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Neamah HA, Tandio J. Towards the development of foods 3D printer: Trends and technologies for foods printing. Heliyon 2024; 10:e33882. [PMID: 39050479 PMCID: PMC11268349 DOI: 10.1016/j.heliyon.2024.e33882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/11/2024] [Accepted: 06/28/2024] [Indexed: 07/27/2024] Open
Abstract
3D printing of food materials is among the innovations that could revolutionize people's food choices and consumption. Food innovation and production have advanced considerably in recent years and its market has shown rapid annual expansion. Printing food technologies are considered as a potential solution for producing customized foods such as military food, and astronaut food. The printable food ink material still lacks standardization and superior extrusion process compared to other 3D printing industries. This review paper aimed to provide a comprehensive review of the current foods 3D printing and the latest technology in certain terms and with its concrete applications. In particular, the following issues are discussed: the printing techniques, exudations classes, business prospects, technologies, printing parameters, food materials, safety, and challenges and limitations of food 3D printing along with possible improvement recommendations. Significant printing parameters have been summarized and the safety of the food printing has been evaluated. Moreover, this article also contains a detailed, tabular evaluation of technical approaches employed across researched based and commercially available systems. One of the major limitations that need to be resolved was standardization of food printing safety.
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Affiliation(s)
- Husam A. Neamah
- Department of Electrical and Mechatronics Engineering, University of Debrecen, Debrecen, 4028, Hungary
- Technical Engineering College, Al-Ayen University, Thi-Qar, 64001, Iraq
- Department of Business Management, Al-imam University College, Balad, Iraq
| | - Joseph Tandio
- Mechatronic Systems Design, Eindhoven University of Technology, Eindhoven, 5612, Netherlands
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2
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Shen C, Ding M, Wu X, Cai G, Cai Y, Gai S, Wang B, Liu D. Identifying the quality characteristics of pork floss structure based on deep learning framework. Curr Res Food Sci 2023; 7:100587. [PMID: 37727873 PMCID: PMC10506091 DOI: 10.1016/j.crfs.2023.100587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/21/2023] Open
Abstract
Pork floss is a traditional Chinese food with a long history. Nowadays, pork floss is known to consumers as a leisure food. It is made from pork through a unique process in which the muscle fibers become flaky or granular and tangled. In this study, a deep learning-based approach is proposed to detect the quality characteristics of pork floss structure. Describe that the experiments were conducted using widely recognized brands of pork floss available in the grocery market, omitting the use of abbreviations. A total of 8000 images of eight commercially available pork flosses were collected and processed using sharpening, image gray coloring, real-time shading correction, and binarization. After the machine learning model learned the features of the pork floss, the images were labeled using a manual mask. The coupling of residual enhancement mask and region-based convolutional neural network (CRE-MRCNN) based deep learning framework was used to segment the images. The results showed that CRE-MRCNN could be used to identify the knot features and pore features of different brands of pork floss to evaluate their quality. The combined results of the models based on the sensory tests and machine vision showed that the pork floss from TC was the best, followed by YJJ, DD and HQ. This also shows the potential of machine vision to help people recognize the quality characteristics of pork floss structure.
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Affiliation(s)
- Che Shen
- College of Food Science and Technology, Bohai University, Jinzhou 121013, China
- Key Laboratory for Agricultural Products Processing of Anhui Province, School of Food Science and Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Meiqi Ding
- College of Food Science and Technology, Bohai University, Jinzhou 121013, China
| | - Xinnan Wu
- College of Food Science and Technology, Bohai University, Jinzhou 121013, China
| | - Guanhua Cai
- College of Food Science and Technology, Bohai University, Jinzhou 121013, China
| | - Yun Cai
- College of Food Science and Technology, Bohai University, Jinzhou 121013, China
| | - Shengmei Gai
- College of Food Science and Technology, Bohai University, Jinzhou 121013, China
| | - Bo Wang
- College of Food Science and Technology, Bohai University, Jinzhou 121013, China
- Key Laboratory of Meat Processing and Quality Control, MOE, Key Laboratory of Meat Processing, MARA, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
- Institute of Ocean Research, Bohai University, Jinzhou 121013, Liaoning, China
| | - Dengyong Liu
- College of Food Science and Technology, Bohai University, Jinzhou 121013, China
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Sánchez CN, Orvañanos-Guerrero MT, Domínguez-Soberanes J, Álvarez-Cisneros YM. Analysis of beef quality according to color changes using computer vision and white-box machine learning techniques. Heliyon 2023; 9:e17976. [PMID: 37519729 PMCID: PMC10375562 DOI: 10.1016/j.heliyon.2023.e17976] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 08/01/2023] Open
Abstract
The quality of beef products relies on the presence of a cherry red color, as any deviation toward brownish tones indicates a loss in quality. Existing studies typically analyze individual color channels separately, establishing acceptable ranges. In contrast, our proposed approach involves conducting a multivariate analysis of beef color changes using white-box machine learning techniques. Our proposal encompasses three phases. (1) We employed a Computer Vision System (CVS) to capture the color of beef pieces, implementing a color correction pre-processing step within a specially designed cabin. (2) We examined the differences among three color spaces (RGB, HSV, and CIELab*) (3) We evaluated the performance of three white-box classifiers (decision tree, logistic regression, and multivariate normal distributions) for predicting color in both fresh and non-fresh beef. These models demonstrated high accuracy and enabled a comprehensive understanding of the prediction process. Our results affirm that conducting a multivariate analysis yields superior beef color prediction outcomes compared to the conventional practice of analyzing each channel independently.
