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Fakhlaei R, Babadi AA, Sun C, Ariffin NM, Khatib A, Selamat J, Xiaobo Z. Application, challenges and future prospects of recent nondestructive techniques based on the electromagnetic spectrum in food quality and safety. Food Chem 2024; 441:138402. [PMID: 38218155 DOI: 10.1016/j.foodchem.2024.138402] [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] [Received: 11/15/2023] [Revised: 12/26/2023] [Accepted: 01/06/2024] [Indexed: 01/15/2024]
Abstract
Safety and quality aspects of food products have always been critical issues for the food production and processing industries. Since conventional quality measurements are laborious, time-consuming, and expensive, it is vital to develop new, fast, non-invasive, cost-effective, and direct techniques to eliminate those challenges. Recently, non-destructive techniques have been applied in the food sector to improve the quality and safety of foodstuffs. The aim of this review is an effort to list non-destructive techniques (X-ray, computer tomography, ultraviolet-visible spectroscopy, hyperspectral imaging, infrared, Raman, terahertz, nuclear magnetic resonance, magnetic resonance imaging, and ultrasound imaging) based on the electromagnetic spectrum and discuss their principle and application in the food sector. This review provides an in-depth assessment of the different non-destructive techniques used for the quality and safety analysis of foodstuffs. We also discussed comprehensively about advantages, disadvantages, challenges, and opportunities for the application of each technique and recommended some solutions and developments for future trends.
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Affiliation(s)
- Rafieh Fakhlaei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Arman Amani Babadi
- School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Chunjun Sun
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Naziruddin Mat Ariffin
- Department of Food Science, Faculty of Food Science and Technology, University Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Alfi Khatib
- Pharmacognosy Research Group, Department of Pharmaceutical Chemistry, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan 25200, Pahang Darul Makmur, Malaysia; Faculty of Pharmacy, Airlangga University, Surabaya 60155, Indonesia
| | - Jinap Selamat
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Zou Xiaobo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
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Phuong NTH, Tanaka F, Wardana AA, Van TT, Yan X, Nkede FN, Tanaka F. Persimmon preservation using edible coating of chitosan enriched with ginger oil and visualization of internal structure changes using X-ray computed tomography. Int J Biol Macromol 2024; 262:130014. [PMID: 38340933 DOI: 10.1016/j.ijbiomac.2024.130014] [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] [Received: 11/08/2023] [Revised: 01/19/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
The incorporation of ginger oil (GO) influenced the physical, optical, and structural properties of the chitosan (CH) film including the decreases of moisture content (60.15 %), water solubility (35.37 %) and water vapor permeability (WVP) (32.79 %) and the increases of tensile strength (TS) (125 %), elongation at break (EAB) (2.74 %) and opacity (131.08 %). Antifungal capacity of the CH film was enhanced when GO was added to the film. The CH + GO film showed a less homogeneous surface that the presence of the oil droplets on the film surface. Moreover, the CH and CH + GO coatings reduced weight loss of persimmon by 14.87 %, and 21.13 %, respectively, compared to the control. Moisture content loss of the coated CH- and the coated CH + GO- persimmons was decreased by 1.94 % and 4.92 %, respectively, compared to that of the control persimmon. Furthermore, the CH and CH + GO coatings decreased in color changes, respiration rate, ethylene production, changes in pH and TSS, and remained firmness of persimmon during storage at 25 °C. In addition, X-ray CT images can be used to monitor internal changes and observe the tissue breakdown during storage period. The ΔGS value can be used as a predictor of persimmon internal qualities. Thus, the CH film containing GO can be applied as an active packaging material.
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Affiliation(s)
- Nguyen Thi Hang Phuong
- Department of Food Technology, Faculty of Agriculture and Food Technology, Tien Giang University, 119 Ap Bac, My Tho city, Viet Nam.
