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Abdanan Mehdizadeh S, Noshad M, Hojjati M. A modified sequential wavenumber selection-discriminant analysis with Bayesian optimization strategy for detection and identification of chia seed oil adulteration using Raman spectroscopy. Talanta 2024; 277:126439. [PMID: 38897011 DOI: 10.1016/j.talanta.2024.126439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 06/12/2024] [Accepted: 06/14/2024] [Indexed: 06/21/2024]
Abstract
The detection of oil fraud can be accomplished through the use of Raman spectroscopy, which is a potent analytical technique for identifying the adulteration of edible oils with inferior or less expensive oils. However, appropriate data reduction and classification methods are required to achieve high accuracy and reliability in the analysis of Raman spectra. In this study, data reduction algorithms such as principal component analysis (PCA) and modified sequential wavenumber selection (MSWS) were applied, along with discriminant analysis (DA) as a classifier for detecting oil fraud. The parameters of DA, such as the discriminant type, the amount of regularization, and the linear coefficient threshold, were optimized using Bayesian optimization. The methods were tested on a dataset of chia oil mixed with 5-40 % sunflower oil, which is a common form of fraud in the market. The results showed that MSWS-DA achieved 100 % classification accuracy, while PCA-DA achieved 91.3 % accuracy. Therefore, it was demonstrated that Raman spectroscopy combined with MSWS-DA and Bayesian optimization can effectively detect oil fraud with high accuracy and robustness.
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Affiliation(s)
- Saman Abdanan Mehdizadeh
- Department of Mechanics of Biosystems Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran.
| | - Mohammad Noshad
- Department of Food Science and Technology, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
| | - Mohammad Hojjati
- Department of Food Science and Technology, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
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2
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Shi J, Li R, Wang Y, Zhang C, Lyu X, Wan Y, Yu Z. Detection of lung cancer through SERS analysis of serum. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 314:124189. [PMID: 38569385 DOI: 10.1016/j.saa.2024.124189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/11/2024] [Accepted: 03/24/2024] [Indexed: 04/05/2024]
Abstract
Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.
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Affiliation(s)
- Jiamin Shi
- Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People's Republic of China; School of Physics, Dalian University of Technology, Dalian, 116023, People's Republic of China
| | - Rui Li
- Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People's Republic of China; School of Physics, Dalian University of Technology, Dalian, 116023, People's Republic of China; State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China
| | - Yuchen Wang
- Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People's Republic of China; School of Physics, Dalian University of Technology, Dalian, 116023, People's Republic of China
| | - Chenlei Zhang
- Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People's Republic of China
| | - Xiaohong Lyu
- Department of Radiology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121000, People's Republic of China
| | - Yuan Wan
- The Pq Laboratory of BiomeDx/Rx, Department of Biomedical Engineering, Binghamton University, Vestal, 13850 NY, USA
| | - Zhanwu Yu
- Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, People's Republic of China.
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3
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Sun X, Hu Y, Liu C, Zhang S, Yan S, Liu X, Zhao K. Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms. Foods 2024; 13:1420. [PMID: 38731791 PMCID: PMC11083255 DOI: 10.3390/foods13091420] [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: 03/30/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024] Open
Abstract
Due to the significant price differences among different types of edible oils, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate, and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.
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Affiliation(s)
- Xiaorong Sun
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Yiran Hu
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Cuiling Liu
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Shanzhe Zhang
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Sining Yan
- College of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; (X.S.); (Y.H.); (S.Z.)
