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Kong Y, Wu Z, Li Y, Kang Z, Wang L, Xie F, Yu D. Analyzing changes in volatile flavor compounds of soy protein isolate during ultrasonic-thermal synergistic treatments using electronic nose and HS-SPME-GC-MS combined with chemometrics. Food Chem 2024; 445:138795. [PMID: 38382257 DOI: 10.1016/j.foodchem.2024.138795] [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: 10/24/2023] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
The beany flavor of soy protein isolate (SPI) creates barriers to their application in food processing. This study investigated the effect of ultrasonic-thermal synergistic treatments, combined with vacuum degassing, on the removal of volatile compounds from SPI. The results revealed that ultrasonic-thermal synergistic treatments altered protein secondary structure and increased fluorescence intensity and surface hydrophobicity, which affected the flavor-binding ability of protein, resulting in reduced electronic nose sensor response values. At synergistic treatment (350 W, 120 ℃ and 150 s), the content of hexanal, (E)-2-hexenal, and 1-octen-3-ol reduced by 70.60 %, 95.60 % and 61.23 %. (E)-2-nonenal and 2-pentylfuran were not detected. Chemometric analysis indicated significant flavor differences between control and treated SPI. Furthermore, α-helix, β-sheet, β-turn, and surface hydrophobicity highly correlated with volatile compounds through correlation analysis, indicating that altered protein structure affected interactions with volatile compounds. The study reduced beany flavor and further expanded the range of applications of plant protein in food industry.
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Affiliation(s)
- Yue Kong
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Zenan Wu
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Yanhui Li
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Zimeng Kang
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Lu Wang
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Fengying Xie
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China.
| | - Dianyu Yu
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China.
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2
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Ku HH, Lung CF, Chi CH. Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms. Foods 2023; 12:4024. [PMID: 37959143 PMCID: PMC10648032 DOI: 10.3390/foods12214024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/15/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.
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Affiliation(s)
- Hao-Hsiang Ku
- Institute of Food Safety and Risk Management, National Taiwan Ocean University, Keelung City 202301, Taiwan
| | - Ching-Fu Lung
- Department of Food Science, National Taiwan Ocean University, Keelung City 202301, Taiwan;
| | - Ching-Ho Chi
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, National Cheng Kung University, Tainan City 701401, Taiwan;
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3
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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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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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5
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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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Momeny M, Neshat AA, Jahanbakhshi A, Mahmoudi M, Ampatzidis Y, Radeva P. Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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7
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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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8
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Determination of cooking state of a Chinese traditional fish dish(Suantangyu)and aroma and taste characterization bymodeling, sensory evaluation, and instrumental analysis. J FOOD PROCESS PRES 2022. [DOI: 10.1111/jfpp.16953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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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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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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11
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Classification and authentication of tea according to their geographical origin based on FT-IR fingerprinting using pattern recognition methods. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104321] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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12
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Zhang Y, Wang M, Zhang X, Qu Z, Gao Y, Li Q, Yu X. Mechanism, indexes, methods, challenges, and perspectives of edible oil oxidation analysis. Crit Rev Food Sci Nutr 2021:1-15. [PMID: 34845958 DOI: 10.1080/10408398.2021.2009437] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Edible oils are indispensable food components, because they are used for cooking or frying. However, during processing, transport, storage, and consumption, edible oils are susceptible to oxidation, during which various primary and secondary oxidative products are generated. These products may reduce the nutritional value and safety of edible oils and even harm human health. Therefore, analyzing the oxidation of edible oil is essential to ensure the quality and safety of oil. Oxidation is a complex process with various oxidative products, and the content of these products can be evaluated by corresponding indexes. According to the structure and properties of the oxidative products, analytical methods have been employed to quantify these products to analyze the oxidation of oil. Combined with proper chemometric analytical methods, qualitative identification has been performed to discriminate oxidized and nonoxidized oils. Oxidative products are complex and diverse. Thus, proper indexes and analytical methods should be selected depending on specific research objectives. Expanding the mechanism of the correspondence between oxidative products and analytical methods is crucial. The underlying mechanism, conventional indexes, and applications of analytical methods are summarized in this review. The challenges and perspectives for future applications of several methods in determining oxidation are also discussed. This review may serve as a reference in the selection, establishment, and improvement of methods for analyzing the oxidation of edible oil. HighlightsThe mechanism of edible oil oxidation analysis was elaborated.Conventional oxidation indexes and their limited values were discussed.Analytical methods for the determination of oxidative products and qualitative identification of oxidized and non-oxidized oils were reviewed.
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Affiliation(s)
- Yan Zhang
- College of Food Science and Engineering, Northwest A&F University, Shaanxi, P. R. China
| | - Mengzhu Wang
- College of Food Science and Engineering, Northwest A&F University, Shaanxi, P. R. China.,State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, P. R. China
| | - Xuping Zhang
- College of Food Science and Engineering, Northwest A&F University, Shaanxi, P. R. China
| | - Zhihao Qu
- College of Food Science and Engineering, Northwest A&F University, Shaanxi, P. R. China
| | - Yuan Gao
- College of Food Science and Engineering, Northwest A&F University, Shaanxi, P. R. China
| | - Qi Li
- College of Food Science and Engineering, Northwest A&F University, Shaanxi, P. R. China
| | - Xiuzhu Yu
- College of Food Science and Engineering, Northwest A&F University, Shaanxi, P. R. China
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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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