101
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Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle. FOOD ENGINEERING REVIEWS 2016. [DOI: 10.1007/s12393-016-9147-1] [Citation(s) in RCA: 111] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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102
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Li JL, Sun DW, Cheng JH. Recent Advances in Nondestructive Analytical Techniques for Determining the Total Soluble Solids in Fruits: A Review. Compr Rev Food Sci Food Saf 2016; 15:897-911. [DOI: 10.1111/1541-4337.12217] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2016] [Revised: 05/22/2016] [Accepted: 05/24/2016] [Indexed: 12/13/2022]
Affiliation(s)
- Jiang-Lin Li
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Academy of Contemporary Food Engineering, South China Univ. of Technology; 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
- Academy of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre; Univ. College Dublin, Natl. Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Jun-Hu Cheng
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Academy of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
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103
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Kamruzzaman M, Makino Y, Oshita S. Online monitoring of red meat color using hyperspectral imaging. Meat Sci 2016; 116:110-7. [DOI: 10.1016/j.meatsci.2016.02.004] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 12/08/2015] [Accepted: 02/01/2016] [Indexed: 11/30/2022]
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104
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Wold JP, Kermit M, Segtnan VH. Chemical Imaging of Heterogeneous Muscle Foods Using Near-Infrared Hyperspectral Imaging in Transmission Mode. APPLIED SPECTROSCOPY 2016; 70:953-61. [PMID: 27257302 DOI: 10.1177/0003702816641260] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Accepted: 08/31/2015] [Indexed: 05/25/2023]
Abstract
Foods and biomaterials are, in general, heterogeneous and it is often a challenge to obtain spectral data which are representative for the chemical composition and distribution. This paper presents a setup for near-infrared (NIR) transmission imaging where the samples are completely trans-illuminated, probing the entire sample. The system measures falling samples at high speed and consists of an NIR imaging scanner covering the spectral range 760-1040 nm and a powerful line light source. The investigated samples were rather big: whole pork bellies of thickness up to 5 cm, salmon fillets with skin, and 3 cm thick model samples of ground pork meat. Partial least square regression models for fat were developed for ground pork and salmon fillet with high correlations (R = 0.98 and R = 0.95, respectively). The regression models were applied at pixel level in the hyperspectral transmission images and resulted in images of fat distribution where also deeply embedded fat clearly contributed to the result. The results suggest that it is possible to use transmission imaging for rapid, nondestructive, and representative sampling of very heterogeneous foods. The proposed system is suitable for industrial use.
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Affiliation(s)
- Jens Petter Wold
- Nofima, Norwegian Institute for Food and Fisheries Research, Tromsø, Norway
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105
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Dai Q, Cheng JH, Sun DW, Zeng XA. Advances in feature selection methods for hyperspectral image processing in food industry applications: a review. Crit Rev Food Sci Nutr 2016; 55:1368-82. [PMID: 24689555 DOI: 10.1080/10408398.2013.871692] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.
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Affiliation(s)
- Qiong Dai
- a College of Light Industry and Food Sciences, South China University of Technology , Guangzhou 510641 , China
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106
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Xiong Z, Xie A, Sun DW, Zeng XA, Liu D. Applications of hyperspectral imaging in chicken meat safety and quality detection and evaluation: a review. Crit Rev Food Sci Nutr 2016; 55:1287-301. [PMID: 24689678 DOI: 10.1080/10408398.2013.834875] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Currently, the issue of food safety and quality is a great public concern. In order to satisfy the demands of consumers and obtain superior food qualities, non-destructive and fast methods are required for quality evaluation. As one of these methods, hyperspectral imaging (HSI) technique has emerged as a smart and promising analytical tool for quality evaluation purposes and has attracted much interest in non-destructive analysis of different food products. With the main advantage of combining both spectroscopy technique and imaging technique, HSI technique shows a convinced attitude to detect and evaluate chicken meat quality objectively. Moreover, developing a quality evaluation system based on HSI technology would bring economic benefits to the chicken meat industry. Therefore, in recent years, many studies have been conducted on using HSI technology for the safety and quality detection and evaluation of chicken meat. The aim of this review is thus to give a detailed overview about HSI and focus on the recently developed methods exerted in HSI technology developed for microbiological spoilage detection and quality classification of chicken meat. Moreover, the usefulness of HSI technique for detecting fecal contamination and bone fragments of chicken carcasses are presented. Finally, some viewpoints on its future research and applicability in the modern poultry industry are proposed.
