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Kurniawan H, Arief MAA, Lohumi S, Kim MS, Baek I, Cho BK. Dual imaging technique for a real-time inspection system of foreign object detection in fresh-cut vegetables. Curr Res Food Sci 2024; 9:100802. [PMID: 39100806 PMCID: PMC11294706 DOI: 10.1016/j.crfs.2024.100802] [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: 04/10/2024] [Revised: 06/25/2024] [Accepted: 07/03/2024] [Indexed: 08/06/2024] Open
Abstract
Fresh-cut vegetables are a food product susceptible to contamination by foreign materials (FMs). To detect a range of potential FMs in fresh-cut vegetables, a dual imaging technique (fluorescence and color imaging) with a simple and effective image processing algorithm in a user-friendly software interface was developed for a real-time inspection system. The inspection system consisted of feeding and sensing units, including two cameras positioned in parallel, illuminations (white LED and UV light), and a conveyor unit. A camera equipped with a long-pass filter was used to collect fluorescence images. Another camera collected color images of fresh-cut vegetables and FMs. The feeding unit fed FMs mixed with fresh-cut vegetables onto a conveyor belt. Two cameras synchronized programmatically in the software interface simultaneously collected fluorescence and color image samples based on the region of interest as they moved through the conveyor belt. Using simple image processing algorithms, FMs could be detected and depicted in two different image windows. The results demonstrated that the dual imaging technique can effectively detect potential FMs in two types of fresh-cut vegetables (cabbage and green onion), as indicated by the combined fluorescence and color imaging accuracy. The test results showed that the real-time inspection system could detect FMs measuring 0.5 mm in fresh-cut vegetables. The results showed that the combined detection accuracy of FMs in the cabbage (95.77%) sample was superior to that of green onion samples (87.89%). Therefore, the inspection system was more effective at detecting FMs in cabbage samples than in green onion samples.
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Affiliation(s)
- Hary Kurniawan
- Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, South Korea
- Department of Agricultural Engineering, Faculty of Food Technology and Agroindustry, University of Mataram, West Nusa Tenggara, 83126, Indonesia
| | - Muhammad Akbar Andi Arief
- Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, South Korea
| | - Santosh Lohumi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, South Korea
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Department of Agriculture, Beltsville, MD, 20705, USA
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Department of Agriculture, Beltsville, MD, 20705, USA
| | - Byoung-Kwan Cho
- Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, South Korea
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, South Korea
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2
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Onyeaka H, Jalata DD, Mekonnen SA. Mitigating physical hazards in food processing: Risk assessment and preventive strategies. Food Sci Nutr 2023; 11:7515-7522. [PMID: 38107102 PMCID: PMC10724640 DOI: 10.1002/fsn3.3727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/29/2023] [Accepted: 09/15/2023] [Indexed: 12/19/2023] Open
Abstract
Physical contaminants in food, such as glass, metal, and plastic, can cause significant health risks and economic loss. This study explores these understudied physical hazards, aiming to provide comprehensive risk analysis and preventive solutions. Our research identified several key infiltration points in the food supply chain, including raw material sourcing and packaging stages. These hazards can be effectively mitigated by employing advanced technologies like metal detectors and optical sorting machines, along with stringent quality control measures. The findings offer valuable insights for stakeholders in the food industry, emphasizing the need for regulatory compliance and consumer education to ensure food safety.
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Affiliation(s)
- Helen Onyeaka
- School of Chemical EngineeringUniversity of BirminghamBirminghamUK
| | - Dassalegn Daraje Jalata
- Department of Food Science and NutritionEthiopian Institute of Agricultural ResearchAddis AbabaEthiopia
| | - Solomon Abate Mekonnen
- Department of Food Science and NutritionEthiopian Institute of Agricultural ResearchAddis AbabaEthiopia
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3
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Shu Z, Li X, Liu Y. Detection of Chili Foreign Objects Using Hyperspectral Imaging Combined with Chemometric and Target Detection Algorithms. Foods 2023; 12:2618. [PMID: 37444353 DOI: 10.3390/foods12132618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/17/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
Chilies undergo multiple stages from field production to reaching consumers, making them susceptible to contamination with foreign materials. Visually similar foreign materials are difficult to detect manually or using color sorting machines, which increases the risk of their presence in the market, potentially affecting consumer health. This paper aims to enhance the detection of visually similar foreign materials in chilies using hyperspectral technology, employing object detection algorithms for fast and accurate identification and localization to ensure food safety. First, the samples were scanned using a hyperspectral camera to obtain hyperspectral image information. Next, a spectral pattern recognition algorithm was used to classify the pixels in the images. Pixels belonging to the same class were assigned the same color, enhancing the visibility of foreign object targets. Finally, an object detection algorithm was employed to recognize the enhanced images and identify the presence of foreign objects. Random forest (RF), support vector machine (SVM), and minimum distance classification algorithms were used to enhance the hyperspectral images of the samples. Among them, RF algorithm showed the best performance, achieving an overall recognition accuracy of up to 86% for randomly selected pixel samples. Subsequently, the enhanced targets were identified using object detection algorithms including R-CNN, Faster R-CNN, and YoloV5. YoloV5 exhibited a recognition rate of over 96% for foreign objects, with the shortest detection time of approximately 12 ms. This study demonstrates that the combination of hyperspectral imaging technology, spectral pattern recognition techniques, and object detection algorithms can accurately and rapidly detect challenging foreign objects in chili peppers, including red stones, red plastics, red fabrics, and red paper. It provides a theoretical reference for online batch detection of chili pepper products, which is of significant importance for enhancing the overall quality of chili pepper products. Furthermore, the detection of foreign objects in similar particulate food items also holds reference value.
