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Måge I, Wubshet SG, Wold JP, Solberg LE, Böcker U, Dankel K, Lintvedt TA, Kafle B, Cattaldo M, Matić J, Sorokina L, Afseth NK. The role of biospectroscopy and chemometrics as enabling technologies for upcycling of raw materials from the food industry. Anal Chim Acta 2023; 1284:342005. [PMID: 37996160 DOI: 10.1016/j.aca.2023.342005] [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: 03/29/2023] [Revised: 09/25/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023]
Abstract
It is important to utilize the entire animal in meat and fish production to ensure sustainability. Rest raw materials, such as bones, heads, trimmings, and skin, contain essential nutrients that can be transformed into high-value products. Enzymatic protein hydrolysis (EPH) is a bioprocess that can upcycle these materials to create valuable proteins and fats. This paper focuses on the role of spectroscopy and chemometrics in characterizing the quality of the resulting protein product and understanding how raw material quality and processing affect it. The article presents recent developments in chemical characterisation and process modelling, with a focus on rest raw materials from poultry and salmon production. Even if some of the technology is relatively mature and implemented in many laboratories and industries, there are still open challenges and research questions. The main challenges are related to the transition of technology and insights from laboratory to industrial scale, and the link between peptide composition and critical product quality attributes.
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Affiliation(s)
- Ingrid Måge
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway.
| | - Sileshi Gizachew Wubshet
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Jens Petter Wold
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Lars Erik Solberg
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Ulrike Böcker
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Katinka Dankel
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Tiril Aurora Lintvedt
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway; Norwegian University of Life Sciences, Faculty of Science and Technology, 1432, Ås, Norway
| | - Bijay Kafle
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway; Norwegian University of Life Sciences, Faculty of Science and Technology, 1432, Ås, Norway
| | - Marco Cattaldo
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway; Universidad Politécnica de Valencia, Department of Applied Statistics, Operations Research and Quality, 46022, Valencia, Spain
| | - Josipa Matić
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
| | - Liudmila Sorokina
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway; University of Oslo, Department of Chemistry, 0371, Oslo, Norway
| | - Nils Kristian Afseth
- Nofima - Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291, Tromsø, Norway
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2
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Early identification of mushy Halibut syndrome with hyperspectral image analysis. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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3
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Zhang Y, Lin Y, Tian H, Tian S, Xu H. Non-destructive evaluation of the edible rate for pomelo using X-ray imaging method. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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4
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Near-infrared spectroscopy and machine learning for classification of food powders under moving conditions. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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5
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Browning CM, Mayes S, Mayes SA, Rich TC, Leavesley SJ. Microscopy is better in color: development of a streamlined spectral light path for real-time multiplex fluorescence microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:3751-3772. [PMID: 35991911 PMCID: PMC9352297 DOI: 10.1364/boe.453657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Spectroscopic image data has provided molecular discrimination for numerous fields including: remote sensing, food safety and biomedical imaging. Despite the various technologies for acquiring spectral data, there remains a trade-off when acquiring data. Typically, spectral imaging either requires long acquisition times to collect an image stack with high spectral specificity or acquisition times are shortened at the expense of fewer spectral bands or reduced spatial sampling. Hence, new spectral imaging microscope platforms are needed to help mitigate these limitations. Fluorescence excitation-scanning spectral imaging is one such new technology, which allows more of the emitted signal to be detected than comparable emission-scanning spectral imaging systems. Here, we have developed a new optical geometry that provides spectral illumination for use in excitation-scanning spectral imaging microscope systems. This was accomplished using a wavelength-specific LED array to acquire spectral image data. Feasibility of the LED-based spectral illuminator was evaluated through simulation and benchtop testing and assessment of imaging performance when integrated with a widefield fluorescence microscope. Ray tracing simulations (TracePro) were used to determine optimal optical component selection and geometry. Spectral imaging feasibility was evaluated using a series of 6-label fluorescent slides. The LED-based system response was compared to a previously tested thin-film tunable filter (TFTF)-based system. Spectral unmixing successfully discriminated all fluorescent components in spectral image data acquired from both the LED and TFTF systems. Therefore, the LED-based spectral illuminator provided spectral image data sets with comparable information content so as to allow identification of each fluorescent component. These results provide proof-of-principle demonstration of the ability to combine output from many discrete wavelength LED sources using a double-mirror (Cassegrain style) optical configuration that can be further modified to allow for high speed, video-rate spectral image acquisition. Real-time spectral fluorescence microscopy would allow monitoring of rapid cell signaling processes (i.e., Ca2+ and other second messenger signaling) and has potential to be translated to clinical imaging platforms.
