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Payne K, O'Bryan CA, Marcy JA, Crandall PG. Detection and prevention of foreign material in food: A review. Heliyon 2023; 9:e19574. [PMID: 37809834 PMCID: PMC10558841 DOI: 10.1016/j.heliyon.2023.e19574] [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: 10/06/2022] [Revised: 08/22/2023] [Accepted: 08/26/2023] [Indexed: 10/10/2023] Open
Abstract
This review highlights the critical concern foreign material contamination poses across the food processing industry and provides information on methods and implementations to minimize the hazards caused by foreign materials. A foreign material is defined as any non-food, foreign bodies that may cause illness or injury to the consumer and are not typically part of the food. Foreign materials can enter the food processing plant as part of the raw materials such as fruit pits, bones, or contaminants like stones, insects, soil, grit, or pieces of harvesting equipment. Over the past 20 years, foreign materials have been responsible for about one out of ten recalls of foods, with plastic fragments being the most common complaint. The goal of this paper is to further the understanding of the risks foreign materials are to consumers and the tools that could be used to minimize the risk of foreign objects in foods.
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Affiliation(s)
- Keila Payne
- Food Safety and Quality Assurance, Tyson Foods, Springdale, AR, USA
| | - Corliss A. O'Bryan
- Department of Food Science, University of Arkansas, Fayetteville, AR, USA
| | - John A. Marcy
- Center of Excellence for Poultry Science, Dept. of Poultry Science, University of Arkansas, Fayetteville, AR, USA
| | - Philip G. Crandall
- Department of Food Science, University of Arkansas, Fayetteville, AR, USA
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Soni A, Dixit Y, Reis MM, Brightwell G. Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants. Compr Rev Food Sci Food Saf 2022; 21:3717-3745. [PMID: 35686478 DOI: 10.1111/1541-4337.12983] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 02/03/2023]
Abstract
Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf-stable food products. The conventional culture-based methods for microbial detection are time and labor-intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity.
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Affiliation(s)
- Aswathi Soni
- Food System Integrity, Consumer Food Interface, AgResearch Ltd, Palmerston North, New Zealand
| | - Yash Dixit
- Food Informatics, Smart Foods, AgResearch Ltd, Palmerston North, New Zealand
| | - Marlon M Reis
- Food Informatics, Smart Foods, AgResearch Ltd, Palmerston North, New Zealand
| | - Gale Brightwell
- Food System Integrity, Consumer Food Interface, AgResearch Ltd, Palmerston North, New Zealand.,New Zealand Food Safety Science Research Centre, Palmerston North, New Zealand
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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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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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Mohd Khairi MT, Ibrahim S, Md Yunus MA, Faramarzi M. Noninvasive techniques for detection of foreign bodies in food: A review. J FOOD PROCESS ENG 2018. [DOI: 10.1111/jfpe.12808] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Mohd Taufiq Mohd Khairi
- Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering; Universiti Teknologi Malaysia; Skudai Johor 81310 Malaysia
| | - Sallehuddin Ibrahim
- Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering; Universiti Teknologi Malaysia; Skudai Johor 81310 Malaysia
| | - Mohd Amri Md Yunus
- Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering; Universiti Teknologi Malaysia; Skudai Johor 81310 Malaysia
| | - Mahdi Faramarzi
- Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering; Universiti Teknologi Malaysia; Skudai Johor 81310 Malaysia
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Lobos GA, Hancock JF. Breeding blueberries for a changing global environment: a review. FRONTIERS IN PLANT SCIENCE 2015; 6:782. [PMID: 26483803 PMCID: PMC4588112 DOI: 10.3389/fpls.2015.00782] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Accepted: 09/10/2015] [Indexed: 05/23/2023]
Abstract
Today, blueberries are recognized worldwide as one of the foremost health foods, becoming one of the crops with the highest productive and commercial projections. Over the last 100 years, the geographical area where highbush blueberries are grown has extended dramatically into hotter and drier environments. The expansion of highbush blueberry growing into warmer regions will be challenged in the future by increases in average global temperature and extreme fluctuations in temperature and rainfall patterns. Considerable genetic variability exists within the blueberry gene pool that breeders can use to meet these challenges, but traditional selection techniques can be slow and inefficient and the precise adaptations of genotypes often remain hidden. Marker assisted breeding (MAB) and phenomics could aid greatly in identifying those individuals carrying adventitious traits, increasing selection efficiency and shortening the rate of cultivar release. While phenomics have begun to be used in the breeding of grain crops in the last 10 years, their use in fruit breeding programs it is almost non-existent.
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Affiliation(s)
- Gustavo A. Lobos
- Faculty of Agricultural Sciences, Plant Breeding and Phenomic Center, Universidad de TalcaTalca, Chile
- Department of Horticulture, Michigan State UniversityEast Lansing, MI, USA
| | - James F. Hancock
- Department of Horticulture, Michigan State UniversityEast Lansing, MI, USA
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Feng YZ, Sun DW. Application of hyperspectral imaging in food safety inspection and control: a review. Crit Rev Food Sci Nutr 2012; 52:1039-58. [PMID: 22823350 DOI: 10.1080/10408398.2011.651542] [Citation(s) in RCA: 200] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.
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Affiliation(s)
- Yao-Ze Feng
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems Engineering, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin, Ireland
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Tsuta M, Sugiyama J, Sagara Y. Development of Food Quality Measurement Methods Based on Near-infrared Imaging Spectroscopy-Applications to Visualization of Sugar Content Distribution in Fresh Fruits and Fruit Sorting-. J JPN SOC FOOD SCI 2011. [DOI: 10.3136/nskkk.58.73] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Sugiyama T, Sugiyama J, Tsuta M, Fujita K, Shibata M, Kokawa M, Araki T, Nabetani H, Sagara Y. NIR spectral imaging with discriminant analysis for detecting foreign materials among blueberries. J FOOD ENG 2010. [DOI: 10.1016/j.jfoodeng.2010.06.026] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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ElMasry G, Wold JP. High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2008; 56:7672-7677. [PMID: 18656933 DOI: 10.1021/jf801074s] [Citation(s) in RCA: 71] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
A nondestructive method using online spectral imaging has been developed for quantitative measurements of moisture and fat distribution in six species of fish fillets: Atlantic halibut (Hippoglossus hippoglossus), catfish (Icatalurus punctatus), cod (Gadus morhua), mackerel (Scomber japonicus), herring (Clupea harengus), and saithe (Pollachius virens). A spectral image cube was acquired for each fish fillet, and a subsampling approach for relating spectral and chemical features was applied. Spectral data was first analyzed by partial least-squares regression (PLSR), and then the regression coefficients were applied pixel-wise to convert the pixel spectra to a meaningful distribution map of moisture and fat contents. The resulting images are called "chemical images", which illustrate the distribution of fat and/or water content in the fillets. The pixel-wise prediction models for water and fat content had a correlation value of 0.94 with root-mean-square error estimated by a cross-validation (RMSECV) of 2.73% and a correlation value of 0.91 with RMSECV of 2.99%, respectively. This technique is suitable for high-speed assessment of quality parameters of biomaterials and should thus be implemented in industrial applications. The product could comprehensively be defined not only in terms of its external features such as size, shape, and color but also in terms of its chemical composition and its spatial distribution.
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Affiliation(s)
- Gamal ElMasry
- Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt.
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