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Affiliation(s)
- Zhan Shu
- School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
| | - Xiong Li
- School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
| | - Yande Liu
- School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
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4
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Shi Y, Wang Y, Hu X, Li Z, Huang X, Liang J, Zhang X, Zheng K, Zou X, Shi J. Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging. Food Chem 2023; 411:135431. [PMID: 36681022 DOI: 10.1016/j.foodchem.2023.135431] [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: 08/12/2022] [Revised: 01/02/2023] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Abstract
Analogous density foreign matter (ADFM) embedded in soy protein meat semi-finished (SFSPM) is hidden by SFSPM and has similar acoustic impedance features to SFSPM, which makes non-destructive testing techniques such as computer vision (CV), reflectance spectroscopy and ultrasound imaging inappropriate for ADFM, which not only seriously affects the quality of soy protein meat (SPM) products but also increases the safety risk to consumers. In this study, to locate and separate ADFM by using transmission hyperspectral imaging (T-HSI) technique which is sensitive to chemical composition and highlight internal contours. The optimal discrimination model SVM + PCA + MSC + SPA was constructed using transmission spectral information with an accuracy of 95.00 %. The visualization results based on the optimal model showed clearer localization results than CV and ultrasound imaging. The study demonstrated that the advantages of T-HSI technology in detecting and locating ADFM inside SFSPM, which provides a basis for improving the production quality and safety of SPM.
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Affiliation(s)
- Yu Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Yueying Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xuetao Hu
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Zhihua Li
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xiaowei Huang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Jing Liang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xinai Zhang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Kaiyi Zheng
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xiaobo Zou
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Jiyong Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China.
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5
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Cui F, Zheng S, Wang D, Tan X, Li Q, Li J, Li T. Recent advances in shelf life prediction models for monitoring food quality. Compr Rev Food Sci Food Saf 2023; 22:1257-1284. [PMID: 36710649 DOI: 10.1111/1541-4337.13110] [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: 08/03/2022] [Revised: 12/30/2022] [Accepted: 01/10/2023] [Indexed: 01/31/2023]
Abstract
Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real-time food quality monitoring.
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Affiliation(s)
- Fangchao Cui
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Shiwei Zheng
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Dangfeng Wang
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
- College of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Xiqian Tan
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Qiuying Li
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Jianrong Li
- College of Food Science and Technology, Bohai University; National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, China
| | - Tingting Li
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, College of Life Science, Dalian Minzu University, Dalian, China
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6
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Bowler A, Ozturk S, di Bari V, Glover ZJ, Watson NJ. Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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7
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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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8
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Kushida T, Tahara K, Chiba H, Kagawa Y, Tanaka K, Funatomi T, Mukaigawa Y. Descattering for transmissive inspection in production line using slanted linear image sensors. OPTICS EXPRESS 2022; 30:38016-38026. [PMID: 36258376 DOI: 10.1364/oe.469424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
We propose a descattering method that can be easily applied to food production lines. The system consists of several sets of linear image sensors and linear light sources slanted at different angles. The images captured by these sensors are partially clear along the direction perpendicular to the sensors. We computationally integrate these images on the frequency domain into a single clear image. The effectiveness of the proposed method is assessed by simulation and real-world experiments. The results show that our method recovers clear images. We demonstrate the applicability of the proposed method to a real production line by a prototype system.
