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Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [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: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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2
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Kumarajith TM, Powell SM, Breadmore MC. Isotachophoretic quantification of total viable bacteria on meat and surfaces. Anal Chim Acta 2024; 1296:342253. [PMID: 38401922 DOI: 10.1016/j.aca.2024.342253] [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: 08/25/2023] [Revised: 12/18/2023] [Accepted: 01/13/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND The quantification of microbes, particularly live bacteria, is of utmost importance in assessing the quality of meat products. In the context of meat processing facilities, prompt identification and removal of contaminated carcasses or surfaces is crucial to ensuring the continuous production of safe meat for human consumption. The plate count method and other traditional detection methods are not only labour-intensive but also time-consuming taking 24-48 h. RESULTS In this report, we present a novel isotachophoretic quantification method utilizing two nucleic acid stains, SYTO9 and propionic iodide, for the detection of total viable bacteria. The study employed E. coli M23 bacteria as a model organism, with an analysis time of only 30 min. The method demonstrated a limit of detection (LOD) of 184 CFU mL-1 and 14 cells mL-1 for total viable count and total cell count, respectively. Furthermore, this new approach is capable of detecting the microbial quality standard limits for food contacting surfaces (10 CFU cm-2) and meat (1.99 × 104 CFU cm-2) by swabbing an area of 10 × 10 cm2. SIGNIFICANCE In contrast to the culture-based methods usually employed in food processing facilities, this isotachophoretic technique enables easy and rapid detection (<30 min) of microorganisms, facilitating crucial decision-making essential for maintaining product quality and safety.
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Affiliation(s)
- Thisara M Kumarajith
- Australia Centre for Research on Separation Science, Chemistry, School of Natural Sciences, Tasmania, Australia; Tasmanian Institute of Agriculture, Tasmania, Australia
| | | | - Michael C Breadmore
- Australia Centre for Research on Separation Science, Chemistry, School of Natural Sciences, Tasmania, Australia.
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3
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Jin S, Liu X, Wang J, Pan L, Zhang Y, Zhou G, Tang C. Hyperspectral imaging combined with fluorescence for the prediction of microbial growth in chicken breasts under different packaging conditions. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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4
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Sun Y, Zhang H, Liu G, He J, Cheng L, Li Y, Pu F, Wang H. Quantitative Detection of Myoglobin Content in Tan Mutton During Cold Storage by Near-infrared Hyperspectral Imaging. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02275-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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An FPGA-Based Machine Learning Tool for In-Situ Food Quality Tracking Using Sensor Fusion. BIOSENSORS-BASEL 2021; 11:bios11100366. [PMID: 34677322 PMCID: PMC8534206 DOI: 10.3390/bios11100366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/27/2021] [Accepted: 09/29/2021] [Indexed: 11/17/2022]
Abstract
The continuous development of more accurate and selective bio- and chemo-sensors has led to a growing use of sensor arrays in different fields, such as health monitoring, cell culture analysis, bio-signals processing, or food quality tracking. The analysis and information extraction from the amount of data provided by these sensor arrays is possible based on Machine Learning techniques applied to sensor fusion. However, most of these computing solutions are implemented on costly and bulky computers, limiting its use in in-situ scenarios outside complex laboratory facilities. This work presents the application of machine learning techniques in food quality assessment using a single Field Programmable Gate Array (FPGA) chip. The characteristics of low-cost, low power consumption as well as low-size allow the application of the proposed solution even in space constrained places, as in food manufacturing chains. As an example, the proposed system is tested on an e-nose developed for beef classification and microbial population prediction.
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Su WH, Xue H. Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality. Foods 2021; 10:2146. [PMID: 34574253 PMCID: PMC8472741 DOI: 10.3390/foods10092146] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.
