No abstract

In bioanalytical chemistry, a detailed chemical understanding of biomaterials is often difficult to obtain due to the sheer number of analytes contained in a sample along with the samples' generally low reproducibility. This study presents a Fourier transform infrared (FT-IR) spectroscopic technique in conjunction with innovations in sample preparation and chemometric data preprocessing to overcome these limitations. These methodologies were applied to quantitative analyses of 31 representative compounds commonly found in biomaterial, which have been incorporated into a spectroscopic calibration database, that is, albumin (protein); D-alanine, glycine, histidine, valine, arginine, cysteine, phenylalanine, tyrosine, methionine, L-glutamine, and glutamic acid, (amino acids); glucose, fructose, galactose, mannose, sucrose, lactose, glycogen, agarose, and starch (carbohydrates); DNA (salmon sperm), sulphonoquinovosyl diglyceride ( sulpho-lipid ), and 1,2-diacyl-sn-glycero-3-phospho-L-serine ( phospho-lipid ); succinic acid and malic acid ( carboxylic acids ); glycolic acid (a -hydroxy acid), sodium pyruvate, b -carotene, frustules (microalgae silica-shells), and ammonium formate. Two proof-of-principle applications were based on calibration models incorporating these solids, i.e., characterization of E. coli and microalgae. The former aims for detection of bacterial contamination and the latter to enable investigations of changes in chemical composition of microalgae cells in response to shifting environmental conditions. Chemometric preprocessing steps have been developed for handling sample-to-sample fluctuations of absorption path lengths and baselines; the former incorporated mass normalization while the latter utilized a novel baseline correction method that requires no a priori information. Data preprocessing, chemometric calibration, and evaluation algorithms have been combined, together with an extensive spectral database of the aforementioned compounds (∼1500 samples), for quantitative calibration purposes through the remotely accessible Virtual Chemometrics Lab , which can be utilized for a multitude of applications through a graphical user interface.

Many chemical processes are involved in the interactions of living cells with their environment; however, monitoring such processes often requires sophisticated analyzers. In this study, a sensing strategy based on imaging techniques has been developed to (i) enable cell discrimination based on their physical appearance such as size and shape and (ii) to build predictive models that relate the measured cell appearance to chemical parameters in their environment. Both goals aim at innovative and straightforward sensing strategies for analyzing cell–environment interactions. Image analyses offer several advantages such as the use of simpler, more robust sensors and the omission of extensive sample/sensor preparations. Imaging can analyze numerous cells and thus gains a culture representative insight rather than a potentially nonrepresentative single‐cell response. As a proof‐of‐principle application, different species of microalgae cells have been exposed to various nutrient conditions. Microalgae are known to sensitively adapt to changing nutrient conditions and could potentially become biological “probes” for chemical shifts in ecosystems. Because of considerable spreads of cell size and shapes within one class, size and shape distributions have been derived from visible images of cell cultures. It is shown that the novel image analyses are capable of discriminating different cell species based on their cell shapes and sizes. It is also demonstrated that in conjunction with the recently introduced, nonlinear multivariate “predictor surfaces”, the nutrient availability has a quantifiable impact on the cell size distributions. In this application, predictor surfaces are somewhat more precise than partial least squares. Copyright © 2012 John Wiley & Sons, Ltd.