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9
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Molinero AL. A mobile phone digital image method designed for efficient durum wheat flour characterization. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:3416-3422. [PMID: 35993378 DOI: 10.1039/d2ay01046a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The characterization and counting of foreign bodies and impurities in flour and semolina from wheat are decisive analytical parameters of safety and quality which should be evaluated in the context of efficiency and low cost. Moreover, their recognition and counting are required by the regulatory and quality norms of the sector. International standards such as UNI 10941:2001 could be applied but this has subjective interpretation. In this paper, a new analytical semi-automatic method based on digital images is assessed and proposed. The main features of the method are based on the use of representative sample images that could be taken under controlled illumination conditions with a smartphone. The images were then analyzed using a macro-script adapted to the free software Fiji-ImageJ for image processing. By changing the image color format to grey and its contrast, a threshold intensity, or cutoff, was selected to distinguish foreign bodies from the rest of the product. And the number of particles was counted. Finally, four different fractions of components in the product were recognized which characterized the type of product. The estimated processing time was less than 60 s. The method has been validated against 14 reference samples that were previously studied using the standard UNI 10941:2001. These samples presented low, medium and high particle content, as well as different background colors of the matrix product, from white to yellow. The results were obtained with an average of 6 ROIs taken in different locations of the same digital image. Figures of merit of the procedure such as the biases presented relative differences of less than ±20%, against reference values in the worst case. The reproducibility of the measurements, examining different locations, is better than 30-40% RSD. Likewise, the reproducibility in the same ROI but with short scans in the threshold of selection produced response ranges of less than 30% RSD. These parameters are consistent with those prevalent in the sector and this type of product.
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Affiliation(s)
- Angel López Molinero
- Nanosensors and Bioanalytical Systems Group (N&SB), Analytical Department, Faculty of Sciences, University of Zaragoza, S-50009 Zaragoza, Spain.
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10
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Tunny SS, Amanah HZ, Faqeerzada MA, Wakholi C, Kim MS, Baek I, Cho BK. Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables. SENSORS (BASEL, SWITZERLAND) 2022; 22:1775. [PMID: 35270921 PMCID: PMC8914723 DOI: 10.3390/s22051775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/08/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
Ensuring the quality of fresh-cut vegetables is the greatest challenge for the food industry and is equally as important to consumers (and their health). Several investigations have proven the necessity of advanced technology for detecting foreign materials (FMs) in fresh-cut vegetables. In this study, the possibility of using near infrared spectral analysis as a potential technique was investigated to identify various types of FMs in seven common fresh-cut vegetables by selecting important wavebands. Various waveband selection methods, such as the weighted regression coefficient (WRC), variable importance in projection (VIP), sequential feature selection (SFS), successive projection algorithm (SPA), and interval PLS (iPLS), were used to investigate the optimal multispectral wavebands to classify the FMs and vegetables. The application of selected wavebands was further tested using NIR imaging, and the results showed good potentiality by identifying 99 out of 107 FMs. The results indicate the high applicability of the multispectral NIR imaging technique to detect FMs in fresh-cut vegetables for industrial application.
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Affiliation(s)
- Salma Sultana Tunny
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Hanim Z. Amanah
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
- Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada University, Yogyakarta 55281, Indonesia
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Collins Wakholi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA; (M.S.K.); (I.B.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA; (M.S.K.); (I.B.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (S.S.T.); (H.Z.A.); (M.A.F.); (C.W.)
- Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
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11
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Shimonomura K, Chang T, Murata T. Detection of Foreign Bodies in Soft Foods Employing Tactile Image Sensor. Front Robot AI 2021; 8:774080. [PMID: 34926592 PMCID: PMC8678492 DOI: 10.3389/frobt.2021.774080] [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: 09/10/2021] [Accepted: 11/16/2021] [Indexed: 11/13/2022] Open
Abstract
In the inspection work involving foodstuffs in food factories, there are cases where people not only visually inspect foodstuffs, but must also physically touch foodstuffs with their hands to find foreign or undesirable objects mixed in the product. To contribute to the automation of the inspection process, this paper proposes a method for detecting foreign objects in food based on differences in hardness using a camera-based tactile image sensor. Because the foreign objects to be detected are often small, the tactile sensor requires a high spatial resolution. In addition, inspection work in food factories requires a sufficient inspection speed. The proposed cylindrical tactile image sensor meets these requirements because it can efficiently acquire high-resolution tactile images with a camera mounted inside while rolling the cylindrical sensor surface over the target object. By analyzing the images obtained from the tactile image sensor, we detected the presence of foreign objects and their locations. By using a reflective membrane-type sensor surface with high sensitivity, small and hard foreign bodies of sub-millimeter size mixed in with soft food were successfully detected. The effectiveness of the proposed method was confirmed through experiments to detect shell fragments left on the surface of raw shrimp and bones left in fish fillets.
