1
|
Fulladosa E, Barnés-Calle C, Cruz J, Martínez B, Giró-Candanedo M, Comaposada J, Font-I-Furnols M, Gou P. Near infrared sensors for the precise characterization of salt content in canned tuna fish. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123217. [PMID: 37544216 DOI: 10.1016/j.saa.2023.123217] [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: 05/31/2023] [Revised: 07/11/2023] [Accepted: 07/27/2023] [Indexed: 08/08/2023]
Abstract
Non-invasive technologies could help to guarantee quality standards of canned tuna fish. The aim of this study was to investigate the ability of bench-top (FT-NIR) and low-cost (LC-NIR) near infrared spectrometers to determine salt content and texture in canned tuna. Salt content distribution was also investigated using hyperspectral imaging (HSI) and computed tomography. Spectra were acquired on canned tuna and reference analysis performed. Partial least squares regression and discriminant analysis were used to develop salt content predictive and texture classification models. Salt content predictive errors were 0.10%, 0.22% and 0.22% for FT-NIR, LC-NIR and HSI, respectively. Salt content was not always homogeneously distributed in the can which was attributed to the salt content differences between internal and external parts of the tuna fish. Low-cost sensors could be a suitable solution to standardise the production and enable precise nutritional labelling, but more sophisticated algorithms are needed to identify textural defects.
Collapse
Affiliation(s)
- E Fulladosa
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain.
| | - C Barnés-Calle
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - J Cruz
- Escola Universitària Salesiana de Sarrià, Passeig Sant Joan Bosco, 74, 08017 Barcelona, Spain
| | - B Martínez
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - M Giró-Candanedo
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - J Comaposada
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - M Font-I-Furnols
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| | - P Gou
- IRTA, Food Quality and Technology Program, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain
| |
Collapse
|
2
|
Giró-Candanedo M, Muñoz I, Gou P, Fulladosa E. Precise nutritional labelling of sliced packaged dry-cured ham using multi-energy X-ray absorptiometry. Meat Sci 2023; 204:109260. [PMID: 37369164 DOI: 10.1016/j.meatsci.2023.109260] [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: 03/21/2023] [Revised: 06/16/2023] [Accepted: 06/18/2023] [Indexed: 06/29/2023]
Abstract
New consumer demands have increased the need for improved food processing and new value-added products that meet the latest quality standards. Changes in eating habits may lead to a preference for lower sodium products, making accurate labelling and nutritional claims important for the industry. The aim of this work was to study the application of Multi-energy X-ray absorptiometry (MEXA) for the determination of nutritional information in sliced packaged dry-cured ham for the industry. The effect of the acquisition conditions, the analysis approach, fat content and measured area of slices, as well as the potential of this technology for the inclusion of a verified 'salt reduced' nutritional claim, were analyzed in two industrial case studies. Two hundred and ten packets of sliced dry-cured ham were scanned using MEXA equipment. Two regions of interest were selected to study the effect of thickness on the model's precision. Salt content could be predicted with a RMSEP of 0.346% and 0.403% when acquisition conditions were 80 keV and 110 keV respectively. When used by the industry, the classification performance for a 'salt reduced' labelling claim depends on the mean salt content and heterogeneity of the company's production and on the threshold value selected for class definition. However, to support consumers personalized nutrition through precise labelling, implementation of MEXA technology together with labelling system is necessary.
Collapse
Affiliation(s)
- Mar Giró-Candanedo
- IRTA, Food Quality and Technology program, Finca Camps i Armet, s/n, 17121, Monells, Girona, Spain
| | - Israel Muñoz
- IRTA, Food Quality and Technology program, Finca Camps i Armet, s/n, 17121, Monells, Girona, Spain
| | - Pere Gou
- IRTA, Food Quality and Technology program, Finca Camps i Armet, s/n, 17121, Monells, Girona, Spain
| | - Elena Fulladosa
- IRTA, Food Quality and Technology program, Finca Camps i Armet, s/n, 17121, Monells, Girona, Spain.
