首页|Studies from Mizoram University Further Understanding of Support Vector Machines (Analysis of gasoline quality by ATR-FTIR spectroscopy with multivariate techni ques)
Studies from Mizoram University Further Understanding of Support Vector Machines (Analysis of gasoline quality by ATR-FTIR spectroscopy with multivariate techni ques)
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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."
Mizoram UniversityMizoramIndiaCh emometricEmerging TechnologiesMachine LearningSupport Vector MachinesVec tor Machines
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