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Zhang L, Huang Z, Zhang X. Quantitative analysis of spectral data based on stochastic configuration networks. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024. [PMID: 38961818 DOI: 10.1039/d4ay00656a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
In quantitative analysis of spectral data, traditional linear models have fewer parameters and faster computation speed. However, when encountering nonlinear problems, their predictive accuracy tends to be lower. Nonlinear models provide higher computational accuracy in such situations but may suffer from drawbacks such as slow convergence speed and susceptibility to get stuck in local optima. Taking into account the advantages of these two algorithms, this paper introduces the single-hidden layer feedforward neural network named stochastic configuration networks (SCNs) into chemometrics analysis. Firstly, the model termination parameters, that is, the error tolerance and the allowed maximum number of hidden nodes are analyzed. Secondly, times of random configuration are discussed and analyzed, and then the appropriate number is determined by considering the efficiency and stability comprehensively. Finally, predictions made by the SCN are tested on two public datasets. The performance of the SCN is then compared with that of other techniques, including principal component regression (PCR), partial least squares (PLS), back propagation neural network (BPNN), and extreme learning machine (ELM). Experimental results show that the SCN has good stability, high prediction accuracy and efficiency, making it suitable for quantitative analysis of spectral data.
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Affiliation(s)
- Lixin Zhang
- School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing 210014, Jiangsu 210014, China.
- College of Information Engineering, Tarim University, Alar, Xinjiang 843300, China
- Key Laboratory of Tarim Oasis Agriculture, Tarim University, Ministry of Education, China
| | - Zhensheng Huang
- School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing 210014, Jiangsu 210014, China.
| | - Xiao Zhang
- College of Information Engineering, Tarim University, Alar, Xinjiang 843300, China
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Zhang S, Qi Y, Tan SPH, Bi R, Olivo M. Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review. BIOSENSORS 2023; 13:bios13050557. [PMID: 37232918 DOI: 10.3390/bios13050557] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.
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Affiliation(s)
- Shuyan Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Yi Qi
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Sonia Peng Hwee Tan
- Department of Biomedical Engineering, National University of Singapore (NUS), 4 Engineering Drive 3 Block 4, #04-08, Singapore 117583, Singapore
| | - Renzhe Bi
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Malini Olivo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
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3
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Optical spectroscopy and chemometrics in intraoperative tumor margin assessment. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Kanemura Y, Kanazawa M, Hashimoto S, Hayashi Y, Fujiwara E, Suzuki A, Ishii T, Goto M, Nozaki H, Inoue T, Takanari H. Assessment of skin inflammation using near-infrared Raman spectroscopy combined with artificial intelligence analysis in an animal model. Analyst 2022; 147:2843-2850. [DOI: 10.1039/d2an00193d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Near-infrared (NIR) Raman spectroscopy was applied to detect skin inflammation in an animal model. Artificial intelligence (AI) analysis improved prediction accuracy for skin inflammation.
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Affiliation(s)
- Yohei Kanemura
- Department of Interdisciplinary Researches for Medicine and Photonics, Institute of Post-LED Photonics, Tokushima University, 3-18-15, Kuramoto, Tokushima 770-8503, Japan
- Tokushima University, Faculty of Science and Technology, 2-1, Minami-Josanjima, Tokushima 770-8506, Japan
| | - Meiko Kanazawa
- Department of Interdisciplinary Researches for Medicine and Photonics, Institute of Post-LED Photonics, Tokushima University, 3-18-15, Kuramoto, Tokushima 770-8503, Japan
- Tokushima University, Faculty of Medicine, 3-18-15 Kuramoto, Tokushima 770-8503, Japan
| | - Satoru Hashimoto
- Division of Applied Chemistry, Faculty of Science and Technology, Oita University Graduate School of Engineering, 700, Dan-noharu, Oita 870-1124, Japan
| | - Yuri Hayashi
- Department of Interdisciplinary Researches for Medicine and Photonics, Institute of Post-LED Photonics, Tokushima University, 3-18-15, Kuramoto, Tokushima 770-8503, Japan
- Tokushima University, Faculty of Medicine, 3-18-15 Kuramoto, Tokushima 770-8503, Japan
| | - Erina Fujiwara
- Division of Applied Chemistry, Faculty of Science and Technology, Oita University Graduate School of Engineering, 700, Dan-noharu, Oita 870-1124, Japan
| | - Ayako Suzuki
- Division of Applied Chemistry, Faculty of Science and Technology, Oita University Graduate School of Engineering, 700, Dan-noharu, Oita 870-1124, Japan
| | - Takashige Ishii
- Division of DX Promotion, OEC Co., Ltd., 17-57, Higashi-Kasuga, Oita 870-0037, Japan
| | - Masakazu Goto
- Division of DX Promotion, OEC Co., Ltd., 17-57, Higashi-Kasuga, Oita 870-0037, Japan
| | - Hiroshi Nozaki
- Division of DX Promotion, OEC Co., Ltd., 17-57, Higashi-Kasuga, Oita 870-0037, Japan
| | - Takanori Inoue
- Division of Applied Chemistry, Faculty of Science and Technology, Oita University Graduate School of Engineering, 700, Dan-noharu, Oita 870-1124, Japan
| | - Hiroki Takanari
- Department of Interdisciplinary Researches for Medicine and Photonics, Institute of Post-LED Photonics, Tokushima University, 3-18-15, Kuramoto, Tokushima 770-8503, Japan
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Ferguson D, Henderson A, McInnes EF, Lind R, Wildenhain J, Gardner P. Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique. Analyst 2022; 147:3709-3722. [DOI: 10.1039/d2an00775d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
A meta-analysis of various multivariate/Machine Learning (ML) classifiers trained on IR Micro-spectroscopy tissue datasets for cancer classification are directly compared using a calculated F1-Score metric alongside study pre-processing techniques.
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Affiliation(s)
- Dougal Ferguson
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemical Engineering and Analytical Science, School of Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Alex Henderson
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemical Engineering and Analytical Science, School of Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | | | - Rob Lind
- Syngenta, International Research Centre, Jealotts Hill, Bracknell, RG42 6EY, UK
| | - Jan Wildenhain
- Syngenta, International Research Centre, Jealotts Hill, Bracknell, RG42 6EY, UK
| | - Peter Gardner
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemical Engineering and Analytical Science, School of Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
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