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Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification
A single feature set is often unable to effectively classify complex biological samples due to their similar morphology and sizes. This paper proposes a protocol for the fast identification of seed medicinal materials based on micro-structural and infrared spectroscopic characteristics. Three differ...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004588/ https://www.ncbi.nlm.nih.gov/pubmed/35425064 http://dx.doi.org/10.1039/d2ra00239f |
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author | Wang, Hanqiu Rehmetulla, Aybek Guo, Shanshan Kong, Xin Lü, Zhiwei Guan, Yu Xu, Cong Sulaiman, Kaiser Wei, Gongxiang Liu, Huiqiang |
author_facet | Wang, Hanqiu Rehmetulla, Aybek Guo, Shanshan Kong, Xin Lü, Zhiwei Guan, Yu Xu, Cong Sulaiman, Kaiser Wei, Gongxiang Liu, Huiqiang |
author_sort | Wang, Hanqiu |
collection | PubMed |
description | A single feature set is often unable to effectively classify complex biological samples due to their similar morphology and sizes. This paper proposes a protocol for the fast identification of seed medicinal materials based on micro-structural and infrared spectroscopic characteristics. Three different feature datasets, namely micro-CT, FTIR, and mixed datasets, were established via principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) and then used to train a back-propagation neural network. The mixed dataset consists of 34-dimensional micro-CT eigenvalues and 13-dimensional FTIR eigenvalues, optimized by PCA and CARS processing and then used to train a BP neural network. The results showed that the classification accuracy reached 89.5% for the micro-CT dataset and 93.3% for the FTIR dataset, and the classification accuracy of the mixed dataset achieved 99.2%, much higher than those of the traditional single feature datasets. This study provides a new protocol for multi-dimensional characteristic architecture with excellent performance for the classification and identification of Chinese medicinal materials. |
format | Online Article Text |
id | pubmed-9004588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-90045882022-04-13 Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification Wang, Hanqiu Rehmetulla, Aybek Guo, Shanshan Kong, Xin Lü, Zhiwei Guan, Yu Xu, Cong Sulaiman, Kaiser Wei, Gongxiang Liu, Huiqiang RSC Adv Chemistry A single feature set is often unable to effectively classify complex biological samples due to their similar morphology and sizes. This paper proposes a protocol for the fast identification of seed medicinal materials based on micro-structural and infrared spectroscopic characteristics. Three different feature datasets, namely micro-CT, FTIR, and mixed datasets, were established via principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) and then used to train a back-propagation neural network. The mixed dataset consists of 34-dimensional micro-CT eigenvalues and 13-dimensional FTIR eigenvalues, optimized by PCA and CARS processing and then used to train a BP neural network. The results showed that the classification accuracy reached 89.5% for the micro-CT dataset and 93.3% for the FTIR dataset, and the classification accuracy of the mixed dataset achieved 99.2%, much higher than those of the traditional single feature datasets. This study provides a new protocol for multi-dimensional characteristic architecture with excellent performance for the classification and identification of Chinese medicinal materials. The Royal Society of Chemistry 2022-04-12 /pmc/articles/PMC9004588/ /pubmed/35425064 http://dx.doi.org/10.1039/d2ra00239f Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Chemistry Wang, Hanqiu Rehmetulla, Aybek Guo, Shanshan Kong, Xin Lü, Zhiwei Guan, Yu Xu, Cong Sulaiman, Kaiser Wei, Gongxiang Liu, Huiqiang Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification |
title | Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification |
title_full | Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification |
title_fullStr | Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification |
title_full_unstemmed | Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification |
title_short | Machine learning based on structural and FTIR spectroscopic datasets for seed autoclassification |
title_sort | machine learning based on structural and ftir spectroscopic datasets for seed autoclassification |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004588/ https://www.ncbi.nlm.nih.gov/pubmed/35425064 http://dx.doi.org/10.1039/d2ra00239f |
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