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Victor de Sousa Dutra J, Salles MO, Michel RC, Vale DL. Computer vision with artificial intelligence for a fast, low-cost, eco-friendly and accurate prediction of beer styles and brands. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:4285-4290. [PMID: 38884156 DOI: 10.1039/d4ay00617h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Beer is the most consumed alcoholic beverage worldwide and are highly susceptible to fraudulent processes. Traditional sensory analysis can lack precision. With the growth of Industry 4.0, new techniques using artificial intelligence are being developed to address this issue. This scenario makes it appealing to propose low-cost techniques with broad classification capabilities based on sample fingerprints, such as computer vision (CV). CV involves image acquisition, processing, and classification using machine learning. In this work, a computer vision prototype associated with an artificial neural network was developed to classify beer in terms of style and brand. A total of 111 samples were analyzed in triplicate, with the data separated into training and testing sets. Accuracy and precision above 96% were obtained for the training set and 78% for the test set. The computer vision method proved to be a simple, low-cost, eco-friendly, and fast tool for detecting fraud in the brewing industry.
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Affiliation(s)
- João Victor de Sousa Dutra
- Universidade Federal do Rio de Janeiro, Avenida Athos da Silveira Ramos, Rio de Janeiro, 21491-909, Brazil.
| | - Maiara Oliveira Salles
- Universidade Federal do Rio de Janeiro, Avenida Athos da Silveira Ramos, Rio de Janeiro, 21491-909, Brazil.
| | - Ricardo Cunha Michel
- Universidade Federal do Rio de Janeiro, Avenida Athos da Silveira Ramos, Rio de Janeiro, 21491-909, Brazil.
| | - Daniella Lopez Vale
- Universidade Federal do Rio de Janeiro, Avenida Athos da Silveira Ramos, Rio de Janeiro, 21491-909, Brazil.
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2
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Vinicius da Silva Ferreira M, Barbosa JL, Kamruzzaman M, Barbin DF. Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:6120-6138. [PMID: 37937362 DOI: 10.1039/d3ay01192e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
An electronic nose (e-nose) is a device designed to recognize and classify odors. The equipment is built around a series of sensors that detect the presence of odors, especially volatile organic compounds (VOCs), and generate an electric signal (voltage), known as e-nose data, which contains chemical information. In the food business, the use of e-noses for analyses and quality control of fruits and plantation crops has increased in recent years. Their use is particularly relevant due to the lack of non-invasive and inexpensive methods to detect VOCs in crops. However, the majority of reports in the literature involve commercial e-noses, with only a few studies addressing low-cost e-nose (LC-e-nose) devices or providing a data-oriented description to assist researchers in choosing their setup and appropriate statistical methods to analyze crop data. Therefore, the objective of this study is to discuss the hardware of the two most common e-nose sensors: electrochemical (EC) sensors and metal oxide sensors (MOSs), as well as a critical review of the literature reporting MOS-based low-cost e-nose devices used for investigating plantations and fruit crops, including the main features of such devices. Miniaturization of equipment from lab-scale to portable and convenient gear, allowing producers to take it into the field, as shown in many appraised systems, is one of the future advancements in this area. By utilizing the low-cost designs provided in this review, researchers can develop their own devices based on practical demands such as quality control and compare results with those reported in the literature. Overall, this review thoroughly discusses the applications of low-cost e-noses based on MOSs for fruits, tea, and coffee, as well as the key features of their equipment (i.e., advantages and disadvantages) based on their technical parameters (i.e., electronic and physical parts). As a final remark, LC-e-nose technology deserves significant attention as it has the potential to be a valuable quality control tool for emerging countries.
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Affiliation(s)
- Marcus Vinicius da Silva Ferreira
- Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Tecnologia de Alimentos, Seropédica 23890-000, Rio de Janeiro, Brazil.
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jose Lucena Barbosa
- Universidade Federal Rural do Rio de Janeiro (UFRRJ), Departamento de Tecnologia de Alimentos, Seropédica 23890-000, Rio de Janeiro, Brazil.
| | - Mohammed Kamruzzaman
- Department of Agriculture and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Douglas Fernandes Barbin
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Campinas, SP, Brazil
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3
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Review of technology advances to assess rice quality traits and consumer perception. Food Res Int 2023; 172:113105. [PMID: 37689840 DOI: 10.1016/j.foodres.2023.113105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 09/11/2023]
Abstract
The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis, 02600 Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., México 64849, Mexico.
