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Obi CC, Nwabanne JT, Igwegbe CA, Abonyi MN, Umembamalu CJ, Kamuche TT. Intelligent algorithms-aided modeling and optimization of the deturbidization of abattoir wastewater by electrocoagulation using aluminium electrodes. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 353:120161. [PMID: 38290261 DOI: 10.1016/j.jenvman.2024.120161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/05/2024] [Accepted: 01/20/2024] [Indexed: 02/01/2024]
Abstract
The removal of turbidity from abattoir wastewater (AWW) by electrocoagulation (EC) was modeled and optimized using Artificial Intelligence (AI) algorithms. Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), particle swarm optimization (PSO), and genetic algorithms (GA) were the AI tools employed. Five input variables were considered: pH, current intensity, electrolysis time, settling time, and temperature. The ANN model was evaluated using the Levenberg-Marquardt (trainlm) algorithm, while the ANFIS modeling was accomplished using the Sugeno-type FIS. The ANN and ANFIS models demonstrated linear adequacy with the experimental data, with an R2 value of 0.9993 in both cases. The corresponding statistical error indices were RMSE (ANN = 5.65685E-05; ANFIS = 2.82843E-05), SSE (ANN = 1.60E-07; ANFIS = 3.4E-08), and MSE (ANN = 3.2E-09; ANFIS = 8E-10). The error indices revealed that the ANFIS model had the least performance error and is considered the most reliable of the two. The process optimization performed with GA and PSO considered turbidity removal efficiency, energy requirement, and electrode material loss. An optimal turbidity removal efficiency of 99.39 % was predicted at pH (3.1), current intensity (2 A), electrolysis time (20 min), settling time (50 min), and operating temperature (50 °C). This represents a potential for the delivery of cleaner water without the use of chemicals. The estimated power consumption and the theoretical mass of the aluminium electrode dissolved at the optimum condition were 293.33 kW h/m3 and 0.2237 g, respectively. The work successfully affirmed the effectiveness of the EC process in the removal of finely divided suspended particles from AWW and demonstrated the suitability of the AI algorithms in the modeling and optimization of the process.
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Affiliation(s)
| | - Joseph Tagbo Nwabanne
- Department of Chemical Engineering, Nnamdi Azikiwe University, P.M.B. 5025, Awka, 420218, Nigeria.
| | - Chinenye Adaobi Igwegbe
- Department of Chemical Engineering, Nnamdi Azikiwe University, P.M.B. 5025, Awka, 420218, Nigeria; Department of Applied Bioeconomy, Wroclaw University of Environmental and Life Sciences, Wrocław, Poland.
| | - Matthew Ndubuisi Abonyi
- Department of Chemical Engineering, Nnamdi Azikiwe University, P.M.B. 5025, Awka, 420218, Nigeria.
| | | | - Toochukwu ThankGod Kamuche
- Department of Chemical Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State, Nigeria.
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Vellur S, Pavadai P, Pandian SRK, Palanichamy C, Kabilan SJ, Sundar K, Kannan S, Kunjiappan S. Optimization of ultrasound-assisted extraction of bioactive chemicals from Hemidesmus indicus (L.) R.Br. using response surface methodology and adaptive neuro-fuzzy inference system. Food Sci Biotechnol 2024; 33:327-341. [PMID: 38222910 PMCID: PMC10786805 DOI: 10.1007/s10068-023-01351-9] [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: 01/16/2023] [Revised: 04/13/2023] [Accepted: 05/22/2023] [Indexed: 01/16/2024] Open
Abstract
This study was designed to optimize the ultrasound-assisted extraction (UAE) of bioactive chemicals from Hemidesmus indicus (L.) R.Br. through RSM (response surface methodology) and ANFIS (adaptive neuro-fuzzy inference system). The effect of four independent parameters, methanol concentration (X1: 55-65%), temperature (X2: 30-40 °C), time (X3: 15-20 min) and particle size (X4: 0.5-1.00 mm) at five levels (- 2 ,- 1, 0, + 1, + 2) with respect to dependent parameters, total polyphenols content (TP) (y1), total flavonoids content (TF) (y2), %DPPHsc (y3), %ABTSsc (y4) and %H2O2sc (y5) were selected. The optimal extraction condition was observed at X1 = 65%, X2 = 40 °C, X3 = 20 min and X4 = 0.5 mm; under this circumstance, y1 = 352.85 mg gallic acid equivalents (GA)/g, y2 = 300.204 mg rutin equivalents (RU)/g and their antioxidant potentials (y3 = 81.33%, y4 = 65.04%, and y5 = 71.01%) has been attained. ANFIS was used to compare and confirm the optimized extraction parameter values. Further, GC-MS and LC-MS were performed to investigate the bioactive chemicals present in the optimized extract. Supplementary Information The online version contains supplementary material available at 10.1007/s10068-023-01351-9.
