Effects of pre‐processing of Raman spectra on in vivo classification of nutrient status of microalgal cells

Abstract

AbstractRaman spectra were obtained from cells of the chlorophyte unicellular eukaryotic alga Dunaliella tertiolecta, which had been grown either under nutrient‐replete conditions or starved of nitrogen for 4 days. Spectra were rich in bands which could all be attributed to either chlorophyll a or β‐carotene. A cursory examination of the differences between the spectra of replete and starved cells indicated a decline in chlorophyll a and an increase in β‐carotene in chlorophytes. Unprocessed spectra showed pronounced baseline effects. A variety of pre‐processing techniques were used in an attempt to visualise the spectral, and hence chemical, differences in the transformed data and perform classification based upon these differences. Six types of spectral pre‐processing were compared: baseline correction with vector normalisation; Multiplicative Scatter Correction (MSC), Extended Multiplicative Signal Correction (EMSC); Standard Normal Variate (SNV); and vector normalised 1st and 2nd derivative spectra. Results for Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares (PLS) discriminant analysis were compared. All pre‐processing methods allowed spectral differences between N‐replete and N‐starved spectra to be visualised, with derivatives and EMSC scoring the lowest RMSEC and RMSEV values with PLS and also the best overall classification results. SIMCA was not suited to classifying the nutrient classes under any of the pre‐treatments, due to the small model distances involved. Copyright © 2006 John Wiley & Sons, Ltd.