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Martinez-Velasco JD, Filomena-Ambrosio A, Garzón-Castro CL. Technological tools for the measurement of sensory characteristics in food: A review. F1000Res 2024; 12:340. [PMID: 38322308 PMCID: PMC10844804 DOI: 10.12688/f1000research.131914.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 02/08/2024] Open
Abstract
The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.
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Affiliation(s)
- José D Martinez-Velasco
- Engineering Faculty - Research Group CAPSAB, Universidad de La Sabana, Campus del Puente del Común, Km 7 Autopista Norte de Bogotá, Chia, Cundinamarca, 250001, Colombia
| | - Annamaria Filomena-Ambrosio
- International School of Economics and Administrative Science - Research Group Alimentación, Gestión de Procesos y Servicio de la Universidad de La Sabana Research Group, Universidad de La Sabana, Campus del Puente del Común, Km 7 Autopista Norte de Bogotá, Chía, Cundinamarca, 250001, Colombia
| | - Claudia L Garzón-Castro
- Engineering Faculty - Research Group CAPSAB, Universidad de La Sabana, Campus del Puente del Común, Km 7 Autopista Norte de Bogotá, Chia, Cundinamarca, 250001, Colombia
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2
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He HJ, Wang Y, Ou X, Ma H, Liu H, Yan J. Rapid determination of chemical compositions in chicken flesh by mining hyperspectral data. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.105069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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3
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Munekata PES, Finardi S, de Souza CK, Meinert C, Pateiro M, Hoffmann TG, Domínguez R, Bertoli SL, Kumar M, Lorenzo JM. Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:672. [PMID: 36679464 PMCID: PMC9860605 DOI: 10.3390/s23020672] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/21/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.
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Affiliation(s)
- Paulo E. S. Munekata
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Sarah Finardi
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Carolina Krebs de Souza
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Caroline Meinert
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Mirian Pateiro
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Tuany Gabriela Hoffmann
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
- Department of Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam, Germany
| | - Rubén Domínguez
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
| | - Sávio Leandro Bertoli
- Food Preservation & Innovation Laboratory, Department of Chemical Engineering, University of Blumenau, 3250 São Paulo St., Blumenau 89030-000, Brazil
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR–Central Institute for Research on Cotton Technology, Mumbai 400019, India
| | - José M. Lorenzo
- Centro Tecnológico de la Carne de Galicia, Rúa Galicia N° 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
- Facultade de Ciencias, Universidade de Vigo, Área de Tecnoloxía dos Alimentos, 32004 Ourense, Spain
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4
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Kamruzzaman M. Optical sensing as analytical tools for meat tenderness measurements - A review. Meat Sci 2023; 195:109007. [DOI: 10.1016/j.meatsci.2022.109007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 09/11/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022]
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5
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Zhuang Q, Peng Y, Nie S, Guo Q, Li Y, Zuo J, Chen Y. Non-destructive detection of frozen pork freshness based on portable fluorescence spectroscopy. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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6
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Hyperspectral imaging and chemometrics assessment of intramuscular fat in pork Longissimus thoracic et lumborum primal cut. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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7
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Li W, Yang Z, Lv J, Zheng T, Li M, Sun C. Detection of Small-Sized Insects in Sticky Trapping Images Using Spectral Residual Model and Machine Learning. FRONTIERS IN PLANT SCIENCE 2022; 13:915543. [PMID: 35837447 PMCID: PMC9274131 DOI: 10.3389/fpls.2022.915543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly (Bemisia tabaci) and thrips (Frankliniella occidentalis) are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one. To alleviate this situation, this study proposed an automated detection approach to meet the need for continuous monitoring of pests in greenhouse conditions. Candidate targets were firstly located using a spectral residual model and then different color features were extracted. Ultimately, Whitefly and thrips were identified using a support vector machine classifier with an accuracy of 93.9 and 89.9%, a true positive rate of 93.1 and 80.1%, and a false positive rate of 9.9 and 12.3%, respectively. Identification performance was further tested via comparison between manual and automatic counting with a coefficient of determination, R 2, of 0.9785 and 0.9582. The results show that the proposed method can provide a comparable performance with previous handcrafted feature-based methods, furthermore, it does not require the support of high-performance hardware compare with deep learning-based method. This study demonstrates the potential of developing a vision-based identification system to facilitate rapid gathering of information pertaining to numbers of small-sized pests in greenhouse agriculture and make a reliable estimation of overall population density.
