang Yue
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Jiaxin Yin
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Jingyi Xie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Shufang Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Hui Ding
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Lifeng Han
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Songtao Bie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Wen Song
- Tianjin HongRenTang Pharmaceutical Co., Ltd., Tianjin 300385, China; (W.S.); (Y.Z.)
| | - Ying Zhang
- Tianjin HongRenTang Pharmaceutical Co., Ltd., Tianjin 300385, China; (W.S.); (Y.Z.)
| | - Xinbo Song
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; (Y.Y.); (J.Y.); (J.X.); (S.W.); (H.D.); (L.H.); (S.B.); (X.S.)
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
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Gan Z, Zhou Q, Zheng C, Wang J. Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis. Biosens Bioelectron 2023; 237:115540. [PMID: 37523812 DOI: 10.1016/j.bios.2023.115540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 07/09/2023] [Accepted: 07/17/2023] [Indexed: 08/02/2023]
Abstract
Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.
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Affiliation(s)
- Ziyu Gan
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Qin'an Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Chengyu Zheng
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
| | - Jun Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China.
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Gonçalves WB, Teixeira WSR, Sampaio ANDCE, Martins OA, Cervantes EP, Mioni MDSR, Gruber J, Pereira JG. Combination of the electronic nose with microbiology as a tool for rapid detection of Salmonella. J Microbiol Methods 2023; 212:106805. [PMID: 37558057 DOI: 10.1016/j.mimet.2023.106805] [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: 05/24/2023] [Revised: 06/26/2023] [Accepted: 08/05/2023] [Indexed: 08/11/2023]
Abstract
Salmonella is one of the most important foodborne pathogens and its analysis in raw and processed products is mandatory in the food industry. Although microbiological analysis is the standard practice for Salmonella determination, these assays are commonly laborious and time-consuming, thus, alternative techniques based on easy operation, few manipulation steps, low cost, and reduced time are desirable. In this paper, we demonstrate the use of an e-nose based on ionogel composites (ionic liquid + gelatine + Fe3O4 particles) as a complementary tool for the conventional microbiological detection of Salmonella. We used the proposed methodology for differentiating Salmonella from Escherichia coli, Pseudomonas fluorescens, Pseudomonas aeruginosa, and Staphylococcus aureus in nonselective medium: pre-enrichment in brain heart infusion (BHI) (incubation at 35 °C, 24 h) and enrichment in tryptone soy agar (TSA) (incubation at 35 °C, 24 h), whereas Salmonella differentiation from E. coli and P. fluorescens was also evaluated in selective media, bismuth sulfite agar (BSA), xylose lysine deoxycholate agar (XLD), and brilliant green agar (BGA) (incubation at 35 °C, 24 h). The obtained data were compared by principal component analysis (PCA) and different machine learning algorithms: multilayer perceptron (MLP), linear discriminant analysis (LDA), instance-based (IBk), and Logistic Model Trees (LMT). For the nonselective media, under optimized conditions, taking merged data of BHI + TSA (total incubation time of 48 h), an accuracy of 85% was obtained with MLP, LDA, and LMT, while five separated clusters were presented in PCA, each cluster corresponding to a bacterium. In addition, for evaluation of the e-nose for discrimination of Salmonella using selective media, considering the combination of BSA + XLD and total incubation of 72 h, the PCA showed three separated and well-defined clusters corresponding to Salmonella, E. coli, and P. fluorescens, and an accuracy of 100% was obtained for all classifiers.