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Xuecong Liu
- College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China;
| | - Kun Zhao
- College of New Energy and Materials, China University of Petroleum, Beijing 102249, China
- Key Laboratory of Oil and Gas Terahertz Spectroscopy and Photoelectric Detection, Petroleum and Chemical Industry Federation, China University of Petroleum, Beijing 102249, China
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4
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Teng Y, Chen Y, Chen X, Zuo S, Li X, Pan Z, Shao K, Du J, Li Z. Revealing the adulteration of sesame oil products by portable Raman spectrometer and 1D CNN vector regression: A comparative study with chemometrics and colorimetry. Food Chem 2024; 436:137694. [PMID: 37844509 DOI: 10.1016/j.foodchem.2023.137694] [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: 06/28/2023] [Revised: 09/28/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023]
Abstract
Identification and quantification of sesame oil products are crucial due to the existing problems of adulteration with lower-priced oils and false labeling of sesame proportions. In this study, 1D CNN models were established to achieve discrimination of oil types and multiple quantification of adulteration using portable Raman spectrometer. An improved data augmentation method involving discarding transformations that alter peak positions was proposed, and synchronously injecting noise during geometric transformations. Furthermore, a novel neural network structure was introduced incorporating vector regression to accurately predict each component simultaneously. The proposed method has achieved higher accuracy in detecting multi-component adulteration compared with chemometrics (100 % accuracy in classifying different oils; R2 over 0.99 and RMSE within 2 % in predicting unknown adulterated samples). Finally, commercially available sesame oil products were tested and compared with gas chromatography and colorimetric methods, demonstrating the effectiveness of our proposed model in achieving higher detection accuracy at low-concentration adulteration.
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Affiliation(s)
- Yuanjie Teng
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
| | - Yingxin Chen
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xiangou Chen
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Shaohua Zuo
- School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China; Engineering Research Center of Nanoelectronic Integration and Advanced Equipment, Ministry of Education, China.
| | - Xin Li
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zaifa Pan
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Kang Shao
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jinglin Du
- Grain and Oil Products Quality Inspection Center of Zhejiang Province, Hangzhou 310012, China
| | - Zuguang Li
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
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Gao C, Fan Q, Zhao P, Sun C, Dang R, Feng Y, Hu B, Wang Q. Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124036. [PMID: 38367343 DOI: 10.1016/j.saa.2024.124036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/04/2024] [Accepted: 02/10/2024] [Indexed: 02/19/2024]
Abstract
Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality. In this paper, we propose a method of utilizing a spectral encoder to extract effective spectral features, which can significantly enhance the reliability and precision of quantitative analysis. We built a latent encoded feature regression model; in the process of utilizing the autoencoder for reconstructing the spectrometer output, the latent feature obtained from the intermediate bottleneck layer is extracted. Then, these latent features are fed into a deep regression model for component concentration prediction. Through detailed ablation and comparative experiments, our proposed model demonstrates superior performance to common methods on single-component and multi-component mixture datasets, remarkably improving regression precision while without needing user-selected parameters and eliminating the interference of irrelevant and redundant information. Furthermore, in-depth analysis reveals that latent encoded feature possesses strong nonlinear feature representation capabilities, low computational costs, wide adaptability, and robustness against noise interference. This highlights its effectiveness in spectral regression tasks and indicates its potential in other application fields. Sufficient experimental results show that our proposed method provides a novel and effective feature extraction approach for spectral analysis, which is simple, suitable for various methods, and can meet the measurement needs of different real-world scenarios.
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Affiliation(s)
- Chi Gao
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qi Fan
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Peng Zhao
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chao Sun
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Ruochen Dang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yutao Feng
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China
| | - Bingliang Hu
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China.
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6
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Li H, Luo X, Haruna SA, Zareef M, Chen Q, Ding Z, Yan Y. Au-Ag OHCs-based SERS sensor coupled with deep learning CNN algorithm to quantify thiram and pymetrozine in tea. Food Chem 2023; 428:136798. [PMID: 37423106 DOI: 10.1016/j.foodchem.2023.136798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 06/29/2023] [Accepted: 07/01/2023] [Indexed: 07/11/2023]
Abstract
Pesticide residue detection in food has become increasingly important. Herein, surface-enhanced Raman scattering (SERS) coupled with an intelligent algorithm was developed for the rapid and sensitive detection of pesticide residues in tea. By employing octahedral Cu2O templates, Au-Ag octahedral hollow cages (Au-Ag OHCs) were developed, which improved the surface plasma effect via rough edges and hollow inner structure, amplifying the Raman signals of pesticide molecules. Afterward, convolutional neural network (CNN), partial least squares (PLS), and extreme learning machine (ELM) algorithms were applied for the quantitative prediction of thiram and pymetrozine. CNN algorithms performed optimally for thiram and pymetrozine, with correlation values of 0.995 and 0.977 and detection limits (LOD) of 0.286 and 29 ppb, respectively. Accordingly, no significant difference (P greater than 0.05) was observed between the developed approach and HPLC in detecting tea samples. Hence, the proposed Au-Ag OHCs-based SERS technique could be utilized for quantifying thiram and pymetrozine in tea.
