摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-调查人员讨论了年的新发现。根据NewsRx编辑在印度米佐拉姆的新闻报道，研究称：“在本文中，使用化学计量学方法，使用傅立叶变换红外光谱(FTIR)数据对汽油燃料进行探索性分析、分类和定量。”这项研究的资助者包括科学和工程研究委员会；印度科学技术部科学和技术部科学和技术部。我们的新闻编辑引用了美佐兰大学的研究:“采用Du环探索性分析,主成分分析(PCA),用支持向量机分类(SVMC),线性判别分析(LDA),偏最小二乘判别分析(PLS-DA)对汽油样品进行分类,苯、甲基叔丁基醚R(MTBE),苯、甲基叔丁基醚R(MTBE),苯、甲基叔丁基醚、用偏最小二乘回归(PLSR)、主成分回归(PCR)、结果表明,SVM分类技术对含氯和掺氯样品均有较高的预测精度,PLSR和PCR均可作为高浓度数据的定量分析方法.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in . According to news reporting out of Mizoram, India, by NewsRx editors, research stated, "In this paper, chemometric methods were used for exploratory analysis, categorization, and quantification of gasoline fuel using Fourier Transform Inf rared Spectroscopy (FTIR) data." Funders for this research include Science And Engineering Research Board; Depart ment of Science And Technology, Ministry of Science And Technology, India. Our news editors obtained a quote from the research from Mizoram University: "Du ring exploratory analysis, Principal Component Analysis (PCA) was employed, and to categorise the gasoline samples, Support Vector Machine Classification (SVMC) , Linear Discrimination Analysis (LDA), and Partial Least Squares Discriminant A nalysis (PLS-DA) were used. The concentration of Benzene, Methyl Tert-butyl Ethe r (MTBE), and Public Distribution System (PDS) Kerosene were determined using th e Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Machine Regression (SVMR). All of the chemometric models had 100% accuracy and high R-square and RMSEC significance values. Th e SVM classification techniques were identified as a suitable choice for both cl assifying oxygenated and adulterated samples among all approaches. Both PLSR and PCR can also be suitable choices for quantification when dealing with high dime nsional data."
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