{"title":"基于紫外光谱和主成分分析的β-苯乙胺、NaCl 和 NaOH 的物种鉴别,结合快速搜索和密度峰查找算法改进聚类方法","authors":"","doi":"10.1016/j.microc.2024.111502","DOIUrl":null,"url":null,"abstract":"<div><p>This paper aimed at putting forward an approach integrating the improved clustering by fast search and find of density peaks <strong>(</strong>I-CFSFDP) algorithm with Ultraviolet (UV) spectroscopy for identifying the species of NaCl, NaOH, β-phenylethylamine(PEA) and their mixtures. For solving the issue that the clustering precision of the CFSFDP algorithm relies on the density forecast of the dataset and the manually selection of the truncated distance <em>d<sub>c</sub></em>. The idea of kernel density forecast was adopted to the I-CFSFDP algorithm. The I-CFSFDP algorithm can observe the clusters of arbitrary shapes and use an adaptive method to evaluate the truncated distance <em>d<sub>c</sub></em>, thereby generating more accurate clusters and identifying the core points in the clusters effectively. The dimensions of the UV spectra was reduced with principal component analysis (PCA), and the results of PCA were invoked as the input of the I-CFSFDP algorithm. Meanwhile, the effect of PCA-I-CFSFDP was evaluated by recall, accuracy, F-Score and precision. Besides, the DBSCAN and PCA-CFSFDP algorithms were used to compare with the PCA-I-CFSFDP algorithm. All of the classification outcomes displayed that the PCA-I-CFSFDP algorithm has better performance than the DBSCAN and PCA-CFSFDP algorithms. Therefore, the PCA-I-CFSFDP algorithm integrated with UV spectroscopy is a simple, quick and credible identification approach for detecting PEA, NaCl, NaOH and the mixtures.</p></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Species discrimination of β-phenylethylamine, NaCl and NaOH based on Ultraviolet spectroscopy and principal component analysis combined with improved clustering by fast search and find of density peaks algorithm\",\"authors\":\"\",\"doi\":\"10.1016/j.microc.2024.111502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper aimed at putting forward an approach integrating the improved clustering by fast search and find of density peaks <strong>(</strong>I-CFSFDP) algorithm with Ultraviolet (UV) spectroscopy for identifying the species of NaCl, NaOH, β-phenylethylamine(PEA) and their mixtures. For solving the issue that the clustering precision of the CFSFDP algorithm relies on the density forecast of the dataset and the manually selection of the truncated distance <em>d<sub>c</sub></em>. The idea of kernel density forecast was adopted to the I-CFSFDP algorithm. The I-CFSFDP algorithm can observe the clusters of arbitrary shapes and use an adaptive method to evaluate the truncated distance <em>d<sub>c</sub></em>, thereby generating more accurate clusters and identifying the core points in the clusters effectively. The dimensions of the UV spectra was reduced with principal component analysis (PCA), and the results of PCA were invoked as the input of the I-CFSFDP algorithm. Meanwhile, the effect of PCA-I-CFSFDP was evaluated by recall, accuracy, F-Score and precision. Besides, the DBSCAN and PCA-CFSFDP algorithms were used to compare with the PCA-I-CFSFDP algorithm. All of the classification outcomes displayed that the PCA-I-CFSFDP algorithm has better performance than the DBSCAN and PCA-CFSFDP algorithms. Therefore, the PCA-I-CFSFDP algorithm integrated with UV spectroscopy is a simple, quick and credible identification approach for detecting PEA, NaCl, NaOH and the mixtures.</p></div>\",\"PeriodicalId\":391,\"journal\":{\"name\":\"Microchemical Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Microchemical Journal\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0026265X2401614X\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microchemical Journal","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0026265X2401614X","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Species discrimination of β-phenylethylamine, NaCl and NaOH based on Ultraviolet spectroscopy and principal component analysis combined with improved clustering by fast search and find of density peaks algorithm
This paper aimed at putting forward an approach integrating the improved clustering by fast search and find of density peaks (I-CFSFDP) algorithm with Ultraviolet (UV) spectroscopy for identifying the species of NaCl, NaOH, β-phenylethylamine(PEA) and their mixtures. For solving the issue that the clustering precision of the CFSFDP algorithm relies on the density forecast of the dataset and the manually selection of the truncated distance dc. The idea of kernel density forecast was adopted to the I-CFSFDP algorithm. The I-CFSFDP algorithm can observe the clusters of arbitrary shapes and use an adaptive method to evaluate the truncated distance dc, thereby generating more accurate clusters and identifying the core points in the clusters effectively. The dimensions of the UV spectra was reduced with principal component analysis (PCA), and the results of PCA were invoked as the input of the I-CFSFDP algorithm. Meanwhile, the effect of PCA-I-CFSFDP was evaluated by recall, accuracy, F-Score and precision. Besides, the DBSCAN and PCA-CFSFDP algorithms were used to compare with the PCA-I-CFSFDP algorithm. All of the classification outcomes displayed that the PCA-I-CFSFDP algorithm has better performance than the DBSCAN and PCA-CFSFDP algorithms. Therefore, the PCA-I-CFSFDP algorithm integrated with UV spectroscopy is a simple, quick and credible identification approach for detecting PEA, NaCl, NaOH and the mixtures.
期刊介绍:
The Microchemical Journal is a peer reviewed journal devoted to all aspects and phases of analytical chemistry and chemical analysis. The Microchemical Journal publishes articles which are at the forefront of modern analytical chemistry and cover innovations in the techniques to the finest possible limits. This includes fundamental aspects, instrumentation, new developments, innovative and novel methods and applications including environmental and clinical field.
Traditional classical analytical methods such as spectrophotometry and titrimetry as well as established instrumentation methods such as flame and graphite furnace atomic absorption spectrometry, gas chromatography, and modified glassy or carbon electrode electrochemical methods will be considered, provided they show significant improvements and novelty compared to the established methods.