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Affiliation(s)
- Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Xiaofeng Luo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
| | - Zhen Ding
- Changzhou Jintan Jiangnan Powder Co., Ltd, Changzhou 213200, PR China
| | - Yiyong Yan
- Shenzhen Bioeasy Biotechnology Co. Ltd, Shenzhen 518101, PR China; Shenzhen Senlanthy Technology Co., Ltd, Shenzhen 518107, PR China
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Joshi R, Gg LP, Faqeerzada MA, Bhattacharya T, Kim MS, Baek I, Cho BK. Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115020. [PMID: 37299748 DOI: 10.3390/s23115020] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/18/2023] [Accepted: 05/20/2023] [Indexed: 06/12/2023]
Abstract
Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with machine learning and deep learning technique, Fourier transform infrared (FT-IR) spectroscopy was employed in this investigation for the nondestructive quantitative measurement of eight different concentrations of melamine and cyanuric acid added to pet food. The effectiveness of the one-dimensional convolutional neural network (1D CNN) technique was compared with that of partial least squares regression (PLSR), principal component regression (PCR), and a net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO). The 1D CNN model developed for the FT-IR spectra attained correlation coefficients of 0.995 and 0.994 and root mean square error of prediction values of 0.090% and 0.110% for the prediction datasets on the melamine- and cyanuric acid-contaminated pet food samples, respectively, which were superior to those of the PLSR and PCR models. Therefore, when FT-IR spectroscopy is employed in conjunction with a 1D CNN model, it serves as a potentially rapid and nondestructive method for identifying toxic chemicals added to pet food.
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Affiliation(s)
- Rahul Joshi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Lakshmi Priya Gg
- Department of Multimedia, VIT School of Design (V-SIGN), Vellore Institute of Technology (VIT), Vellore 632014, India
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Tanima Bhattacharya
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Moon Sung Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea
- Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Republic of Korea
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8
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Teng Y, Wang Z, Zuo S, Li X, Chen Y. Identification of antibiotic residues in aquatic products with surface-enhanced Raman scattering powered by 1-D convolutional neural networks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 289:122195. [PMID: 36549071 DOI: 10.1016/j.saa.2022.122195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/22/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Universal and fast antibiotic residues detection technology is imperative for the control of food safety in aquatic products. However, accurate surface-enhanced Raman scattering (SERS) quantitative detection of complicated samples is still a challenge. A recognition method powered by deep learning and took advantage of the unique fingerprint information merits of SERS was proposed. Herein, the spectra were collected by Ag nanofilm SERS substrate prepared by self-assembly of Ag nanoparticles on water/oil interface. A SERS-based database of commonly used antibiotics in aquatic products was set up, which is suitable for employed as input data for learning and training. The results show that the five types of antibiotics are successfully distinguished through principal component analysis (PCA) and each antibiotic in every type was successfully distinguished. Furthermore, one-dimensional convolutional neural networks (1-D CNN) was used to distinguish the antibiotics, and the results show that all the test samples were correctly predicted by 1-D CNN model. The results of this research suggest the great potential of the combination of SERS spectra with deep learning as a method for rapid and highly accurate identification of antibiotic residues in aquatic products.
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Affiliation(s)
- Yuanjie Teng
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China.
| | - Zhenni Wang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Shaohua Zuo
- Engineering Research Center for Nanophotonics & Advanced Instrument, Ministry of Education, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China; Engineering Research Center of Nanoelectronic Integration and Advanced Equipment, Ministry of Education, China.
| | - Xin Li
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Yinxin Chen
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
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Wu X, Xu B, Niu Y, Gao S, Zhao Z, Ma R, Liu H, Zhang Y. Detection of antioxidants in edible oil by two-dimensional correlation spectroscopy combined with convolutional neural network. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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10
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Wang XZ, Wu HL, Wang T, Chen AQ, Sun HB, Ding ZW, Chang HY, Yu RQ. Rapid identification and semi-quantification of adulteration in walnut oil by using excitation–emission matrix fluorescence spectroscopy coupled with chemometrics and ensemble learning. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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11
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Momeny M, Neshat AA, Jahanbakhshi A, Mahmoudi M, Ampatzidis Y, Radeva P. Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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