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Affiliation(s)
- Wellington Belarmino Gonçalves
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Av. Prof Lineu Prestes, 748, 05508-000, São Paulo, SP, Brazil.
| | - Wanderson Sirley Reis Teixeira
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Aryele Nunes da Cruz Encide Sampaio
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Otávio Augusto Martins
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Evelyn Perez Cervantes
- Instituto de Matemática e Estatística, Universidade de São Paulo, 05508-090, São Paulo, SP, Brazil.
| | - Mateus de Souza Ribeiro Mioni
- Departamento de Patologia, Reprodução e Saúde Única, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 14884-900, Jaboticabal, SP, Brazil.
| | - Jonas Gruber
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Av. Prof Lineu Prestes, 748, 05508-000, São Paulo, SP, Brazil.
| | - Juliano Gonçalves Pereira
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
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Tian H, Wu D, Chen B, Yuan H, Yu H, Lou X, Chen C. Rapid identification and quantification of vegetable oil adulteration in raw milk using a flash gas chromatography electronic nose combined with machine learning. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Xing Z, Zogona D, Wu T, Pan S, Xu X. Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food. Food Chem 2023; 415:135650. [PMID: 36868065 DOI: 10.1016/j.foodchem.2023.135650] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 01/27/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023]
Abstract
Bionic nose, a technology that mimics the human olfactory system, has been widely used to assess food quality due to their high sensitivity, low cost, portability and simplicity. This review briefly describes that bionic noses with multiple transduction mechanisms are developed based on gas molecules' physical properties: electrical conductivity, visible optical absorption, and mass sensing. To enhance their superior sensing performance and meet the growing demand for applications, a range of strategies have been developed, such as peripheral substitutions, molecular backbones, and ligand metals that can finely tune the properties of sensitive materials. In addition, challenges and prospects coexist are covered. Cross-selective receptors of bionic nose will help and guide the selection of the best array for a particular application scenario. It provides an odour-based monitoring tool for rapid, reliable and online assessment of food safety and quality.
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Affiliation(s)
- Zheng Xing
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China; Shenzhen Institute of Nutrition and Health, Shenzhen, Guangdong 518038, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture,Genome Analysis Laboratory of the Ministry of Agriculture,Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518038, China
| | - Daniel Zogona
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China
| | - Ting Wu
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China
| | - Siyi Pan
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China
| | - Xiaoyun Xu
- Key Laboratory of Environment Correlative Dietology (Ministry of Education), Huazhong Agricultural University, Wuhan, Hubei 430072, China; Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control, Huazhong Agricultural University, Wuhan, Hubei 430072, China; Shenzhen Institute of Nutrition and Health, Shenzhen, Guangdong 518038, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture,Genome Analysis Laboratory of the Ministry of Agriculture,Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518038, China.
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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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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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He S, Zhang B, Dong X, Wei Y, Li H, Tang B. Differentiation of Goat Meat Freshness Using Gas Chromatography with Ion Mobility Spectrometry. Molecules 2023; 28:molecules28093874. [PMID: 37175284 PMCID: PMC10179894 DOI: 10.3390/molecules28093874] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/17/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
To investigate the flavor changes in goat meat upon storage, the volatile components observed in goat meat after different storage periods were determined using gas chromatography-ion mobility spectrometry (GC-IMS). A total of 38 volatile organic compounds (VOCs) were determined from the goat meat samples, including alcohols, ketones, aldehydes, esters, hydrocarbons, ethers, and amine compounds. 1-Hexanol, 3-Hydroxy-2-butanone, and Ethyl Acetate were the main volatile substances in fresh goat meat, and they rapidly decreased with increasing storage time and can be used as biomarkers for identifying fresh meat. When combined with the contents of total volatile basic-nitrogen (TVB-N) and the total numbers of bacterial colonies observed in physical and chemical experiments, the characteristic volatile components of fresh, sub-fresh, and spoiled meat were determined by principal component analysis (PCA). This method will help with the detection of fraudulent production dates in goat meat sales.
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Affiliation(s)
- Shan He
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Bin Zhang
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Xuan Dong
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Yuqing Wei
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Hongtu Li
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
| | - Bo Tang
- College of Food and Bioengineering, Bengbu University, Bengbu 233000, China
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11
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Feyzioglu A, Taspinar YS. Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types. SENSORS (BASEL, SWITZERLAND) 2023; 23:2222. [PMID: 36850817 PMCID: PMC9958759 DOI: 10.3390/s23042222] [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: 01/21/2023] [Revised: 02/08/2023] [Accepted: 02/12/2023] [Indexed: 06/18/2023]
Abstract
Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.
