title: Effect of data pre-treatment procedures on principal component analysis: a case study for mangrove surface sediment datasets.
abstract: Principal component analysis (PCA) is capable of handling large sets of data. However, lack of consistent method in data pre-treatment and its importance are the limitations in PCA applications. This study examined pre-treatments methods (log (x + 1) transformation, outlier removal, and granulometric and geochemical normalization) on dataset of Mengkabong Lagoon, Sabah, mangrove surface sediment at high and low tides. The study revealed that geochemical normalization using Al with outliers removal resulted in a better classification of the mangrove surface sediment than that outliers removal, granulometric normalization using clay and log (x + 1) transformation. PCA output using geochemical normalization with outliers removal demonstrated associations between environmental variables and tides of mangrove surface sediment, Mengkabong Lagoon, Sabah. The PCA outputs at high and low tides also provided to better interpret information about the sediment and its controlling factors in the intertidal zone. The study showed data pre-treatment method to be a useful procedure to standardize the datasets and reducing the influence of outliers.
authors.affiliation: Center of Marine Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. smpraveena@gmail.com
authors.forename: Sarva Mangala
authors.initials: SM
authors.lastname: Praveena
authors.forename: Ong Wei
authors.initials: OW
authors.lastname: Kwan
authors.forename: Ahmad Zaharin
authors.initials: AZ
authors.lastname: Aris
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D019015:Geologic Sediments / Q000737:chemistry*; D008296:Malaysia; D025341:Principal Component Analysis / Q000379:methods*; D014874:Water Pollutants, Chemical / Q000032:analysis; D014877:Water Pollution, Chemical
chemical_list: D014874:Water Pollutants, Chemical