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Wang W, Shi S, Liu Y, Hou Z, Qi J, Guo L. Staging classification of omicron variant SARS-CoV-2 infection based on dual-spectrometer LIBS (DS-LIBS) combined with machine learning. OPTICS EXPRESS 2023; 31:42413-42427. [PMID: 38087616 DOI: 10.1364/oe.504640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023]
Abstract
Effective differentiation of the infection stages of omicron can provide significant assistance in transmission control and treatment strategies. The combination of LIBS serum detection and machine learning methods, as a novel disease auxiliary diagnostic approach, has a high potential for rapid and accurate staging classification of Omicron infection. However, conventional single-spectrometer LIBS serum detection methods focus on detecting the spectra of major elements, while trace elements are more closely related to the progression of COVID-19. Here, we proposed a rapid analytical method with dual-spectrometer LIBS (DS-LIBS) assisted with machine learning to classify different infection stages of omicron. The DS-LIBS, including a broadband spectrometer and a narrowband spectrometer, enables synchronous collection of major and trace elemental spectra in serum, respectively. By employing the RF machine learning models, the classification accuracy using the spectra data collected from DS-LIBS can reach 0.92, compared to 0.84 and 0.73 when using spectra data collected from single-spectrometer LIBS. This significant improvement in classification accuracy highlights the efficacy of the DS-LIBS approach. Then, the performance of four different models, SVM, RF, IGBT, and ETree, is compared. ETree demonstrates the best, with cross-validation and test set accuracies of 0.94 and 0.93, respectively. Additionally, it achieves classification accuracies of 1.00, 0.92, 0.92, and 0.89 for the four stages B1-acute, B1-post, B2, and B3. Overall, the results demonstrate that DS-LIBS combined with the ETree machine learning model enables effective staging classification of omicron infection.
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Skalny AV, Korobeinikova TV, Aschner M, Baranova OV, Barbounis EG, Tsatsakis A, Tinkov AA. Medical application of laser-induced breakdown spectroscopy (LIBS) for assessment of trace element and mineral in biosamples: Laboratory and clinical validity of the method. J Trace Elem Med Biol 2023; 79:127241. [PMID: 37393771 DOI: 10.1016/j.jtemb.2023.127241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 07/04/2023]
Abstract
BACKGROUND Biomedical application is based on the use of LIBS-derived data on chemical contents of tissues in diagnosis of diseases, forensic investigation, as well as a mechanism for providing online feedback for laser surgery. Although LIBS has certain advantages, the issue of correlation of LIBS-derived data on chemical element content in different human and animal tissues with other methods, and especially ICP-MS, remains pertinent. The objective of the present review was to discuss the application of laser-induced breakdown spectroscopy (LIBS) for elemental analysis of human biosamples or tissues from experimental models of human diseases. METHODS A systematic search in the PubMed-Medline, Scopus, and Google Scholar databases using the terms laser-induced breakdown spectroscopy, LIBS, metals, trace elements, minerals, and names of particular chemical elements was performed up through 25 February, 2023. Of all extracted studies only those dealing with human subjects, human tissues, in vivo animal and in vitro cell line models of human diseases were reviewed in detail. RESULTS The majority of studies revealed a wide number of metals and metalloids in solid tissues including teeth (As, Ag, Ca, Cd, Cr, Cu, Fe, Hg, Mg, Ni, P, Pb, Sn, Sr, Ti, and Zn), bones (Al, Ba, Ca, Cd, Cr, K, Mg, Na, Pb, Sr), and nails (Al, As, Ca, Fe, K, Mg, Na, P, Pb, Si, Sr, Ti, Zn). At the same time, LIBS was also used for estimation of trace element and mineral content in hair (Ca, Cu, Fe, K, Mg, Na, Zn), blood (Al, Ca, Co, Cd, Cu, Fe, Mg, Mn, Ni, Pb, Si, Sn, Zn), cancer tissues (Ca, Cu, Fe, Mg, K, Na, Zn) and other tissues. Single studies revealed satisfactory correspondence between quantitative LIBS and ICP-OES/MS data on the level of As (81-93 %), Pb (94-98 %), Cd (50-94 %) in teeth, Cu (97-105 %), Fe (117 %), Zn (88-117 %) in hair, Ca (97-99 %), Zn (90-95 %), and Pb (61-82 %) in kidney stones. LIBS also estimated specific patterns of trace element and mineral content associated with multiple pathologies, including caries, cancer, skin disorders, and other systemic diseases including diabetes mellitus type 2, osteoporosis, hypothyroidism, etc. Data obtained from in situ tissue LIBS analysis were profitably used for discrimination between tissue types. CONCLUSIONS Taken together, the existing data demonstrate the applicability of LIBS for medical studies, although further increase in its sensitivity, calibration range, cross-validation, and quality control is required.