| |
Collapse
|
3
|
Campos MI, Debán L, Antolín G, Pardo R. A quantitative on-line analysis of salt in cured ham by near-infrared spectroscopy and chemometrics. Meat Sci 2023; 200:109167. [PMID: 36947977 DOI: 10.1016/j.meatsci.2023.109167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 02/08/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023]
Abstract
In this work, non-invasive near-infrared spectroscopy (NIRS) combined with chemometrics was evaluated as a possible online analytical technique to categorize pieces of cured ham on the industrial production line based on their maximum sodium content. Stifle muscle was selected for the development of the NIRS prediction models because it is the one with the highest sodium content and the easiest in terms of accessibility for spectral measurement. In the study, samples with varying thicknesses were taken. The suitability of this method is demonstrated when a 5 mm sample is used for the construction of the model, obtaining the best fit with an R2cv of 92% and a prediction error of 0.11% sodium that coincides with the error of the reference method. In conclusion, a method is proposed for the direct determination of sodium content on the production line which allows the different pieces of ham to be quickly categorized according to their salt content.
Collapse
Affiliation(s)
- M Isabel Campos
- CARTIF Technology Center, Agrofood and Sustainable Processes Division, Parque Tecnológico de Boecillo, 205, 47151 Valladolid, Spain; Analytical Chemistry Department, Faculty of Sciences, University of Valladolid, P° de Belén, 7, 47011 Valladolid, Spain.
| | - Luis Debán
- Analytical Chemistry Department, Faculty of Sciences, University of Valladolid, P° de Belén, 7, 47011 Valladolid, Spain
| | - Gregorio Antolín
- CARTIF Technology Center, Agrofood and Sustainable Processes Division, Parque Tecnológico de Boecillo, 205, 47151 Valladolid, Spain; Chemical Engineering and Environmental Technology Department, E.I.I. (School of Industrial Engineering), University of Valladolid, P° del Cauce 59, 47011 Valladolid, Spain
| | - Rafael Pardo
- Analytical Chemistry Department, Faculty of Sciences, University of Valladolid, P° de Belén, 7, 47011 Valladolid, Spain
| |
Collapse
|
4
|
Shi Y, Wang Y, Hu X, Li Z, Huang X, Liang J, Zhang X, Zhang D, Zou X, Shi J. Quantitative characterization of the diffusion behavior of sucrose in marinated beef by HSI and FEA. Meat Sci 2023; 195:109002. [DOI: 10.1016/j.meatsci.2022.109002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 11/09/2022]
|
5
|
Tian XY, Aheto JH, Huang X, Zheng K, Dai C, Wang C, Bai JW. An evaluation of biochemical, structural and volatile changes of dry-cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:5972-5983. [PMID: 33856705 DOI: 10.1002/jsfa.11251] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/04/2021] [Accepted: 04/15/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2-thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography-ion mobility spectrometry (GC-IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm. RESULTS Prediction results for MC and TBARS using multiplicative scatter correction pre-processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC-IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity. CONCLUSION The synergistic application of HSI, CLSM and GC-IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry.
Collapse
Affiliation(s)
- Xiao-Yu Tian
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Joshua H Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Kaiyi Zheng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Chunxia Dai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, PR China
| | - Chengquan Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Jun-Wen Bai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| |
Collapse
|
6
|
Tian XY, Aheto JH, Dai C, Ren Y, Bai JW. Monitoring microstructural changes and moisture distribution of dry-cured pork: a combined confocal laser scanning microscopy and hyperspectral imaging study. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2727-2735. [PMID: 33124042 DOI: 10.1002/jsfa.10899] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/13/2020] [Accepted: 10/30/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Various spectral profiles, including reflectance, absorbance, and Kubelka-Munk spectra, have been derived from hyperspectral images and used to develop multivariate models to evaluate changes in the quality of meat and meat products as a function of processing. However, none of these has the capacity to produce images of the structural changes often associated with processing. This study explored the feasibility of combining hyperspectral imaging (HSI) with confocal laser scanning microscopy (CLSM) to examine the impact of processing on microstructural changes and the evolution of moisture. Reflectance spectra features were obtained and transformed into absorbance and Kubelka-Munk spectra and their ability to predict moisture content using models established on partial least-squares regression were evaluated. RESULTS The partial least-squares regression model (full-band wavelength) dubbed Rs-MSC yielded the best result, with R c 2 = 0.967 , RMSEC = 0.127, R cv 2 = 0.949 , RMSECV = 0.418, R p 2 = 0.937 , RMSEP = 0.824. Next, a total of 16 optimum wavelengths were selected using the competitive adaptive reweighted sampling algorithm. These wavelengths also yielded good results for Rs-MSC, with R c 2 = 0.958 , RMSEC = 0.840, R cv 2 = 0.931 , RMSECV = 0.118, R p 2 = 0.926 , RMSEP = 0.121. Regarding moisture distribution and microstructure analysis, HSI and CLSM were able to reveal moisture content distribution and conformational differences in microstructure in the test samples. CONCLUSION Using HSI in synergy with CLSM may offer a reliable means for assessing both the chemical and structural changes that occur in other congener food products during processing. © 2020 Society of Chemical Industry.
