Land-use classification of Malaysian soils by ultra-high performance liquid chromatography (UHPLC)-based untargeted data combined with chemometrics for forensic provenance - Mohd Rosdi, Nur Ain Najihah Binti; Abdul Halim, Nur Izzma Hanis; Sashidharan, Jeevna A/P; Abd Hamid, Nadirah; Abdul Halim, Azhar; Sino, Hukil; Lee, Loong Chuen (Microchemical journal, 2024)
[Display omitted] •Non-volatile organic fingerprints of soils were acquired via an isocratic elution in a UHPLC system.•The non-volatile organic fingerprints were varied by different land-use class of soils.•PLS-DA model outperforms CART in predicting land-use class of mixed soils based on UHPLC fingerprints.•UHPLC coupled with chemometric are worth developing and utilizing for forensic soil provenance. Soil has become the most precious trace evidence attributed to its transference and persistence, but little is known about the capability of non-volatile organic fractions of soil in predicting land-use class. Hence, this study devoted to discriminating soils into four land-use classes based on chromatographic data and predictive modelling. Nine sites representing four different land-use classes were selected for collecting 73 soil samples via a grid method. Then, all the soil samples were dried, homogenized, sieved and eventually extracted using acetonitrile. The extracts were further analyzed via an isocratic elution in an ultra-high performance liquid chromatography (UHPLC) system. The pixel-level chromatograms were carefully assessed via diverse data preprocessing (DP) methods including baseline correction, normalization and derivative algorithms. Furthermore, the retention time (RT) window was also segmented into several sub-windows and thoroughly evaluated. Prediction accuracy of classification and regression tree (CART) modelling as estimated using 100 subsets of training samples on the corresponding testing and blind samples were ranked using TOPSIS method for identifying the most desired sub-window and DP strategy. The sub-RT window covering 0–5 min preprocessed via modified polynomial fitting algorithm followed by vector normalization emerged to be the most desired sub-dataset. The best sub-dataset modelled using CART and partial least squares-discriminant analysis (PLS2-DA) algorithms achieved 79.5 % and 97.4 % accuracy in predicting the land-use classes of 13 mixed samples. In conclusion, UHPLC-based fingerprint technique coupled with predictive modelling shows great potential in inferring land-use class of trace amount of soil.