QUANTIFICATION OF MICROBIAL PRODUCTIVITY VIA MULTI-ANGLE LIGHT-SCATTERING AND SUPERVISED LEARNING

This article describes the use of chemometric methods for prediction o f biological parameters of cell suspensions on the basis of their ligh t scattering profiles. Laser light is directed into a vial or flow cel l containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods are then used to calibrate a model relating the parameter of interest to the in tensity values. Using such models opens up the possibility of estimati ng the biological properties of fermenter broths extremely rapidly (ty pically every 4 sec), and, using the flow cell, without user interacti on. Our work has demonstrated the usefulness of this approach for esti mation of yeast cell counts over a wide range of values (10(5)-10(9) c ells mL(-1)), although it was less successful in predicting cell viabi lity in such suspensions. (C) 1998 John Wiley & Sons, Inc.