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Affiliation(s)
- Anatoly V Skalny
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", IM Sechenov First Moscow State Medical University (Sechenov University), 119435 Moscow, Russia
| | - Tatiana V Korobeinikova
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", IM Sechenov First Moscow State Medical University (Sechenov University), 119435 Moscow, Russia
| | - Michael Aschner
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Oksana V Baranova
- Institute of Bioelementology, Orenburg State University, 460001, Orenburg
| | | | - Aristides Tsatsakis
- Laboratory of Toxicology, Medical School, University of Crete, Voutes, 700 13 Heraklion, Greece
| | - Alexey A Tinkov
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", IM Sechenov First Moscow State Medical University (Sechenov University), 119435 Moscow, Russia; Laboratory of Ecobiomonitoring and Quality Control, Yaroslavl State University, 150003 Yaroslavl, Russia.
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Idrees BS, Teng G, Israr A, Zaib H, Jamil Y, Bilal M, Bashir S, Khan MN, Wang Q. Comparison of whole blood and serum samples of breast cancer based on laser-induced breakdown spectroscopy with machine learning. BIOMEDICAL OPTICS EXPRESS 2023; 14:2492-2509. [PMID: 37342687 PMCID: PMC10278612 DOI: 10.1364/boe.489513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 06/23/2023]
Abstract
To identify cancer from non-cancer is one of the most challenging issues nowadays in the early diagnosis of cancer. The primary issue of early detection is to choose a suitable type of sample collection to diagnose cancer. A comparison of whole blood and serum samples of breast cancer was studied using laser-induced breakdown spectroscopy (LIBS) with machine learning methods. For LIBS spectra measurement, blood samples were dropped on a substrate of boric acid. For the discrimination of breast cancer and non-cancer samples, eight machine learning models were applied to LIBS spectral data, including decision tree, discrimination analysis, logistic regression, naïve byes, support vector machine, k-nearest neighbor, ensemble and neural networks classifiers. Discrimination between whole blood samples showed that narrow neural networks and trilayer neural networks both provided 91.7% highest prediction accuracy and serum samples showed that all the decision tree models provided 89.7% highest prediction accuracy. However, using whole blood as sample achieved the strong emission lines of spectra, better discrimination results of PCA and maximum prediction accuracy of machine learning models as compared to using serum samples. These merits concluded that whole blood samples could be a good option for the rapid detection of breast cancer. This preliminary research may provide the complementary method for early detection of breast cancer.
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Affiliation(s)
- Bushra Sana Idrees
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, 100081 Beijing, China
| | - Geer Teng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7LD, United Kingdom
| | - Ayesha Israr
- Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan
| | - Huma Zaib
- Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan
| | - Yasir Jamil
- Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan
| | - Muhammad Bilal
- Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sajid Bashir
- Punjab Institute of Nuclear Medicine Hospital, Faisalabad 2019, Pakistan
| | - M Nouman Khan
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, 100081 Beijing, China
| | - Qianqian Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, 100081 Beijing, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
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Alexeree SM, Youssef D, Abdel-Harith M. Using biospeckle and LIBS techniques with artificial intelligence to monitor phthalocyanine-gold nanoconjugates as a new drug delivery mediator for in vivo PDT. J Photochem Photobiol A Chem 2023. [DOI: 10.1016/j.jphotochem.2023.114687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
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Wang W, Liu Y, Chu Y, Xiao S, Nie J, Zhang J, Qi J, Guo L. Stable sensing platform for diagnosing electrolyte disturbance using laser-induced breakdown spectroscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:6778-6790. [PMID: 36589579 PMCID: PMC9774860 DOI: 10.1364/boe.477565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/10/2022] [Accepted: 11/10/2022] [Indexed: 06/17/2023]
Abstract
Electrolyte disturbance is very common and harmful, increasing the mortality of critical patients. Hence, rapid and accurate detection of electrolyte levels is vital in clinical practice. Laser-induced breakdown spectroscopy (LIBS) has the advantage of rapid and simultaneous detection of multiple elements, which meets the needs of clinical electrolyte detection. However, the cracking caused by serum drying and the effect of the coffee-ring led to the unstable spectral signal of LIBS and inaccurate detection results. Herein, we propose the ordered microarray silicon substrates (OMSS) obtained by laser microprocessing, to solve the disturbance caused by cracking and the coffee-ring effect in LIBS detection. Moreover, the area of OMSS is optimized to obtain the optimal LIBS detection effect; only a 10 uL serum sample is required. Compared with the silicon wafer substrates, the relative standard deviation (RSD) of the serum LIBS spectral reduces from above 80.00% to below 15.00% by the optimized OMSS, improving the spectral stability. Furthermore, the OMSS is combined with LIBS to form a sensing platform for electrolyte disturbance detection. A set of electrolyte disturbance simulation samples (80% of the ingredients are human serum) was prepared for this platform evaluation. Finally, the platform can achieve an accurate quantitative detection of Na and K elements (Na: RSD < 6.00%, R2 = 0.991; K: RSD < 4.00%, R2 = 0.981), and the detection time is within 5 min. The LIBS sensing platform has a good prospect in clinical electrolyte detection and other blood-related clinical diagnoses.