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Affiliation(s)
- Senthilkumar Vellur
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126 India
| | - Parasuraman Pavadai
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M S R Nagar, Bengaluru, 560054 India
| | | | - Chandrasekar Palanichamy
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126 India
| | | | - Krishnan Sundar
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126 India
| | - Suthendran Kannan
- Department of Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126 India
| | - Selvaraj Kunjiappan
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126 India
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Prabhakar DAP, Korgal A, Shettigar AK, Herbert MA, Chandrashekharappa MPG, Pimenov DY, Giasin K. A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process. JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING 2023; 7:181. [DOI: 10.3390/jmmp7050181] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
This review reports on the influencing parameters on the joining parts quality of tools and techniques applied for conducting process analysis and optimizing the friction stir welding process (FSW). The important FSW parameters affecting the joint quality are the rotational speed, tilt angle, traverse speed, axial force, and tool profile geometry. Data were collected corresponding to different processing materials and their process outcomes were analyzed using different experimental techniques. The optimization techniques were analyzed, highlighting their potential advantages and limitations. Process measurement techniques enable feedback collection during the process using sensors (force, torque, power, and temperature data) integrated with FSW machines. The use of signal processing coupled with artificial intelligence and machine learning algorithms produced better weld quality was discussed.
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Affiliation(s)
- D. A. P. Prabhakar
- Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, Karnataka, India
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576104, Karnataka, India
| | - Akash Korgal
- Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, Karnataka, India
| | - Arun Kumar Shettigar
- Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, Karnataka, India
| | - Mervin A. Herbert
- Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, Karnataka, India
| | | | - Danil Yurievich Pimenov
- Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, 454080 Chelyabinsk, Russia
| | - Khaled Giasin
- School of Mechanical and Design Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK
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Metekia WA, Ulusoy BH. Antimicrobial activity of Spirulina platensis extract on total mesophilic and psychrophilic bacteria of fresh tilapia fillet. Sci Rep 2023; 13:13081. [PMID: 37567905 PMCID: PMC10421913 DOI: 10.1038/s41598-023-40260-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 08/08/2023] [Indexed: 08/13/2023] Open
Abstract
Spirulina platensis has a wide range of activities, notably antibacterial property against food pathogens. This study investigates the antibacterial activity of S. platensis extract on Total Mesophilic and Psychrophilic Aerobic Bacteria. The results were compared using statistical analysis and the predicted model values using artificial intelligence-based models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) Models. The extraction of spirulina was done by using the freeze-thaw method with a concentration of 0.5, 1 and 5% w/v. Before the application of the extract, initial microbial load of fillets was analyzed the and the results were used as control. After application analysis was performed at 1, 24 and 48 h of storage at 4 °C. Based on the statistical analysis result the S. platensis extracts' antimicrobial activity over TMAB of fresh tilapia fish fillets at 1, 24 and 48 h was using EA from 2.5 log10 CFU/g during the control stage to 1.8, 1.1 and 0.7 log10 CFU/g respectively whereas EB and EC was from 2.1 and 2.2 log10 CFU/g at control to 1.5, 0.8, 0.5 log10 CFU/g and 1.23, 0.6 and 0.32 log10 CFU/g respectively at the specified hour interval. Similarly, the three extracts over TPAB were from 2.8 log10 CFU/g at control time to 2.1, 1.5 and 0.9 in EA, while using EB reduces from 2.8 log10 CFU/g to 1.9, 1.3 and 0.8 log10 CFU/g at 1, 24 and 48 h respectively. Although EC presented the reduction from 1.9 log10 CFU/g to 1.4, 1 and 0.5 log10 CFU/g. This was supported by ANN and ANFIS models prediction.
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Affiliation(s)
- Wubshet Asnake Metekia
- Ethiopian Ministry of Agriculture, Food and Nutrition Office, Food Safety and Quality Desk, P. O. Box. 62347, Addis Ababa, Ethiopia.
| | - Beyza Hatice Ulusoy
- Food Hygiene and Technology Department, Faculty of Veterinary Medicine, Near East University, 99138, Nicosia, Cyprus
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Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis. Foods 2023; 12:foods12061242. [PMID: 36981168 PMCID: PMC10048131 DOI: 10.3390/foods12061242] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.
