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Tian X, Zheng X, Chen L, Wang Z, Liu BT, Bi Y, Li L, Shi H, Li S, Li C, Zhang D. Recent advances in photoluminescent fluorescent probe technology for food flavor compounds analysis. Food Chem 2024; 459:140455. [PMID: 39029422 DOI: 10.1016/j.foodchem.2024.140455] [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: 04/16/2024] [Revised: 06/24/2024] [Accepted: 07/12/2024] [Indexed: 07/21/2024]
Abstract
The real-time, precise qualitative and quantitative sensing of food flavor compounds is crucial for ensuring food safety, quality, and consumer acceptance. As indicators for food flavor labeling, it is vital to delve deep into the specific ingredient and content of food flavor compounds to assess the food flavor quality, but still facing huge challenges. Photoluminescent fluorescent probe technology, with fast detection and high sensitivity, has shown immense potentials in detecting food flavor compounds. In this review, the classification and optical sensing mechanism of photoluminescent fluorescent probe technology are described in detail. Besides, challenges in applying photoluminescent fluorescent probe technology to analyze food flavor compounds are outlined to indicate future research directions. We hope this review can provide an insight for the applications of photoluminescent fluorescent probe technology in the evaluation of food flavor quality in future.
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Affiliation(s)
- Xiaoxian Tian
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Xiaochun Zheng
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Li Chen
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Zhenyu Wang
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Bai-Tong Liu
- Department of Chemistry, The University of Hong Kong, 999077, Hong Kong Special Administrative Region
| | - Yongzhao Bi
- Food Laboratory of Zhongyuan, Beijing Technology and Business University, Beijing 100048, China
| | - Liang Li
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Haonan Shi
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Shaobo Li
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Cheng Li
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Dequan Zhang
- Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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Xun Z, Wang X, Xue H, Zhang Q, Yang W, Zhang H, Li M, Jia S, Qu J, Wang X. Deep machine learning identified fish flesh using multispectral imaging. Curr Res Food Sci 2024; 9:100784. [PMID: 39005497 PMCID: PMC11246001 DOI: 10.1016/j.crfs.2024.100784] [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: 04/18/2024] [Revised: 06/03/2024] [Accepted: 06/13/2024] [Indexed: 07/16/2024] Open
Abstract
Food fraud is widespread in the aquatic food market, hence fast and non-destructive methods of identification of fish flesh are needed. In this study, multispectral imaging (MSI) was used to screen flesh slices from 20 edible fish species commonly found in the sea around Yantai, China, by combining identification based on the mitochondrial COI gene. We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. We then employed eight models to compare their prediction performances based on the hold-out method with 70% training and 30% test sets. Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. CNN and QDA achieved more than 99% accuracy on the test set. By extracting the CNN features for optimization, a very high degree of separation was obtained for all species. Furthermore, based on the Gini index in RF, 11 bands were selected as key classification features for CNN, and an accuracy of 98% was achieved. Our study developed a successful pipeline for employing machine learning models (especially CNN) on MSI identification of fish flesh, and provided a convenient and non-destructive method to determine the marketing of fish flesh in the future.
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Affiliation(s)
- Zhuoran Xun
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Xuemeng Wang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hao Xue
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Qingzheng Zhang
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Wanqi Yang
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Hua Zhang
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Mingzhu Li
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Shangang Jia
- College of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jiangyong Qu
- College of Life Sciences, Yantai University, Yantai, 264005, China
| | - Xumin Wang
- College of Life Sciences, Yantai University, Yantai, 264005, China
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Ma Y, Chen R, Zhang R, Liang J, Ren S, Gao Z. Application of DNA-fueled molecular machines in food safety testing. Compr Rev Food Sci Food Saf 2024; 23:1-22. [PMID: 38284608 DOI: 10.1111/1541-4337.13299] [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: 05/31/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/30/2024]
Abstract
Food is consumed by humans, which is indispensable to human life. Therefore, considerable attention of the whole society has been paid to food safety. Over the last few years, dramatic social development has brought new challenges to food safety, making developing new and quick methods for on-site food safety testing an important necessity. As a result, DNA-fueled molecular machines, characterized by high efficiency, accuracy, and sensitivity in testing, have come into the spotlight, based on which sensors can be constructed to detect toxic and harmful substances in food products. This study reviewed recent research on several DNA-fueled molecular machines, including DNA tweezers, DNA walkers, and DNA origami, for rapidly detecting toxic and harmful substances. Based on the above studies, the sensitivity and timeliness of several DNA molecular machines were summarized and compared, and the development prospect of DNA fuel molecular machines in the field of food safety detection was prospected.