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Affiliation(s)
- Weiliang Wang
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yuanchao Liu
- Department of Physics, City University of Hong Kong, Hong Kong SAR, China
| | - Yanwu Chu
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, 610209, China
| | - Siyi Xiao
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Junfei Nie
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Junlong Zhang
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jianwei Qi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Contributed equally
| | - Lianbo Guo
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Contributed equally
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Zhang D, Nie J, Niu X, Chen F, Hu Z, Wen X, Li Y, Guo L. Time-resolved spectral-image laser-induced breakdown spectroscopy for precise qualitative and quantitative analysis of milk powder quality by fully excavating the matrix information. Food Chem 2022; 386:132763. [PMID: 35364495 DOI: 10.1016/j.foodchem.2022.132763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 02/17/2022] [Accepted: 03/19/2022] [Indexed: 11/25/2022]
Abstract
A novel and effective method named time-resolved spectral-image laser-induced breakdown spectroscopy (TRSI-LIBS) was proposed to achieve precise qualitative and quantitative analysis of milk powder quality. To verify the feasibility of TRSI-LIBS, qualitative and quantitative analysis of milk powder quality was carried out. For qualitative analysis of foreign protein adulteration, the accuracy of models based on TRSI-LIBS was higher than those based on LIBS, with an accuracy improvement of about 5% to 10%. For the quantitative analysis of foreign protein adulteration and element content, the quantitative analysis models based on TSRI-LIBS also had better effect. For instance, limit of detection (LOD),determination coefficient of prediction (R2p), root-mean-square error of prediction (RMSEP) and average relative error of prediction (AREP) of quantitative model of calcium (Ca) content based on TRSI-LIBS improved from 1.47 mg/g, 0.95, 0.35 mg/g and 23.29% to 0.81 mg/g, 0.98, 0.20 mg/g and 12.60%.
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Affiliation(s)
- Deng Zhang
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Junfei Nie
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Xuechen Niu
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Feng Chen
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Zhenlin Hu
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Xuelin Wen
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Yuqiong Li
- Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, PR China.
| | - Lianbo Guo
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China.
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7
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Identification of adulterated milk powder based on convolutional neural network and laser-induced breakdown spectroscopy. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107190] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhao Z, Wang Q, Xu X, Chen F, Teng G, Wei K, Chen G, Cai Y, Guo L. Accurate Identification and Quantification of Chinese Yam Powder Adulteration Using Laser-Induced Breakdown Spectroscopy. Foods 2022; 11:foods11091216. [PMID: 35563939 PMCID: PMC9104410 DOI: 10.3390/foods11091216] [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: 03/15/2022] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 11/16/2022] Open
Abstract
As a popular food, Chinese yam (CY) powder is widely used for healthy and commercial purposes. Detecting adulteration of CY powder has become essential. In this work, chemometric methods combined with laser-induced breakdown spectroscopy (LIBS) were developed for identification and quantification of CY powder adulteration. Pure powders (CY, rhizome of winged yam (RY) and cassava (CS)) and adulterated powders (CY adulterated with CS) were pressed into pellets to obtain LIBS spectra for identification and quantification experiments, respectively. After variable number optimization by principal component analysis and random forest (RF), the best model random forest-support vector machine (RF-SVM) decreased 48.57% of the input variables and improved the accuracy to 100% in identification. Following the better feature extraction method RF, the Gaussian process regression (GPR) method performed the best in the prediction of the adulteration rate, with a correlation coefficient of prediction (Rp2) of 0.9570 and a root-mean-square error of prediction (RMSEP) of 7.6243%. Besides, the variable importance of metal elements analyzed by RF revealed that Na and K were significant due to the high metabolic activity and maximum metal content of CY powder, respectively. These results demonstrated that chemometric methods combined with LIBS can identify and quantify CY powder adulteration accurately.