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Affiliation(s)
- Craig M. Browning
- Chemical and Biomolecular Engineering, University of South Alabama, AL 36688, USA
- Systems Engineering, University of South Alabama, AL 36688, USA
- These authors contributed equally to this work
| | - Samantha Mayes
- Chemical and Biomolecular Engineering, University of South Alabama, AL 36688, USA
- These authors contributed equally to this work
| | - Samuel A. Mayes
- Chemical and Biomolecular Engineering, University of South Alabama, AL 36688, USA
- Systems Engineering, University of South Alabama, AL 36688, USA
| | - Thomas C. Rich
- Pharmacology, University of South Alabama, AL 36688, USA
- Center for Lung Biology, University of South Alabama, AL 36688, USA
| | - Silas J. Leavesley
- Chemical and Biomolecular Engineering, University of South Alabama, AL 36688, USA
- Pharmacology, University of South Alabama, AL 36688, USA
- Center for Lung Biology, University of South Alabama, AL 36688, USA
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6
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Anderssen KE, Stormo SK, Skåra T, Skjelvareid MH, Heia K. Predicting liquid loss of frozen and thawed cod from hyperspectral imaging. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.110093] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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7
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Qin J, Vasefi F, Hellberg RS, Akhbardeh A, Isaacs RB, Yilmaz AG, Hwang C, Baek I, Schmidt WF, Kim MS. Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107234] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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Prabhakar PK, Vatsa S, Srivastav PP, Pathak SS. A comprehensive review on freshness of fish and assessment: Analytical methods and recent innovations. Food Res Int 2020; 133:109157. [PMID: 32466909 DOI: 10.1016/j.foodres.2020.109157] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 02/25/2020] [Accepted: 03/07/2020] [Indexed: 11/26/2022]
Abstract
Fish, a highly nutritious, containing a good amount of protein and fatty acids, has TMA and TVB-N present as major factors responsible for quality deterioration during storage and maintaining of fish freshness. Freshness is one of the most important parameters in the fish market. There are many methods of estimating fish freshness, out of which some are very costly while others are not user-friendly. However, with more technological innovations, there have been efforts to make a more reliable method of calculating and analyzing freshness. Parameters chosen for assessing the freshness are sensory, physical, chemical and microbiological including the recent trends such as SDS-PAGE, fast protein liquid chromatography, hyper Spectral Imaging Technique, etc. focused on reducing time, destruction and labor. Traditional and recent methods of evaluation of freshness along with their comparison based on several parameters are needed to link them and making it convenient for upcoming researchers to have a detailed study for having a universal indicator for assessing the freshness of fish. Information in the present article has all the methods of assessing the fish freshness been discussed in detail. There has also been focus on bringing the readers knowledge about the comprehensive information related to recent developments. The recommended limit for different indicators signifies the time period for which the particular fish can be stored and it depends upon several factors like species, surrounding environment and enzymatic and non-enzymatic actions. Based on these demands, this paper is uniquely worked upon to review the different literature which brought all the discussions from the past including the recent innovations in assessing the freshness of different fishes with the help of various indicators as well as a complete study of spoilage and toxicity mechanism leading to deterioration in quality, making it easy for the reader and researchers to have quick glance over the trends and innovations.
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Affiliation(s)
- Pramod K Prabhakar
- Department of Food Science and Technology, National Institute of Food Technology Entrepreneurship and Management, Kundli, HR, India.
| | - Siddhartha Vatsa
- Department of Food Science and Technology, National Institute of Food Technology Entrepreneurship and Management, Kundli, HR, India
| | - Prem P Srivastav
- Department of Agricultural and Food Engineering, Indian Institute of Technology Kharagpur, West Bengal, India
| | - Sant S Pathak
- Department of Electronics & Electrical Communication Engineering, Indian Institute of Technology Kharagpur, West Bengal, India
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9
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Islam K, Mahbub SB, Clement S, Guller A, Anwer AG, Goldys EM. Autofluorescence excitation-emission matrices as a quantitative tool for the assessment of meat quality. JOURNAL OF BIOPHOTONICS 2020; 13:e201900237. [PMID: 31587525 DOI: 10.1002/jbio.201900237] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 09/26/2019] [Accepted: 09/29/2019] [Indexed: 06/10/2023]
Abstract
Commercially produced meat is currently graded by a complex and partly subjective multiparameter methodology; a quantitative method of grading, using small samples would be desirable. Here, we investigate the correlation between commercial grades of beef and spectral signatures of native fluorophores in such small samples. Beef samples of different commercial grades were characterized by fluorescence spectroscopy complemented by biochemical and histological assessment. The excitation-emission matrices of the specimens reveal five prominent native autofluorescence signatures in the excitation range from 250 to 350 nm, derived mainly from tryptophan and intramuscular fat. We found that these signatures reflect meat grade and can be used for its determination.