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Affiliation(s)
- Wen-Hao Su
- Department of Agricultural Engineering, College of Engineering, China Agricultural University, Beijing 100083, China;
| | - Huidan Xue
- School of Economics and Management, Beijing University of Technology, Beijing 100124, China
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Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
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Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
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Anthocyanin Colorimetric Strip for Volatile Amine Determination. INTERNATIONAL JOURNAL OF FOOD SCIENCE 2020; 2020:1672851. [PMID: 32656261 PMCID: PMC7320287 DOI: 10.1155/2020/1672851] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/17/2020] [Indexed: 11/18/2022]
Abstract
Food freshness is one of the main concerns of consumers. Food spoilage is mainly caused by contamination and microbial growth in which the latter produces volatile amines in the process. Several methods have been used to determine volatile amines to indicate food freshness, and indicator films are deemed as the most time-efficient and economical. In this study, anthocyanin was extracted from mangosteen rind as a natural dye indicator and was incorporated in a chitosan/PVA polymer matrix. The film with different concentrations of anthocyanin extract (5%, 15%, and 25%) was prepared and tested for their sensitivity to 136 ppm ammonia vapor followed by colorimetric analysis using ImageJ software. The film with 25% anthocyanin yielded the most visible color change upon exposure to ammonia vapor. The color changed from pink to yellowish-brown within 14 minutes of exposure. The RGB-converted images of the film with 25% anthocyanin extract showed gradual loss of red coloration being replaced by cyan spots. FTIR spectra showed incorporation of anthocyanin to the chitosan/PVA matrix with the decrease in the intensity of the C-N stretching peak. Thermogravimetric analysis showed that the film has high thermal stability with onset temperature of 310.43°C. Thus, the film developed is an excellent candidate for optimization and production of a thermally stable amine detector for food products.
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Liang QI, Maocheng ZHAO, Jie ZHAO, Yuweiyi TANG. Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively. FOOD SCIENCE AND TECHNOLOGY 2019. [DOI: 10.1590/fst.25718] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- QI Liang
- Nanjing Forestry University, China; Nanjing Normal University, China
| | - ZHAO Maocheng
- Nanjing Forestry University, China; Taizhou University, China
| | - ZHAO Jie
- Nanjing Forestry University, China; Nanjing Institute of Industrial Professional Technology, China
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Ma J, Sun DW, Pu H, Cheng JH, Wei Q. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu Rev Food Sci Technol 2019; 10:197-220. [DOI: 10.1146/annurev-food-032818-121155] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Hyperspectral imaging (HSI) is a technology integrating optical sensing technologies of imaging, spectroscopy, and chemometrics. The sensor of HSI can obtain both spatial and spectral information simultaneously. Therefore, the chemical and physical information of food products can be monitored in a rapid, nondestructive, and noncontact manner. There are numerous reports and papers and much research dealing with the applications of HSI in food in recent years. This review introduces the principle of HSI technology, summarizes its recent applications in food, and pinpoints future trends.
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Affiliation(s)
- Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland;,
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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11
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Kutsanedzie FYH, Guo Z, Chen Q. Advances in Nondestructive Methods for Meat Quality and Safety Monitoring. FOOD REVIEWS INTERNATIONAL 2019. [DOI: 10.1080/87559129.2019.1584814] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
| | - Zhiming Guo
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
| | - Quansheng Chen
- School of Food & Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
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12
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Su WH, Sun DW. Advanced Analysis of Roots and Tubers by Hyperspectral Techniques. ADVANCES IN FOOD AND NUTRITION RESEARCH 2018; 87:255-303. [PMID: 30678816 DOI: 10.1016/bs.afnr.2018.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.
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Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland.
