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Li L, Jia X, Fan K. Recent advance in nondestructive imaging technology for detecting quality of fruits and vegetables: a review. Crit Rev Food Sci Nutr 2024:1-19. [PMID: 39291966 DOI: 10.1080/10408398.2024.2404639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
As an integral part of daily dietary intake, the market demand for fruits and vegetables is continuously growing. However, traditional methods for assessing the quality of fruits and vegetables are prone to subjective influences, destructive to samples, and fail to comprehensively reflect internal quality, thereby resulting in various shortcomings in ensuring food safety and quality control. Over the past few decades, imaging technologies have rapidly evolved and been widely employed in nondestructive detection of fruit and vegetable quality. This paper offers a thorough overview of recent advancements in nondestructive imaging technologies for assessing the quality of fruits and vegetables, including hyperspectral imaging (HSI), fluorescence imaging (FI), magnetic resonance imaging (MRI), thermal imaging (TI), terahertz imaging, X-ray imaging (XRI), ultrasonic imaging, and microwave imaging (MWI). The principles and applications of these imaging techniques in nondestructive testing are summarized. The challenges and future trends of these technologies are discussed.
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Affiliation(s)
- Lijing Li
- College of Life Science, Yangtze University, Jingzhou, Hubei, China
| | - Xiwu Jia
- College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, Hubei, China
| | - Kai Fan
- College of Life Science, Yangtze University, Jingzhou, Hubei, China
- Institute of Food Science and Technology, Yangtze University, Jingzhou, Hubei, China
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Aung Moon S, Wongsakul S, Kitazawa H, Kittiwachana S, Saengrayap R. Application of ATR-FTIR for Green Arabica Bean Shelf-Life Determination in Accelerated Storage. Foods 2024; 13:2331. [PMID: 39123523 PMCID: PMC11311548 DOI: 10.3390/foods13152331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Revised: 07/21/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
Coffee bean oxidation is associated with enzymatic and non-enzymatic browning, the degradation of desirable aromatic compounds, the development of undesirable flavors, increased susceptibility to microbial spoilage, and volatile compound losses. This study investigated natural dry process (DP) and honey process (HP) green coffee beans stored in GrainPro® bags for 0, 5, 10, and 20 days under accelerated storage conditions at 30 °C, 40 °C, and 50 °C with relative humidity of 50%. A kinetic model was used to estimate the shelf life of the green coffee beans. DP recorded durability of 45.67, 29.9, and 24.92 days at 30 °C, 40 °C, and 50 °C, respectively, with HP 60.34, 38.07, and 19.22 days. Partial least squares (PLS) analysis was performed to build the models in order to predict the shelf life of coffee based on peroxide (PV) and thiobarbituric acid reactive substances (TBARS) values. In terms of prediction with leave-one-out cross-validation (LOOCV), PLS provided a higher accuracy for TBARS (R2 = 0.801), while PV was lower (R2 = 0.469). However, the auto-prediction showed good agreement among the observed and predicted values in both PV (R2 = 0.802) and TBARS (R2 = 0.932). Based on the variable importance of projection (VIP) scores, the ATR-FTIR peaks as 3000-2825, 2154-2150, 1780-1712, 1487-2483, 1186-1126, 1107-1097, and 1012-949 cm-1 were identified to be the most related to PV and TBARS on green coffee beans shelf life. ATR-FITR showed potential as a fast and accurate technique to evaluate the oxidation reaction that related to the loss of coffee quality during storage.
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Affiliation(s)
- Sai Aung Moon
- School of Agro-Industry, Mae Fah Luang University, Chiang Rai 57100, Thailand; (S.A.M.); (S.W.)
| | - Sirirung Wongsakul
- School of Agro-Industry, Mae Fah Luang University, Chiang Rai 57100, Thailand; (S.A.M.); (S.W.)
- Coffee Quality Research Group, Mae Fah Luang University, Chiang Rai 57100, Thailand
- Integrated AriTech Ecosystems Research Group, Mae Fah Luang University, Chiang Rai 57100, Thailand
| | - Hiroaki Kitazawa
- Department of Food and Nutrition, Faculty of Human Sciences and Design, Japan Women’s University, 2-8-1 Mejirodai, Bunkyo-ku, Tokyo 112-8681, Japan;
| | - Sila Kittiwachana
- Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand;
| | - Rattapon Saengrayap
- School of Agro-Industry, Mae Fah Luang University, Chiang Rai 57100, Thailand; (S.A.M.); (S.W.)