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Affiliation(s)
- Kashif Islam
- ARC Centre of Excellence Centre for Nanoscale Biophotonics, Macquarie University, Sydney, New South Wales, Australia
| | - Saabah B Mahbub
- ARC Centre of Excellence Centre for Nanoscale Biophotonics, Macquarie University, Sydney, New South Wales, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | - Sandhya Clement
- ARC Centre of Excellence Centre for Nanoscale Biophotonics, Macquarie University, Sydney, New South Wales, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | - Anna Guller
- ARC Centre of Excellence Centre for Nanoscale Biophotonics, Macquarie University, Sydney, New South Wales, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
- Sechenov University, Moscow, Russia
| | - Ayad G Anwer
- ARC Centre of Excellence Centre for Nanoscale Biophotonics, Macquarie University, Sydney, New South Wales, Australia
| | - Ewa M Goldys
- ARC Centre of Excellence Centre for Nanoscale Biophotonics, Macquarie University, Sydney, New South Wales, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
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10
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A Clustering-Based Partial Least Squares Method for Improving the Freshness Prediction Model of Crucian Carps Fillets by Hyperspectral Image Technology. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01541-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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11
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Yeong TJ, Pin Jern K, Yao LK, Hannan MA, Hoon STG. Applications of Photonics in Agriculture Sector: A Review. Molecules 2019; 24:E2025. [PMID: 31137897 PMCID: PMC6571790 DOI: 10.3390/molecules24102025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/10/2019] [Accepted: 05/12/2019] [Indexed: 11/17/2022] Open
Abstract
The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.
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Affiliation(s)
- Tan Jin Yeong
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Ker Pin Jern
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Lau Kuen Yao
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - M A Hannan
- Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.
| | - Shirley Tang Gee Hoon
- Microbiology Unit, Department of Pre-clinical, International Medical School, Management and Science University, University Drive, Off Persiaran Olahraga, Seksyen 13, Shah Alam 40100, Selangor, Malaysia.
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12
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Rapid prediction of yellow tea free amino acids with hyperspectral images. PLoS One 2019; 14:e0210084. [PMID: 30785888 PMCID: PMC6382264 DOI: 10.1371/journal.pone.0210084] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 12/16/2018] [Indexed: 01/19/2023] Open
Abstract
Free amino acids are an important indicator of the freshness of yellow tea. This study investigated a novel procedure for predicting the free amino acid (FAA) concentration of yellow tea. It was developed based on the combined spectral and textural features from hyperspectral images. For the purposes of exploration and comparison, hyperspectral images of yellow tea (150 samples) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing. To reduce the dimension of spectral data, five feature wavelengths were extracted using the successive projections algorithm (SPA). Five textural features (angular second moment, entropy, contrast, correlation, and homogeneity) were extracted as textural variables from the characteristic grayscale images of the five characteristic wavelengths using the gray-level co-occurrence matrix (GLCM). The FAA content prediction model with different variables was established by a genetic algorithm-support vector regression (GA-SVR) algorithm. The results showed that better prediction results were obtained by combining the feature wavelengths and textural variables. Compared with other data, this prediction result was still very satisfactory in the GA-SVR model, indicating that data fusion was an effective way to enhance hyperspectral imaging ability for the determination of free amino acid values in yellow tea.