| | - Fumina Tanaka
- Division of Bio-production Environmental Sciences, Department of Agro-environmental Sciences, Faculty of Agriculture, Kyushu University, W5-873,744 Motooka, Nishi-ku Fukuoka shi 819-0395, Japan
| | - Ata Aditya Wardana
- Department of Food Technology, Faculty of Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| | - Tran Thi Van
- Division of Bio-production Environmental Sciences, Department of Agro-environmental Sciences, Faculty of Agriculture, Kyushu University, W5-873,744 Motooka, Nishi-ku Fukuoka shi 819-0395, Japan; Department of Preservation Technology Research on Agricultural Product, Vietnam Institution of Agricultural Engineering and Postharvest Technology, Hanoi 10000, Viet Nam
| | - Xirui Yan
- Division of Bio-production Environmental Sciences, Department of Agro-environmental Sciences, Faculty of Agriculture, Kyushu University, W5-873,744 Motooka, Nishi-ku Fukuoka shi 819-0395, Japan
| | - Francis Ngwane Nkede
- Division of Bio-production Environmental Sciences, Department of Agro-environmental Sciences, Faculty of Agriculture, Kyushu University, W5-873,744 Motooka, Nishi-ku Fukuoka shi 819-0395, Japan
| | - Fumihiko Tanaka
- Division of Bio-production Environmental Sciences, Department of Agro-environmental Sciences, Faculty of Agriculture, Kyushu University, W5-873,744 Motooka, Nishi-ku Fukuoka shi 819-0395, Japan
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Xue Q, Miao P, Miao K, Yu Y, Li Z. An online automatic sorting system for defective et using deep learning. CHINESE HERBAL MEDICINES 2023. [PMID: 37538869 PMCID: PMC10394327 DOI: 10.1016/j.chmed.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
Objective To establish a deep-learning architecture based on faster region-based convolutional neural networks (Faster R-CNN) algorithm for detection and sorting of red ginseng (Ginseng Radix et Rhizoma Rubra) with internal defects automatically on an online X-ray machine vision system. Methods A Faster R-CNN based classifier was trained with around 20 000 samples with mean average precision value (mAP) of 0.95. A traditional image processing method based on feedforward neural network (FNN) obtained a bad performance with the accuracy, recall and specificity of 69.0%, 68.0%, and 70.0%, respectively. Therefore, the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system. Results An independent set of 2 000 red ginsengs were used to validate the performance of the Faster R-CNN based online sorting system in three parallel tests, achieving accuracy of 95.8%, 95.2% and 96.2%, respectively. Conclusion The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.
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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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Yang X, Zhang D, Wu B, Zhang K, Yang B, Wang Z, Wu X. Accurate Characterization of the Adhesive Layer Thickness of Ceramic Bonding Structures Using Terahertz Time-Domain Spectroscopy. MATERIALS (BASEL, SWITZERLAND) 2022; 15:6972. [PMID: 36234313 PMCID: PMC9572604 DOI: 10.3390/ma15196972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Ceramic adhesive structures have been increasingly used in aerospace applications. However, the peaks of the signal on the upper and lower surface of the adhesive layer are difficult to measure directly due to the thin thickness of the adhesive layer and the effect of the attenuation dispersion of the ceramic layer. Thus, the existing non-destructive testing techniques have been ineffective in detecting adhesive quality. In this paper, the thickness of the adhesive layer is measured using terahertz time-domain spectroscopy. A sparse deconvolution method is proposed for the terahertz time-domain spectral signal of ceramic adhesive structures with different adhesive layer thicknesses. The results show that the methods proposed in this paper can realize the separation of reflection signals for glue layers with a thickness of 0.20 mm. By comparing with a wavelet denoising method and a modified covariance method (AR/MCM), the effectiveness of the sparse deconvolution method in estimating the thickness of the glue layer is demonstrated. This work will provide the theoretical and experimental basis for using terahertz time-domain spectroscopy to detect the homogeneity of ceramic adhesive structures.
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Affiliation(s)
- Xiuwei Yang
- Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Dehai Zhang
- Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Biyuan Wu
- Shandong Institute of Advanced Technology, Jinan 250100, China
- Basic Research Center, School of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, China
| | - Kaihua Zhang
- Henan Key Laboratory of Infrared Materials and Spectrum Measures and Applications, School of Physics, Henan Normal University, Xinxiang 453007, China
| | - Bing Yang
- Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China
| | - Zhongmin Wang
- Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Xiaohu Wu
- Shandong Institute of Advanced Technology, Jinan 250100, China
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Mohd Ali M, Hashim N, Abd Aziz S, Lasekan O. Characterisation of Pineapple Cultivars under Different Storage Conditions Using Infrared Thermal Imaging Coupled with Machine Learning Algorithms. AGRICULTURE 2022; 12:1013. [DOI: 10.3390/agriculture12071013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
The non-invasive ability of infrared thermal imaging has gained interest in various food classification and recognition tasks. In this work, infrared thermal imaging was used to distinguish different pineapple cultivars, i.e., MD2, Morris, and Josapine, which were subjected to different storage temperatures, i.e., 5, 10, and 25 °C and a relative humidity of 85% to 90%. A total of 14 features from the thermal images were obtained to determine the variation in terms of image parameters among the different pineapple cultivars. Principal component analysis was applied for feature reduction in order to prevent any effect of significant difference between the selected features. Several types of machine learning algorithms were compared, including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and naïve Bayes, to obtain the best performance for the classification of pineapple cultivars. The results showed that support vector machine achieved the best performance from the combination of optimal image parameters with the highest classification rate of 100%. The ability of infrared thermal imaging coupled with machine learning approaches can be potentially used to distinguish pineapple cultivars, which could enhance the grading and sorting processes of the fruit.