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Affiliation(s)
- Claudia N. Sánchez
- Universidad Panamericana. Facultad de Ingeniería. Aguascalientes, 20296, Mexico
| | | | | | - Yenizey M. Álvarez-Cisneros
- Departamento de Biotecnología, Universidad Autónoma Metropolitana, Unidad Iztapalapa. Ciudad de México, 09310, Mexico
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4
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Image based beef and lamb slice authentication using convolutional neural networks. Meat Sci 2023; 195:108997. [DOI: 10.1016/j.meatsci.2022.108997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/11/2022] [Accepted: 09/30/2022] [Indexed: 11/09/2022]
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5
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Wang C, Liu Y, Xia Z, Wang Q, Duan S, Gong Z, Chen J. Convolutional neural network‐based portable computer vision system for freshness assessment of crayfish (
Prokaryophyllus clarkii
). J Food Sci 2022; 87:5330-5339. [DOI: 10.1111/1750-3841.16377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/21/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022]
Affiliation(s)
- Chao Wang
- College of Food Science and Engineering Wuhan Polytechnic University Wuhan China
| | - Yan Liu
- College of Food Science and Engineering Wuhan Polytechnic University Wuhan China
- Key Laboratory for Deep Processing of Major Grain and Oil (Wuhan Polytechnic University), Ministry of Education, College of Food Science and Engineering Wuhan Polytechnic University Wuhan P.R. China
- Hubei Key Laboratory for Processing and Transformation of Agricultural Products (Wuhan Polytechnic University), College of Food Science and Engineering Wuhan Polytechnic University Wuhan P.R. China
| | - Zhenzhen Xia
- Institute of Agricultural Quality Standards and Testing Technology Research Hubei Academy of Agricultural Science Wuhan China
| | - Qiao Wang
- College of Food Science and Engineering Wuhan Polytechnic University Wuhan China
| | - Shuo Duan
- College of Food Science and Engineering Wuhan Polytechnic University Wuhan China
| | - Zhiyong Gong
- College of Food Science and Engineering Wuhan Polytechnic University Wuhan China
| | - Jiwang Chen
- College of Food Science and Engineering Wuhan Polytechnic University Wuhan China
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Banwari A, Joshi RC, Sengar N, Dutta MK. Computer vision technique for freshness estimation from segmented eye of fish image. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Fan KJ, Su WH. Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review. BIOSENSORS 2022; 12:bios12020076. [PMID: 35200337 PMCID: PMC8869398 DOI: 10.3390/bios12020076] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 05/12/2023]
Abstract
Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.
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Computer Vision and Machine Learning for Tuna and Salmon Meat Classification. INFORMATICS 2021. [DOI: 10.3390/informatics8040070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.
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Khaled AY, Parrish CA, Adedeji A. Emerging nondestructive approaches for meat quality and safety evaluation-A review. Compr Rev Food Sci Food Saf 2021; 20:3438-3463. [PMID: 34151512 DOI: 10.1111/1541-4337.12781] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/29/2021] [Accepted: 05/11/2021] [Indexed: 11/28/2022]
Abstract
Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.
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Affiliation(s)
- Alfadhl Y Khaled
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Chadwick A Parrish
- Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA
| | - Akinbode Adedeji
- Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, USA
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Taheri-Garavand A, Fatahi S, Omid M, Makino Y. Meat quality evaluation based on computer vision technique: A review. Meat Sci 2019; 156:183-195. [PMID: 31202093 DOI: 10.1016/j.meatsci.2019.06.002] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 05/30/2019] [Accepted: 06/04/2019] [Indexed: 01/11/2023]
Abstract
Nowadays people tend to include more meat in their diet thanks to the improvement in standards of living as well as an increase in awareness of meat nutritive values. To ensure public health, therefore, there is a need for a rise in worldwide meat production and consumption. Further attention is also required as to how the safety and the quality of meat production process should be assessed. Classical methods of meat quality assessment, however, have some disadvantages; expensive and time-consuming. This study intends to introduce an alternative method known as Computer Vision (CV) for the assessment of various quality parameters of muscle foods. CV has several advantages over the traditional methods. It is non-destructive, easy, and quick, hence, more efficient in meat quality assessments. This study aims to investigate different quality characteristics of some muscle foods using CV. It closes with a discussion on the future challenges and expected opportunities of the practical application of CV in the meat industry.
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Affiliation(s)
- Amin Taheri-Garavand
- Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran.
| | - Soodabeh Fatahi
- Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran
| | - Mahmoud Omid
- Department of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
| | - Yoshio Makino
- Graduate School of Agricultural and Life Science, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-Ku, Tokyo 113-8657, Japan
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