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Affiliation(s)
- Zhenjie Xiong
- a College of Light Industry and Food Sciences , South China University of Technology , Guangdong 510641 , P. R. China
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107
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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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108
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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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109
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Parsimonious model development for real-time monitoring of moisture in red meat using hyperspectral imaging. Food Chem 2016; 196:1084-91. [DOI: 10.1016/j.foodchem.2015.10.051] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Revised: 10/08/2015] [Accepted: 10/11/2015] [Indexed: 11/23/2022]
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110
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Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen–thawed fish muscle. Food Chem 2016; 197:855-63. [DOI: 10.1016/j.foodchem.2015.11.019] [Citation(s) in RCA: 119] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 10/07/2015] [Accepted: 11/04/2015] [Indexed: 01/08/2023]
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111
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Wojtasik-Kalinowska I, Guzek D, Górska-Horczyczak E, Głąbska D, Brodowska M, Sun DW, Wierzbicka A. Volatile compounds and fatty acids profile in Longissimus dorsi muscle from pigs fed with feed containing bioactive components. Lebensm Wiss Technol 2016. [DOI: 10.1016/j.lwt.2015.11.023] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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112
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Hyperspectral imaging for real-time monitoring of water holding capacity in red meat. Lebensm Wiss Technol 2016. [DOI: 10.1016/j.lwt.2015.11.021] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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113
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Pan TT, Sun DW, Cheng JH, Pu H. Regression Algorithms in Hyperspectral Data Analysis for Meat Quality Detection and Evaluation. Compr Rev Food Sci Food Saf 2016; 15:529-541. [DOI: 10.1111/1541-4337.12191] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Revised: 12/12/2015] [Accepted: 12/16/2015] [Indexed: 01/06/2023]
Affiliation(s)
- Ting-Tiao Pan
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
| | - Da-Wen Sun
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology, Agriculture and Food Science Centre, Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Jun-Hu Cheng
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
| | - Hongbin Pu
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
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114
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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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115
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Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2015.08.023] [Citation(s) in RCA: 127] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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116
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Pu YY, Sun DW. Prediction of moisture content uniformity of microwave-vacuum dried mangoes as affected by different shapes using NIR hyperspectral imaging. INNOV FOOD SCI EMERG 2016. [DOI: 10.1016/j.ifset.2015.11.003] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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117
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Wang L, Pu H, Sun DW. Estimation of chlorophyll-a concentration of different seasons in outdoor ponds using hyperspectral imaging. Talanta 2016; 147:422-9. [DOI: 10.1016/j.talanta.2015.09.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 09/04/2015] [Accepted: 09/07/2015] [Indexed: 10/23/2022]
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118
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119
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120
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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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121
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He HJ, Sun DW. Microbial evaluation of raw and processed food products by Visible/Infrared, Raman and Fluorescence spectroscopy. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.10.004] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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122
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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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123
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Dai Q, Cheng JH, Sun DW, Zhu Z, Pu H. Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis). Food Chem 2015; 197:257-65. [PMID: 26616948 DOI: 10.1016/j.foodchem.2015.10.073] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Revised: 10/07/2015] [Accepted: 10/18/2015] [Indexed: 11/25/2022]
Abstract
A visible/near-infrared hyperspectral imaging (HSI) system (400-1000 nm) coupled with wavelet analysis was used to determine the total volatile basic nitrogen (TVB-N) contents of prawns during cold storage. Spectral information was denoised by conducting wavelet analysis and uninformative variable elimination (UVE) algorithm, and then three wavelet features (energy, entropy and modulus maxima) were extracted. Quantitative models were established between the wavelet features and the reference TVB-N contents by using three regression algorithms. As a result, the LS-SVM model with modulus maxima features was considered as the best model for determining the TVB-N contents of prawns, with an excellent RP(2) of 0.9547, RMSEP=0.7213 mg N/100g and RPD=4.799. Finally, an image processing algorithm was developed for generating a TVB-N distribution map. This study demonstrated the possibility of applying the HSI imaging system in combination with wavelet analysis to the monitoring of TVB-N values in prawns.