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13
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Cheng JH, Sun DW, Liu G, Chen YN. Developing a multispectral model for detection of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) changes in fish fillet using physarum network and genetic algorithm (PN-GA) method. Food Chem 2019; 270:181-188. [DOI: 10.1016/j.foodchem.2018.07.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 05/27/2018] [Accepted: 07/02/2018] [Indexed: 12/22/2022]
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14
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Nguyen-Do-Trong N, Dusabumuremyi JC, Saeys W. Cross-polarized VNIR hyperspectral reflectance imaging for non-destructive quality evaluation of dried banana slices, drying process monitoring and control. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2018.06.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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15
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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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16
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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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17
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Skjelvareid MH, Heia K, Olsen SH, Stormo SK. Detection of blood in fish muscle by constrained spectral unmixing of hyperspectral images. J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2017.05.029] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Tao F, Ngadi M. Recent advances in rapid and nondestructive determination of fat content and fatty acids composition of muscle foods. Crit Rev Food Sci Nutr 2017; 58:1565-1593. [PMID: 28118034 DOI: 10.1080/10408398.2016.1261332] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Conventional methods for determining fat content and fatty acids (FAs) composition are generally based on the solvent extraction and gas chromatography techniques, respectively, which are time consuming, laborious, destructive to samples and require use of hazard solvents. These disadvantages make them impossible for large-scale detection or being applied to the production line of meat factories. In this context, the great necessity of developing rapid and nondestructive techniques for fat and FAs analyses has been highlighted. Measurement techniques based on near-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance and hyperspectral imaging have provided interesting and promising results for fat and FAs prediction in varieties of foods. Thus, the goal of this article is to give an overview of the current research progress in application of the four important techniques for fat and FAs analyses of muscle foods, which consist of pork, beef, lamb, chicken meat, fish and fish oil. The measurement techniques are described in terms of their working principles, features, and application advantages. Research advances for these techniques for specific food are summarized in detail and the factors influencing their modeling results are discussed. Perspectives on the current situation, future trends and challenges associated with the measurement techniques are also discussed.
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Affiliation(s)
- Feifei Tao
- a Department of Bioresource Engineering , McGill University , Ste-Anne-de-Bellevue , Quebec , Canada
| | - Michael Ngadi
- a Department of Bioresource Engineering , McGill University , Ste-Anne-de-Bellevue , Quebec , Canada
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19
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Washburn KE, Stormo SK, Skjelvareid MH, Heia K. Non-invasive assessment of packaged cod freeze-thaw history by hyperspectral imaging. J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2017.02.025] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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20
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Huang H, Shen Y, Guo Y, Yang P, Wang H, Zhan S, Liu H, Song H, He Y. Characterization of moisture content in dehydrated scallops using spectral images. J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2017.02.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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21
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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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22
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Hassoun A, Karoui R. Quality evaluation of fish and other seafood by traditional and nondestructive instrumental methods: Advantages and limitations. Crit Rev Food Sci Nutr 2017; 57:1976-1998. [PMID: 26192079 DOI: 10.1080/10408398.2015.1047926] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Although being one of the most vulnerable and perishable products, fish and other seafoods provide a wide range of health-promoting compounds. Recently, the growing interest of consumers in food quality and safety issues has contributed to the increasing demand for sensitive and rapid analytical technologies. Several traditional physicochemical, textural, sensory, and electrical methods have been used to evaluate freshness and authentication of fish and other seafood products. Despite the importance of these standard methods, they are expensive and time-consuming, and often susceptible to large sources of variation. Recently, spectroscopic methods and other emerging techniques have shown great potential due to speed of analysis, minimal sample preparation, high repeatability, low cost, and, most of all, the fact that these techniques are noninvasive and nondestructive and, therefore, could be applied to any online monitoring system. This review describes firstly and briefly the basic principles of multivariate data analysis, followed by the most commonly traditional methods used for the determination of the freshness and authenticity of fish and other seafood products. A special focus is put on the use of rapid and nondestructive techniques (spectroscopic techniques and instrumental sensors) to address several issues related to the quality of these products. Moreover, the advantages and limitations of each technique are reviewed and some perspectives are also given.