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Neo YT, Chia WY, Lim SS, Ngan CL, Kurniawan TA, Chew KW. Smart systems in producing algae-based protein to improve functional food ingredients industries. Food Res Int 2023; 165:112480. [PMID: 36869493 DOI: 10.1016/j.foodres.2023.112480] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/29/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.
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Affiliation(s)
- Yi Ting Neo
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Wen Yi Chia
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Siew Shee Lim
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Cheng Loong Ngan
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor Darul Ehsan, Malaysia
| | | | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62, Nanyang Drive, Singapore 637459, Singapore.
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Chong JWR, Khoo KS, Chew KW, Ting HY, Show PL. Trends in digital image processing of isolated microalgae by incorporating classification algorithm. Biotechnol Adv 2023; 63:108095. [PMID: 36608745 DOI: 10.1016/j.biotechadv.2023.108095] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 12/17/2022] [Accepted: 01/01/2023] [Indexed: 01/05/2023]
Abstract
Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.
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Affiliation(s)
- Jun Wei Roy Chong
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia
| | - Kuan Shiong Khoo
- Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan.
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459 Singapore
| | - Huong-Yong Ting
- Drone Research and Application Centre, University of Technology Sarawak, No.1, Jalan Universiti, 96000 Sibu, Sarawak, Malaysia
| | - Pau Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India.
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Homayoonfal M, Malekjani N, Baeghbali V, Ansarifar E, Hedayati S, Jafari SM. Optimization of spray drying process parameters for the food bioactive ingredients. Crit Rev Food Sci Nutr 2022; 64:5631-5671. [PMID: 36547397 DOI: 10.1080/10408398.2022.2156976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Spray drying (SD) is one of the most important thermal processes used to produce different powders and encapsulated materials. During this process, quality degradation might happen. The main objective of applying optimization methods in SD processes is maximizing the final nutritional quality of the product besides sensory attributes. Optimization regarding economic issues might be also performed. Applying optimization approaches in line with mathematical models to predict product changes during thermal processes such as SD can be a promising method to enhance the quality of final products. In this review, the application of the response surface methodology (RSM), as the most widely used approach, is introduced along with other optimization techniques such as factorial, Taguchi, and some artificial intelligence-based methods like artificial neural networks (ANN), genetic algorithms (GA), Fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS). Also, probabilistic methods such as Monte Carlo are briefly introduced. Some recent case studies regarding the implementation of these methods in SD processes are also exemplified and discussed.
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Affiliation(s)
- Mina Homayoonfal
- Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Narjes Malekjani
- Department of Food Science and Technology, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Vahid Baeghbali
- Department of Food Hygiene and Quality Control, School of Nutrition and Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Elham Ansarifar
- Department of Public Health, Faculty of Health, Birjand University of Medical Sciences, Birjand, Iran
| | - Sara Hedayati
- Nutrition Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Seid Mahdi Jafari
- Department of Food Materials and Process Design Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
- Department of Analytical Chemistry and Food Science, Faculty of Science, Universidade de Vigo, Nutrition and Bromatology Group, Ourense, Spain
- College of Food Science and Technology, Hebei Agricultural University, Baoding, China
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Andrade Cruz I, Chuenchart W, Long F, Surendra KC, Renata Santos Andrade L, Bilal M, Liu H, Tavares Figueiredo R, Khanal SK, Fernando Romanholo Ferreira L. Application of machine learning in anaerobic digestion: Perspectives and challenges. BIORESOURCE TECHNOLOGY 2022; 345:126433. [PMID: 34848330 DOI: 10.1016/j.biortech.2021.126433] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/21/2021] [Accepted: 11/22/2021] [Indexed: 06/13/2023]
Abstract
Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions.