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Affiliation(s)
- Qiong Dai
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China
| | - Jun-Hu Cheng
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China
| | - Da-Wen Sun
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China; Food Refrigeration and Computerized Food Technology, Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
| | - Zhiwei Zhu
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China
| | - Hongbin Pu
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China
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124
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Freshness estimation of intact frozen fish using fluorescence spectroscopy and chemometrics of excitation–emission matrix. Talanta 2015; 143:145-156. [DOI: 10.1016/j.talanta.2015.05.031] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 05/11/2015] [Accepted: 05/12/2015] [Indexed: 11/23/2022]
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125
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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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126
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Cheng W, Cheng JH, Sun DW, Pu H. Marbling Analysis for Evaluating Meat Quality: Methods and Techniques. Compr Rev Food Sci Food Saf 2015. [DOI: 10.1111/1541-4337.12149] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Weiwei Cheng
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
| | - Jun-Hu Cheng
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
| | - Da-Wen Sun
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre, Univ. College Dublin, Natl. Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Hongbin Pu
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
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127
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Santos MI, Gerbino E, Tymczyszyn E, Gomez-Zavaglia A. Applications of Infrared and Raman Spectroscopies to Probiotic Investigation. Foods 2015; 4:283-305. [PMID: 28231205 PMCID: PMC5224548 DOI: 10.3390/foods4030283] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 07/01/2015] [Accepted: 07/09/2015] [Indexed: 11/16/2022] Open
Abstract
In this review, we overview the most important contributions of vibrational spectroscopy based techniques in the study of probiotics and lactic acid bacteria. First, we briefly introduce the fundamentals of these techniques, together with the main multivariate analytical tools used for spectral interpretation. Then, four main groups of applications are reported: (a) bacterial taxonomy (Subsection 4.1); (b) bacterial preservation (Subsection 4.2); (c) monitoring processes involving lactic acid bacteria and probiotics (Subsection 4.3); (d) imaging-based applications (Subsection 4.4). A final conclusion, underlying the potentialities of these techniques, is presented.
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Affiliation(s)
- Mauricio I Santos
- Center for Research and Development in Food Cryotechnology (CIDCA, CCT-CONICET La Plata), 1900 La Plata, Argentina.
| | - Esteban Gerbino
- Center for Research and Development in Food Cryotechnology (CIDCA, CCT-CONICET La Plata), 1900 La Plata, Argentina.
| | - Elizabeth Tymczyszyn
- Laboratory for Molecular Microbiology, Department of Food Science and Technology, National University of Quilmes, 1876 Buenos Aires, Argentina.
| | - Andrea Gomez-Zavaglia
- Center for Research and Development in Food Cryotechnology (CIDCA, CCT-CONICET La Plata), 1900 La Plata, Argentina.