Collapse
Affiliation(s)
- Xiao-Yu Tian
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Joshua H Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Chunxia Dai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Yi Ren
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
- School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, Suzhou, P. R. China
| | - Jun-Wen Bai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| |
Collapse
|
7
|
Liu Y, Wang H, Fei Y, Liu Y, Shen L, Zhuang Z, Zhang X. Research on the Prediction of Green Plum Acidity Based on Improved XGBoost. SENSORS 2021; 21:s21030930. [PMID: 33573249 PMCID: PMC7866513 DOI: 10.3390/s21030930] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 11/19/2022]
Abstract
The acidity of green plum has an important influence on the fruit’s deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis—linear discriminant analysis—extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.
Collapse
|
8
|
Daikos O, Scherzer T. Monitoring of the residual moisture content in finished textiles during converting by NIR hyperspectral imaging. Talanta 2021; 221:121567. [PMID: 33076115 DOI: 10.1016/j.talanta.2020.121567] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/29/2020] [Accepted: 08/10/2020] [Indexed: 12/11/2022]
Abstract
Hyperspectral imaging was used for large-scale monitoring of the residual moisture in wide textile webs at the end of the drying process that follows their washing or finishing by impregnation in aqueous solutions or dispersions. Such data are essential for optimizing the energy efficiency and the precise control of the drying process. Quantitative analysis of the recorded spectral data was carried out with multivariate regression methods such as the partial least squares (PLS) algorithm. Reference data for calibration of the prediction models were determined by gravimetry. The drying of textile materials from both natural or synthetic fibers possessing different water absorption capacities (cotton, polyamide, polyester), which were partially finished with an optical brightener, was investigated. Moisture contents in the range from 0 to about 12 wt% were considered in the calibration models. For all systems, the root mean square error of prediction (RMSEP) for the residual moisture was found to be about 0.5 wt%, that is, about 1 g/m2. In addition to the quantitative determination of the water content, hyperspectral imaging provides detailed information about its spatial distribution across the textile web, which may help to improve the control of the drying process. In particular, it was demonstrated that the developed methods were capable of detecting and visualizing inhomogeneous moisture distributions. Averaging of the individual values of the moisture content predicted from all spectra across the surface of the textile samples resulted in a very close correlation with the corresponding gravimetric reference values. Due to the averaging process, the difference between both values is generally lower than RMSEP even in case of samples with inhomogeneous distribution of the moisture. The high precision and the broad capabilities of the developed analytic methods for in-line monitoring of the moisture content hold the potential for an efficient process control in technical textile converting processes.