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Affiliation(s)
- Zhifang Zhao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Qianqian Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
- Correspondence: (Q.W.); (L.G.)
| | - Xiangjun Xu
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
| | - Feng Chen
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China;
| | - Geer Teng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
| | - Kai Wei
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Guoyan Chen
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China; (Z.Z.); (X.X.); (G.T.); (K.W.); (G.C.)
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yu Cai
- College of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China;
| | - Lianbo Guo
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China;
- Correspondence: (Q.W.); (L.G.)
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Wang S, He H, Lv R, He W, Li C, Cai N. Classification modeling method for hyperspectral stamp-pad ink data based on one-dimensional convolutional neural network. J Forensic Sci 2021; 67:550-561. [PMID: 34617278 DOI: 10.1111/1556-4029.14909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 08/25/2021] [Accepted: 09/21/2021] [Indexed: 11/28/2022]
Abstract
In the questioned document, the examination of stamp-pad ink is crucial scientific evidence to discern the difference between genuine and forged documents. In this study, a new method for rapid and non-destructive identification of types of stamp-pad inks by combining hyperspectral imaging (HSI) technology and deep learning was developed. Twenty stamp-pad inks of different brands and models were collected and numbered in turn, and then, each of them was sealed six times repeatedly on the A4 printing paper for the test. After that, the hyperspectral imager was used to collect the hyperspectral images and the reflectance spectral data were obtained after pixel fusion. Principal component analysis (PCA) and non-negative matrix factorization (NMF) were used to deal with the dataset, but visual results were not good. Then, back propagation neural network (BPNN) and one-dimensional convolutional neural network (1D-CNN) were constructed and their merits and drawbacks were compared. The final loss function of the BPNN of training set and validation set was stable at 0.27 and 0.42, and the classification accuracy of the training set and validation set reached 90.02% and 83.99%, respectively. Compared with the BPNN, the 1D-CNN had better stability and efficiency for the classification. The loss function of the training set and validation set was as low as 0.068 and 0.075, and the final classification accuracy reached 98.30% and 97.94%, respectively. Therefore, the combination of hyperspectral imaging technology and 1D-CNN represents a potentially simple, non-destructive, and rapid method for stamp-pad inks detection and classification.
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Affiliation(s)
- Shuyue Wang
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Hongyuan He
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Rulin Lv
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Weiwen He
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Chunyu Li
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Nengbin Cai
- Shanghai Key Laboratory of Criminal Scene Evidence, Shanghai, China
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Guo L, Zheng W, Chen F, Wang W, Zhang D, Hu Z, Chu Y. Meat species identification accuracy improvement using sample set portioning based on joint x-y distance and laser-induced breakdown spectroscopy. APPLIED OPTICS 2021; 60:5826-5831. [PMID: 34263801 DOI: 10.1364/ao.430980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 06/09/2021] [Indexed: 06/13/2023]
Abstract
Laser-induced breakdown spectroscopy (LIBS) was suitable for the identification of meat species due to fast and less sample preparation. However, the problem of low accuracy rate of the recognition model caused by improper selection of training set samples by random split has severely restricted the development of LIBS in meat detection. Sample set portioning based on the joint x-y distance (SPXY) method was applied for dividing the meat spectra into a training set and a test set. Then, the five kinds of meat samples (shrimp, chicken, beef, scallop, and pig liver) were classified by the support vector machine (SVM). With the random split method, Kennard-Stone method, and SPXY method, the recognition accuracies of the SVM model were 90.44%, 91.95%, and 94.35%, respectively. The multidimensional scaling method was used to visualize the results of the sample split for the interpretation of the classification. The results showed that the identification performance of the SPXY method combined with the SVM model was best, and the accuracy rates of shrimp, chicken, beef, scallop, and pig liver were 100.00%, 100.00%, 100.00%, 78.57%, and 92.00%, respectively. Moreover, to verify the broad adaptability of the SPXY method, the linear discriminant analysis model, the K-nearest neighbor model, and the ensemble learning model were applied as the meat species identification model. The results demonstrated that the accuracy rate of the classification model can be improved with the SPXY method. In light of the findings, the proposed sample portioning method can improve the accuracy rate of the recognition model using LIBS.
