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Vinothkanna A, Dar OI, Liu Z, Jia AQ. Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents. Food Chem 2024; 446:138893. [PMID: 38432137 DOI: 10.1016/j.foodchem.2024.138893] [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: 12/02/2023] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.
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Affiliation(s)
- Annadurai Vinothkanna
- School of Life and Health Sciences, Hainan University, Haikou 570228, China; Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China.
| | - Owias Iqbal Dar
- School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China
| | - Zhu Liu
- School of Life and Health Sciences, Hainan University, Haikou 570228, China.
| | - Ai-Qun Jia
- Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China.
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2
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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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3
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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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4
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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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Osmólska E, Stoma M, Starek-Wójcicka A. Juice Quality Evaluation with Multisensor Systems-A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4824. [PMID: 37430738 DOI: 10.3390/s23104824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 07/12/2023]
Abstract
E-nose and e-tongue are advanced technologies that allow for the fast and precise analysis of smells and flavours using special sensors. Both technologies are widely used, especially in the food industry, where they are implemented, e.g., for identifying ingredients and product quality, detecting contamination, and assessing their stability and shelf life. Therefore, the aim of this article is to provide a comprehensive review of the application of e-nose and e-tongue in various industries, focusing in particular on the use of these technologies in the fruit and vegetable juice industry. For this purpose, an analysis of research carried out worldwide over the last five years, concerning the possibility of using the considered multisensory systems to test the quality and taste and aroma profiles of juices is included. In addition, the review contains a brief characterization of these innovative devices through information such as their origin, mode of operation, types, advantages and disadvantages, challenges and perspectives, as well as the possibility of their applications in other industries besides the juice industry.
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Affiliation(s)
- Emilia Osmólska
- Department of Power Engineering and Transportation, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
| | - Monika Stoma
- Department of Power Engineering and Transportation, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
| | - Agnieszka Starek-Wójcicka
- Department of Biological Bases of Food and Feed Technologies, Faculty of Production Engineering, University of Life Sciences in Lublin, 20-612 Lublin, Poland
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Kumar A, Castro M, Feller JF. Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages. SENSORS (BASEL, SWITZERLAND) 2023; 23:4017. [PMID: 37112358 PMCID: PMC10141392 DOI: 10.3390/s23084017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
Food quality control is an important area to address, as it directly impacts the health of the whole population. To evaluate the food authenticity and quality, the organoleptic feature of the food aroma is very important, such that the composition of volatile organic compounds (VOC) is unique in each aroma, providing a basis to predict the food quality. Different types of analytical approaches have been used to assess the VOC biomarkers and other parameters in the food. The conventional approaches are based on targeted analyses using chromatography and spectroscopies coupled with chemometrics, which are highly sensitive, selective, and accurate to predict food authenticity, ageing, and geographical origin. However, these methods require passive sampling, are expensive, time-consuming, and lack real-time measurements. Alternately, gas sensor-based devices, such as the electronic nose (e-nose), bring a potential solution for the existing limitations of conventional methods, offering a real-time and cheaper point-of-care analysis of food quality assessment. Currently, research advancement in this field involves mainly metal oxide semiconductor-based chemiresistive gas sensors, which are highly sensitive, partially selective, have a short response time, and utilize diverse pattern recognition methods for the classification and identification of biomarkers. Further research interests are emerging in the use of organic nanomaterials in e-noses, which are cheaper and operable at room temperature.
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The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies. FERMENTATION-BASEL 2023. [DOI: 10.3390/fermentation9010068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control.
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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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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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Ma P, Zhang Z, Jia X, Peng X, Zhang Z, Tarwa K, Wei CI, Liu F, Wang Q. Neural network in food analytics. Crit Rev Food Sci Nutr 2022; 64:4059-4077. [PMID: 36322538 DOI: 10.1080/10408398.2022.2139217] [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] [Indexed: 06/16/2023]
Abstract
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.