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Affiliation(s)
- Wenyong Li
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Zhankui Yang
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- College of Computer Science and Technology, Beijing University of Technology, Beijing, China
| | - Jiawei Lv
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Tengfei Zheng
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- College of Information, Shanghai Ocean University, Shanghai, China
| | - Ming Li
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chuanheng Sun
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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Lee HJ, Koh YJ, Kim YK, Lee SH, Lee JH, Seo DW. MSENet: Marbling score estimation network for automated assessment of Korean beef. Meat Sci 2022; 188:108784. [DOI: 10.1016/j.meatsci.2022.108784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 10/19/2022]
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9
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Prediction and visualization of fat content in polythene-packed meat using near-infrared hyperspectral imaging and chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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10
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Tan WK, Husin Z, Yasruddin ML, Ismail MAH. Recent technology for food and beverage quality assessment: a review. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2022; 60:1681-1694. [PMID: 35463865 PMCID: PMC9014778 DOI: 10.1007/s13197-022-05439-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 03/13/2022] [Accepted: 03/16/2022] [Indexed: 12/02/2022]
Abstract
Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.
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Affiliation(s)
- Wei Keong Tan
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis Malaysia
| | - Zulkifli Husin
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis Malaysia
| | - Muhammad Luqman Yasruddin
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis Malaysia
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Zhuang Q, Peng Y, Yang D, Wang Y, Zhao R, Chao K, Guo Q. Detection of frozen pork freshness by fluorescence hyperspectral image. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2021.110840] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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Jiang R, Zhao K. Using machine learning method on calculation of boundary layer height. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-05865-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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13
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Chen D, Wu P, Wang K, Wang S, Ji X, Shen Q, Yu Y, Qiu X, Xu X, Liu Y, Tang G. Combining computer vision score and conventional meat quality traits to estimate the intramuscular fat content using machine learning in pigs. Meat Sci 2021; 185:108727. [PMID: 34971942 DOI: 10.1016/j.meatsci.2021.108727] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/16/2021] [Accepted: 12/19/2021] [Indexed: 11/16/2022]
Abstract
Intramuscular fat content (IMF%) is an important factor that affects the quality of pork. The traditional testing method (Soxhlet extraction) is accurate; however, it has a long preprocessing time. In this study, a total of 1481 photographs of 200 pigs' loin muscles were used to obtain a computer vision score (IIMF %). Then, actual IMF%, meat color, marbling score, pH value, and drip loss of 200 pigs were measured. Stepwise regression (SR) and gradient boosting machine (GBM) were used to construct the estimation model of IMF%. The results showed that the correlation coefficients between IMF% and IIMF%, marbling score, backfat thickness, percentage of moisture (POM), and pH value were 0.68, 0.64, 0.48, 0.45, and 0.25, respectively. The model accuracies of SR and GBM base on residuals distribution were 0.875 and 0.89, respectively. This study presents a method for estimating IMF% using computer vision technology and meat quality traits.
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Affiliation(s)
- Dong Chen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Pingxian Wu
- Chongqing Academy of Animal Sciences, Chongqing, China
| | - Kai Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Shujie Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Xiang Ji
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Qi Shen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yang Yu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Xiaotian Qiu
- National Animal Husbandry Service, Beijing, China
| | - Xu Xu
- National Animal Husbandry Service, Beijing, China
| | - Yihui Liu
- National Animal Husbandry Service, Beijing, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China.
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15
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Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA. Application of Artificial Intelligence in Food Industry—a Guideline. FOOD ENGINEERING REVIEWS 2021. [PMCID: PMC8350558 DOI: 10.1007/s12393-021-09290-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.
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Affiliation(s)
- Nidhi Rajesh Mavani
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Jarinah Mohd Ali
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Suhaili Othman
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
- Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, 43400 Selangor, Malaysia
| | - M. A. Hussain
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Haslaniza Hashim
- Department of Food Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Norliza Abd Rahman
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
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Shi Y, Wang X, Borhan MS, Young J, Newman D, Berg E, Sun X. A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies. Food Sci Anim Resour 2021; 41:563-588. [PMID: 34291208 PMCID: PMC8277176 DOI: 10.5851/kosfa.2021.e25] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 11/09/2022] Open
Abstract
Increasing meat demand in terms of both quality and quantity in conjunction with
feeding a growing population has resulted in regulatory agencies imposing
stringent guidelines on meat quality and safety. Objective and accurate rapid
non-destructive detection methods and evaluation techniques based on artificial
intelligence have become the research hotspot in recent years and have been
widely applied in the meat industry. Therefore, this review surveyed the key
technologies of non-destructive detection for meat quality, mainly including
ultrasonic technology, machine (computer) vision technology, near-infrared
spectroscopy technology, hyperspectral technology, Raman spectra technology, and
electronic nose/tongue. The technical characteristics and evaluation methods
were compared and analyzed; the practical applications of non-destructive
detection technologies in meat quality assessment were explored; and the current
challenges and future research directions were discussed. The literature
presented in this review clearly demonstrate that previous research on
non-destructive technologies are of great significance to ensure
consumers’ urgent demand for high-quality meat by promoting automatic,
real-time inspection and quality control in meat production. In the near future,
with ever-growing application requirements and research developments, it is a
trend to integrate such systems to provide effective solutions for various grain
quality evaluation applications.