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4
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Zhang W, Zhang C, Cao L, Liang F, Xie W, Tao L, Chen C, Yang M, Zhong L. Application of digital-intelligence technology in the processing of Chinese materia medica. Front Pharmacol 2023; 14:1208055. [PMID: 37693890 PMCID: PMC10484343 DOI: 10.3389/fphar.2023.1208055] [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: 04/18/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.
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Affiliation(s)
- Wanlong Zhang
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Changhua Zhang
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
- Nanchang Research Institute, Sun Yat-sen University, Nanchang, Jiangxi, China
| | - Lan Cao
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Fang Liang
- College of Physical Culture, Yuzhang Normal University, Nanchang, Jiangxi, China
| | - Weihua Xie
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Liang Tao
- Nanchang Research Institute, Sun Yat-sen University, Nanchang, Jiangxi, China
| | - Chen Chen
- School of Biomedical Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Ming Yang
- Key Laboratory of Modern Chinese Medicine Preparation of Ministry of Education, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Lingyun Zhong
- College of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
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Zhang W, Zhang C, Cao L, Liang F, Xie W, Tao L, Chen C, Yang M, Zhong L. Application of digital-intelligence technology in the processing of Chinese materia medica. Front Pharmacol 2023; 14. [DOI: https:/doi.org/10.3389/fphar.2023.1208055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2024] Open
Abstract
Processing of Chinese Materia Medica (PCMM) is the concentrated embodiment, which is the core of Chinese unique traditional pharmaceutical technology. The processing includes the preparation steps such as cleansing, cutting and stir-frying, to make certain impacts on the quality and efficacy of Chinese botanical drugs. The rapid development of new computer digital technologies, such as big data analysis, Internet of Things (IoT), blockchain and cloud computing artificial intelligence, has promoted the rapid development of traditional pharmaceutical manufacturing industry with digitalization and intellectualization. In this review, the application of digital intelligence technology in the PCMM was analyzed and discussed, which hopefully promoted the standardization of the process and secured the quality of botanical drugs decoction pieces. Through the intellectualization and the digitization of production, safety and effectiveness of clinical use of traditional Chinese medicine (TCM) decoction pieces were ensured. This review also provided a theoretical basis for further technical upgrading and high-quality development of TCM industry.
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Fermentation for Designing Innovative Plant-Based Meat and Dairy Alternatives. Foods 2023; 12:foods12051005. [PMID: 36900522 PMCID: PMC10000644 DOI: 10.3390/foods12051005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 03/02/2023] Open
Abstract
Fermentation was traditionally used all over the world, having the preservation of plant and animal foods as a primary role. Owing to the rise of dairy and meat alternatives, fermentation is booming as an effective technology to improve the sensory, nutritional, and functional profiles of the new generation of plant-based products. This article intends to review the market landscape of fermented plant-based products with a focus on dairy and meat alternatives. Fermentation contributes to improving the organoleptic properties and nutritional profile of dairy and meat alternatives. Precision fermentation provides more opportunities for plant-based meat and dairy manufacturers to deliver a meat/dairy-like experience. Seizing the opportunities that the progress of digitalization is offering would boost the production of high-value ingredients such as enzymes, fats, proteins, and vitamins. Innovative technologies such as 3D printing could be an effective post-processing solution following fermentation in order to mimic the structure and texture of conventional products.
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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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Abstract
Fermented foods and beverages have become a part of daily diets in several societies around the world. Emitted volatile organic compounds play an important role in the determination of the chemical composition and other information of fermented foods and beverages. Electronic nose (E-nose) technologies enable non-destructive measurement and fast analysis, have low operating costs and simplicity, and have been employed for this purpose over the past decades. In this work, a comprehensive review of the recent progress in E-noses is presented according to the end products of the main fermentation types, including alcohol fermentation, lactic acid fermentation, acetic acid fermentation and alkaline fermentation. The benefits, research directions, limitations and challenges of current E-nose systems are investigated and highlighted for fermented foods and beverage applications.
