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Yang SW, Xie Y, Liu JZ, Zhang D, Huang J, Liang P. A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 317:124427. [PMID: 38754205 DOI: 10.1016/j.saa.2024.124427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 04/21/2024] [Accepted: 05/07/2024] [Indexed: 05/18/2024]
Abstract
The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.
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Affiliation(s)
- Si-Wei Yang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - Yuhao Xie
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - Jia-Zhen Liu
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - De Zhang
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Jie Huang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China.
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Liu Z, Ettabib MA, Bowden BM, Bartlett PN, Wilkinson JS, Zervas MN. Multiframe-based non-local means denoising for Raman spectra. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 310:123931. [PMID: 38301572 DOI: 10.1016/j.saa.2024.123931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/03/2024]
Abstract
A method for denoising Raman spectra is presented in this paper. The approach is based on the principle that the original signal can be restored by averaging pixels based on structure similarity. Similarity searching and averaging are not limited to the neighbouring pixels but extended throughout the entire signal range across different frames. This approach is distinguished from the conventional single-frame neighbour pixel-based filtering. The effectiveness and robustness of the proposed method are demonstrated through denoising simulated and experimental Raman data sets with fixed denoising parameters. Several denoised results and statistical indicators are presented for the simulated data. Recovery of the experimental Raman spectrum from our newly developed cost-effective waveguide-enhanced Raman spectroscopy system is also presented and compared to the spectrum from a conventional expensive Raman microscope for the same analyte.
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Affiliation(s)
- Zhen Liu
- Zepler Institute for Photonics and Nanoelectronics, University of Southampton, Southampton SO17 1BJ, United Kingdom.
| | - Mohamed A Ettabib
- Zepler Institute for Photonics and Nanoelectronics, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Bethany M Bowden
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Philip N Bartlett
- School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - James S Wilkinson
- Zepler Institute for Photonics and Nanoelectronics, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Michalis N Zervas
- Zepler Institute for Photonics and Nanoelectronics, University of Southampton, Southampton SO17 1BJ, United Kingdom
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Zhang P, Liu B, Mu X, Xu J, Du B, Wang J, Liu Z, Tong Z. Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms. Molecules 2023; 29:197. [PMID: 38202780 PMCID: PMC10780255 DOI: 10.3390/molecules29010197] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/21/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.
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Affiliation(s)
| | | | | | | | | | | | | | - Zhaoyang Tong
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (P.Z.); (B.L.); (X.M.); (J.X.); (B.D.); (J.W.); (Z.L.)
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Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model. Int J Mol Sci 2022; 23:ijms232113633. [DOI: 10.3390/ijms232113633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.
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Sun L, Shan X, Dong Q, Wu C, Shan M, Guo H, Lu R. Ultrasonic Elastography Combined with Human Papilloma Virus Detection Based on Intelligent Denoising Algorithm in Diagnosis of Cervical Intraepithelial Neoplasia. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:8066133. [PMID: 34987601 PMCID: PMC8720634 DOI: 10.1155/2021/8066133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/28/2021] [Accepted: 12/08/2021] [Indexed: 11/17/2022]
Abstract
The aim of this research was to study the application of ultrasonic elastography combined with human papilloma virus (HPV) detection based on bilateral filter intelligent denoising algorithm in the diagnosis of cervical intraepithelial neoplasia (CIN) and provide a theoretical basis for clinical diagnosis and treatment of CIN. In this study, 100 patients with cervical lesions were selected as research objects and randomly divided into control group and experimental group, with 50 cases in each group. Patients in control group and experimental group were diagnosed by ultrasonic elastography combined with HPV detection. The experimental group used the optimized image map of bilateral filter intelligent denoising algorithm for denoising and optimization, while the control group did not use optimization, and the differences between them were analyzed and compared. The diagnostic effects of the two groups were compared. As a result, the three accuracy rates of the experimental group were 95%, 95%, and 98%, respectively; the three sensitivity rates were 96%, 92%, and 94%, respectively; and the three specificity rates were 99%, 97%, and 98%, respectively. In the control group, the three accuracy rates were 84%, 86%, and 84%, respectively; the three sensitivity rates were 88%, 84%, and 86%, respectively; and the three specificity rates were 81%, 83%, and 88%, respectively. The accuracy, sensitivity, and specificity of experiment group were significantly higher than those of control group, and the difference was statistically significant (P < 0.05). In summary, the bilateral filter intelligent denoising algorithm has a good denoising effect on the ultrasonic elastography. The ultrasonic image processed by the algorithm combined with HPV detection has a better diagnosis of CIN.
