1
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Wang J, Wang J, Qiao L, Zhang N, Sun B, Li H, Sun J, Chen H. From Traditional to Intelligent, A Review of Application and Progress of Sensory Analysis in Alcoholic Beverage Industry. Food Chem X 2024; 23:101542. [PMID: 38974198 PMCID: PMC11225692 DOI: 10.1016/j.fochx.2024.101542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 06/01/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
Sensory analysis is an interdisciplinary field that combines multiple disciplines to analyze food qualitatively and quantitatively. At present, this analysis method has been widely used in product development, quality control, marketing, flavor analysis, safety supervision and inspection of alcoholic beverages. Due to the changing needs of analysis, new and more optimized methods are still emerging. Thereinto, intelligent and biometric technologies with growing attention have also been applied to sensory analysis. This work summarized the sensory analysis methods from three aspects, including traditional artificial sensory analysis, intelligent sensory technology, and innovative technologies. Meanwhile, the application sensory analysis in alcoholic beverages and its industrial production was scientifically emphasized. Moreover, the future tendency of sensory analysis in the alcoholic beverage industry is also highlights.
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Affiliation(s)
- Junyi Wang
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Jing Wang
- Beijing Key Laboratory of Flavor Chemistry, Beijing Technology & Business University, Beijing 100048, China
| | - Lina Qiao
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Ning Zhang
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing 100048, China
- Beijing Key Laboratory of Flavor Chemistry, Beijing Technology & Business University, Beijing 100048, China
| | - Baoguo Sun
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Hehe Li
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Jinyuan Sun
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Haitao Chen
- Beijing Key Laboratory of Flavor Chemistry, Beijing Technology & Business University, Beijing 100048, China
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2
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Guan Y, Xu X, Liu C, Wang J, Niu C, Zheng F, Li Q. Evaluating the physiology and fermentation performance of the lager yeast during very high gravity brewing with increased temperature. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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3
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Evaluation of taste characteristics of chinese rice wine by quantitative description analysis, dynamic description sensory and electronic tongue. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01637-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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4
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Li S, Du D, Wang J, Wei Z. Application progress of intelligent flavor sensing system in the production process of fermented foods based on the flavor properties. Crit Rev Food Sci Nutr 2022; 64:3764-3793. [PMID: 36259959 DOI: 10.1080/10408398.2022.2134982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Fermented foods are sensitive to the production conditions because of microbial and enzymatic activities, which requires intelligent flavor sensing system (IFSS) to monitor and optimize the production process based on the flavor properties. As the simulation system of human olfaction and gustation, IFSS has been widely used in the field of food with the characteristics of nondestructive, pollution-free, and real-time detection. This paper reviews the application of IFSS in the control of fermentation, ripening, and shelf life, and the potential in the identification of quality differences and flavor-producing microbes in fermented foods. The survey found that electronic nose (tongue) is suitable to monitor fermentation process and identify food authenticity in real time based on the changes of flavor profile. Gas chromatography-ion mobility spectrometry and nuclear magnetic resonance technology can be used to analyze the flavor metabolism of fermented foods at various production stages and explore the correlation between flavor substances and microorganisms.
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Affiliation(s)
- Siying Li
- Department of Biosystems Engineering, Zhejiang University, Hangzhou, China
| | - Dongdong Du
- Department of Biosystems Engineering, Zhejiang University, Hangzhou, China
| | - Jun Wang
- Department of Biosystems Engineering, Zhejiang University, Hangzhou, China
| | - Zhenbo Wei
- Department of Biosystems Engineering, Zhejiang University, Hangzhou, China
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5
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Ao L, Guo K, Dai X, Dong W, Sun X, Sun B, Sun J, Liu G, Li A, Li H, Zheng F. Quick classification of strong-aroma types of base Baijiu using potentiometric and voltammetric electronic tongue combined with chemometric techniques. Front Nutr 2022; 9:977929. [PMID: 36172528 PMCID: PMC9512042 DOI: 10.3389/fnut.2022.977929] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 08/18/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, the classification of strong-aroma types of base Baijiu (base SAB) is mainly achieved by human sensory evaluation. However, prolonged tasting brings difficulties for sommeliers in guaranteeing the consistency of results, and may even cause health problems. Herein, an electronic tongue (E-Tongue) combined with a gas chromatography-mass spectrometry (GC-MS) method was successfully developed to grade high-alcoholic base SAB. The E-tongue was capable of identifying base SAB samples into four grades by a discriminant function analysis (DFA) model based on human sensory evaluation results. More importantly, it could effectively and rapidly predict the quality grade of unknown base SAB with an average accuracy up to 95%. The differences of chemical components between base SAB samples were studied by the GC-MS analysis and 52 aroma compounds were identified. The qualitative and quantitative results showed that with the increase of base SAB grade, the varieties and contents of aroma compounds increased. Overall, the comprehensive analysis of E-tongue data and GC-MS results could be in good agreement with human sensory evaluation results, which also proved that the newly developed method has a potential to be a useful alternative to the overall quality grading of base Baijiu.
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Affiliation(s)
- Ling Ao
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
| | - Kai Guo
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
| | - Xinran Dai
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
| | - Wei Dong
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
- *Correspondence: Wei Dong,
| | - Xiaotao Sun
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
| | - Baoguo Sun
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
| | - Jinyuan Sun
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
- Jinyuan Sun,
| | - Guoying Liu
- Center for Solid-state Fermentation Engineering of Anhui Province, Bozhou, China
| | - Anjun Li
- Center for Solid-state Fermentation Engineering of Anhui Province, Bozhou, China
| | - Hehe Li
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
| | - Fuping Zheng
- Beijing Laboratory of Food Quality and Safety, Beijing Technology and Business University, Beijing, China
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, School of Light Industry, Beijing, China
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Liu L, Wan X, Li J, Wang W, Gao Z. An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:6391. [PMID: 36080850 PMCID: PMC9460293 DOI: 10.3390/s22176391] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/10/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value.
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Affiliation(s)
- Lilan Liu
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, China
| | - Xiang Wan
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, China
| | - Jiaying Li
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, China
| | - Wenxi Wang
- Aerospace System Engineering Shanghai, Shanghai 201108, China
| | - Zenggui Gao
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, China
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Han L, Chen M, Li Y, Wu S, Zhang L, Tu K, Pan L, Wu J, Song L. Discrimination of different oil types and adulterated safflower seed oil based on electronic nose combined with gas chromatography-ion mobility spectrometry. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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8
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Fabrication and application of three-dimensional nanocomposites modified electrodes for evaluating the aging process of Huangjiu (Chinese rice wine). Food Chem 2022; 372:131158. [PMID: 34601421 DOI: 10.1016/j.foodchem.2021.131158] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 08/08/2021] [Accepted: 09/14/2021] [Indexed: 02/08/2023]
Abstract
In this study, three modified glassy carbon electrodes based on three-dimensional conducting polymer nanocomposites (TDCPNs) were fabricated for evaluating the aging process of Huangjiu (Chinese rice wines). The electrochemical activity and experimental conditions of the TDCPNs modified electrodes were investigated by cyclic voltammetry, the aging information obtained by the modified electrodes were optimized by variance inflation factor (VIF). Principal components analysis (PCA), locally linear embedding (LLE), and locality preserving projection (LPP, which presented the best classification result) based on the optimized data were applied to classify the wine samples. Then, the dimensionality reduction data of PCA, LLE, and LPP were used as input variables of the logistic regression and extreme learning machine (ELM) for evaluating the aging process of Huangjiu, and the LLE-ELM method exhibited the best prediction results. These results demonstrated that the TDCPNs modified electrodes presented the potential for the quality analysis of food and beverages.
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Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition. SENSORS 2022; 22:s22062132. [PMID: 35336301 PMCID: PMC8950699 DOI: 10.3390/s22062132] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/05/2022] [Accepted: 03/07/2022] [Indexed: 02/04/2023]
Abstract
Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.
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Ma T, Wang H, Wei M, Lan T, Wang J, Bao S, Ge Q, Fang Y, Sun X. Application of smart-phone use in rapid food detection, food traceability systems, and personalized diet guidance, making our diet more health. Food Res Int 2022; 152:110918. [DOI: 10.1016/j.foodres.2021.110918] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/11/2021] [Accepted: 12/20/2021] [Indexed: 12/11/2022]
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Calvini R, Pigani L. Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22020577. [PMID: 35062537 PMCID: PMC8778015 DOI: 10.3390/s22020577] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 05/02/2023]
Abstract
Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the "smell", "taste", and "color" of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review's purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.
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Affiliation(s)
- Rosalba Calvini
- Department of Life Sciences, University of Modena and Reggio Emilia, Pad. Besta Via Amendola 2, 42122 Reggio Emilia, Italy;
| | - Laura Pigani
- Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Via G. Campi 103, 41125 Modena, Italy
- Correspondence:
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12
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Mao Y, Li N, Shi B, Zhao L, Cheng S, Tian S, Wang H. Geographical origin determination of Red Huajiao in China using the electronic nose combined with ensemble recognition algorithm. J Food Sci 2021; 86:4922-4931. [PMID: 34642944 DOI: 10.1111/1750-3841.15933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/15/2021] [Accepted: 09/17/2021] [Indexed: 12/29/2022]
Abstract
Red Huajiao was the most important Zanthoxylum species in China, and its quality was highly determined the geographical region. This study was aimed to establish a determination method for the geographical origin recognition of Red Huajiao by using the electronic nose and ensemble recognition algorithm. Six origins of samples were detected by the electronic nose, and two categories of electronic nose sensors characteristic values, named as "optimized characteristic value" and "filtered characteristic value," were obtained by the principal component analysis and discrimination index method and Filter-Wrapper method. Based on the two categories of characteristic values, 22 kinds of model analysis methods, which belonged to five categories of ensemble recognition algorithms were used to recognize the geographical origin. The total recognition accuracy rate of the two categories of characteristic values were 83.9% and 85.7%, respectively. Furthermore, during 22 kinds of model analysis method, the ensemble Subspace KNN and Bagged Trees methods in Ensemble Learning algorithm exhibited the best distinguishing ability with the accuracy rate more than 90%. Therefore, the electronic nose combined with Ensemble Learning would be promising for the geographical origin determination application. PRACTICAL APPLICATION: This work demonstrates that the Red Huajiao can be simply and rapidly determined by using electronic nose combined with ensemble recognition algorithm, allowing to effectively distinguish geographical origin of Red Huajiao, which can provide an important reference for the quality assessment of Huajiao.
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Affiliation(s)
- Yuezhong Mao
- School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China
| | - Na Li
- School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China
| | - Bolin Shi
- China National Institute of Standardization, Beijing, China
| | - Lei Zhao
- China National Institute of Standardization, Beijing, China
| | - Shiwen Cheng
- School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China
| | - Shiyi Tian
- School of Food Science and Biotechnology, Zhejiang GongShang. University, Zhejiang, China
| | - Houyin Wang
- China National Institute of Standardization, Beijing, China
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Abstract
This paper is focused on the assessment of a multi-sensor approach to improve the overall characterization of sparkling wines (cava wines). Multi-sensor, low-level data fusion can provide more comprehensive and more accurate vision of results compared with the study of simpler data sets from individual techniques. Data from different instrumental platforms were combined in an enriched matrix, integrating information from spectroscopic (UV/Vis and FTIR), chromatographic, and other techniques. Sparkling wines belonging to different classes, which differed in the grape varieties, coupages, and wine-making processes, were analyzed to determine organic acids (e.g., tartaric, lactic, malic, and acetic acids), pH, total acidity, polyphenols, total antioxidant capacity, ethanol, or reducing sugars. The resulting compositional values were treated chemometrically for a more efficient recovery of the underlaying information. In this regard, exploratory methods such as principal component analysis showed that phenolic compounds were dependent on varietal and blending issues while organic acids were more affected by fermentation features. The analysis of the multi-sensor data set provided a more comprehensive description of cavas according to grape classes, blends, and vinification processes. Hierarchical Cluster Analysis (HCA) allowed specific groups of samples to be distinguished, featuring malolactic fermentation and the chardonnay and red grape classes. Partial Least Squares-Discriminant Analysis (PLS-DA) also classified samples according to the type of grape varieties and fermentations. Bar charts and complementary statistic test were performed to better define the differences among the studied samples based on the most significant markers of each cava wine type. As a conclusion, catechin, gallic, gentisic, caftaric, caffeic, malic, and lactic acids were the most remarkable descriptors that contributed to their discrimination based on varietal, blending, and oenological factors.
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Rapid Non-Destructive Quantification of Eugenol in Curdlan Biofilms by Electronic Nose Combined with Gas Chromatography-Mass Spectrometry. SENSORS 2020; 20:s20164441. [PMID: 32784818 PMCID: PMC7472399 DOI: 10.3390/s20164441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/20/2020] [Accepted: 08/06/2020] [Indexed: 02/02/2023]
Abstract
Eugenol is hepatotoxic and potentially hazardous to human health. This paper reports on a rapid non-destructive quantitative method for the determination of eugenol concentration in curdlan (CD) biofilms by electronic nose (E-nose) combined with gas chromatography-mass spectrometry (GC-MS). Different concentrations of eugenol were added to the film-forming solution to form a series of biofilms by casting method, and the actual eugenol concentration in the biofilm was determined. Analysis of the odor collected on the biofilms was carried out by GC-MS and an E-nose. The E-nose data was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) in order to establish a discriminant model for determining eugenol concentrations in the biofilms. Further analyses involving the application of all sensors and featured sensors, the prediction model-based partial least squares (PLS) and support vector machines (SVM) were carried out to determine eugenol concentration in the CD biofilms. The results showed that the optimal prediction model for eugenol concentration was obtained by PLS at R2p of 0.952 using 10 sensors. The study described a rapid, non-destructive detection and quantitative method for determining eugenol concentration in bio-based packaging materials.
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15
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Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review. CHEMOSENSORS 2020. [DOI: 10.3390/chemosensors8030059] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.
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