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The odor information produced in Saccharomyces cerevisiae culture is one of the important characteristics of yeast growth status. This work innovatively presents the quantitative monitoring of cell concentration during the yeast culture process using a homemade color sensor. First, a color sensor array, which could visually represent the odor changes produced during the yeast culture process, was developed using eleven porphyrins and one pH indicator. Second, odor information of the culture substrate was obtained during the process using the homemade color sensor. Next, color components, which came from different color sensitive spots, were extracted first and then optimized using the ant colony optimization (ACO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized feature color components for quantitative monitoring of cell concentration. Results demonstrated that BPNN models, which were developed using two color components from FTPPFeCl (component B) and MTPPTE (component B), can obtain better results on the basis of both the comprehensive consideration of the model performance and the economic benefit. In the validation set, the average of determination coefficient R P 2 was 0.8837 and the variance was 0.0725, while the average of root mean square error of prediction (RMSEP) was 1.0033 and the variance was 0.1452. The overall results sufficiently demonstrate that the optimized sensor array can satisfy the monitoring accuracy and stability of the cell concentration in the process of yeast culture.

Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO