1
|
Gao W, Wu X, Ye R, Zeng X, Brennan MA, Brennan CS, Ma J. Analysis of protein denaturation, and chemical visualisation, of frozen grass carp surimi containing soluble soybean polysaccharides. Int J Food Sci Technol 2022. [DOI: 10.1111/ijfs.15888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Wenhong Gao
- School of Food Science and Engineering South China University of Technology Guangzhou 510641 China
| | - Xinru Wu
- School of Food Science and Engineering South China University of Technology Guangzhou 510641 China
| | - Ruisen Ye
- Midea Household Appliance Division Midea Group Foshan 528311 China
| | - Xin‐an Zeng
- School of Food Science and Engineering South China University of Technology Guangzhou 510641 China
| | - Margaret A. Brennan
- Department of Wine, Food and Molecular Biosciences Lincoln University Lincoln 7647 Christchurch New Zealand
| | | | - Ji Ma
- School of Food Science and Engineering South China University of Technology Guangzhou 510641 China
- State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation‐Induced Emission South China University of Technology Guangzhou 510640 China
| |
Collapse
|
2
|
He HJ, Wang Y, Zhang M, Wang Y, Ou X, Guo J. Rapid determination of reducing sugar content in sweet potatoes using NIR spectra. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104641] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
3
|
Seafood Processing, Preservation, and Analytical Techniques in the Age of Industry 4.0. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031703] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Fish and other seafood products are essential dietary components that are highly appreciated and consumed worldwide. However, the high perishability of these products has driven the development of a wide range of processing, preservation, and analytical techniques. This development has been accelerated in recent years with the advent of the fourth industrial revolution (Industry 4.0) technologies, digitally transforming almost every industry, including the food and seafood industry. The purpose of this review paper is to provide an updated overview of recent thermal and nonthermal processing and preservation technologies, as well as advanced analytical techniques used in the seafood industry. A special focus will be given to the role of different Industry 4.0 technologies to achieve smart seafood manufacturing, with high automation and digitalization. The literature discussed in this work showed that emerging technologies (e.g., ohmic heating, pulsed electric field, high pressure processing, nanotechnology, advanced mass spectrometry and spectroscopic techniques, and hyperspectral imaging sensors) are key elements in industrial revolutions not only in the seafood industry but also in all food industry sectors. More research is still needed to explore how to harness the Industry 4.0 innovations in order to achieve a green transition toward more profitable and sustainable food production systems.
Collapse
|
4
|
Manthou E, Karnavas A, Fengou LC, Bakali A, Lianou A, Tsakanikas P, Nychas GJE. Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables. Int J Food Microbiol 2022; 361:109458. [PMID: 34743052 DOI: 10.1016/j.ijfoodmicro.2021.109458] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 09/23/2021] [Accepted: 10/24/2021] [Indexed: 12/23/2022]
Abstract
Based on both new and previously utilized experimental data, the present study provides a comparative assessment of sensors and machine learning approaches for evaluating the microbiological spoilage of ready-to-eat leafy vegetables (baby spinach and rocket). Fourier-transform infrared (FTIR), near-infrared (NIR), visible (VIS) spectroscopy and multispectral imaging (MSI) were used. Two data partitioning approaches and two algorithms, namely partial least squares regression and support vector regression (SVR), were evaluated. Concerning baby spinach, when model testing was performed on samples randomly selected, the performance was better than or similar to the one attained when testing was performed based on dynamic temperatures data, depending on the applied analytical technology. The two applied algorithms yielded similar model performances for the majority of baby spinach cases. Regarding rocket, the random data partitioning approach performed considerably better results in almost all cases of sensor/algorithm combination. Furthermore, SVR algorithm resulted in considerably or slightly better model performances for the FTIR, VIS and NIR sensors, depending on the data partitioning approach. However, PLSR algorithm provided better models for the MSI sensor. Overall, the microbiological spoilage of baby spinach was better assessed by models derived mainly from the VIS sensor, while FTIR and MSI were more suitable in rocket. According to the findings of this study, a distinct sensor and computational analysis application is needed for each vegetable type, suggesting that there is not a single combination of analytical approach/algorithm that could be applied successfully in all food products and throughout the food supply chain.
Collapse
Affiliation(s)
- Evanthia Manthou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food & Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - Apostolos Karnavas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food & Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - Lemonia-Christina Fengou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food & Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - Anastasia Bakali
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food & Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - Alexandra Lianou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food & Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; Division of Genetics, Cell Biology and Development, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Panagiotis Tsakanikas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food & Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - George-John E Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food & Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
| |
Collapse
|
5
|
Li Y, Sun J, Wu X, Chen Q, Lu B, Dai C. Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model. J FOOD PROCESS PRES 2019. [DOI: 10.1111/jfpp.14238] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yating Li
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Bing Lu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Chunxia Dai
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| |
Collapse
|
6
|
Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9183926] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.
Collapse
|
7
|
Su WH, Sun DW. Multispectral Imaging for Plant Food Quality Analysis and Visualization. Compr Rev Food Sci Food Saf 2018; 17:220-239. [DOI: 10.1111/1541-4337.12317] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| |
Collapse
|
8
|
Classification of organic beef freshness using VNIR hyperspectral imaging. Meat Sci 2017; 129:20-27. [DOI: 10.1016/j.meatsci.2017.02.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 01/30/2017] [Accepted: 02/06/2017] [Indexed: 12/24/2022]
|
9
|
Vejarano R, Siche R, Tesfaye W. Evaluation of biological contaminants in foods by hyperspectral imaging: A review. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2017. [DOI: 10.1080/10942912.2017.1338729] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Ricardo Vejarano
- Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo (UNT), Ciudad Universitaria, Trujillo, Peru
- Facultad de Ingeniería, Universidad Privada del Norte (UPN), Trujillo, Peru
| | - Raúl Siche
- Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo (UNT), Ciudad Universitaria, Trujillo, Peru
| | - Wendu Tesfaye
- Departamento de Química y Tecnología de Alimentos, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| |
Collapse
|
10
|
Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat ( Triticum spp.) flour. J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2016.12.014] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|
11
|
Nychas GJE, Panagou EZ, Mohareb F. Novel approaches for food safety management and communication. Curr Opin Food Sci 2016. [DOI: 10.1016/j.cofs.2016.06.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
12
|
Su WH, Sun DW. Facilitated wavelength selection and model development for rapid determination of the purity of organic spelt (Triticum spelta L.) flour using spectral imaging. Talanta 2016; 155:347-57. [DOI: 10.1016/j.talanta.2016.04.041] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Revised: 04/14/2016] [Accepted: 04/19/2016] [Indexed: 10/21/2022]
|
13
|
Ye X, Iino K, Zhang S. Monitoring of bacterial contamination on chicken meat surface using a novel narrowband spectral index derived from hyperspectral imagery data. Meat Sci 2016; 122:25-31. [PMID: 27471794 DOI: 10.1016/j.meatsci.2016.07.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 07/12/2016] [Accepted: 07/20/2016] [Indexed: 11/17/2022]
Abstract
This study presents a novel narrowband spectral index for monitoring bacterial contamination on chicken meat surface. Fresh chicken meats were prepared and stored aerobically in a refrigerator at 4°C for 11d. Hyperspectral images and the total viable count (TVC) of bacteria for meat samples were obtained every 24h. A new two band freshness index (TBFI) method was proposed for developing the bacteria prediction models. Results indicated that the model with the TBFI based on the wavelengths 650 and 700nm achieved the optimal estimation of TVC (R(2)=0.6833). The TBFI value for each image pixel was calculated using the above two wavelengths, and then used to predict the TVC for the corresponding pixel on the image. Finally, the predicted TVC were visualized to illustrate the temporal variation and spatial distribution of viable bacteria on meat surface over storage. The results demonstrate the promising potential of the developed TBFI for the detection of viable bacteria contamination on chicken meat surface.
Collapse
Affiliation(s)
- Xujun Ye
- Faculty of Agriculture and Life Science, Hirosaki University, Aomori 036-8561, Japan.
| | - Kanako Iino
- Faculty of Agriculture and Life Science, Hirosaki University, Aomori 036-8561, Japan
| | - Shuhuai Zhang
- Faculty of Agriculture and Life Science, Hirosaki University, Aomori 036-8561, Japan
| |
Collapse
|
14
|
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]
|
15
|
Cheng JH, Sun DW. Recent Applications of Spectroscopic and Hyperspectral Imaging Techniques with Chemometric Analysis for Rapid Inspection of Microbial Spoilage in Muscle Foods. Compr Rev Food Sci Food Saf 2015. [DOI: 10.1111/1541-4337.12141] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Jun-Hu Cheng
- College of Light Industry and Food Science; South China Univ. of Technology; Guangzhou 510641 China
| | - Da-Wen Sun
- College of Light Industry and Food Science; 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
| |
Collapse
|