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Affiliation(s)
- Ianny Andrade Cruz
- Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
| | - Wachiranon Chuenchart
- Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA; Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA
| | - Fei Long
- Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA
| | - K C Surendra
- Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA; Global Institute for Interdisciplinary Studies, 44600 Kathmandu, Nepal
| | - Larissa Renata Santos Andrade
- Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
| | - Muhammad Bilal
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Hong Liu
- Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA
| | - Renan Tavares Figueiredo
- Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil; Institute of Technology and Research, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
| | - Samir Kumar Khanal
- Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA; Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA.
| | - Luiz Fernando Romanholo Ferreira
- Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil; Institute of Technology and Research, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
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Image Processing Technology in Remote Monitoring and Intelligent Medical System. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6549891. [PMID: 34853671 PMCID: PMC8629634 DOI: 10.1155/2021/6549891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022]
Abstract
In order to study the application of image processing technology in remote monitoring and intelligent medical systems, the principle and implementation method of a remote intelligent image monitoring system based on virtual local area network is proposed; this method analyzes the key technologies to be considered in the remote realization of image monitoring, adopts advanced digital image compression coding and decoding technology and digital image transmission technology, and applies intelligent image processing and recognition technology to display, adjust, and track images; it overcomes the defects that the general monitoring system requires excessive intervention by monitoring personnel and low intelligence. After verification, the experimental results show that the proposed model can accurately and efficiently segment nonoverlapping cervical cell images, and compared with other existing models, this model has both higher segmentation accuracy and faster calculation speed. The application of multicast is still only in the laboratory or small local area network; with the further development of network technology, its application prospects will be very broad.
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11
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Chasiotis V, Nadi F, Filios A. Evaluation of multilayer perceptron neural networks and adaptive neuro-fuzzy inference systems for the mass transfer modeling of Echium amoenum Fisch. & C. A. Mey. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:6514-6524. [PMID: 34000064 DOI: 10.1002/jsfa.11323] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 02/25/2021] [Accepted: 05/17/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Multilayer perceptron (MLP) feed-forward artificial neural networks (ANN) and first-order Takagi-Sugeno-type adaptive neuro-fuzzy inference systems (ANFIS) are utilized to model the fluidized bed-drying process of Echium amoenum Fisch. & C. A. Mey. The moisture ratio evolution is calculated based on the drying temperature, airflow velocity and process time. Different ANN topologies are examined by evaluating the number of neurons (3 to 20), the activation functions and the addition of a second hidden layer. Different numbers (2 to 5) and shapes of membership functions are examined for the ANFIS, using the grid partitioning method. The models with the best performance in terms of prediction accuracy, as evaluated by the statistical indices, are compared with the best fit thin-layer model and the available data from the experimental cases of 40 °C, 50 °C and 60 °C temperatures at 0.5, 0.75 and 1 ms-1 airflow velocity. RESULTS The best performed ANFIS model, comprised by 5-2-2 of π-shaped andtriangular membership functions for time, temperature and airflow velocityinputs respectively, was able to describe the moisture ratio evolution of E. amoenum more precisely than the best ANN topology, achieving higher values of coefficientof determination (R2 ), root mean square error (RMSE) and sum of squared errors(SSE). The best thin-layer model involving six adjustable parameters, managedto describe experimental data most accurately with R2 = 0.9996, RMSE = 0.0057and SSE = 7.3·10-4 . CONCLUSION The results of the comparative study indicate that empirical regression models with increased numbers of adjustable parameters, constitute a simpler and more accurate modeling approach for estimating the moisture ratio of E. amoenum Fisch. & C. A. Mey under fluidized bed drying. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Vasileios Chasiotis
- Laboratory of Thermo Fluid Systems (LTFS), Department of Mechanical Engineering, University of West Attica, Egaleo, Greece
| | - Fatemeh Nadi
- Department of Agricultural Machinery Mechanics, Azadshahr Branch, Islamic Azad University, Azadshahr, Iran
| | - Andronikos Filios
- Laboratory of Thermo Fluid Systems (LTFS), Department of Mechanical Engineering, University of West Attica, Egaleo, Greece
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Asadi M, McPhedran K. Biogas maximization using data-driven modelling with uncertainty analysis and genetic algorithm for municipal wastewater anaerobic digestion. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 293:112875. [PMID: 34062425 DOI: 10.1016/j.jenvman.2021.112875] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 06/12/2023]
Abstract
Anaerobic digestion processes create biogases that can be useful sources of energy. The development of data-driven models of anaerobic digestion processes via operating parameters can lead to increased biogas production rates, resulting in greater energy production, through process modification and optimization. This study assessed processed and unprocessed input operating parameter variables for the development of regression models with transparent structures ('white-box' models) to: (1) estimate biogas production rates from municipal wastewater treatment plant (MWTP) anaerobic digestors; (2) compare their performances to artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models with opaque structures ('black-box' models) using Monte Carlo Simulation for uncertainty analysis; and (3) integrate the models with a genetic algorithm (GA) to optimize operating parameters for maximization of MWTP biogas production rates. The input variables were anaerobic digestion operating parameters from a MWTP including volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate, which were processed via correlation tests and principal component analysis. Overall, the results indicated that the processed data did not improve regression model performances. Additionally, the developed non-linear regression model with the unprocessed inputs had the best performance based on values including R = 0.81, RMSE = 0.95, and IA = 0.89. However, this model was less accurate, but interestingly had less uncertainty, as compared to ANN and ANFIS models which indicates the compromise between model accuracy and uncertainty. Thus, all three models were coupled with GA optimization with maximum biogas production rate estimates of 22.0, 23.1, and 28.6 m3/min for ANN, ANFIS, and non-linear regression models, respectively.
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Affiliation(s)
- Mohsen Asadi
- Department of Civil, Geological & Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Kerry McPhedran
- Department of Civil, Geological & Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
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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: 22] [Impact Index Per Article: 7.3] [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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Ebrahimi-Khusfi Z, Taghizadeh-Mehrjardi R, Nafarzadegan AR. Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:6796-6810. [PMID: 33011943 DOI: 10.1007/s11356-020-10957-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/20/2020] [Indexed: 06/11/2023]
Abstract
Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, therefore, the uncertainty of the ANFIS model was quantified using uncertainty estimation based on local errors and clustering methods. Furthermore, we used a model-agnostic interpretation to make the ANFIS model interpretable. In addition, we used the bat optimization algorithm (BAT) to increase the prediction accuracy of the ANFIS model. Seven explanatory variables were chosen for predicting DC in the cold and warm months across semi-arid regions of Iran. The results showed that the ANFIS+BAT model increased the correlation coefficient by 10% and 16% for predicting DC in the cold and warm months, respectively, compared with the ANFIS model. Furthermore, the uncertainty analysis indicated a lower prediction interval (i.e., lower uncertainty) for the ANFIS+BAT model compared with the ANFIS model for predicting DC in the cold and warm months. In addition, the model-agnostic interpretation tool findings indicated the highest contributions of air temperature and maximum wind speed for predicting DC in the cold and warm months, respectively. Prediction of DC using the proposed model will allow decision-makers to better plan for measures to mitigate the risks of wind erosion and air pollution.
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Affiliation(s)
- Zohre Ebrahimi-Khusfi
- Department of Natural Science, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran.
| | - Ruhollah Taghizadeh-Mehrjardi
- Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, Tubingen, Germany.
- Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Iran.
| | - Ali Reza Nafarzadegan
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran
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Bakhshipour A, Zareiforoush H, Bagheri I. Mathematical and intelligent modeling of stevia ( Stevia Rebaudiana) leaves drying in an infrared-assisted continuous hybrid solar dryer. Food Sci Nutr 2021; 9:532-543. [PMID: 33473314 PMCID: PMC7802544 DOI: 10.1002/fsn3.2022] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/01/2020] [Accepted: 11/02/2020] [Indexed: 12/04/2022] Open
Abstract
Drying characteristics of stevia leaves were investigated in an infrared (IR)-assisted continuous-flow hybrid solar dryer. Drying experiments were conducted at the inlet air temperatures of 30, 40, and 50°C, air inlet velocities of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The results indicated that inlet air temperature and IR lamp input power had significant effect on drying time (p < .05). A comparative study was performed among mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy System (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia leaves during the drying process. The ANN model was the most accurate MR predictor with coefficient of determination (R2), root mean squared error (RMSE), and chi-squared error (χ2) values of 0.9995, 0.0005, and 0.0056, respectively, on test dataset. These values of the ANFIS model on test dataset were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the Midilli model was the best-fitted model to experimental MR values in most of the drying conditions. It was concluded that artificial intelligence modeling is an effective approach for accurate prediction of the drying kinetics of stevia leaves in the continuous-flow IR-assisted hybrid solar dryer.
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Affiliation(s)
- Adel Bakhshipour
- Department of Agricultural Mechanization EngineeringFaculty of Agricultural SciencesUniversity of GuilanRashtIran
| | - Hemad Zareiforoush
- Department of Agricultural Mechanization EngineeringFaculty of Agricultural SciencesUniversity of GuilanRashtIran
| | - Iraj Bagheri
- Department of Agricultural Mechanization EngineeringFaculty of Agricultural SciencesUniversity of GuilanRashtIran
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Bhagya Raj GVS, Dash KK. Comprehensive study on applications of artificial neural network in food process modeling. Crit Rev Food Sci Nutr 2020; 62:2756-2783. [PMID: 33327740 DOI: 10.1080/10408398.2020.1858398] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed.HIGHLIGHTS1. This paper discusses application of ANN in different emerging trends in food process.2. Application of ANN to develop non-linear multivariate modeling is illustrated.3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy.4. ANN resulted in fast response making it suitable for application in real time systems.