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Affiliation(s)
- Lu Sun
- Department of Obstetrics and Gynecology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, 223600 Jiangsu, China
| | - Xiuling Shan
- Department of Obstetrics and Gynecology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, 223600 Jiangsu, China
| | - Qihu Dong
- Department of Obstetrics and Gynecology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, 223600 Jiangsu, China
| | - Chong Wu
- Department of Obstetrics and Gynecology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, 223600 Jiangsu, China
| | - Mei Shan
- Department of Obstetrics and Gynecology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, 223600 Jiangsu, China
| | - Hongxia Guo
- Department of Obstetrics and Gynecology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, 223600 Jiangsu, China
| | - Rui Lu
- Department of Obstetrics and Gynecology, Shuyang Hospital of Traditional Chinese Medicine, Shuyang, 223600 Jiangsu, China
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Zhang D, Liang P, Chen W, Tang Z, Li C, Xiao K, Jin S, Ni D, Yu Z. Rapid field trace detection of pesticide residue in food based on surface-enhanced Raman spectroscopy. Mikrochim Acta 2021; 188:370. [PMID: 34622367 DOI: 10.1007/s00604-021-05025-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/19/2021] [Indexed: 12/17/2022]
Abstract
Surface-enhanced Raman spectroscopy is an alternative detection tool for monitoring food security. However, there is still a lack of a conclusion of SERS detection with respect to pesticides and real sample analysis, and the summary of intelligent algorithms in SERS is also a blank. In this review, a comprehensive report of pesticides detection using SERS technology is given. The SERS detection characteristics of different types of pesticides and the influence of substrate on inspection are discussed and compared by the typical ways of classification. The key points, including the progress in real sample analysis and Raman data processing methods with intelligent algorithm, are highlighted. Lastly, major challenges and future research trends of SERS analysis of pesticide residue are also addressed. SERS has been proven to be a powerful technique for rapid test of residue pesticides in complex food matrices, but there still is a tremendous development space for future research.
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Affiliation(s)
- De Zhang
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China.
| | - Wenwen Chen
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhexiang Tang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Chen Li
- Jiangxi Sericulture and Tea Research Institute, Nanchang, 330203, China
| | - Kunyue Xiao
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shangzhong Jin
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Dejiang Ni
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhi Yu
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China.
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Gouda M, Huang Z, Liu Y, He Y, Li X. Physicochemical impact of bioactive terpenes on the microalgae biomass structural characteristics. BIORESOURCE TECHNOLOGY 2021; 334:125232. [PMID: 33965853 DOI: 10.1016/j.biortech.2021.125232] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/23/2021] [Accepted: 04/25/2021] [Indexed: 06/12/2023]
Abstract
This study aimed to evaluate the functionality of bioactive terpenes on Spirulina (Arthrospira platensis; AP) and Chlorella (Chlorella vulgaris; CV) biomasses. The two microalgae species were treated with 0.01%, 0.05%, and 0.1% of thymol (THY), trans-cinnamaldehyde (TC), menthol (MEN), and vanillin (VAN). Raman micro-spectroscopy (RMS) was correlated with other physicochemical methods to confirm their functional mechanisms. In results, THY (0.1%) decreased (P < 0.05) RMS intensity at 1196 cm-1 that represents the protein's secondary amines wavenumber. Also, VAN (0.1%) decreased significantly A. platensis α-helix to 16.60 ± 0.52% compared to the control with 19.83 ± 0.32%. While, 0.1% TC increased (P < 0.05) the viscosity to 2.52 ± 0.61 Pa.s. This work demonstrated that terpenes could differently affect the physicochemical structure of microalgae biomass. The RMS's uniqueness comes from its ability to evaluate the functionality of terpenes during microalgae cultivation. Besides, chemometrics led to focus on the most important variances.
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Affiliation(s)
- Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Department of Nutrition & Food Science, National Research Centre, Dokki, Giza, Egypt.
| | - Zhenxiong Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
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