Faster, reduced cost calibration method development methods for the analysis of fermentation product using near-infrared spectroscopy (NIRS). (5th June 2021)

• Record Type: Journal Article
• Authors: Agbonkonkon, Nosa; Wojciechowski, Greg; Abbott, Derek A; Gaucher, Sara P; Yim, Daniel R; Thompson, Andrew W; Leavell, Michael D
• Is Part Of: Journal of industrial microbiology & biotechnology. Volume 48:Number 5/6(2021)
• Publication Date: 2021-06-05
• Subjects: NIR spectroscopy -- Fermentation -- Generalized calibration model (GCM) -- Sibling model -- RMMM; Industrial microbiology -- Periodicals; 660.62
• DOI: 10.1093/jimb/kuab033
• Languages: English
• ISSNs: 1367-5435

Abstract

Abstract: 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.