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Affiliation(s)
- G V S Bhagya Raj
- Department of Food Engineering and Technology, Tezpur University, Tezpur, Assam, India
| | - Kshirod K Dash
- Department of Food Engineering and Technology, Tezpur University, Tezpur, Assam, India
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Bayesian Regularized Neural Network for Prediction of the Dose in Gamma Irradiated Milk Products. CYBERNETICS AND INFORMATION TECHNOLOGIES 2020. [DOI: 10.2478/cait-2020-0022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Gamma irradiation is a well-known method for sterilizing different foodstuffs, including fresh cow milk. Many studies witness that the low dose irradiation of milk and milk products affects the fractions of the milk protein, thus reducing its allergenic effect and make it potentially appropriate for people with milk allergy. The purpose of this study is to evaluate the relationship between the gamma radiation dose and size of the protein fractions, as potential approach to decrease the allergenic effect of the milk. In this paper, an approach for prediction of the dose in gamma irradiated products by using a Bayesian regularized neural network as a mean to save recourses for expensive electrophoretic experiments, is developed. The efficiency of the proposed neural network model is proved on data for two dairy products – lyophilized cow milk and curd.
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Drying characteristics of yam slices ( Dioscorea rotundata) in a convective hot air dryer: application of ANFIS in the prediction of drying kinetics. Heliyon 2020; 6:e03555. [PMID: 32190764 PMCID: PMC7068632 DOI: 10.1016/j.heliyon.2020.e03555] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 02/06/2020] [Accepted: 03/04/2020] [Indexed: 11/22/2022] Open
Abstract
This study applied Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the moisture ratio (MR) during the drying process of yam slices (Dioscorea rotundata) in a hot air convective dryer. Also the effective diffusivity, activation energy, and rehydration ratio were calculated. The experiments were carried out at three (3) drying air temperatures (50, 60, and 70 °C), air velocities (0.5, 1, and 1.5 m/s), and slice thickness (3, 6, and 9 mm), and the obtained experimental data were used to check the usefulness of ANFIS in the yam drying process. The result showed efficient applicability of ANFIS in predicting the MR at any time of the drying process with a correlation value (R2) of 0.98226 and root mean square error value (RMSE) of 0.01702 for the testing stage. The effective diffusivity increased with an increase in air velocity, air temperature, and thickness and the values (6.382E -09 to 1.641E -07 m2/s). The activation energy increased with an increase in air velocity, but fluctuate within the air temperatures and thickness used (10.59–54.93 KJ/mol). Rehydration ratio was highest at air velocity×air temperature×thickness (1.5 m/s×70 °C × 3 mm), and lowest at air velocity × air temperature×thickness (0.5 m/s×70 °C × 3 mm). The result showed that the drying kinetics of Dioscorea rotundata existed in the falling rate period. The drying time decreased with increased temperature, air velocity, and decreased slice thickness. These established results are applicable in process and equipment design, analysis and prediction of hot air convective drying of yam (Dioscorea rotundata) slices.
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Alizadeh Behbahani B, Noshad M, Jooyandeh H. Improving oxidative and microbial stability of beef using Shahri Balangu seed mucilage loaded with Cumin essential oil as a bioactive edible coating. BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY 2020. [DOI: 10.1016/j.bcab.2020.101563] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Lu Y, Liang X, Cheng L, Fang S. Microencapsulation of Pigments by Directly Spray-Drying of Anthocyanins Extracts from Blueberry Pomace: Chemical Characterization and Extraction Modeling. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2020. [DOI: 10.1515/ijfe-2019-0247] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
AbstractThe aim of this study was to develop an environmentally friendly process to extract anthocyanins from blueberry pomace using water as a solvent and directly microencapsulate anthocyanins by spray drying. The anthocyanins in water and ethanol extracts were characterized by high-performance liquid chromatography and mass spectrometry. The malvidin-3-O-galactoside and malvidin-3-O-glucoside were identified as the main anthocyanins in the blueberry pomace. The anthocyanins profiles of water extracts were similar to that by ethanol extraction. The effects of extraction parameters including solid-to-liquid ratio and temperature on the extraction efficiency and anthocyanins concentration were studied. The blueberry anthocyanins degraded at temperatures higher than 60 °C and all anthocyanins showed similar degradation tendency. The result showed that the artificial neural network (ANN) modeling could be well used to portray the effects of these parameters. Finally, the water extracts were successfully spray dried to produce microencapsulation of blueberry anthocyanins with maltodextrin (MD) as wall materials.