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Affiliation(s)
- Yinyan Shi
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA.,College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
| | - Xiaochan Wang
- College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
| | - Md Saidul Borhan
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
| | - Jennifer Young
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - David Newman
- Department of Animal Science, Arkansas State University, Jonesboro, AR 72467, USA
| | - Eric Berg
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
| | - Xin Sun
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA
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Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
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Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
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18
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Wang Y, Wang X. Research on the framework of traditional culture innovation system based on artificial intelligence. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-219145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In order to improve the effect of traditional cultural innovation, this paper proposes a cultural algorithm with dual knowledge, and improves the effect of the algorithm to obtain a cultural algorithm with dual knowledge. Each individual corresponds to its unique dual knowledge, so that the individual’s evolution can move towards the current optimal solution. This paper constructs a traditional cultural innovation system architecture based on artificial intelligence, analyzes its functional modules, and constructs the system structure from the perspectives of cultural classification and cultural innovation. After constructing the system, this paper designs experiments to verify the system performance. The research results show that the system constructed in this paper performs well in traditional cultural analysis and traditional cultural innovation, and can provide references for related research.
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Affiliation(s)
- Yanzhen Wang
- School of Architecture and Art, Shijiazhuang Tiedao University, Shijiazhuang, Hebei, China
| | - Xiaofen Wang
- School of Architecture and Art, Shijiazhuang Tiedao University, Shijiazhuang, Hebei, China
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19
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Zhang H, An H. Shanxi merchant economic history education system based on fuzzy control and quantum evolution algorithm. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-219151] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In order to improve the economic history education effect of Shanxi merchants, this paper combines fuzzy control and quantum evolution algorithm to construct Shanxi merchant economic history education system. The purpose of this paper to construct the Shanxi merchant economic history education system is to establish a learning platform on the Internet or local area network that allows students to learn outside the classroom. This system will consist of multiple sub-modules, and it will provide knowledge points and networks, problem sets, student assignments, teacher-student interaction links, and teaching resource management for Shanxi merchant economic history teaching. Moreover, this system will be designed as an open network-assisted teaching system. In addition, this paper designs experiments to verify the performance of the algorithm constructed in this paper. The research shows that the Shanxi merchant economic history education system based on fuzzy control and quantum evolution algorithm constructed in this paper performs well in data mining and also has good performance in practical education.
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Affiliation(s)
- Haoran Zhang
- Center for Studies of Song History, Hebei University, Baoding, Hebei, China
| | - Haifeng An
- Hebei College of Science and Technology, Baoding, Hebei, China
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20
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Zhang T, Liu S. Evaluation of the effect of music education on improving students’ mental health based on intelligent fuzzy system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-219053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Traditional control methods and modern accurate mathematical model control methods do not perform well in the evaluation of students’ mental health. In order to improve the evaluation effect of students’ mental health, this paper takes the intelligent fuzzy system as the control center and proposes an evaluation system to evaluate the effect of music education in promoting students’ mental health based on fuzzy neural network. Moreover, according to the working characteristics of the music education system, this paper interprets the design requirements of its control system in detail, and has an in-depth understanding of the fuzzy principle, neural network principle and fuzzy god network principle. Secondly, this paper completes the design of the actual orthosis control algorithm applied to the fuzzy neural network control system and the optimization of the fuzzy neural network algorithm. Finally, this paper realizes the intelligent processing of the non-linear pressure signal output by the corresponding strain, and uses music education to evaluate the students’ mental health and manage the rehabilitation effect. From the experimental research results, it can be seen that the system constructed in this paper has a certain effect.
