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Alfieri G, Modesti M, Riggi R, Bellincontro A. Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry. SENSORS (BASEL, SWITZERLAND) 2024; 24:2293. [PMID: 38610504 PMCID: PMC11014050 DOI: 10.3390/s24072293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/26/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024]
Abstract
Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.
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Affiliation(s)
| | | | | | - Andrea Bellincontro
- Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, Via S. Camillo de Lellis, 01100 Viterbo, Italy; (G.A.); (M.M.); (R.R.)
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Sun Y, Li M, Li X, Du J, Li W, Lin Y, Zhang Y, Wang Y, He W, Chen Q, Zhang Y, Wang X, Luo Y, Xiong A, Tang H. Characterization of Volatile Organic Compounds in Five Celery ( Apium graveolens L.) Cultivars with Different Petiole Colors by HS-SPME-GC-MS. Int J Mol Sci 2023; 24:13343. [PMID: 37686147 PMCID: PMC10488006 DOI: 10.3390/ijms241713343] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Celery (Apium graveolens L.) is an important vegetable crop cultivated worldwide for its medicinal properties and distinctive flavor. Volatile organic compound (VOC) analysis is a valuable tool for the identification and classification of species. Currently, less research has been conducted on aroma compounds in different celery varieties and colors. In this study, five different colored celery were quantitatively analyzed for VOCs using HS-SPME, GC-MS determination, and stoichiometry methods. The result revealed that γ-terpinene, d-limonene, 2-hexenal,-(E)-, and β-myrcene contributed primarily to the celery aroma. The composition of compounds in celery exhibited a correlation not only with the color of the variety, with green celery displaying a higher concentration compared with other varieties, but also with the specific organ, whereby the content and distribution of volatile compounds were primarily influenced by the leaf rather than the petiole. Seven key genes influencing terpenoid synthesis were screened to detect expression levels. Most of the genes exhibited higher expression in leaves than petioles. In addition, some genes, particularly AgDXS and AgIDI, have higher expression levels in celery than other genes, thereby influencing the regulation of terpenoid synthesis through the MEP and MVA pathways, such as hydrocarbon monoterpenes. This study identified the characteristics of flavor compounds and key aroma components in different colored celery varieties and explored key genes involved in the regulation of terpenoid synthesis, laying a theoretical foundation for understanding flavor chemistry and improving its quality.
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Affiliation(s)
- Yue Sun
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Mengyao Li
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Xiaoyan Li
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Jiageng Du
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Weilong Li
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Yuanxiu Lin
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Yunting Zhang
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Yan Wang
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Wen He
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Qing Chen
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Yong Zhang
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Xiaorong Wang
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Ya Luo
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
| | - Aisheng Xiong
- College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China;
| | - Haoru Tang
- College of Horticulture, Sichuan Agricultural University, Chengdu 611130, China; (Y.S.); (M.L.); (X.L.); (J.D.); (W.L.); (Y.L.); (Y.Z.); (Y.W.); (W.H.); (Q.C.); (Y.Z.); (X.W.); (Y.L.)
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Viciano-Tudela S, Parra L, Navarro-Garcia P, Sendra S, Lloret J. Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:5812. [PMID: 37447662 DOI: 10.3390/s23135812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/13/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.
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Affiliation(s)
- Sandra Viciano-Tudela
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
| | - Lorena Parra
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
| | - Paula Navarro-Garcia
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
| | - Sandra Sendra
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
| | - Jaime Lloret
- Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain
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Khorramifar A, Karami H, Lvova L, Kolouri A, Łazuka E, Piłat-Rożek M, Łagód G, Ramos J, Lozano J, Kaveh M, Darvishi Y. Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:5716. [PMID: 37420880 PMCID: PMC10300923 DOI: 10.3390/s23125716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.
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Affiliation(s)
- Ali Khorramifar
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199, Iran; (A.K.); (A.K.)
| | - Hamed Karami
- Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq;
| | - Larisa Lvova
- Department of Chemical Science and Technologies, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Alireza Kolouri
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199, Iran; (A.K.); (A.K.)
| | - Ewa Łazuka
- Department of Applied Mathematics, Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland; (E.Ł.); (M.P.-R.)
| | - Magdalena Piłat-Rożek
- Department of Applied Mathematics, Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland; (E.Ł.); (M.P.-R.)
| | - Grzegorz Łagód
- Department of Water Supply and Wastewater Disposal, Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland;
| | - Jose Ramos
- College of Computing and Engineering, Nova Southeastern University (NSU), 3301 College Avenue, Fort Lauderdale, FL 33314-7796, USA;
| | - Jesús Lozano
- Department of Electric Technology, Electronics and Automation, University of Extremadura, Avda. De Elvas S/n, 06006 Badajoz, Spain;
| | - Mohammad Kaveh
- Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq;
| | - Yousef Darvishi
- Department of Biosystems Engineering, University of Tehran, Tehran P.O. Box 113654117, Iran;
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Cho S, Park TH. Advances in the Production of Olfactory Receptors for Industrial Use. Adv Biol (Weinh) 2023; 7:e2200251. [PMID: 36593488 DOI: 10.1002/adbi.202200251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/11/2022] [Indexed: 01/04/2023]
Abstract
In biological olfactory systems, olfactory receptors (ORs) can recognize and discriminate between thousands of volatile organic compounds with very high sensitivity and specificity. The superior properties of ORs have led to the development of OR-based biosensors that have shown promising potential in many applications over the past two decades. In particular, newly designed technologies in gene synthesis, protein expression, solubilization, purification, and membrane mimetics for membrane proteins have greatly opened up the previously inaccessible industrial potential of ORs. In this review, gene design, expression and solubilization strategies, and purification and reconstitution methods available for modern industrial applications are examined, with a focus on ORs. The limitations of current OR production technology are also estimated, and future directions for further progress are suggested.
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Affiliation(s)
- Seongyeon Cho
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Tai Hyun Park
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
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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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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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9
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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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10
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A Conventional VOC-PID Sensor for a Rapid Discrimination among Aromatic Plant Varieties: Classification Models Fitted to a Rosemary Case-Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136399] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This study explores the use of a photoionization detector (PID) to distinguish varieties of rosemary plant, based on their volatile organic compound (VOC) emissions. The aim was to be able to distinguish plant varieties using a simple, quick, and inexpensive method. Two varieties were studied, Rosmarinus officinalis L. “Prostratus” and “Erectus”. First, the PID was used to detect VOCs emitted by leaves from each variety, and subsequently essential oil was extracted from the same leaves. Then, the well-established GC-MS method was used to characterize and differentiate the oil from each of the two varieties. The PID was able to capture different signals, and a ‘fingerprint’ for each of the two varieties was obtained. To validate the PID performance, the data set obtained was analyzed by means of advanced statistical models (principal component analysis, cluster and support vector machine and artificial neural network) which were able to discriminate the two varieties with high accuracy (over 80%). Therefore, the results confirm that the PID was able to detect differences in VOC emissions. In conclusion, PID proved be an interesting instrument for the classification of rosemary plants, and in this sense could be applied to other aromatic plants.
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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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