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Affiliation(s)
- Yushuang Lu
- School of Food Science and Biotechnology, Zhejiang Gongshang University, HangzhouZhejiang, China
| | - Xianrui Liang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, HangzhouZhejiang, China
| | - Lishuang Cheng
- School of Food Science and Biotechnology, Zhejiang Gongshang University, HangzhouZhejiang, China
| | - Sheng Fang
- School of Food Science and Biotechnology, Zhejiang Gongshang University, HangzhouZhejiang, China
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Abbaspour‐Gilandeh Y, Jahanbakhshi A, Kaveh M. Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS. Food Sci Nutr 2020; 8:594-611. [PMID: 31993183 PMCID: PMC6977499 DOI: 10.1002/fsn3.1347] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/03/2019] [Accepted: 11/05/2019] [Indexed: 12/02/2022] Open
Abstract
This study aimed to predict the drying kinetics, energy utilization (Eu ), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (Deff ), Eu , EUR, exergy efficiency, and exergy loss. The value of the Deff was varied from 4.19 × 10-10 to 1.18 × 10-9 m2/s. The highest value Eu , EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R 2) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict Eu , EUR, exergy efficiency, and exergy loss, with R 2 of .9989, .9988, .9986, and .9978, respectively.
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Affiliation(s)
- Yousef Abbaspour‐Gilandeh
- Department of Biosystems EngineeringCollege of Agriculture and Natural ResourcesUniversity of Mohaghegh ArdabiliArdabilIran
| | - Ahmad Jahanbakhshi
- Department of Biosystems EngineeringCollege of Agriculture and Natural ResourcesUniversity of Mohaghegh ArdabiliArdabilIran
| | - Mohammad Kaveh
- Department of Biosystems EngineeringCollege of Agriculture and Natural ResourcesUniversity of Mohaghegh ArdabiliArdabilIran
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Arabameri M, Nazari RR, Abdolshahi A, Abdollahzadeh M, Mirzamohammadi S, Shariatifar N, Barba FJ, Mousavi Khaneghah A. Oxidative stability of virgin olive oil: evaluation and prediction with an adaptive neuro-fuzzy inference system (ANFIS). JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019; 99:5358-5367. [PMID: 31056745 DOI: 10.1002/jsfa.9777] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/04/2019] [Accepted: 05/01/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND An adaptive neuro-fuzzy inference system (ANFIS) was employed to predict the oxidative stability of virgin olive oil (VOO) during storage as a function of time, storage temperature, total polyphenol, α-tocopherol, fatty acid profile, ultraviolet (UV) extinction coefficient (K268 ), and diacylglycerols (DAGs). RESULTS The mean total quantities of polyphenols and DAGs were 1.1 and 1.9 times lower in VOOs stored at 25 °C than in the initial samples, and the mean total quantities of polyphenols and DAGs were 1.3 and 2.26 times lower in VOOs stored at 37 °C than in the initial samples, respectively. In a single sample, α-tocopherol was reduced by between 0.52 and 0.91 times during storage, regardless of the storage temperature. The mean specific UV extinction coefficients (K268 ) for VOO stored at 25 and 37 °C were reported as 0.15 (ranging between 0.06-0.39) and 0.13 (ranging between 0.06-0.35), respectively. The ANFIS model created a multi-dimensional correlation function, which used compositional variables and environmental conditions to assess the quality of VOO. The ANFIS model, with a generalized bell-shaped membership function and a hybrid learning algorithm (R2 = 0.98; MSE = 0.0001), provided more precise predictions than other algorithms. CONCLUSION Minor constituents were found to be the most important factors influencing the preservation status and freshness of VOO during storage. Relative changes (increases and reductions) in DAGs were good indicators of oil oxidative stability. The observed effectiveness of ANFIS for modeling oxidative stability parameters confirmed its potential use as a supplemental tool in the predictive quality assessment of VOO. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Majid Arabameri
- Vice-chancellery of Food and Drug, Shahroud University of Medical Sciences, Shahroud, Iran
| | | | - Anna Abdolshahi
- Food Safety Research Center(salt), School of Nutrition and Food Sciences, Semnan University of Medical Sciences, Semnan, Iran
| | - Mohammad Abdollahzadeh
- Vice-Chancellery of Food and Drug, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Solmaz Mirzamohammadi
- Vice-chancellery of Food and Drug, Shahroud University of Medical Sciences, Shahroud, Iran
- School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iran
| | - Nabi Shariatifar
- Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Halal research center of IRI.FDA.MOH, Tehran, Iran
- Food safety research center, Shahid Beheshti University of Medical Science, Tehran, Iran
| | - Francisco J Barba
- Universitat de València, Faculty of Pharmacy, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Nutrition and Food Science Area, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, València, Spain
| | - Amin Mousavi Khaneghah
- Department of Food Science, Faculty of Food Engineering, State University of Campinas (UNICAMP), Monteiro Lobato, 80. Caixa. CEP: 13083-862, Campinas, São Paulo
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FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction. TECHNOLOGIES 2018. [DOI: 10.3390/technologies6040090] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.