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Xue Q, Miao P, Miao K, Yu Y, Li Z. X‐ray‐based machine vision technique for detection of internal defects of sterculia seeds. J Food Sci 2022; 87:3386-3395. [DOI: 10.1111/1750-3841.16237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/25/2022] [Accepted: 06/09/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Qilong Xue
- College of Pharmaceutical Engineering of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China
- State Key Laboratory of Component Traditional Chinese Medicine Tianjin China
| | - Peiqi Miao
- Tianjin Modern Innovative TCM Technology Co. Ltd Tianjin China
| | - Kunhong Miao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China
- State Key Laboratory of Component Traditional Chinese Medicine Tianjin China
| | - Yang Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China
- State Key Laboratory of Component Traditional Chinese Medicine Tianjin China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China
- State Key Laboratory of Component Traditional Chinese Medicine Tianjin China
- Haihe Laboratory of Modern Chinese Medicine Tianjin China
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Mohd Ali M, Hashim N. Non-destructive methods for detection of food quality. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00003-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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MA X, LUO H, ZHANG F, GAO F. A bibliometric and visual analysis of fruit quality detection research. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.72322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Xueting MA
- Tarim University, China; Tarim University, China
| | - Huaping LUO
- Tarim University, China; Tarim University, China
| | - Fei ZHANG
- Tarim University, China; Tarim University, China
| | - Feng GAO
- Tarim University, China; Tarim University, China
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10
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Sharabiani VR, Sabzi S, Pourdarbani R, Szymanek M, Michałek S. Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based Methods. Foods 2021; 10:2967. [PMID: 34945518 PMCID: PMC8700664 DOI: 10.3390/foods10122967] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/12/2021] [Accepted: 11/29/2021] [Indexed: 11/16/2022] Open
Abstract
Fruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive and time-consuming methods that have no application for online operations. For that, various researchers have conducted various studies on non-destructive methods, which are currently in the research and development stage. Thus, the present paper focusses on a non-destructive method based on spectral data in the 200-1100-nm region for estimation of total soluble solids and BrimA in Gala apples. The work steps included: (1) collecting different samples of Gala apples at different stages of maturity; (2) extracting spectral data of samples and pre-preprocessing them; (3) measuring the chemical properties of TSS and BrimA; (4) selecting optimal (effective) wavelengths using artificial neural network-simulated annealing algorithm (ANN-SA); and (5) estimating chemical properties based on partial least squares regression (PLSR) and hybrid artificial neural network known as the imperialist competitive algorithm (ANN-ICA). It should be noted that, in order to investigate the validity of the methods, the estimation algorithm was repeated 500 times. In the end, the results displayed that, in the best training, the ANN-ICA predicted the TSS and BrimA with correlation coefficients of 0.963 and 0.965 and root mean squared error of 0.167% and 0.596%, respectively.
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Affiliation(s)
- Vali Rasooli Sharabiani
- Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; (S.S.); (R.P.)
| | - Sajad Sabzi
- Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; (S.S.); (R.P.)
| | - Razieh Pourdarbani
- Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran; (S.S.); (R.P.)
| | - Mariusz Szymanek
- Department of Machine Science, University of Life Sciences in Lublin, 20-950 Lublin, Poland;
| | - Sławomir Michałek
- Department of Botany and Plant Physiology, University of Life Sciences in Lublin, 20-950 Lublin, Poland
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Watson NJ, Bowler AL, Rady A, Fisher OJ, Simeone A, Escrig J, Woolley E, Adedeji AA. Intelligent Sensors for Sustainable Food and Drink Manufacturing. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2021. [DOI: 10.3389/fsufs.2021.642786] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.
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Andriiashen V, van Liere R, van Leeuwen T, Batenburg KJ. Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry. J Imaging 2021; 7:104. [PMID: 39080892 PMCID: PMC8321356 DOI: 10.3390/jimaging7070104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 06/11/2021] [Accepted: 06/21/2021] [Indexed: 11/16/2022] Open
Abstract
X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.
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Affiliation(s)
- Vladyslav Andriiashen
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (R.v.L.); (T.v.L.); (K.J.B.)
| | - Robert van Liere
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (R.v.L.); (T.v.L.); (K.J.B.)
- Faculteit Wiskunde en Informatica, Technical University Eindhoven, Groene Loper 5, 5612 AZ Eindhoven, The Netherlands
| | - Tristan van Leeuwen
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (R.v.L.); (T.v.L.); (K.J.B.)
- Mathematical Institute, Utrecht University, Budapestlaan 6, 3584 CD Utrecht, The Netherlands
| | - Kees Joost Batenburg
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands; (R.v.L.); (T.v.L.); (K.J.B.)
- Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
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Detection of Early Core Browning in Pears Based on Statistical Features in Vibro-Acoustic Signals. FOOD BIOPROCESS TECH 2021. [DOI: 10.1007/s11947-021-02613-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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14
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Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce. Processes (Basel) 2020. [DOI: 10.3390/pr8111431] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.
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