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Affiliation(s)
- Peihua Ma
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Zhikun Zhang
- CISPA Helmholtz Center for Information Security, Saarbrucken, Germany
| | - Xiaoxue Jia
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Xiaoke Peng
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Zhi Zhang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Kevin Tarwa
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Cheng-I Wei
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Fuguo Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Qin Wang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
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11
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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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12
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Physicochemical, Electronic Nose and Tongue, Sensory Evaluation Determination Combined with Chemometrics to Characterize Ficus hirta Vahl. (Moraceae) Beer. J FOOD QUALITY 2022. [DOI: 10.1155/2022/8948603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Ficus hirta Vahl. (FHV) is widely consumed because of its functional and aromatic compounds. The incorporation of adjuncts contributes to the functional and flavor properties of beers. This study aims to enrich FHV extractions to develop beers with satisfactory physicochemical, antioxidant, and sensory characteristics. As a result, beers with 0.1 g/mL (P1) and 0.067 g/mL (P3) FHV extraction showed the highest values of physicochemical properties including °Brix, antioxidant activity, foam, lightness, and color intensity. Electronic nose and tongue results show that the aroma of P1 and taste of P3 were quite different from those of other FHV beers, resulting in substantially high consumer preference. The liking drivers of FHV beers were color appearance, hop and malty odor, sweet and malty flavor, thickness, and carbonation mouthfeel. However, the astringency flavor attribute was the disliking factor for beers. The results of this study may provide some references and guidelines for the development of Ficus hirta Vahl. functional beer to control the physicochemical, antioxidative, and sensory properties of the beer.
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Lee CH, Chen IT, Yang HC, Chen YJ. An AI-powered Electronic Nose System with Fingerprint Extraction for Aroma Recognition of Coffee Beans. MICROMACHINES 2022; 13:1313. [PMID: 36014234 PMCID: PMC9414376 DOI: 10.3390/mi13081313] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin.
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Affiliation(s)
- Chung-Hong Lee
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
| | - I-Te Chen
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Medical Research, Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung 80756, Taiwan
| | - Hsin-Chang Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung 811726, Taiwan
| | - Yenming J. Chen
- Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung 824005, Taiwan
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14
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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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Khorramifar A, Rasekh M, Karami H, Covington JA, Derakhshani SM, Ramos J, Gancarz M. Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27113508. [PMID: 35684450 PMCID: PMC9182414 DOI: 10.3390/molecules27113508] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 12/19/2022]
Abstract
Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.
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Affiliation(s)
- Ali Khorramifar
- 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;
- Correspondence: (M.R.); (H.K.); (M.G.); Tel.: +98-451-551-2081-9 (M.R.); +98-912-083-9910 (H.K.)
| | - Hamed Karami
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
- Correspondence: (M.R.); (H.K.); (M.G.); Tel.: +98-451-551-2081-9 (M.R.); +98-912-083-9910 (H.K.)
| | | | - Sayed M. Derakhshani
- Wageningen Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, 6700AA Wageningen, The Netherlands;
| | - Jose Ramos
- College of Computing and Engineering, Nova Southeastern University (NSU), 3301 College Avenue, Fort Lauderdale, FL 33314-7796, USA;
| | - 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: (M.R.); (H.K.); (M.G.); Tel.: +98-451-551-2081-9 (M.R.); +98-912-083-9910 (H.K.)
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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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Classification and Identification of Essential Oils from Herbs and Fruits Based on a MOS Electronic-Nose Technology. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9060142] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The frequent occurrence of adulterated or counterfeit plant products sold in worldwide commercial markets has created the necessity to validate the authenticity of natural plant-derived palatable products, based on product-label composition, to certify pricing values and for regulatory quality control (QC). The necessity to confirm product authenticity before marketing has required the need for rapid-sensing, electronic devices capable of quickly evaluating plant product quality by easily measurable volatile (aroma) emissions. An experimental MAU-9 electronic nose (e-nose) system, containing a sensor array with 9 metal oxide semiconductor (MOS) gas sensors, was developed with capabilities to quickly identify and classify volatile essential oils derived from fruit and herbal edible-plant sources. The e-nose instrument was tested for efficacy to discriminate between different volatile essential oils present in gaseous emissions from purified sources of these natural food products. Several chemometric data-analysis methods, including pattern recognition algorithms, principal component analysis (PCA), and support vector machine (SVM) were utilized and compared. The classification accuracy of essential oils using PCA, LDA and QDA, and SVM methods was at or near 100%. The MAU-9 e-nose effectively distinguished between different purified essential oil aromas from herbal and fruit plant sources, based on unique e-nose sensor array responses to distinct, essential-oil specific mixtures of volatile organic compounds (VOCs).
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Wu L, Xing Y, Jia S, Pan J. Study of freshness monitoring on small larimichthys polyactis based on multiple sensor array system and non-linear data analysis method. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2021. [DOI: 10.1080/10942912.2021.1946079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Lili Wu
- College of Science, Henan Agricultural University, Zhengzhou, PR China
| | - Yuqing Xing
- College of Science, Henan Agricultural University, Zhengzhou, PR China
| | - Shuheng Jia
- College of Science, Henan Agricultural University, Zhengzhou, PR China
| | - Jianbin Pan
- College of Science, Henan Agricultural University, Zhengzhou, PR China
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