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Fakhlaei R, Babadi AA, Sun C, Ariffin NM, Khatib A, Selamat J, Xiaobo Z. Application, challenges and future prospects of recent nondestructive techniques based on the electromagnetic spectrum in food quality and safety. Food Chem 2024; 441:138402. [PMID: 38218155 DOI: 10.1016/j.foodchem.2024.138402] [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: 11/15/2023] [Revised: 12/26/2023] [Accepted: 01/06/2024] [Indexed: 01/15/2024]
Abstract
Safety and quality aspects of food products have always been critical issues for the food production and processing industries. Since conventional quality measurements are laborious, time-consuming, and expensive, it is vital to develop new, fast, non-invasive, cost-effective, and direct techniques to eliminate those challenges. Recently, non-destructive techniques have been applied in the food sector to improve the quality and safety of foodstuffs. The aim of this review is an effort to list non-destructive techniques (X-ray, computer tomography, ultraviolet-visible spectroscopy, hyperspectral imaging, infrared, Raman, terahertz, nuclear magnetic resonance, magnetic resonance imaging, and ultrasound imaging) based on the electromagnetic spectrum and discuss their principle and application in the food sector. This review provides an in-depth assessment of the different non-destructive techniques used for the quality and safety analysis of foodstuffs. We also discussed comprehensively about advantages, disadvantages, challenges, and opportunities for the application of each technique and recommended some solutions and developments for future trends.
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Affiliation(s)
- Rafieh Fakhlaei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Arman Amani Babadi
- School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Chunjun Sun
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Naziruddin Mat Ariffin
- Department of Food Science, Faculty of Food Science and Technology, University Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Alfi Khatib
- Pharmacognosy Research Group, Department of Pharmaceutical Chemistry, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan 25200, Pahang Darul Makmur, Malaysia; Faculty of Pharmacy, Airlangga University, Surabaya 60155, Indonesia
| | - Jinap Selamat
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Zou Xiaobo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
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Bai Y, Lu Y, Yang P, Ding Y, Zheng Y, Ke Z, Liu S, Ding Y, Zhou X. Simultaneous determination of multiple quality indices of dried shrimp (Parapenaeopsis hardwickii) during storage using Raman spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:4226-4233. [PMID: 38299755 DOI: 10.1002/jsfa.13304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Dried shrimp is a high-value fishery product worldwide, but rapid and accurate assessment of its quality remains challenging. In the present study, a new method based on Raman spectroscopy was developed for assessing the quality changes in dried shrimp (Parapenaeopsis hardwickii) during storage. RESULTS A high-quality Raman spectrum of astaxanthin (AST) was obtained from the third abdominal segment of dried shrimp. The intensity ratio (I1520/I1446) of the band from 1520 cm-1 to that at 1446 cm-1, which was ascribed to AST and protein/lipid, respectively, was calculated. I1520/I1446 can probe AST degradation in dried shrimp during storage at both 37 and 4 °C and further reflect quality changes of dried shrimp, as indicated by indices including total volatile basic nitrogen, pH and thiobarbituric acid reactive substances. CONCLUSION Compared to conventional methods, the proposed method avoids complex and time-consuming preprocessing and provides significant advantages including cost-effectiveness and rapid detection. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Yan Bai
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Hangzhou, China
- National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou, China
| | - Yilin Lu
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Peng Yang
- Hangzhou Hengmei Food Science & Technology Co., Ltd., Hangzhou, China
| | - Yicheng Ding
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Hangzhou, China
- National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou, China
| | - Yadan Zheng
- Hangzhou Hengmei Food Science & Technology Co., Ltd., Hangzhou, China
| | - Zhigang Ke
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Hangzhou, China
- National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou, China
| | - Shulai Liu
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Hangzhou, China
- National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou, China
| | - Yuting Ding
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Hangzhou, China
- National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou, China
| | - Xuxia Zhou
- College of Food Science and Technology, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Hangzhou, China
- National R&D Branch Center for Pelagic Aquatic Products Processing (Hangzhou), Hangzhou, China
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Chen X, Chen C, Tian X, He L, Zuo E, Liu P, Xue Y, Yang J, Chen C, Lv X. DBAN: An improved dual branch attention network combined with serum Raman spectroscopy for diagnosis of diabetic kidney disease. Talanta 2024; 266:125052. [PMID: 37574605 DOI: 10.1016/j.talanta.2023.125052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/02/2023] [Accepted: 08/05/2023] [Indexed: 08/15/2023]
Abstract
Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%-40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis.
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Affiliation(s)
- Xinya Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Xuecong Tian
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Liang He
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China; Xinjiang Key Laboratory of Signal Detection and Processing, Urumqi, 830017,China; Department of Electronic Engineering, and Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Enguang Zuo
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Pei Liu
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - You Xue
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Jie Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, 830046, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, 840046, China.
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Hou S, Dong Y, Li Y, Yan Q, Wang M, Fang S. Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio. Sci Rep 2023; 13:10736. [PMID: 37400501 DOI: 10.1038/s41598-023-37165-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 06/16/2023] [Indexed: 07/05/2023] Open
Abstract
Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio (SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. In recent years, joint recognition of multiple networks has become increasingly popular. In complex wireless environments, there are multiple signal types and diversity of characteristics between different signals. Also, the existence of multiple interference in wireless environment makes the signal characteristics more complex. It is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time-frequency domain joint recognition model that combines two deep learning networks (DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN (multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component (IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3 (three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT (Fast Fourier Transform) is used to obtain frequency domain amplitude and phase (FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.
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Affiliation(s)
- Shunhu Hou
- Graduate School, Space Engineering University, Beijing, 101416, China
| | - Yaoyao Dong
- School of Space Information, Space Engineering University, Beijing, 101416, China
| | - Yuhai Li
- Graduate School, Space Engineering University, Beijing, 101416, China
| | - Qingqing Yan
- Xichang Satellite Launch Center, Xichang, 615000, China
| | - Mengtao Wang
- Graduate School, Space Engineering University, Beijing, 101416, China
| | - Shengliang Fang
- School of Space Information, Space Engineering University, Beijing, 101416, China.
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