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Editorial: Special Issue “Implementation of Digital Technologies on Beverage Fermentation”. FERMENTATION-BASEL 2022. [DOI: 10.3390/fermentation8030127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products’ quality traits and international competitiveness, especially within the past five years [...]
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WANG A, ZHU Y, QIU J, CAO R, ZHU H. Application of intelligent sensory technology in the authentication of alcoholic beverages. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.32622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Affiliation(s)
| | | | - Ju QIU
- China Agricultural University, China
| | - Ruge CAO
- Tianjin University of Science and Technology, China
| | - Hong ZHU
- Ministry of Agriculture and Rural Affairs, China
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Wang YT, Yang ZX, Piao ZH, Xu XJ, Yu JH, Zhang YH. Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method. RSC Adv 2021; 11:36942-36950. [PMID: 35494377 PMCID: PMC9044825 DOI: 10.1039/d1ra06551c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/30/2021] [Indexed: 11/28/2022] Open
Abstract
In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure. Towards this goal, the flavor compounds in beer from existing literature were collected. The database was classified into four groups as aromatic, bitter, sulfury, and others. The RI values on a non-polar SE-30 column and a polar Carbowax 20M column from the National Institute of Standards Technology (NIST) were investigated. The structures were converted to molecular descriptors calculated by molecular operating environment (MOE), ChemoPy and Mordred, respectively. By combining the pretreatment of the descriptors, machine learning models, including support vector machine (SVM), random forest (RF) and k-nearest neighbour (kNN) were utilized for beer flavor models. Principal component regression (PCR), random forest regression (RFR) and partial least squares (PLS) regression were employed to predict the RI. The accuracy of the test set was obtained by SVM, RF, and kNN. Among them, the combination of descriptors calculated by Mordred and RF model afforded the highest accuracy of 0.686. R2 of the optimal regression model achieved 0.96. The results indicated that the models can be used to predict the flavor of a specific compound in beer and its RI value. In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure.![]()
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Affiliation(s)
- Yu-Tang Wang
- Department of Food Science, Northeast Agricultural University Harbin 150030 PR China.,Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University China
| | - Zhao-Xia Yang
- State Key Laboratory of Biological Fermentation Engineering of Beer, Tsingtao Brewery Co., Ltd Qingdao 266061 Shandong China
| | - Zan-Hao Piao
- Department of Food Science, Northeast Agricultural University Harbin 150030 PR China.,Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University China
| | - Xiao-Juan Xu
- Department of Food Science, Northeast Agricultural University Harbin 150030 PR China.,Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University China
| | - Jun-Hong Yu
- State Key Laboratory of Biological Fermentation Engineering of Beer, Tsingtao Brewery Co., Ltd Qingdao 266061 Shandong China
| | - Ying-Hua Zhang
- Department of Food Science, Northeast Agricultural University Harbin 150030 PR China.,Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University China
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Fuentes S, Tongson E, Gonzalez Viejo C. Novel digital technologies implemented in sensory science and consumer perception. Curr Opin Food Sci 2021. [DOI: 10.1016/j.cofs.2021.03.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence. FERMENTATION 2021. [DOI: 10.3390/fermentation7030117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification ma-chine learning (ML) modelling. Six different ML models were developed; Model 1 (M1) and M2 were developed using the NIR absorbance values (100 inputs from 1596–2396 nm) and e-nose (nine sensor readings) as inputs, respectively, to classify the samples into control, low and high concentration of faults. Model 3 (M3) and M4 were based on NIR and M5 and M6 based on the e-nose readings as inputs with 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neu-ron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 95.6%, M2 = 95.3%, M3 = 98.9%, M4 = 98.3%, M5 = 96.8%, and M6 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies.
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Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity. SENSORS 2021; 21:s21062016. [PMID: 33809248 PMCID: PMC7998415 DOI: 10.3390/s21062016] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/10/2021] [Accepted: 03/11/2021] [Indexed: 12/26/2022]
Abstract
Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products.
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