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128
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Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2015.02.013] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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129
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Cheng L, Sun DW, Zhu Z, Zhang Z. Emerging techniques for assisting and accelerating food freezing processes: A review of recent research progresses. Crit Rev Food Sci Nutr 2015; 57:769-781. [DOI: 10.1080/10408398.2015.1004569] [Citation(s) in RCA: 133] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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130
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Liu J, Cao Y, Wang Q, Pan W, Ma F, Liu C, Chen W, Yang J, Zheng L. Rapid and non-destructive identification of water-injected beef samples using multispectral imaging analysis. Food Chem 2015. [PMID: 26213059 DOI: 10.1016/j.foodchem.2015.06.056] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Water-injected beef has aroused public concern as a major food-safety issue in meat products. In the study, the potential of multispectral imaging analysis in the visible and near-infrared (405-970 nm) regions was evaluated for identifying water-injected beef. A multispectral vision system was used to acquire images of beef injected with up to 21% content of water, and partial least squares regression (PLSR) algorithm was employed to establish prediction model, leading to quantitative estimations of actual water increase with a correlation coefficient (r) of 0.923. Subsequently, an optimized model was achieved by integrating spectral data with feature information extracted from ordinary RGB data, yielding better predictions (r = 0.946). Moreover, the prediction equation was transferred to each pixel within the images for visualizing the distribution of actual water increase. These results demonstrate the capability of multispectral imaging technology as a rapid and non-destructive tool for the identification of water-injected beef.
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Affiliation(s)
- Jinxia Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Yue Cao
- School of Medical Engineering, Hefei University of Technology, Hefei 230009, China
| | - Qiu Wang
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Wenjuan Pan
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Fei Ma
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Changhong Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Wei Chen
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Jianbo Yang
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Lei Zheng
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China; School of Medical Engineering, Hefei University of Technology, Hefei 230009, China.
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131
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Khoshtaghaza MH, Khojastehnazhand M, Mojaradi B, Goodarzi M, Saeys W. Texture Quality Analysis of Rainbow Trout Using Hyperspectral Imaging Method. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2015. [DOI: 10.1080/10942912.2015.1042111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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132
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Rapid Detection of Surface Color of Shatian Pomelo Using Vis-NIR Spectrometry for the Identification of Maturity. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-015-0188-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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133
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Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat. Food Chem 2015; 175:417-22. [DOI: 10.1016/j.foodchem.2014.11.161] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Revised: 11/28/2014] [Accepted: 11/29/2014] [Indexed: 12/22/2022]
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134
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Garrido-Novell C, Garrido-Varo A, Pérez-Marín D, Guerrero-Ginel J, Kim M. Quantification and spatial characterization of moisture and NaCl content of Iberian dry-cured ham slices using NIR hyperspectral imaging. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2014.09.035] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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135
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Pullanagari RR, Yule IJ, Agnew M. On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy. Meat Sci 2015; 100:156-63. [DOI: 10.1016/j.meatsci.2014.10.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 10/03/2014] [Accepted: 10/07/2014] [Indexed: 10/24/2022]
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136
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Xiong Z, Sun DW, Pu H, Xie A, Han Z, Luo M. Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem 2015; 179:175-81. [PMID: 25722152 DOI: 10.1016/j.foodchem.2015.01.116] [Citation(s) in RCA: 146] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Revised: 01/14/2015] [Accepted: 01/25/2015] [Indexed: 11/18/2022]
Abstract
This study examined the potential of hyperspectral imaging (HSI) for rapid prediction of 2-thiobarbituric acid reactive substances (TBARS) content in chicken meat during refrigerated storage. Using the spectral data and the reference values of TBARS, a partial least square regression (PLSR) model was established and yielded acceptable results with regression coefficients in prediction (Rp) of 0.944 and root mean squared errors estimated by prediction (RMSEP) of 0.081. To simplify the calibration model, ten optimal wavelengths were selected by successive projections algorithm (SPA). Then, a new SPA-PLSR model based on the selected wavelengths was built and showed good results with Rp of 0.801 and RMSEP of 0.157. Finally, an image algorithm was developed to achieve image visualization of TBARS values in some representative samples. The encouraging results of this study demonstrated that HSI is suitable for determination of TBARS values for freshness evaluation in chicken meat.
