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Liu Y, Chen C, Xie X, Lv X, Chen C. For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123147. [PMID: 37517264 DOI: 10.1016/j.saa.2023.123147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 08/01/2023]
Abstract
Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.
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Affiliation(s)
- Yang Liu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Xiaodong Xie
- Xinjiang Uygur Autonomous Region People's Hospital, Urumqi 830046, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
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2
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Zhao Y, Zhang Z, Ning Y, Miao P, Li Z, Wang H. Simultaneous quantitative analysis of Escherichia coli, Staphylococcus aureus and Salmonella typhimurium using surface-enhanced Raman spectroscopy coupled with partial least squares regression and artificial neural networks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 293:122510. [PMID: 36812753 DOI: 10.1016/j.saa.2023.122510] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Simultaneous detection of mixed bacteria accurately and sensitively is a major challenge in microbial quality control field. In this study, we proposed a label-free SERS technique coupled with partial least squares regression (PLSR) and artificial neural networks (ANNs) for quantitative analysis of Escherichia coli, Staphylococcus aureus and Salmonella typhimurium simultaneously. SERS-active and reproducible Raman spectra can be acquired directly upon the bacteria and Au@Ag@SiO2 nanoparticle composites on the surface of gold foil substrates. After applying different preprocessing models, SERS-PLSR and SERS-ANNs quantitative analysis models were developed to map SERS spectra of concentrations of the Escherichia coli, Staphylococcus aureus and Salmonella typhimurium, respectively. Both models achieved high prediction accuracy and low prediction error, while the performance of SERS-ANNs model in both quality of fit (R2 > 0.95) and accuracy of predictions (RMSE < 0.06) was superior to SERS-PLSR model. Therefore, it is feasible to develop simultaneous quantitative analysis of mixed pathogenic bacteria by proposed SERS methodology.
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Affiliation(s)
- Yuwen Zhao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin 301617, China
| | - Zeshuai Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin 301617, China
| | - Ying Ning
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin 301617, China
| | - Peiqi Miao
- Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300392, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin 301617, China.
| | - Haixia Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin 301617, China.
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3
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Liu W, Sun S, Liu Y, Deng H, Hong F, Liu C, Zheng L. Determination of benzo(a)pyrene in peanut oil based on Raman spectroscopy and machine learning methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122806. [PMID: 37167744 DOI: 10.1016/j.saa.2023.122806] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/26/2023] [Accepted: 04/27/2023] [Indexed: 05/13/2023]
Abstract
Benzo(a)pyrene (BaP) generated in the production process of oil is harmful to human severely as a kind of carcinogenic substance. In this study, the qualitative and quantitative detection of BaP concentration in peanut oil was investigated based on Raman spectroscopy combined with machine learning methods. The glass substrates and magnetron sputtered gold substrates for the Raman spectra were compared and the data preprocessing methods of principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were used to process Raman signal. Back propagation neural network (BPNN), partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) algorithms were developed to obtain the qualitative and quantitative detection model of BaP concentration in peanut oil. The results showed that the Raman spectra with the glass substrate was more suitable for the BaP detection than magnetron sputtered gold substrates. RF combined with t-SNE could achieve an accuracy of 97.5% in the qualitative detection of BaP concentration levels in model validation experiment, and the correlation coefficient of the prediction set (Rp) in the quantitative detection was 0.9932, the root mean square error (RMSEP) was 0.8323 μg/kg and the bias was 0.1316 μg/kg. It can be concluded that Raman spectroscopy combined with machine learning methods could provide an effective method for the rapid determination of BaP concentration in peanut oil.
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Affiliation(s)
- Wei Liu
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
| | - Shengai Sun
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
| | - Yang Liu
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
| | - Haiyang Deng
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
| | - Fei Hong
- Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China
| | - Changhong Liu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China.
| | - Lei Zheng
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China; Research Laboratory of Agricultural Environment and Food Safety, Anhui Modern Agricultural Industry Technology System, Hefei 230009, China.