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Sun X, Young J, Liu JH, Chen Q, Newman D. Predicting Pork Color Scores Using Computer Vision and Support Vector Machine Technology. MEAT AND MUSCLE BIOLOGY 2018. [DOI: 10.22175/mmb2018.06.0015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
The objective of this study was to investigate the ability of image color features to predict subjective pork color scores. Subjective and instrumental color were assessed on the bloomed, cross-sectional surface of pork longissimus thoracis et lumborum chops. Images of pork chop samples were acquired using a computer vision system, and 18 image color features (mean and standard deviation of R, G, B, H, S, I, L*, a*, b*) were extracted for inclusion in partial least squares (PLS) and support vector machine (SVM) regression models. For color scores 2, 3, 4, and 5, the accuracies were 50.4, 75.9, 72.4, and 47.3% classified correctly by PLS, respectively, and 70.7, 72.8, 76.7, and 69.7% by SVM, respectively. The overall prediction accuracies of 2 models for pork color scores were 68.3% for PLS and 73.4% for SVM. There was minimal major misclassification of samples (< 0.5%). Image color features isolated through the development of PLS and SVM models, particularly SVM, show potential as a method to predict pork color scores.
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Affiliation(s)
- Xin Sun
- North Dakota State University Department of Animal Sciences
| | - Jennifer Young
- North Dakota State University Department of Animal Sciences
| | - Jeng Hung Liu
- North Dakota State University Department of Animal Sciences
| | - Quansheng Chen
- Jiangsu University School of Food and Biological Engineering
| | - David Newman
- Arkansas State University Department of Animal Science
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Heterospectral two-dimensional correlation analysis with near-infrared hyperspectral imaging for monitoring oxidative damage of pork myofibrils during frozen storage. Food Chem 2018; 248:119-127. [DOI: 10.1016/j.foodchem.2017.12.050] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 11/18/2017] [Accepted: 12/13/2017] [Indexed: 11/19/2022]
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Yu H, Yang S, Yuan C, Hu Q, Li Y, Chen S, Hu Y. Application of biopolymers for improving the glass transition temperature of hairtail fish meat. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2018; 98:1437-1443. [PMID: 28776690 DOI: 10.1002/jsfa.8611] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 07/02/2017] [Accepted: 07/28/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND Glass transition temperature (Tg ) and food moisture content are closely related, especially in foods with a high moisture content, such as surimi products. In order to improve storage condition and maintain food quality, the influence of six biopolymers on the Tg of hairtail fish meat paste was investigated by differential scanning colorimetry. RESULTS Samples were stored at -8 °C (>Tg ), -14 °C (Tg ) and -18 °C (<Tg ) for 105 days. The results showed that trehalose, maltodextrin and xanthan could obviously improve the Tg of the hairtail fish meat paste. Trehalose at 4.1% (w/w), maltodextrin at 5.0% and xanthan at 0.41% were determined as the optimum amounts, with the predicted value of Tg to be -14.1 °C. The total viable counts (TVC) and total volatile basic nitrogen (TVB-N) of samples stored at -14 and -18 °C were much lower and the gel strength and whiteness maintained much better than those of -8 °C. CONCLUSION Addition of biopolymers could effectively restrain the increase in TVC and TVB-N, as well as the dropping of gel strength and whiteness of fish meat paste during storage. A mixture of the biopolymers could improve Tg and showed great potential in the preservation of hairtail meat products. © 2017 Society of Chemical Industry.