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Affiliation(s)
- Zhenjie Xiong
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
| | - Da-Wen Sun
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China; Food Refrigeration and Computerised Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland.
| | - Hongbin Pu
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
| | - Anguo Xie
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
| | - Zhong Han
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
| | - Man Luo
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
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137
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Assessment of Visible Near-Infrared Hyperspectral Imaging as a Tool for Detection of Horsemeat Adulteration in Minced Beef. FOOD BIOPROCESS TECH 2015. [DOI: 10.1007/s11947-015-1470-7] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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138
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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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139
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Pu H, Sun DW, Ma J, Liu D, Kamruzzaman M. Hierarchical variable selection for predicting chemical constituents in lamb meats using hyperspectral imaging. J FOOD ENG 2014. [DOI: 10.1016/j.jfoodeng.2014.06.025] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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140
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Dai Q, Sun DW, Cheng JH, Pu H, Zeng XA, Xiong Z. Recent Advances in De-Noising Methods and Their Applications in Hyperspectral Image Processing for the Food Industry. Compr Rev Food Sci Food Saf 2014. [DOI: 10.1111/1541-4337.12110] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Qiong Dai
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre; Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre; Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Jun-Hu Cheng
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre; Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Hongbin Pu
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre; Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Xin-An Zeng
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre; Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Zhenjie Xiong
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre; Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
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141
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Dai Q, Sun DW, Xiong Z, Cheng JH, Zeng XA. Recent Advances in Data Mining Techniques and Their Applications in Hyperspectral Image Processing for the Food Industry. Compr Rev Food Sci Food Saf 2014. [DOI: 10.1111/1541-4337.12088] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Qiong Dai
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
| | - Da-Wen Sun
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology; Agriculture and Food Science Centre, Univ. College Dublin, Natl. Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Zhenjie Xiong
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
| | - Jun-Hu Cheng
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
| | - Xin-An Zeng
- College of Light Industry and Food Sciences; South China Univ. of Technology; Guangzhou 510641 China
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142
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Tao F, Peng Y. A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging. FOOD BIOPROCESS TECH 2014. [DOI: 10.1007/s11947-014-1374-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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143
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Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis. Food Res Int 2014. [DOI: 10.1016/j.foodres.2014.03.064] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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144
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Xiong Z, Sun DW, Zeng XA, Xie A. Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review. J FOOD ENG 2014. [DOI: 10.1016/j.jfoodeng.2014.02.004] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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145
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146
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Xiong Z, Sun DW, Dai Q, Han Z, Zeng XA, Wang L. Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat. FOOD ANAL METHOD 2014. [DOI: 10.1007/s12161-014-9853-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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147
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Liu D, Sun DW, Qu J, Zeng XA, Pu H, Ma J. Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process. Food Chem 2014; 152:197-204. [DOI: 10.1016/j.foodchem.2013.11.107] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Revised: 10/21/2013] [Accepted: 11/19/2013] [Indexed: 11/26/2022]
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148
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Panagou EZ, Papadopoulou O, Carstensen JM, Nychas GJE. Potential of multispectral imaging technology for rapid and non-destructive determination of the microbiological quality of beef filets during aerobic storage. Int J Food Microbiol 2014; 174:1-11. [DOI: 10.1016/j.ijfoodmicro.2013.12.026] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 10/25/2013] [Accepted: 12/24/2013] [Indexed: 11/30/2022]
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149
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Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chem 2013; 141:389-96. [DOI: 10.1016/j.foodchem.2013.02.094] [Citation(s) in RCA: 158] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Revised: 02/03/2013] [Accepted: 02/23/2013] [Indexed: 11/22/2022]
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150
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Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry. FOOD BIOPROCESS TECH 2013. [DOI: 10.1007/s11947-013-1193-6] [Citation(s) in RCA: 248] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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