Faster, reduced cost calibration method development methods for the analysis of fermentation product using near-infrared spectroscopy (NIRS).

Recent innovations in synthetic biology, fermentation, and process development have decreased time to market by reducing strain construction cycle time and effort. Faster analytical methods are required to keep pace with these innovations, but current methods of measuring fermentation titers often involve manual intervention and are slow, time-consuming, and difficult to scale. Spectroscopic methods like near-infrared (NIR) spectroscopy address this shortcoming; however, NIR methods require calibration model development that is often costly and time-consuming. Here, we introduce two approaches that speed up calibration model development. First, generalized calibration modeling (GCM) or sibling modeling, which reduces calibration modeling time and cost by up to 50% by reducing the number of samples required. Instead of constructing analyte-specific models, GCM combines a reduced number of spectra from several individual analytes to produce a large pool of spectra for a generalized model predicting all analyte levels. Second, randomized multicomponent multivariate modeling (RMMM) reduces modeling time by mixing multiple analytes into one sample matrix and then taking the spectral measurements. Afterward, individual calibration methods are developed for the various components in the mixture. Time saved from the use of RMMM is proportional to the number of components or analytes in the mixture. When combined, the two methods effectively reduce the associated cost and time for calibration model development by a factor of 10.

Prediction of gas-phase reduced ion mobility constants (K0).

A method of predicting reduced ion mobility values, K0, for use in ion mobility spectrometry is described. While the method is very similar to a previously reported method based on a neural network, the method described in this paper uses a purely statistical regression approach. Furthermore, it has been applied to a wider class of compounds, including chemical agents. Various molecular parameters were evaluated in the predictive model to determine the qualitative dynamics that have the greatest effect on K0. An R2 value of 80.1% was obtained when calculated K0 values were plotted against measured K0 values for 162 compounds for which experimental K0 values were available. However, when chloroacetophenone and 3-xylyl bromide (3-methylbenzyl bromide) were removed from the set due to their large residual values, the predictability increased to an R2 value of 87.4%. This compares well with the value of 88.7%, which was obtained in a regression step of a previous neural network study for a less diverse set of 168 compounds.