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Teng G, Wang Q, Cui X, Chen G, Wei K, Xu X, Idrees BS, Nouman Khan M. Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis. BIOMEDICAL OPTICS EXPRESS 2021; 12:4438-4451. [PMID: 34457424 PMCID: PMC8367271 DOI: 10.1364/boe.431356] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 05/25/2023]
Abstract
Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models.
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Affiliation(s)
| | | | | | | | - Kai Wei
- Beijing Institute of Technology
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Detection of hypokalemia disorder and its relation with hypercalcemia in blood serum using LIBS technique for patients of colorectal cancer grade I and grade II. Lasers Med Sci 2021; 37:1081-1093. [PMID: 34173122 DOI: 10.1007/s10103-021-03355-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/07/2021] [Indexed: 10/21/2022]
Abstract
Cancer continues to be the most dangerous disease around the world; it causes electrolyte imbalance as well as metabolic changes. There is a complicated relationship between electrolyte disorder and cancer. Cancer patients commonly pass with abnormalities in serum electrolyte levels such as hypokalemia, hyperkalemia, hyponatremia, and hypercalcemia. So, these electrolyte imbalances indicate the existence of paraneoplastic processes and help come to a more informed prognosis. Hypokalemia is defined as a serum potassium concentration below 3.5 mmol/L and it is the second common electrolyte imbalance seen in patients with malignant diseases. In this paper, the contribution of serum potassium concentration to tumor progression was studied by applying a promising and non-invasive technique called laser-induced breakdown spectroscopy (LIBS). It was found that there is a correlation between hypokalemia and the colorectal cancer problem. Also, significant serum potassium concentration differences were detected among two different stages of the same cancer and also between two groups of the same stage of a cancer held in common but one of them suffers from hypercalcemia. In addition, the optimum conditions of LIBS setup were arranged such that it will be suitable to work with serum samples on glass substrate.
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Yue Z, Sun C, Chen F, Zhang Y, Xu W, Shabbir S, Zou L, Lu W, Wang W, Xie Z, Zhou L, Lu Y, Yu J. Machine learning-based LIBS spectrum analysis of human blood plasma allows ovarian cancer diagnosis. BIOMEDICAL OPTICS EXPRESS 2021; 12:2559-2574. [PMID: 34123488 PMCID: PMC8176811 DOI: 10.1364/boe.421961] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/18/2021] [Accepted: 03/26/2021] [Indexed: 05/31/2023]
Abstract
Early-stage screening and diagnosis of ovarian cancer represent an urgent need in medicine. Usual ultrasound imaging and cancer antigen CA-125 test when prescribed to a suspicious population still require reconfirmations. Spectroscopic analyses of blood, at the molecular and atomic levels, provide useful supplementary tests when coupled with effective information extraction methods. Laser-induced breakdown spectroscopy (LIBS) was employed in this work to record the elemental fingerprint of human blood plasma. A machine learning data treatment process was developed combining feature selection and regression with a back-propagation neural network, resulting in classification models for cancer detection among 176 blood plasma samples collected from patients, including also ovarian cyst and normal cases. Cancer diagnosis sensitivity and specificity of respectively 71.4% and 86.5% were obtained for randomly selected validation samples.
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Affiliation(s)
- Zengqi Yue
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chen Sun
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Fengye Chen
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuqing Zhang
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weijie Xu
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Sahar Shabbir
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Long Zou
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiguo Lu
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, and Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China
| | - Wei Wang
- Department of Clinical Laboratory, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China
| | - Zhenwei Xie
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, and Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China
| | - Lanyun Zhou
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, and Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China
| | - Yan Lu
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, and Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China
| | - Jin Yu
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
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