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Affiliation(s)
| | - Shengnan Liu
- Shijiazhuang University, Shijiazhuang, Hebei, China
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21
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Liu S, Zhang T. Music psychology using fuzzy models to promote mental health. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-219040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Music psychology can play an important role in the diagnosis and rehabilitation of mental health patients. Under the guidance of music psychology, this paper combines fuzzy models to process data, which effectively solves the problem that the number of categories in the fuzzy c-means algorithm needs to be manually given. The AFCC algorithm effectively combines the idea of semi-supervised clustering with the CA algorithm. Through two sets of must-link and cannot-link, this paper introduces the constraint penalty item into the objective function, which greatly improves the clustering accuracy. On this basis, this paper constructs a fuzzy model of psychological rehabilitation and diagnosis based on music psychology, designs experiments to verify the performance of this model, and conducts research results statistics from two aspects of diagnosis and rehabilitation. The research results show that the model constructed in this paper has certain practical effects.
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Affiliation(s)
- Shengnan Liu
- Shijiazhuang University, Shijiazhuang, Hebei, China
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22
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Lyu Y. Research on the influence of music educational psychology on saxophone players’ mental state and stage performance. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-219039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Saxophone playing is also a complex process of mental activity. In the learning process of saxophone performance, it is necessary to understand the role of psychological factors in saxophone performance on physiological factors, correctly understand the fluctuations in performance during practice, and remove various psychological obstacles during practice. This article uses clustering method to extract and analyze performers’ mental state. At the same time, based on the existing clustering evaluation indicator, a new evaluation indicator is proposed, which solves the problem that the original evaluation indicator is not applicable to non-convex data sets. In addition, this paper uses intelligent algorithms to extract the mental state characteristics of saxophone players, and on this basis, constructs an intelligent system with music education psychology to improve the mental state of saxophone players and stage performance effects. Finally, this paper analyzes the system performance after constructing the system with algorithms. The research results show that the system constructed in this paper has a certain effect.
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Affiliation(s)
- Yukun Lyu
- School of Music, Guiyang University, Guiyang, Guizhou
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Zhang Z, Liao Q, Sun Y, Pan T, Liu S, Miao W, Li Y, Zhou L, Xu G. Lipidomic and Transcriptomic Analysis of the Longissimus Muscle of Luchuan and Duroc Pigs. Front Nutr 2021; 8:667622. [PMID: 34055857 PMCID: PMC8154583 DOI: 10.3389/fnut.2021.667622] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/30/2021] [Indexed: 01/08/2023] Open
Abstract
Meat is an essential food, and pork is the largest consumer meat product in China and the world. Intramuscular fat has always been the basis for people to select and judge meat products. Therefore, we selected the Duroc, a western lean pig breed, and the Luchuan, a Chinese obese pig breed, as models, and used the longissimus dorsi muscle for lipidomics testing and transcriptomics sequencing. The purpose of the study was to determine the differences in intramuscular fat between the two breeds and identify the reasons for the differences. We found that the intramuscular fat content of Luchuan pigs was significantly higher than that of Duroc pigs. The triglycerides and diglycerides related to flavor were higher in Luchuan pigs compared to Duroc pigs. This phenotype may be caused by the difference in the expression of key genes in the glycerolipid metabolism signaling pathway.
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Affiliation(s)
- Zhiwang Zhang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Qichao Liao
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Yu Sun
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Tingli Pan
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Siqi Liu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Weiwei Miao
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Yixing Li
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Lei Zhou
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Gaoxiao Xu
- Teaching and Research Section of Biotechnology, Nanning University, Nanning, China
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24
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Jiang Z, Wang X. Research on air pollution system based on neural network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.
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Affiliation(s)
- Zhiqi Jiang
- Department of Energy and Resources Engineering, College of Engineering, Peking University, Beijing, China
- Beijing Key Laboratory for Solid Waste Utilization and Management, Peking University, Beijing, China
| | - Xidong Wang
- Department of Energy and Resources Engineering, College of Engineering, Peking University, Beijing, China
- Beijing Key Laboratory for Solid Waste Utilization and Management, Peking University, Beijing, China
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26
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Arora M, Mangipudi P. A Computer Vision-Based Method for Classification of Red Meat Quality After Nitrosamine Appendage. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2021. [DOI: 10.1142/s146902682150005x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Nitrosamine is a carcinogenic chemical used as a preservative in red meat whose identification is an ordeal. This paper presents a computer vision-based non-destructive method for identifying quality disparities between preservative treated and untreated (control) red meat. To access the discrepancy in the quality of red meat, both traditional machine learning and deep learning-based methods have been used. Support vector machine (SVM) classifier and artificial neural network (ANN) models have been used to detect the presence of nitrosamine in test samples. The paper also made use of different pre-trained deep convolutional neural networks (DCNN) with transfer learning approach such as ResNet-34, ResNet-50, ResNet-101, VGG-16, VGG-19, AlexNet and MobileNetv2 to examine the presence of nitrosamine in the food samples. While the ANN classifier performed better in comparison to the SVM classifier, the highest testing accuracy and F1-score were obtained using the deep learning model, ResNet-101 with 95.45% and 96.54%, respectively. The experimental results demonstrate an improved performance in comparison to the existing methods; indicating the feasibility of the proposed work for food quality control in real-time applications.
