Complex Chemical Data Classification and Discrimination Using Locality Preserving Partial Least Squares Discriminant Analysis

Abstract

Partial least squares discriminant analysis (PLS-DA) is a well-known technique for feature extraction and discriminant analysis in chemometrics. Despite its popularity, it has been observed that PLS-DA does not automatically lead to extraction of relevant features. Feature learning and extraction depends on how well the discriminant subspace is captured. In this paper, discriminant subspace learning of chemical data is discussed from the perspective of PLS-DA and a recent extension of PLS-DA, which is known as the locality preserving partial least squares discriminant analysis (LPPLS-DA). The objective is twofold: (a) to introduce the LPPLS-DA algorithm to the chemometrics community and (b) to demonstrate the superior discrimination capabilities of LPPLS-DA and how it can be a powerful alternative to PLS-DA. Four chemical data sets are used: three spectroscopic data sets and one that contains compositional data. Comparative performances are measured based on discrimination and classification of these data sets. To compare the classification performances, the data samples are projected onto the PLS-DA and LPPLS-DA subspaces, and classification of the projected samples into one of the different groups (classes) is done using the nearest-neighbor classifier. We also compare the two techniques in data visualization (discrimination) task. The ability of LPPLS-DA to group samples from the same class while at the same time maximizing the between-class separation is clearly shown in our results. In comparison with PLS-DA, separation of data in the projected LPPLS-DA subspace is more well defined.

Metabolomics Analysis and Antioxidant Potential of Endophytic Diaporthe fraxini ED2 Grown in Different Culture Media

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Amplify antimicrobial photo dynamic therapy efficacy with poly-beta-amino esters (PBAEs)

“…We firstly validated the hypothesis of using PBAE to augment PDT on a variety of pathogens representing those most commonly encountered in SSTI 44 , 45 ; subsequently the role of polymer backbone constituents was assessed through a broad library of PBAE and a new discovery, the impact on ROS, beside TBO uptake was also observed. PBAE features critical in this newly noticed phenomenon were assessed through PLS, one of chemometrics data analytic techniques that have been employed in drug design and analytical chemistry 34 , 46 , 47 , to also elicit possible mechanism. Initial in-vitro tests on keratinocytes cells and fibroblasts were also carried out exposing the cells to a selection of the PBAE-TBO complexes that exhibited the greatest enhancement of antimicrobial activity; in order to gather more robust safety data, two independent assays (MTT and LDH) were utilised with both showing the safety of the complexes PBAE-TBO on skin tissue cells.…”

Amplify Antimicrobial Photo Dynamic Therapy Efficacy With Poly-beta-amino Esters (PBAEs)

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