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4
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Dou J, Dawuti W, Zheng X, Zhu Y, Lin R, Lü G, Zhang Y. Rapid discrimination of Brucellosis in sheep using serum Fourier transform infrared spectroscopy combined with PCA-LDA algorithm. Photodiagnosis Photodyn Ther 2023; 42:103567. [PMID: 37084931 DOI: 10.1016/j.pdpdt.2023.103567] [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: 02/25/2023] [Revised: 04/09/2023] [Accepted: 04/11/2023] [Indexed: 04/23/2023]
Abstract
Brucellosis in sheep is an infectious disease caused by Brucella melitensis in sheep. The current conventional serological methods for screening Brucella-infected sheep have the disadvantage of time consuming and low accuracy, so a simple, rapid and highly accurate screening method is needed. The aim of this study was to evaluate the feasibility of diagnosing Brucella-infected sheep by serum samples based on the Fourier transform infrared (FTIR) spectroscopy. In this study, FTIR spectroscopy of serum from Brucella-infected sheep (n=102) and healthy sheep (n=125) revealed abnormal protein and lipid metabolism in serum from Brucella-infected sheep compared to healthy sheep. Principal component analysis-Linear discriminant analysis (PCA-LDA) method was used to differentiate the FTIR spectra of serum from Brucella-infected sheep and healthy sheep in the protein band (3700-3090 cm-1) and lipid band (3000-2800 cm-1), and its overall diagnostic accuracy was 100% (sensitivity 100%, specificity 100%). In conclusion, our results suggest that serum FTIR spectroscopy combined with PCA-LDA algorithm has great potential for brucellosis in sheep screening.
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Affiliation(s)
- Jingrui Dou
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Wubulitalifu Dawuti
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yousen Zhu
- Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi Xinjiang 830054, China
| | - Renyong Lin
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Guodong Lü
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
| | - Yujiang Zhang
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; The Center for Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi 830002, China.
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Lu B, Tian F, Chen C, Wu W, Tian X, Chen C, Lv X. Identification of Chinese red wine origins based on Raman spectroscopy and deep learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 291:122355. [PMID: 36641919 DOI: 10.1016/j.saa.2023.122355] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/07/2022] [Accepted: 01/08/2023] [Indexed: 06/17/2023]
Abstract
In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field.
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Affiliation(s)
- Bingxu Lu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Feng Tian
- National Institute of Metrology, China, Beijing 100000, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Wei Wu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xuecong Tian
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China.
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González-Viveros N, Castro-Ramos J, Gómez-Gil P, Cerecedo-Núñez HH, Gutiérrez-Delgado F, Torres-Rasgado E, Pérez-Fuentes R, Flores-Guerrero JL. Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks. Lasers Med Sci 2022; 37:3537-3549. [PMID: 36063232 PMCID: PMC9708775 DOI: 10.1007/s10103-022-03633-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/14/2022] [Indexed: 01/17/2023]
Abstract
Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.
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Affiliation(s)
- Naara González-Viveros
- Optics Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | - Jorge Castro-Ramos
- Optics Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | - Pilar Gómez-Gil
- Computer Science Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | | | | | - Enrique Torres-Rasgado
- Faculty of Medicine, Meritorious Autonomous University of Puebla (BUAP), 72589, Puebla, Mexico
| | - Ricardo Pérez-Fuentes
- Department of Chronic Disease Physiopathology, East Center of Biomedical Research, Mexican Social Security Institute (CIBIOR), 74360, Puebla, México
| | - Jose L Flores-Guerrero
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, WC1E 7HB, UK.
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Zhao R, An L, Song D, Li M, Qiao L, Liu N, Sun H. Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 259:119768. [PMID: 33971438 DOI: 10.1016/j.saa.2021.119768] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/21/2021] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650-800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.
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Affiliation(s)
- Ruomei Zhao
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Lulu An
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Di Song
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Minzan Li
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affffairs, China Agricultural University, Beijing 100083, China
| | - Lang Qiao
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
| | - Ning Liu
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affffairs, China Agricultural University, Beijing 100083, China
| | - Hong Sun
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
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