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Kaveh M, Jahanbakhshi A, Abbaspour-Gilandeh Y, Taghinezhad E, Moghimi MBF. The effect of ultrasound pre-treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network. J FOOD PROCESS ENG 2018. [DOI: 10.1111/jfpe.12868] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Mohammad Kaveh
- Department of Biosystems Engineering; University of Mohaghegh Ardabili; Ardabil Iran
| | - Ahmad Jahanbakhshi
- Department of Biosystems Engineering; University of Mohaghegh Ardabili; Ardabil Iran
| | | | - Ebrahim Taghinezhad
- Moghan College of Agriculture and Natural Resources; University of Mohaghegh Ardabili; Ardabil Iran
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Rafiei Nazari R, Noorian S, Arabameri M. Migration modelling of phthalate from non-alcoholic beer bottles by adaptive neuro-fuzzy inference system. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2018; 98:2113-2120. [PMID: 28941244 DOI: 10.1002/jsfa.8693] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 09/11/2017] [Accepted: 09/17/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND There are limitations to the basic knowledge regarding various ways by which packaging components migrate into food as well as ways by which various conditions, elements and molecules related to this phenomenon are analysed. This research aimed to model phthalate migration from polyethylene terephthalate bottles containing non-alcoholic beer by performing adaptive neuro-fuzzy inference system (ANFIS) analysis. RESULTS The data showed that storage temperature, contact surface and storage period correlates with the rate of migration. Migration of phthalate increases with storage duration gradually and reduces under different temperatures and contact surface. Moreover, increased temperature and storage duration resulted in an increase in migration level ranging from 0.6 μg L-1 to 2.9 μg L-1 . In summary, the present study used an ANFIS architecture which consists of three inputs (temperature, surface and storage period), Gauss-bell membership functions for each input variable and one output layer, which represent the migration level. The validation and training models showed an excellent match between the experimental and predicted values of ANFIS. CONCLUSION Analysis of the model showed that ANFIS is a powerful tool for predicting phthalate migration from bottles containing non-alcoholic beer. © 2017 Society of Chemical Industry.
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Affiliation(s)
| | - Simin Noorian
- Department of Food Science & Technology, Damghan Branch, Islamic Azad University, Damghan, Iran
| | - Majid Arabameri
- Vice-chancellery of Food and Drug, Shahroud University of Medical Sciences, Shahroud, Iran
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Case Studies in Modelling, Control in Food Processes. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2017; 161:93-120. [PMID: 28447120 DOI: 10.1007/10_2017_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
This chapter discusses the importance of modelling and control in increasing food process efficiency and ensuring product quality. Various approaches to both modelling and control in food processing are set in the context of the specific challenges in this industrial sector and latest developments in each area are discussed. Three industrial case studies are used to demonstrate the benefits of advanced measurement, modelling and control in food processes. The first case study illustrates the use of knowledge elicitation from expert operators in the process for the manufacture of potato chips (French fries) and the consequent improvements in process control to increase the consistency of the resulting product. The second case study highlights the economic benefits of tighter control of an important process parameter, moisture content, in potato crisp (chips) manufacture. The final case study describes the use of NIR spectroscopy in ensuring effective mixing of dry multicomponent mixtures and pastes. Practical implementation tips and infrastructure requirements are also discussed.
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