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- W2803249634 abstract "The objectives of this study are to identify the significant variables and to verify the best statisticalmethod for determining the effect of indoor air quality (IAQ) at 7 different locations in Universiti SultanZainal Abidin, Terengganu, Malaysia. The IAQ data were collected using in-situ measurement. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discrimination analysis (LDA), and agglomerative hierarchical clustering (AHC) were used to classify the significant variables as well as to compare the best method for determining IAQ levels. PCA verifies only 4 out of 9 parameters (PM10, PM2.5, PM1.0, and O3) and is the significant variable in IAQ. The PLS-DA modelclassifies 89.05% correct of the IAQ variables in each station compared to LDA with only 66.67% correct.AHC identifies three cluster groups, which are highly polluted concentration (HPC), moderately pollutedconcentration (MPC), and low-polluted concentration (LPC) area. PLS-DA verifies the groups produced by AHC by identifying the variables that affect the quality at each station without being affected by redundancy. In conclusion, PLS-DA is a promising procedure for differentiating the group classes and determining the correct percentage of variables for IAQ." @default.
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- W2803249634 date "2018-07-09" @default.
- W2803249634 modified "2023-09-22" @default.
- W2803249634 title "Assessing Indoor Air Quality Using Chemometric Models" @default.
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- W2803249634 doi "https://doi.org/10.15244/pjoes/78154" @default.
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