- Coffee Quality Research Group, Mae Fah Luang University, Chiang Rai 57100, Thailand
- Integrated AriTech Ecosystems Research Group, Mae Fah Luang University, Chiang Rai 57100, Thailand
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Rahmati E, Khoshtaghaza MH, Banakar A, Ebadi MT, Hamidi-Esfahani Z. Continuous decontamination of cumin seed by non-contact induction heating technology: Assessment of microbial load and quality changes. Heliyon 2024; 10:e25504. [PMID: 38384505 PMCID: PMC10878883 DOI: 10.1016/j.heliyon.2024.e25504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 02/23/2024] Open
Abstract
Over the past few decades, the demand for high-quality food has increased steadily. Therefore, it is essential to develop innovative technologies that effectively reduce microbial load while minimizing any negative effect on the quality of spices. The objective of this study was to determine the efficacy of a self-designed non-contact induction heating system using contaminated cumin seeds. The non-contact induction heating decontamination process was performed at different temperatures of 115, 135 and 155°C and durations (45, 60 and 75 s) through continuous process (screw conveyor) in Pyrex cylinder chamber. Various parameters including microbial load, color characteristics, essential oil content, surface morphology, sample temperature, and energy consumption were analyzed as dependent variables in the study. The results showed that the treatment combination (155°C - 60 s) reduced the aerobic plate count from 6.21 to 2.97 CFU/g. Mold, yeast and coliforms in the treatment combination (155°C-45 s) were also reduced by 3.26 and 3.6 CFU/g, respectively. The total color difference of the samples increased due to the degradation and alteration of pigments at high temperatures. However, no statistically significant disparity in essential oil content was observed between the treatment groups and the control group. The quantities of essential oil components in the cumin seeds were determined to align with the ISO standard, with the primary constituents identified as follows: Terpinen-7-al γ (38.98%), Cumin aldehyde (20.75%), γ-Terpinene (18.81%), β-Pinene (13.66%), and p-Cymene (6.2%). In summary, non-contact induction heating system shows promise as an effective technology for surface decontamination of spices. The acquired findings contribute to a deeper understanding of the impact of the induction heating process on both the microbial contamination levels and the quality attributes of cumin seeds. This scientific knowledge serves as a foundational framework for the prospective adoption and integration of this technology on a larger industrial scale.
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Affiliation(s)
- Edris Rahmati
- Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran
| | | | - Ahmad Banakar
- Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran
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Yang S, Cao Y, Li C, Castagnini JM, Barba FJ, Shan C, Zhou J. Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis. Curr Res Food Sci 2024; 8:100695. [PMID: 38362161 PMCID: PMC10867766 DOI: 10.1016/j.crfs.2024.100695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/13/2024] [Accepted: 02/07/2024] [Indexed: 02/17/2024] Open
Abstract
This study proposes a recognition model for different drying methods of grain using hyperspectral imaging technology (HSI) and multivariate analysis. Fresh harvested grain samples were dried using three different methods: rotating ventilation drying, mechanical drying, and natural drying. Hyperspectral images of the samples were collected within the 388-1065 nm band range. The spectral features of the samples were extracted using principal component analysis (PCA), while the texture features were extracted using second-order probability statistical filtering. Partial least squares regression (PLSR) drying models with different characteristics were established. At the same time, a BPNN (Back-propagation neural network, BPNN) based on spectral texture fusion features was established to compare the recognition effects of different models. Texture analysis indicated that the mean-image had the clearest contour, and the texture characteristics of mechanical drying were smaller than those of rotating ventilation drying and natural drying. The BPNN model established using spectral-texture feature variables showed the best performance in distinguishing grain in different drying modes, with a prediction model obtained based on the correlation coefficients of special variables. The spectral and texture feature values were fused for pseudo-color visualization expression, and the three drying methods of grain showed different colors. This study provides a reference for non-destructive and rapid detection of grain with different drying methods.
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Affiliation(s)
- Sicheng Yang
- Huanggang Public Testing Center, No.128 Huangzhou Avenue, Huanggang City, Hubei Province, China
| | - Yang Cao
- Academy of State Administration of Grain, Beijing, 100037, China
| | - Chuanjie Li
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural Reclamation University, Daqing, 163319, Heilongjiang, China
| | - Juan Manuel Castagnini
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
| | - Francisco Jose Barba
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
| | - Changyao Shan
- College of Science, Health, Engineering and Education, Murdoch University, Perth, 6150, Australia
| | - Jianjun Zhou
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain
- Department of Biotechnology, Institute of Agrochemistry and Food Technology-National Re-search Council (IATA-CSIC), Agustin Escardino 7, 46980, Paterna, Spain
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Wen T, Li JH, Wang Q, Gao YY, Hao GF, Song BA. Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165626. [PMID: 37481085 DOI: 10.1016/j.scitotenv.2023.165626] [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: 05/04/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.
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Affiliation(s)
- Ting Wen
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Jian-Hong Li
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Qi Wang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, PR China.
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China; Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
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Tang T, Zhang M, Mujumdar AS. Intelligent detection for fresh-cut fruit and vegetable processing: Imaging technology. Compr Rev Food Sci Food Saf 2022; 21:5171-5198. [PMID: 36156851 DOI: 10.1111/1541-4337.13039] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/31/2022] [Accepted: 08/23/2022] [Indexed: 01/28/2023]
Abstract
Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.