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Affiliation(s)
- Yujing Ma
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, China
| | - Ruipeng Chen
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
| | - Rui Zhang
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, China
| | - Jun Liang
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, China
| | - Shuyue Ren
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
| | - Zhixian Gao
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China
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Mukundan A, Tsao Y, Wang HC. Early esophageal detection using hyperspectral engineering and convolutional neural network. OPTICS IN HEALTH CARE AND BIOMEDICAL OPTICS XIII 2023. [DOI: 10.1117/12.2689086] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
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Stasenko N, Shukhratov I, Savinov M, Shadrin D, Somov A. Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples. ENTROPY (BASEL, SWITZERLAND) 2023; 25:987. [PMID: 37509935 PMCID: PMC10378337 DOI: 10.3390/e25070987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 07/30/2023]
Abstract
Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples. The Pix2PixHD model achieved higher results in terms of VNIR images translation from RGB (SSIM = 0.972). Mask R-CNN model was selected as a CNN technique for VNIR images segmentation and achieved 58.861 for postharvest decay zones, 40.968 for fungal zones and 94.800 for both the decayed and fungal zones detection and prediction in stored apples in terms of F1-score metric. In order to verify the effectiveness of this approach, a unique paired dataset containing 1305 RGB and VNIR images of apples of four varieties was obtained. It is further utilized for a GAN model selection. Additionally, we acquired 1029 VNIR images of apples for training and testing a CNN model. We conducted validation on an embedded system equipped with a graphical processing unit. Using Pix2PixHD, 100 VNIR images from RGB images were generated at a rate of 17 frames per second (FPS). Subsequently, these images were segmented using Mask R-CNN at a rate of 0.42 FPS. The achieved results are promising for enhancing the food study and control during the postharvest stage.
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Affiliation(s)
- Nikita Stasenko
- Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | | | - Maxim Savinov
- Saint-Petersburg State University of Aerospace Instrumentation (SUAI), 190000 Saint-Petersburg, Russia
| | - Dmitrii Shadrin
- Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
- Department of Information Technology and Data Science, Irkutsk National Research Technical University, 664074 Irkutsk, Russia
| | - Andrey Somov
- Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
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A coumarin-based small molecular fluorescent probe for detection of the freshness of meat and shrimp. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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Hassoun A, Jagtap S, Garcia-Garcia G, Trollman H, Pateiro M, Lorenzo JM, Trif M, Rusu AV, Aadil RM, Šimat V, Cropotova J, Câmara JS. Food quality 4.0: From traditional approaches to digitalized automated analysis. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Ali A, Wei S, Ali A, Khan I, Sun Q, Xia Q, Wang Z, Han Z, Liu Y, Liu S. Research Progress on Nutritional Value, Preservation and Processing of Fish-A Review. Foods 2022; 11:foods11223669. [PMID: 36429260 PMCID: PMC9689683 DOI: 10.3390/foods11223669] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/09/2022] [Accepted: 11/13/2022] [Indexed: 11/18/2022] Open
Abstract
The global population has rapidly expanded in the last few decades and is continuing to increase at a rapid pace. To meet this growing food demand fish is considered a balanced food source due to their high nutritious value and low cost. Fish are rich in well-balanced nutrients, a good source of polyunsaturated fatty acids and impose various health benefits. Furthermore, the most commonly used preservation technologies including cooling, freezing, super-chilling and chemical preservatives are discussed, which could prolong the shelf life. Non-thermal technologies such as pulsed electric field (PEF), fluorescence spectroscopy, hyperspectral imaging technique (HSI) and high-pressure processing (HPP) are used over thermal techniques in marine food industries for processing of most economical fish products in such a way as to meet consumer demands with minimal quality damage. Many by-products are produced as a result of processing techniques, which have caused serious environmental pollution. Therefore, highly advanced technologies to utilize these by-products for high-value-added product preparation for various applications are required. This review provides updated information on the nutritional value of fish, focusing on their preservation technologies to inhibit spoilage, improve shelf life, retard microbial and oxidative degradation while extending the new applications of non-thermal technologies, as well as reconsidering the values of by-products to obtain bioactive compounds that can be used as functional ingredients in pharmaceutical, cosmetics and food processing industries.
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Affiliation(s)
- Ahtisham Ali
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institute, Guangdong Provincial Engineering Technology Research Centre of Seafood, Zhanjiang 524088, China
| | - Shuai Wei
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institute, Guangdong Provincial Engineering Technology Research Centre of Seafood, Zhanjiang 524088, China
- Correspondence: (S.W.); (S.L.)
| | - Adnan Ali
- Livestock & Dairy Development Department, Abbottabad 22080, Pakistan
| | - Imran Khan
- Department of Food Science and Technology, The University of Haripur, Haripur 22620, Pakistan
| | - Qinxiu Sun
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institute, Guangdong Provincial Engineering Technology Research Centre of Seafood, Zhanjiang 524088, China
| | - Qiuyu Xia
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institute, Guangdong Provincial Engineering Technology Research Centre of Seafood, Zhanjiang 524088, China
| | - Zefu Wang
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institute, Guangdong Provincial Engineering Technology Research Centre of Seafood, Zhanjiang 524088, China
| | - Zongyuan Han
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institute, Guangdong Provincial Engineering Technology Research Centre of Seafood, Zhanjiang 524088, China
| | - Yang Liu
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institute, Guangdong Provincial Engineering Technology Research Centre of Seafood, Zhanjiang 524088, China
| | - Shucheng Liu
- College of Food Science and Technology, Guangdong Ocean University, Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety, Guangdong Province Engineering Laboratory for Marine Biological Products, Key Laboratory of Advanced Processing of Aquatic Product of Guangdong Higher Education Institute, Guangdong Provincial Engineering Technology Research Centre of Seafood, Zhanjiang 524088, China
- Collaborative Innovation Centre of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
- Correspondence: (S.W.); (S.L.)
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Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146986] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.
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