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Affiliation(s)
- Abdo Hassoun
- a Université d'Artois, Institut Régional en Agroalimentaire et Biotechnologie Charles Violette, Equipe Qualité et Sécurité des Aliments (QSA), Unité Ingénierie de Formulation des Aliments et Altération (IFAA), Faculté des Sciences Jean-Perrin , Lens , France
| | - Romdhane Karoui
- a Université d'Artois, Institut Régional en Agroalimentaire et Biotechnologie Charles Violette, Equipe Qualité et Sécurité des Aliments (QSA), Unité Ingénierie de Formulation des Aliments et Altération (IFAA), Faculté des Sciences Jean-Perrin , Lens , France
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23
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Line-Scan Hyperspectral Imaging Techniques for Food Safety and Quality Applications. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7020125] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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24
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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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25
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Huang L, Cheng S, Song Y, Xia K, Xu X, Zhu BW, Tan M. Non-destructive analysis of caviar compositions using low-field nuclear magnetic resonance technique. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2016. [DOI: 10.1007/s11694-016-9431-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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26
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Cheng W, Sun DW, Pu H, Liu Y. Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat. Lebensm Wiss Technol 2016. [DOI: 10.1016/j.lwt.2016.05.003] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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27
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Ivorra E, Sánchez AJ, Verdú S, Barat JM, Grau R. Shelf life prediction of expired vacuum-packed chilled smoked salmon based on a KNN tissue segmentation method using hyperspectral images. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2016.01.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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28
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Cheng JH, Sun DW, Zeng XA, Liu D. Recent advances in methods and techniques for freshness quality determination and evaluation of fish and fish fillets: a review. Crit Rev Food Sci Nutr 2016; 55:1012-225. [PMID: 24915394 DOI: 10.1080/10408398.2013.769934] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The freshness quality of fish plays an important role in human health and the acceptance of consumers as well as in international fishery trade. Recently, with food safety becoming a critical issue of great concern in the world, determination and evaluation of fish freshness is much more significant in research and development. This review renovates and concentrates recent advances of evaluating methods for fish freshness as affected by preharvest and postharvest factors and highlights the determination methods for fish freshness including sensory evaluation, microbial inspection, chemical measurements of moisture content, volatile compounds, protein changes, lipid oxidation, and adenosine triphosphate (ATP) decomposition (K value), physical measurements, and foreign material contamination detection. Moreover, the advantages and disadvantages of these methods and techniques are compared and discussed and some viewpoints about the current work and future trends are also presented.
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Affiliation(s)
- Jun-Hu Cheng
- a College of Light Industry and Food Sciences, South China University of Technology , Guangzhou , China
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29
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Recent Advances for Rapid Identification of Chemical Information of Muscle Foods by Hyperspectral Imaging Analysis. FOOD ENGINEERING REVIEWS 2016. [DOI: 10.1007/s12393-016-9139-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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30
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Liu D, Zeng XA, Sun DW. Recent developments and applications of hyperspectral imaging for quality evaluation of agricultural products: a review. Crit Rev Food Sci Nutr 2016; 55:1744-57. [PMID: 24915395 DOI: 10.1080/10408398.2013.777020] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Food quality and safety is the foremost issue for consumers, retailers as well as regulatory authorities. Most quality parameters are assessed by traditional methods, which are time consuming, laborious, and associated with inconsistency and variability. Non-destructive methods have been developed to objectively measure quality attributes for various kinds of food. In recent years, hyperspectral imaging (HSI) has matured into one of the most powerful tools for quality evaluation of agricultural and food products. HSI allows characterization of a sample's chemical composition (spectroscopic component) and external features (imaging component) in each point of the image with full spectral information. In order to track the latest research developments of this technology, this paper gives a detailed overview of the theory and fundamentals behind this technology and discusses its applications in the field of quality evaluation of agricultural products. Additionally, future potentials of HSI are also reported.
