DEVELOPMENT OF AUTOMATED VISION TECHNIQUES FOR IMMEDIATE ANALYSIS AND CONTROL OF BETALAIN-PRODUCING CELL CULTURES

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  • 1 Dept. of Horticulture, University of Illinois, Urbana, Illinois 61801, and Laboratoire de Phytogénétique Cellulaire, Université de Lausanne, Switzerland

Natural plant pigments (produced as secondary metabolites in cell culture) can replace controversial synthetic chemical colorants to enhance the appearance of processed foods. Intensive bioreactor-based production systems designed for betalain pigment-producing cultures of Beta vulgaris are still not economically competitive, in part due to the slow, prohibitively expensive, and incomplete conventional methods (HPLC analysis, biomass estimates, cell counts) which must be used to assess culture status. As an alternative, software was written using Semper 6 (a high level programming language for image analysis) for collection of exacting morphometric (spatial) and photometric (spectral) process information from an intense violet cell line. Uniform, crisp images of 1 ml culture samples in multiwell plates were captured macroscopically, and the pattern of pigment production was traced at 3 day intervals over the course of a 15 day growth cycle with monochromatic color filters and image grey level data. Rod-shaped cells and aggregates were automatically sorted and measured using parameters of particle size, density, and circularity. The machine vision method offers greater opportunity to fine-tune cell selection for enhanced pigment content.

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