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Frigerio J, Campone L, Giustra MD, Buzzelli M, Piccoli F, Galimberti A, Cannavacciuolo C, Ouled Larbi M, Colombo M, Ciocca G, Labra M. Convergent technologies to tackle challenges of modern food authentication. Heliyon 2024; 10:e32297. [PMID: 38947432 PMCID: PMC11214499 DOI: 10.1016/j.heliyon.2024.e32297] [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: 04/11/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 07/02/2024] Open
Abstract
The authentication process involves all the supply chain stakeholders, and it is also adopted to verify food quality and safety. Food authentication tools are an essential part of traceability systems as they provide information on the credibility of origin, species/variety identity, geographical provenance, production entity. Moreover, these systems are useful to evaluate the effect of transformation processes, conservation strategies and the reliability of packaging and distribution flows on food quality and safety. In this manuscript, we identified the innovative characteristics of food authentication systems to respond to market challenges, such as the simplification, the high sensitivity, and the non-destructive ability during authentication procedures. We also discussed the potential of the current identification systems based on molecular markers (chemical, biochemical, genetic) and the effectiveness of new technologies with reference to the miniaturized systems offered by nanotechnologies, and computer vision systems linked to artificial intelligence processes. This overview emphasizes the importance of convergent technologies in food authentication, to support molecular markers with the technological innovation offered by emerging technologies derived from biotechnologies and informatics. The potential of these strategies was evaluated on real examples of high-value food products. Technological innovation can therefore strengthen the system of molecular markers to meet the current market needs; however, food production processes are in profound evolution. The food 3D-printing and the introduction of new raw materials open new challenges for food authentication and this will require both an update of the current regulatory framework, as well as the development and adoption of new analytical systems.
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Affiliation(s)
- Jessica Frigerio
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20216, Milano, Italy
| | - Luca Campone
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20216, Milano, Italy
| | - Marco Davide Giustra
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20216, Milano, Italy
| | - Marco Buzzelli
- Department of Informatics Systems and Communication, University of Milano-Bicocca, viale Sarca, 336, 20216, Milano, Italy
| | - Flavio Piccoli
- Department of Informatics Systems and Communication, University of Milano-Bicocca, viale Sarca, 336, 20216, Milano, Italy
| | - Andrea Galimberti
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20216, Milano, Italy
| | - Ciro Cannavacciuolo
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20216, Milano, Italy
| | - Malika Ouled Larbi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20216, Milano, Italy
| | - Miriam Colombo
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20216, Milano, Italy
| | - Gianluigi Ciocca
- Department of Informatics Systems and Communication, University of Milano-Bicocca, viale Sarca, 336, 20216, Milano, Italy
| | - Massimo Labra
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza, 2, 20216, Milano, Italy
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Li Y, Guo J, Qiu H, Chen F, Zhang J. Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis. FRONTIERS IN PLANT SCIENCE 2023; 14:1267810. [PMID: 38146275 PMCID: PMC10749533 DOI: 10.3389/fpls.2023.1267810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/14/2023] [Indexed: 12/27/2023]
Abstract
Problems Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models. Aim This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample. Methods Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images. Results and conclusion The test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.
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Affiliation(s)
| | - Jianwen Guo
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, Guangdong, China
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Abedini A, Salimi M, Mazaheri Y, Sadighara P, Alizadeh Sani M, Assadpour E, Jafari SM. Assessment of cheese frauds, and relevant detection methods: A systematic review. Food Chem X 2023; 19:100825. [PMID: 37780280 PMCID: PMC10534187 DOI: 10.1016/j.fochx.2023.100825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/28/2023] [Accepted: 08/02/2023] [Indexed: 10/03/2023] Open
Abstract
Dairy products are widely consumed in the world due to their nutritional and functional characteristics. This group of food products are consumed by all age groups due to their health-giving properties. One of these products is cheese which has a high price compared to other dairy products. Because of this, it can be prone to fraud all over the world. Fraud in food products threatens the world's food safety and can cause serious damage to human health. There are many concerns among food authorities in the world about the fraud of food products. FDA, WHO, and the European Commission provide different legislations and definitions for fraud. The purpose of this review is to identify the most susceptible cheese type for fraud and effective methods for evaluating fraud in all types of cheeses. For this, we examined the Web of Science, Scopus, PubMed, and ScienceDirect databases. Mozzarella cheese had the largest share among all cheeses in terms of adulteration due to its many uses. Also, the methods used to evaluate different types of cheese frauds were PCR, Spectrometry, stable isotope, image analysis, electrophoretic, ELISA, sensors, sensory analysis, near-infrared and NMR. The methods that were most used in detecting fraud were PCR and spectrometry methods. Also, the least used method was sensory evaluation.