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Affiliation(s)
- Ahmet Feyzioglu
- Department of Mechanical Engineering, Marmara University, Istanbul 34722, Turkey
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12
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Cheli F, Ottoboni M, Fumagalli F, Mazzoleni S, Ferrari L, Pinotti L. E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology? Toxins (Basel) 2023; 15:146. [PMID: 36828460 PMCID: PMC9958648 DOI: 10.3390/toxins15020146] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/17/2023] [Accepted: 02/04/2023] [Indexed: 02/16/2023] Open
Abstract
Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.
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Affiliation(s)
- Federica Cheli
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
- CRC I-WE (Coordinating Research Centre: Innovation for Well-Being and Environment), University of Milan, 20100 Milan, Italy
| | - Matteo Ottoboni
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
| | - Francesca Fumagalli
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
| | - Sharon Mazzoleni
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
| | - Luca Ferrari
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
| | - Luciano Pinotti
- Department of Veterinary Medicine and Animal Science, University of Milan, 26900 Lodi, Italy
- CRC I-WE (Coordinating Research Centre: Innovation for Well-Being and Environment), University of Milan, 20100 Milan, Italy
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13
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Grassi S, Tarapoulouzi M, D’Alessandro A, Agriopoulou S, Strani L, Varzakas T. How Chemometrics Can Fight Milk Adulteration. Foods 2022; 12:foods12010139. [PMID: 36613355 PMCID: PMC9819000 DOI: 10.3390/foods12010139] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/10/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022] Open
Abstract
Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.
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Affiliation(s)
- Silvia Grassi
- Department of Food, Environmental and Nutritional Sciences (DeFENS), Università degli Studi di Milano, Via Celoria, 2, 20133 Milano, Italy
| | - Maria Tarapoulouzi
- Department of Chemistry, Faculty of Pure and Applied Science, University of Cyprus, P.O. Box 20537, Nicosia CY-1678, Cyprus
| | - Alessandro D’Alessandro
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Sofia Agriopoulou
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
| | - Lorenzo Strani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
- Correspondence: (L.S.); (T.V.)
| | - Theodoros Varzakas
- Department of Food Science and Technology, University of the Peloponnese, Antikalamos, 24100 Kalamata, Greece
- Correspondence: (L.S.); (T.V.)
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Cardin M, Cardazzo B, Mounier J, Novelli E, Coton M, Coton E. Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods. Foods 2022; 11:3379. [PMID: 36359992 PMCID: PMC9653732 DOI: 10.3390/foods11213379] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/20/2022] [Accepted: 10/22/2022] [Indexed: 08/13/2023] Open
Abstract
Food fraud, corresponding to any intentional action to deceive purchasers and gain an undue economical advantage, is estimated to result in a 10 to 65 billion US dollars/year economical cost worldwide. Dairy products, such as cheese, in particular cheeses with protected land- and tradition-related labels, have been listed as among the most impacted as consumers are ready to pay a premium price for traditional and typical products. In this context, efficient food authentication methods are needed to counteract current and emerging frauds. This review reports the available authentication methods, either chemical, physical, or DNA-based methods, currently used for origin authentication, highlighting their principle, reported application to cheese geographical origin authentication, performance, and respective advantages and limits. Isotope and elemental fingerprinting showed consistent accuracy in origin authentication. Other chemical and physical methods, such as near-infrared spectroscopy and nuclear magnetic resonance, require more studies and larger sampling to assess their discriminative power. Emerging DNA-based methods, such as metabarcoding, showed good potential for origin authentication. However, metagenomics, providing a more in-depth view of the cheese microbiota (up to the strain level), but also the combination of methods relying on different targets, can be of interest for this field.