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Affiliation(s)
- Monika Arora
- Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
| | - Parthasarathi Mangipudi
- Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
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27
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Liu JH, Newman DJ, Young JM, Sun X. Prediction of Whole Pork Loin and Individual Chops’ Intramuscular Fat Using Computer Vision System Technology. MEAT AND MUSCLE BIOLOGY 2020. [DOI: 10.22175/mmb.11127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
The objective of this study was to compare different methods of evaluating intramuscular fat (IMF) in pork and test the accuracy of using a computer vision system (CVS) on different locations of the loin. Whole pork loins (n = 1,400) were obtained from 6 pork processing plants. Subjective marbling scores and CVS IMF percentage (CVS IMF%) were assessed on the ventral lean surface of the whole loin and the 3rd (A) and 10th (B) rib chops. Additionally, the A and B chops were evaluated for crude fat percentage (CF%) using ether extract. The CF% of the whole loin was represented by using the average CF% of A and B chops. A combination of the bootstrap method and stepwise regression models was used to increase prediction and robustness for CF% prediction. To better understand whether plants played an effect, models for individual plants and using all plants together were built, tested, and compared. Results were that subjective marbling score had stronger correlations with CF% compared to CVS IMF% for the whole loin (0.70 vs. 0.58), A chop (0.79 vs.0.62), and B chop (0.74 vs. 0.61). When using the stepwise regression models to predict CF%, B chop (71.8%) had the highest prediction accuracy (estimates within 0.5% residual compared to CF% were considered accurate) followed by A chop (58.1%) and whole loin (48.2%). When comparing individual plant models and overall models, the overall accuracy improved; however, this improvement in accuracy was not consistent through every single plant. In conclusion, CVS has shown potential to estimate pork IMF on all locations, especially the posterior pork chop.
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Affiliation(s)
- Jeng-Hung Liu
- North Dakota State University Department of Animal Sciences
| | | | | | - Xin Sun
- North Dakota State University Department of Agricultural and Biosystems Engineering
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Shi Y, Wang X, Sun X(R. WITHDRAWN: Development and application of an online grading system for pork loin quality based on computer vision technology. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.110007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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29
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Sun X, Young J, Liu JH, Chen Q, Newman D. Predicting Pork Color Scores Using Computer Vision and Support Vector Machine Technology. MEAT AND MUSCLE BIOLOGY 2018. [DOI: 10.22175/mmb2018.06.0015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
The objective of this study was to investigate the ability of image color features to predict subjective pork color scores. Subjective and instrumental color were assessed on the bloomed, cross-sectional surface of pork longissimus thoracis et lumborum chops. Images of pork chop samples were acquired using a computer vision system, and 18 image color features (mean and standard deviation of R, G, B, H, S, I, L*, a*, b*) were extracted for inclusion in partial least squares (PLS) and support vector machine (SVM) regression models. For color scores 2, 3, 4, and 5, the accuracies were 50.4, 75.9, 72.4, and 47.3% classified correctly by PLS, respectively, and 70.7, 72.8, 76.7, and 69.7% by SVM, respectively. The overall prediction accuracies of 2 models for pork color scores were 68.3% for PLS and 73.4% for SVM. There was minimal major misclassification of samples (< 0.5%). Image color features isolated through the development of PLS and SVM models, particularly SVM, show potential as a method to predict pork color scores.
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Affiliation(s)
- Xin Sun
- North Dakota State University Department of Animal Sciences
| | - Jennifer Young
- North Dakota State University Department of Animal Sciences
| | - Jeng Hung Liu
- North Dakota State University Department of Animal Sciences
| | - Quansheng Chen
- Jiangsu University School of Food and Biological Engineering
| | - David Newman
- Arkansas State University Department of Animal Science
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30
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Predicting pork loin intramuscular fat using computer vision system. Meat Sci 2018; 143:18-23. [DOI: 10.1016/j.meatsci.2018.03.020] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Revised: 02/06/2018] [Accepted: 03/23/2018] [Indexed: 11/23/2022]
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