Application of mid-infrared chemical imaging and multivariate chemometrics analyses to characterise a population of microalgae cells.

A suite of multivariate chemometrics methods was applied to a mid-infrared imaging dataset of a eustigmatophyte, marine Nannochloropsis sp. microalgae strain. This includes the improved leader-follower cluster analysis (iLFCA) to interrogate spectra in an unsupervised fashion, a resonant Mie optical scatter correction algorithm (RMieS-EMSC) that improves data linearity, the band-target entropy minimization (BTEM) self-modeling curve resolution for recovering component spectra, and a multi-linear regression (MLR) for estimating relative concentrations and plotting chemical maps of component spectra. A novel Alpha-Stable probability calculation for microalgae cellular lipid-to-protein ratio Λi is introduced for estimating population characteristics.

Self-modeling curve resolution of multi-component vibrational spectroscopic data using automatic band-target entropy minimization (AutoBTEM).

Vibrational spectroscopy is being used routinely to measure multi-component samples and often times these data possess spectroscopic non-idealities such as highly overlapping spectral bands, presence of spectral non-linearities, etc. A multivariate curve resolution algorithm coined as automatic band-target entropy minimization (AutoBTEM) was developed to achieve self-modeling curve resolution of pure component spectra from multi-component vibrational spectroscopic data. This AutoBTEM is a variant extension of the band-target entropy minimization (BTEM) that combines a novel automatic band-targeting numerical strategy with exhaustive BTEM curve resolutions and unsupervised hierarchical clustering analysis in an overall blind search approach. It is also found that the number of components or significant factors and the extent of spectral band shifts can be inferred via the automatic band-targeting computations. The AutoBTEM algorithm is demonstrated herein to be successful when tested on two challenging mixture spectral datasets that are ill-conditioned. One is a two-component mid-infrared FTIR dataset containing spectral non-linearities, and the other is a 10-component Raman dataset with highly overlapping bands from its 10 chemical constituent spectra. The resolved pure component spectra correspond well with reference spectra and have an excellent normalized inner product of above 0.95 upon quantitative comparison.

Utilization of spectral vector properties in multivariate chemometrics analysis of hyperspectral infrared imaging data for cellular studies.

A suite of numerical techniques was utilized in a concerted fashion for the efficacious multivariate chemometrics analysis of hyperspectral infrared imaging data of exfoliated oral mucosa cells. Based on the vector representation of infrared spectrum a1xnu), spectral vector properties (SVP) are demonstrated to possess underpinning spectral information that was exploited in crucial chemometrics analyses; which include outlier spectra identification, selection for a subset of imaged mid-infrared spectra that contain good oral mucosa cell signals, and, for the first time, obtain major biochemical constituent spectra via the band-target entropy minimization (BTEM) curve resolution algorithm. The relative concentration spatial distribution of the major biochemical constituents observed, namely membrane lipids and various cellular protein structures (alpha-helix, beta-sheet, turns and bends), were subsequently acquired through multi-linear regression and were displayed as chemical contour maps. Amongst the set of numerical algorithms employed, two novel unsupervised clustering algorithms were developed and tested. One is useful for outlier spectra detection, and the other aids the selection of pertinent spatially distributed spectra that possess oral mucosa cell mid-infrared spectra with good signal-to-noise ratio. It is anticipated that this developed numerical suite will serve as an effective multivariate chemometrics protocol for cellular studies and biomedical diagnostics via infrared imaging.