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Affiliation(s)
- Marco Cardin
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020 Legnaro, PD, Italy
- Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
| | - Barbara Cardazzo
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020 Legnaro, PD, Italy
| | - Jérôme Mounier
- Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
| | - Enrico Novelli
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020 Legnaro, PD, Italy
| | - Monika Coton
- Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
| | - Emmanuel Coton
- Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
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15
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Roy M, Doddappa M, Yadav BK, Jaganmohan R, Sinija VR, Manickam L, Sarvanan S. Detection of soybean oil adulteration in cow ghee (clarified milk fat): an ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:4097-4108. [PMID: 34997578 DOI: 10.1002/jsfa.11759] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/15/2021] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Cow ghee is one of the expensive edible fats in the dairy sector. Ghee is often adulterated with low-priced edible oils, like soybean oil, owing to its high market demand. The existing adulteration detection methods are time-consuming, requiring sample preparation and expertise in these fields. The possibility of detecting soybean oil adulteration (from 10% to 100%) in pure cow ghee was investigated in this study. The fingerprint information of volatile compounds was collected using a flash gas chromatography electronic nose (FGCEN) instrument. The classification results were studied using the pattern recognition chemometric models principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and discriminant function analysis (DFA). RESULTS The most powerful fingerprint odor of all the samples identified from FGCEN analysis was acetaldehyde (Z)-4-heptenal, 2-propanol, ethyl propanoate, and pentan-2-one. The odor analysis investigation was accomplished with an average analysis time of 90 s. A clear differentiation of all the samples with an excellent classification accuracy of more than 99% was achieved with the PCA and DFA chemometric methods. However, the results of the SIMCA model showed that SIMCA could only be used to detect ghee adulteration at higher concentration levels (30% to 100%). The validation study shows good agreement between FGCEN and gas chromatography-mass spectrometry methods. CONCLUSION The methodology demonstrated coupled with PCA and DFA methods for adulteration detection in ghee using FGCEN apparatus has been an efficient and convenient technique. This study explored the capability of the FGCEN instrument to tackle the adulteration problems in ghee. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Mrinmoy Roy
- Planning and Monitoring Cell, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Manoj Doddappa
- Planning and Monitoring Cell, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Binod K Yadav
- Liaison Office, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Rangarajan Jaganmohan
- Department of Food Product Development, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Vadakkepulppara Rn Sinija
- Food Processing Business Incubation Centre, Indian Institute of Food Processing Technology, Thanjavur, India
| | - Loganathan Manickam
- Department of Academics and Human Resource Development, Indian Institute of Food Processing Technology, Thanjavur, India
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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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17
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Priya RB, Rashmitha R, Preetham GS, Chandrasekar V, Mohan RJ, Sinija VR, Pandiselvam R. Detection of Adulteration in Coconut Oil and Virgin Coconut Oil Using Advanced Analytical Techniques: A Review. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02342-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Huang ZM, Xin JX, Sun SS, Li Y, Wei DX, Zhu J, Wang XL, Wang J, Yao YF. Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints. Foods 2021; 10:3068. [PMID: 34945619 PMCID: PMC8701812 DOI: 10.3390/foods10123068] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/28/2021] [Accepted: 12/03/2021] [Indexed: 11/22/2022] Open
Abstract
Most current approaches applied for the essential identification of adulteration in edible vegetable oils are of limited practical benefit because they require long analysis times, professional training, and costly instrumentation. The present work addresses this issue by developing a novel simple, accurate, and rapid identification approach based on the magnetic resonance relaxation fingerprints obtained from low-field nuclear magnetic resonance spectroscopy measurements of edible vegetable oils. The relaxation fingerprints obtained for six types of edible vegetable oil, including flaxseed oil, olive oil, soybean oil, corn oil, peanut oil, and sunflower oil, are demonstrated to have sufficiently unique characteristics to enable the identification of the individual types of oil in a sample. By using principal component analysis, three characteristic regions in the fingerprints were screened out to create a novel three-dimensional characteristic coordination system for oil discrimination and adulteration identification. Univariate analysis and partial least squares regression were used to successfully quantify the oil adulteration in adulterated binary oil samples, indicating the great potential of the present approach on both identification and quantification of edible oil adulteration.