Collapse
Affiliation(s)
- Olesya Daikos
- Leibniz Institute of Surface Engineering (IOM), Department of Functional Coatings, Permoserstr. 15, D-04318, Leipzig, Germany
| | - Tom Scherzer
- Leibniz Institute of Surface Engineering (IOM), Department of Functional Coatings, Permoserstr. 15, D-04318, Leipzig, Germany.
| |
Collapse
|
9
|
Application of Novel Techniques for Monitoring Quality Changes in Meat and Fish Products during Traditional Processing Processes: Reconciling Novelty and Tradition. Processes (Basel) 2020. [DOI: 10.3390/pr8080988] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
In this review, we summarize the most recent advances in monitoring changes induced in fish and other seafood, and meat and meat products, following the application of traditional processing processes by means of conventional and emerging advanced techniques. Selected examples from the literature covering relevant applications of spectroscopic methods (i.e., visible and near infrared (VIS/NIR), mid-infrared (MIR), Raman, nuclear magnetic resonance (NMR), and fluorescence) will be used to illustrate the topics covered in this review. Although a general reluctance toward using and adopting new technologies in traditional production sectors causes a relatively low interest in spectroscopic techniques, the recently published studies have pointed out that these techniques could be a powerful tool for the non-destructive monitoring and process optimization during the production of muscle food products.
Collapse
|
10
|
Muñoz I, Gou P, Fulladosa E. Computer image analysis for intramuscular fat segmentation in dry-cured ham slices using convolutional neural networks. Food Control 2019. [DOI: 10.1016/j.foodcont.2019.06.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
|
11
|
Aheto JH, Huang X, Xiaoyu T, Bonah E, Ren Y, Alenyorege EA, Chunxia D. Investigation into crystal size effect on sodium chloride uptake and water activity of pork meat using hyperspectral imaging. J FOOD PROCESS PRES 2019. [DOI: 10.1111/jfpp.14197] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
| | - Xingyi Huang
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Tian Xiaoyu
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Ernest Bonah
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
- Laboratory Services Department Food and Drugs Authority Cantonments Accra Ghana
| | - Yi Ren
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
- School of smart agriculture Suzhou Polytechnic Institute of Agriculture Suzhou P.R. China
| | - Evans Adingba Alenyorege
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
- Faculty of Agriculture University for Development Studies Tamale Ghana
| | - Dai Chunxia
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
- School of Electrical and Information Engineering Jiangsu University Zhenjiang P.R. China
| |
Collapse
|
12
|
Aheto JH, Huang X, Tian X, Lv R, Dai C, Bonah E, Chang X. Evaluation of lipid oxidation and volatile compounds of traditional dry‐cured pork belly: The hyperspectral imaging and multi‐gas‐sensory approaches. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13092] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Joshua H. Aheto
- School of Food and Biological Engineering, Jiangsu University Zhenjiang Jiangsu People's Republic of China
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University Zhenjiang Jiangsu People's Republic of China
| | - Xiaoyu Tian
- School of Food and Biological Engineering, Jiangsu University Zhenjiang Jiangsu People's Republic of China
| | - Riqin Lv
- School of Food and Biological Engineering, Jiangsu University Zhenjiang Jiangsu People's Republic of China
| | - Chunxia Dai
- School of Food and Biological Engineering, Jiangsu University Zhenjiang Jiangsu People's Republic of China
- School of Electrical and Information Engineering, Jiangsu University Zhenjiang Jiangsu People's Republic of China
| | - Ernest Bonah
- School of Food and Biological Engineering, Jiangsu University Zhenjiang Jiangsu People's Republic of China
| | - Xianhui Chang
- School of Food and Biological Engineering, Jiangsu University Zhenjiang Jiangsu People's Republic of China
| |
Collapse
|
13
|
Pérez-Santaescolástica C, Fraeye I, Barba FJ, Gómez B, Tomasevic I, Romero A, Moreno A, Toldrá F, Lorenzo JM. Application of non-invasive technologies in dry-cured ham: An overview. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.02.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Fulladosa E, Rubio-Celorio M, Skytte J, Muñoz I, Picouet P. Laser-light backscattering response to water content and proteolysis in dry-cured ham. Food Control 2017. [DOI: 10.1016/j.foodcont.2017.02.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
16
|
|
17
|
Mirschel G, Daikos O, Scherzer T, Steckert C. Near-infrared chemical imaging used for in-line analysis of inside adhesive layers in textile laminates. Anal Chim Acta 2016; 932:69-79. [DOI: 10.1016/j.aca.2016.05.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 05/03/2016] [Accepted: 05/10/2016] [Indexed: 10/21/2022]
|