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Affiliation(s)
- Haixia Yu
- Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan, China
- National Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Shuibing Yang
- Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan, China
- National Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Chunhong Yuan
- Production and Environmental Management, Faculty of Agriculture, Iwate University, Morioka, Japan
| | - Qinglan Hu
- Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan, China
- National Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yuan Li
- Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan, China
- National Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Shiguo Chen
- National Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yaqin Hu
- Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan, China
- National Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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Wang K, Pu H, Sun DW. Emerging Spectroscopic and Spectral Imaging Techniques for the Rapid Detection of Microorganisms: An Overview. Compr Rev Food Sci Food Saf 2018; 17:256-273. [DOI: 10.1111/1541-4337.12323] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 11/01/2017] [Accepted: 11/02/2017] [Indexed: 02/04/2023]
Affiliation(s)
- Kaiqiang Wang
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
| | - Hongbin Pu
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
| | - Da-Wen Sun
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
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Cheng W, Sun DW, Pu H, Wei Q. Characterization of myofibrils cold structural deformation degrees of frozen pork using hyperspectral imaging coupled with spectral angle mapping algorithm. Food Chem 2018; 239:1001-1008. [DOI: 10.1016/j.foodchem.2017.07.011] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 06/02/2017] [Accepted: 07/03/2017] [Indexed: 10/19/2022]
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Evaluation of Freshness in Determination of Volatile Organic Compounds Released from Pork by HS-SPME-GC-MS. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1109-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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19
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Cheng W, Sun DW, Pu H, Wei Q. Chemical spoilage extent traceability of two kinds of processed pork meats using one multispectral system developed by hyperspectral imaging combined with effective variable selection methods. Food Chem 2017; 221:1989-1996. [DOI: 10.1016/j.foodchem.2016.11.093] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 10/19/2016] [Accepted: 11/21/2016] [Indexed: 01/25/2023]
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Ma J, Sun DW, Pu H. Model improvement for predicting moisture content (MC) in pork longissimus dorsi muscles under diverse processing conditions by hyperspectral imaging. J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2016.10.016] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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A Nondestructive Real-Time Detection Method of Total Viable Count in Pork by Hyperspectral Imaging Technique. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7030213] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Identification and Evaluation of Composition in Food Powder Using Point-Scan Raman Spectral Imaging. APPLIED SCIENCES-BASEL 2016. [DOI: 10.3390/app7010001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data. Lebensm Wiss Technol 2016. [DOI: 10.1016/j.lwt.2016.05.031] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Ma J, Sun DW, Pu H. Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles. Food Chem 2016; 197:848-54. [DOI: 10.1016/j.foodchem.2015.11.023] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Revised: 10/31/2015] [Accepted: 11/04/2015] [Indexed: 12/13/2022]
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He HJ, Sun DW. Hyperspectral imaging technology for rapid detection of various microbial contaminants in agricultural and food products. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.08.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Tao F, Peng Y, Gomes CL, Chao K, Qin J. A comparative study for improving prediction of total viable count in beef based on hyperspectral scattering characteristics. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2015.04.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.05.006] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique. Talanta 2015; 139:208-15. [PMID: 25882428 DOI: 10.1016/j.talanta.2015.02.027] [Citation(s) in RCA: 117] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 02/06/2015] [Accepted: 02/17/2015] [Indexed: 11/23/2022]
Abstract
Quality determination of frozen food is a time-consuming and laborious work as it normally takes a long time to thaw the frozen samples before measurements can be carried out. In this research, a rapid and non-destructive determination technique for frozen pork quality was tested with a hyperspectral imaging (HSI) system. In this study, 120 pieces of pork meat were frozen by four kinds of methods with various freezing temperatures from -20 to -120°C. The hyperspectral images of the samples were acquired at the frozen state. Quality indicators including drip loss, pH value, color, cooking loss and Warner-Bratzler shear force (WBSF) of the samples were measured after thawing. The spectral characteristics of the frozen meat samples were studied and it was revealed that the reflectance at 1100nm had a close relationship with the freezing temperature (R=-0.832, p<0.01). Partial least squares regression (PLSR) was applied to establish the spectral models, and the models were then optimized. Results showed that the improved region of interest (ROI) method could be used to extract effective spectral information to withstand the interference of freezing, and choosing appropriate spectral bands and spectral pretreatment techniques were crucial to develop robust mathematical model. The performances of the models established were diverse based on different quality indicators. The coefficients of determination for prediction (Rp(2)) for L*, cooking loss, b*, drip loss and a* were 0.907, 0.845, 0.814, 0.762, and 0.716, respectively. However there were low correlations (Rp(2)) for pH and WBSF measurements. The current study indicated that HSI had the potential for non-destructive determination of frozen meat quality without thawing.
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