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Affiliation(s)
- Amirhossein Abedini
- Students Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
- Division of Food Safety and Hygiene, Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahla Salimi
- Student Research Committee, Department of Food Science and Technology, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Science and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Yeganeh Mazaheri
- Division of Food Safety and Hygiene, Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Parisa Sadighara
- Division of Food Safety and Hygiene, Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahmood Alizadeh Sani
- Division of Food Safety and Hygiene, Department of Environmental Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Elham Assadpour
- Food Industry Research Co., Gorgan, Iran
- Food and Bio-Nanotech International Research Center (Fabiano), Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
| | - Seid Mahdi Jafari
- Department of Food Materials and Process Design Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
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Chen Y, Wu C, Zhang Q, Wu D. Review of visual analytics methods for food safety risks. NPJ Sci Food 2023; 7:49. [PMID: 37699926 PMCID: PMC10497676 DOI: 10.1038/s41538-023-00226-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023] Open
Abstract
With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.
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Affiliation(s)
- Yi Chen
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China.
| | - Caixia Wu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China
| | - Qinghui Zhang
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China
| | - Di Wu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, Northern Ireland, UK
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Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chilli Powder. FOOD ANAL METHOD 2023. [DOI: 10.1007/s12161-023-02445-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Pratondo A, Elfahmi E, Novianty A. Classification of Curcuma longa and Curcuma zanthorrhiza using transfer learning. PeerJ Comput Sci 2022; 8:e1168. [PMID: 37346311 PMCID: PMC10280267 DOI: 10.7717/peerj-cs.1168] [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: 04/12/2022] [Accepted: 11/01/2022] [Indexed: 06/23/2023]
Abstract
Curcuma longa (turmeric) and Curcuma zanthorrhiza (temulawak) are members of the Zingiberaceae family that contain curcuminoids, essential oils, starch, protein, fat, cellulose, and minerals. The nutritional content proportion of turmeric is different from temulawak which implies differences in economic value. However, only a few people who understand herbal plants, can identify the difference between them. This study aims to build a model that can distinguish between the two species of Zingiberaceae based on the image captured from a mobile phone camera. A collection of images consisting of both types of rhizomes are used to build a model through a learning process using transfer learning, specifically pre-trained VGG-19 and Inception V3 with ImageNet weight. Experimental results show that the accuracy rates of the models to classify the rhizomes are 92.43% and 94.29%, consecutively. These achievements are quite promising to be used in various practical use.
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Affiliation(s)
- Agus Pratondo
- Department of Multimedia Engineering, School of Applied Sciences, Telkom University, Bandung, West Java, Indonesia
| | - Elfahmi Elfahmi
- Department of Pharmaceutical Biology, School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, Indonesia
| | - Astri Novianty
- Department of Computer Engineering, School of Electrical Engineering, Telkom University, Bandung, West Jawa, Indonesia
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An YL, Wei WL, Guo DA. Application of Analytical Technologies in the Discrimination and Authentication of Herbs from Fritillaria: A Review. Crit Rev Anal Chem 2022:1-22. [PMID: 36227577 DOI: 10.1080/10408347.2022.2132374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Medicinal plants of Fritillaria are widely distributed in numerous countries around the world and possess excellent antitussive and expectorant effects. In particular, Fritillariae Bulbus (FB) as a precious traditional medicine has thousands of years of medical history in China. Herbs of Fritillaria have a high market value and demand while limited by harsh growing circumstances and scarce wild resources. As a consequence, fraudulent behaviors are regularly engaged by the unscrupulous merchants in an attempt to reap greater profits. It is of an urgent need to evaluate the quality of Fritillaria herbs and their products using various analytical instruments and techniques. This review has scrutinized approximately 160 articles from 1995 to 2022 published on the investigation of Fritillaria herbs and related herbal products. The botanical classification of genus Fritillaria, types of counterfeits, technologies applied for differentiating Fritillaria species were comprehensively summarized and discussed in the current review. Molecular and chromatographic identification were the dominant technologies in the authentication of Fritillaria herbs. Additionally, we brought some potential and promising technologies and analytical strategies into attention, which are worthy attempting in the future researches. This review could conduce to excellent reference value for further investigations of the authenticity assessment of Fritillaria species.
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Affiliation(s)
- Ya-Ling An
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wen-Long Wei
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - De-An Guo
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Research Center for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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The Use of Convolutional Neural Networks and Digital Camera Images in Cataract Detection. ELECTRONICS 2022. [DOI: 10.3390/electronics11060887] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Cataract is one of the major causes of blindness in the world. Its early detection and treatment could greatly reduce the risk of deterioration and blindness. Instruments commonly used to detect cataracts are slit lamps and fundus cameras, which are highly expensive and require domain knowledge. Thus, the problem is that the lack of professional ophthalmologists could result in the delay of cataract detection, where medical treatment is inevitable. Therefore, this study aimed to design a convolutional neural network (CNN) with digital camera images (CNNDCI) system to detect cataracts efficiently and effectively. The designed CNNDCI system can perform the cataract identification process accurately in a user-friendly manner using smartphones to collect digital images. In addition, the existing numerical results provided by the literature were used to demonstrate the performance of the proposed CNNDCI system for cataract detection. Numerical results revealed that the designed CNNDCI system could identify cataracts effectively with satisfying accuracy. Thus, this study concluded that the presented CNNDCI architecture is a feasible and promising alternative for cataract detection.
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