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Affiliation(s)
- Tiantian Tang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.,Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, Jiangsu, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.,China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, Jiangsu, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald Campus, McGill University, Montreal, Quebec, Canada
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Chen Q, Qian J, Yang H, Wu W. Sustainable food cold chain logistics: From microenvironmental monitoring to global impact. Compr Rev Food Sci Food Saf 2022; 21:4189-4209. [PMID: 35904269 DOI: 10.1111/1541-4337.13014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/02/2022] [Accepted: 07/05/2022] [Indexed: 01/28/2023]
Abstract
Food cold chain logistics (FCCL) is a systematic engineering process involving the use of a low-temperature environment to maintain the quality and safety of perishable food and reduce food loss and waste (FLW). From a mechanism perspective, FCCL must balance resource costs for a required level of food quality and safety with the costs of greenhouse gas (GHG) emissions. In the context of global warming, the sustainability trade-off between FLW and environmental impact has recently become an important topic in research on efficient, green FCCL. This is mainly reflected in technological innovation, management optimization, and policy responses. With a focus on three levels (micro, meso, macro), this review analyzes current research areas and the gaps and challenges of FCCL in microenvironmental monitoring, life cycle assessment (LCA), and global impact. Future trends pertaining to FCCL in technology, management, and industry and sustainable development are also summarized. Future trends involving sustainable FCCL must be intelligent, systematic, and low carbon. Industry empowerment through next-generation information technologies (e.g., IoT, AI, big data, blockchain) will promote the multidimensional perception, real-time information transmission, and sustainable control of microenvironmental monitoring, as well as support LCA management transformation from fragmentation to system integration. From a macro level, due to the serious global loss of perishable food, the FCCL scale demand is growing greatly, causing a huge environmental burden. Global cooperation, low-carbon consensus, and appropriate policies will become the basis for promoting sustainable FCCL development.
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Affiliation(s)
- Qian Chen
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jianping Qian
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Han Yang
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenbin Wu
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
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Bankole OE, Verma DK, Chávez González ML, Ceferino JG, Sandoval-Cortés J, Aguilar CN. Recent trends and technical advancements in biosensors and their emerging applications in food and bioscience. FOOD BIOSCI 2022. [DOI: 10.1016/j.fbio.2022.101695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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9
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Bruise Detection and Classification of Strawberries Based on Thermal Images. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02804-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Zeng S, Li M, Li G, Lv W, Liao X, Wang L. Innovative applications, limitations and prospects of energy-carrying infrared radiation, microwave and radio frequency in agricultural products processing. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.01.032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Zhang Z, Lou Y, Guo C, Jia Q, Song Y, Tian JY, Zhang S, Wang M, He L, Du M. Metal–organic frameworks (MOFs) based chemosensors/biosensors for analysis of food contaminants. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.10.024] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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12
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Jiang S, Wang F, Li Q, Sun H, Wang H, Yao Z. Environment and food safety: a novel integrative review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:54511-54530. [PMID: 34431060 PMCID: PMC8384557 DOI: 10.1007/s11356-021-16069-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/16/2021] [Indexed: 04/12/2023]
Abstract
Environment protection and food safety are two critical issues in the world. In this review, a novel approach which integrates statistical study and subjective discussion was adopted to review recent advances on environment and food safety. Firstly, a scientometric-based statistical study was conducted based on 4904 publications collected from the Web of Science Core Collection database. It was found that the research on environment and food safety was growing steadily from 2001 to 2020. Interestingly, the statistical analysis of most-cited papers, titles, abstracts, keywords, and research areas revealed that the research on environment and food safety was diverse and multidisciplinary. In addition to the scientometric study, strategies to protect environment and ensure food safety were critically discussed, followed by a discussion on the emerging research topics, including emerging contaminates (e.g., microplastics), rapid detection of contaminants (e.g., biosensors), and environment friendly food packaging materials (e.g., biodegradable polymers). Finally, current challenges and future research directions were proposed.
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Affiliation(s)
- Shanxue Jiang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
- Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing, 100048, China
| | - Fang Wang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
- Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing, 100048, China
| | - Qirun Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Haishu Sun
- Department of Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Huijiao Wang
- School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
| | - Zhiliang Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China.
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China.
- Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing, 100048, China.
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Liu X, Li Y, Wang S, Huangfu L, Zhang M, Xiang Q. Synergistic antimicrobial activity of plasma-activated water and propylparaben: Mechanism and applications for fresh produce sanitation. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111447] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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14
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Nakashima Y, Shiba N. Nondestructive measurement of intramuscular fat content of fresh beef meat by a hand-held magnetic resonance sensor. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2021. [DOI: 10.1080/10942912.2021.1999261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Yoshito Nakashima
- Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Nobuya Shiba
- Livestock and Forage Research Division, National Agriculture and Food Research Organization (NARO), Tohoku Agricultural Research Center, Morioka, Japan
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