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Affiliation(s)
- Dan Liu
- a College of Light Industry and Food Sciences , South China University of Technology , Guangzhou , 510641 , P. R. China
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31
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Wold JP. On-line and non-destructive measurement of core temperature in heat treated fish cakes by NIR hyperspectral imaging. INNOV FOOD SCI EMERG 2016. [DOI: 10.1016/j.ifset.2015.12.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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32
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Xu JL, Riccioli C, Sun DW. Efficient integration of particle analysis in hyperspectral imaging for rapid assessment of oxidative degradation in salmon fillet. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2015.08.015] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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33
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34
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Siche R, Vejarano R, Aredo V, Velasquez L, Saldaña E, Quevedo R. Evaluation of Food Quality and Safety with Hyperspectral Imaging (HSI). FOOD ENGINEERING REVIEWS 2015. [DOI: 10.1007/s12393-015-9137-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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35
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ElMasry G, Nakauchi S. Noninvasive sensing of thermal treatments of Japanese seafood products using imaging spectroscopy. Int J Food Sci Technol 2015. [DOI: 10.1111/ijfs.12863] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Gamal ElMasry
- Department of Computer Science and Engineering; Toyohashi University of Technology; Toyohashi 441-8580 Japan
- Agricultural Engineering Department; Faculty of Agriculture; Suez Canal University; PO Box 41522 Ismailia Egypt
| | - Shigeki Nakauchi
- Department of Computer Science and Engineering; Toyohashi University of Technology; Toyohashi 441-8580 Japan
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36
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Xu JL, Riccioli C, Sun DW. An Overview on Nondestructive Spectroscopic Techniques for Lipid and Lipid Oxidation Analysis in Fish and Fish Products. Compr Rev Food Sci Food Saf 2015. [DOI: 10.1111/1541-4337.12138] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jun-Li Xu
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems Engineering, Univ. College Dublin, Natl. Univ. of Ireland; Agriculture and Food Science Centre; Belfield Dublin 4 Ireland
| | - Cecilia Riccioli
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems Engineering, Univ. College Dublin, Natl. Univ. of Ireland; Agriculture and Food Science Centre; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems Engineering, Univ. College Dublin, Natl. Univ. of Ireland; Agriculture and Food Science Centre; Belfield Dublin 4 Ireland
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37
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Suitability of hyperspectral imaging for rapid evaluation of thiobarbituric acid (TBA) value in grass carp (Ctenopharyngodon idella) fillet. Food Chem 2015; 171:258-65. [DOI: 10.1016/j.foodchem.2014.08.124] [Citation(s) in RCA: 107] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 08/25/2014] [Accepted: 08/30/2014] [Indexed: 11/21/2022]
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38
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Dai Q, Cheng JH, Sun DW, Pu H, Zeng XA, Xiong Z. Potential of visible/near-infrared hyperspectral imaging for rapid detection of freshness in unfrozen and frozen prawns. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2014.10.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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39
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Selection of Informative Spectral Wavelength for Evaluating and Visualising Enterobacteriaceae Contamination of Salmon Flesh. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-015-0122-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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40
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Cheng JH, Sun DW. Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method. FOOD BIOPROCESS TECH 2015. [DOI: 10.1007/s11947-014-1457-9] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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41
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Kamruzzaman M, Makino Y, Oshita S. Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: A review. Anal Chim Acta 2015; 853:19-29. [DOI: 10.1016/j.aca.2014.08.043] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 07/22/2014] [Accepted: 08/20/2014] [Indexed: 10/24/2022]
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42
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An overview on principle, techniques and application of hyperspectral imaging with special reference to ham quality evaluation and control. Food Control 2014. [DOI: 10.1016/j.foodcont.2014.05.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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43
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He HJ, Wu D, Sun DW. Nondestructive Spectroscopic and Imaging Techniques for Quality Evaluation and Assessment of Fish and Fish Products. Crit Rev Food Sci Nutr 2014; 55:864-86. [DOI: 10.1080/10408398.2012.746638] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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44
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Cheng JH, Sun DW. Hyperspectral imaging as an effective tool for quality analysis and control of fish and other seafoods: Current research and potential applications. Trends Food Sci Technol 2014. [DOI: 10.1016/j.tifs.2014.03.006] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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45
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Comparison of Visible and Long-wave Near-Infrared Hyperspectral Imaging for Colour Measurement of Grass Carp (Ctenopharyngodon idella). FOOD BIOPROCESS TECH 2014. [DOI: 10.1007/s11947-014-1325-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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46
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Ma J, Sun DW, Qu JH, Liu D, Pu H, Gao WH, Zeng XA. Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances. Crit Rev Food Sci Nutr 2014; 56:113-27. [DOI: 10.1080/10408398.2013.873885] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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47
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Mapping of Fat and Moisture Distribution in Atlantic Salmon Using Near-Infrared Hyperspectral Imaging. FOOD BIOPROCESS TECH 2013. [DOI: 10.1007/s11947-013-1228-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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48
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Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends Food Sci Technol 2013. [DOI: 10.1016/j.tifs.2013.08.005] [Citation(s) in RCA: 104] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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49
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Liu D, Qu J, Sun DW, Pu H, Zeng XA. Non-destructive prediction of salt contents and water activity of porcine meat slices by hyperspectral imaging in a salting process. INNOV FOOD SCI EMERG 2013. [DOI: 10.1016/j.ifset.2013.09.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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50
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Wu D, Sun DW. Hyperspectral Imaging Technology: A Nondestructive Tool for Food Quality and Safety Evaluation and Inspection. ACTA ACUST UNITED AC 2013. [DOI: 10.1007/978-1-4614-7906-2_29] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
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