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Affiliation(s)
- Zhi-Ming Huang
- Shanghai Key Laboratory of Magnetic Resonance, College of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; (Z.-M.H.); (J.-X.X.); (Y.L.); (D.-X.W.); (J.Z.); (X.-L.W.); (J.W.)
| | - Jia-Xiang Xin
- Shanghai Key Laboratory of Magnetic Resonance, College of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; (Z.-M.H.); (J.-X.X.); (Y.L.); (D.-X.W.); (J.Z.); (X.-L.W.); (J.W.)
| | - Shan-Shan Sun
- National Institutes for Food and Drug Control, Dongcheng District, Beijing 100050, China;
| | - Yi Li
- Shanghai Key Laboratory of Magnetic Resonance, College of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; (Z.-M.H.); (J.-X.X.); (Y.L.); (D.-X.W.); (J.Z.); (X.-L.W.); (J.W.)
| | - Da-Xiu Wei
- Shanghai Key Laboratory of Magnetic Resonance, College of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; (Z.-M.H.); (J.-X.X.); (Y.L.); (D.-X.W.); (J.Z.); (X.-L.W.); (J.W.)
| | - Jing Zhu
- Shanghai Key Laboratory of Magnetic Resonance, College of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; (Z.-M.H.); (J.-X.X.); (Y.L.); (D.-X.W.); (J.Z.); (X.-L.W.); (J.W.)
| | - Xue-Lu Wang
- Shanghai Key Laboratory of Magnetic Resonance, College of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; (Z.-M.H.); (J.-X.X.); (Y.L.); (D.-X.W.); (J.Z.); (X.-L.W.); (J.W.)
| | - Jiachen Wang
- Shanghai Key Laboratory of Magnetic Resonance, College of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; (Z.-M.H.); (J.-X.X.); (Y.L.); (D.-X.W.); (J.Z.); (X.-L.W.); (J.W.)
| | - Ye-Feng Yao
- Shanghai Key Laboratory of Magnetic Resonance, College of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; (Z.-M.H.); (J.-X.X.); (Y.L.); (D.-X.W.); (J.Z.); (X.-L.W.); (J.W.)
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Bordbar MM, Sheini A, Hashemi P, Hajian A, Bagheri H. Disposable Paper-Based Biosensors for the Point-of-Care Detection of Hazardous Contaminations-A Review. BIOSENSORS 2021; 11:316. [PMID: 34562906 PMCID: PMC8464915 DOI: 10.3390/bios11090316] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 08/29/2021] [Accepted: 09/01/2021] [Indexed: 02/07/2023]
Abstract
The fast detection of trace amounts of hazardous contaminations can prevent serious damage to the environment. Paper-based sensors offer a new perspective on the world of analytical methods, overcoming previous limitations by fabricating a simple device with valuable benefits such as flexibility, biocompatibility, disposability, biodegradability, easy operation, large surface-to-volume ratio, and cost-effectiveness. Depending on the performance type, the device can be used to analyze the analyte in the liquid or vapor phase. For liquid samples, various structures (including a dipstick, as well as microfluidic and lateral flow) have been constructed. Paper-based 3D sensors are prepared by gluing and folding different layers of a piece of paper, being more user-friendly, due to the combination of several preparation methods, the integration of different sensor elements, and the connection between two methods of detection in a small set. Paper sensors can be used in chromatographic, electrochemical, and colorimetric processes, depending on the type of transducer. Additionally, in recent years, the applicability of these sensors has been investigated in various applications, such as food and water quality, environmental monitoring, disease diagnosis, and medical sciences. Here, we review the development (from 2010 to 2021) of paper methods in the field of the detection and determination of toxic substances.
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Affiliation(s)
- Mohammad Mahdi Bordbar
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran 19945, Iran;
| | - Azarmidokht Sheini
- Department of Mechanical Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan 78986, Iran;
| | - Pegah Hashemi
- Research and Development Department, Farin Behbood Tashkhis Ltd., Tehran 16471, Iran;
| | - Ali Hajian
- Institute of Sensor and Actuator Systems, TU Wien, Gusshausstrasse 27-29, 1040 Vienna, Austria;
| | - Hasan Bagheri
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran 19945, Iran;
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