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12
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Saeidan A, Khojastehpour M, Golzarian MR, Mooenfard M, Khan HA. Detection of foreign materials in cocoa beans by hyperspectral imaging technology. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108242] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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13
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S K, M Y, Rawson A, C. K S. Recent Advances in Terahertz Time-Domain Spectroscopy and Imaging Techniques for Automation in Agriculture and Food Sector. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02132-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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14
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Hunt B, Saatchi R, Lacey MM. Infrared thermography can detect previsual bacterial growth in a laboratory setting via metabolic heat detection. J Appl Microbiol 2021; 132:2-7. [PMID: 34260801 PMCID: PMC9292240 DOI: 10.1111/jam.15218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/25/2021] [Accepted: 07/12/2021] [Indexed: 11/30/2022]
Abstract
Aims Detection of bacterial contamination in healthcare and industry takes many hours if not days. Thermal imaging, the measurement of heat by an infrared camera, was investigated as a potential noninvasive method of detecting bacterial growth. Methods and Results Infrared thermography can detect the presence of Escherichia coli and Staphylococcus aureus on solid growth media by an increase in temperature before they are visually observable. A heat decrease is observed after treatment with ultraviolet light and heat increased after incubation with dinitrophenol. Conclusions Infrared thermography can detect early growth of bacteria before they are detectable by other microbiology‐based method. The heat observed is due to the cells being viable and metabolically active, as cells killed with ultraviolet light exhibit reduced increase in temperature and treatment with dinitrophenol increases heat detected. Significance and Impact of the Study Infrared thermography detects bacterial growth without the need for specialized temperature control facilities. The method is statistically robust and can be undertaken in situ, thus is highly versatile. These data support the application of infrared thermography in a laboratory, clinical and industrial setting for vegetative bacteria, thus may become into an important methodology for the timely and straightforward detection of early‐stage bacterial growth.
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Affiliation(s)
- Ben Hunt
- Biomolecular Sciences Research Centre, Sheffield Hallam University, Sheffield, UK
| | - Reza Saatchi
- Centre for Automation and Robotics Research, Materials and Engineering Research Institute, Sheffield Hallam University, Sheffield, UK
| | - Melissa M Lacey
- Biomolecular Sciences Research Centre, Sheffield Hallam University, Sheffield, UK
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15
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Achouri IE, Rhoden A, Hudon S, Gosselin R, Simard JS, Abatzoglou N. Non-invasive detection technologies of solid foreign matter and their applications to lyophilized pharmaceutical products: A review. Talanta 2021; 224:121885. [PMID: 33379094 DOI: 10.1016/j.talanta.2020.121885] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/15/2020] [Accepted: 10/19/2020] [Indexed: 01/28/2023]
Abstract
Good Manufacturing Practice Regulations, under the Food and Drug Administration (FDA), stipulate that all pharmaceutical products must be free of any contaminants, including, namely, any foreign solid objects. Lyophilization is a common manufacturing method that consists of several steps where foreign materials may enter the product. The presence of unintended particles in freeze drying, which will herein be referred to under the term 'Lyophilization', is of great concern to the authorities responsible for drug safety and effectiveness. In the pharmaceutical industry, presently, the inspection of lyophilized products for foreign matter particulates relies on visual inspection where only the outer surface of the lyophilized cake is visible. This review is motivated by the need for new control strategies for foreign matter (FM) detection in lyophilized products; more specifically, it assesses the reliability of non-destructive technologies for FM detection in dried samples. Emerging technologies applied in other industries, such as various types of spectroscopies and imaging (e.g. chemical, X-ray, ultrasound, thermal and terahertz), are evaluated based on compatibility with the intended application, with identification of the possible technical challenges.
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Affiliation(s)
- Inès E Achouri
- Département de Génie Chimique et de Génie Biotechnologique, Université de Sherbrooke, Sherbrooke, QC, Canada.
| | - Alan Rhoden
- Pfizer USA, 100 route 206 North, Peapack, NJ, 07977, USA
| | - Sophie Hudon
- Pfizer Canada, 17300 route transcanadienne, Kirkland, QC, H9J 2M5, Canada
| | - Ryan Gosselin
- Département de Génie Chimique et de Génie Biotechnologique, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Nicolas Abatzoglou
- Département de Génie Chimique et de Génie Biotechnologique, Université de Sherbrooke, Sherbrooke, QC, Canada
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Estrada-Pérez LV, Pradana-López S, Pérez-Calabuig AM, Mena ML, Cancilla JC, Torrecilla JS. Thermal imaging of rice grains and flours to design convolutional systems to ensure quality and safety. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107572] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Izquierdo M, Lastra-Mejías M, González-Flores E, Cancilla JC, Pérez M, Torrecilla JS. Convolutional decoding of thermographic images to locate and quantify honey adulterations. Talanta 2020; 209:120500. [DOI: 10.1016/j.talanta.2019.120500] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 10/19/2019] [Accepted: 10/23/2019] [Indexed: 12/13/2022]
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