Non-invasive fermentation analysis using an artificial neural network algorithm for processing near infrared spectra

The feasibility of using Artificial Neural Networks (ANN) to improve the performance of a fibre optic, near infrared (NIR) spectroscopic probe for monitoring fermentation processes was investigated. A miniature diode array spectrometer, operating between 1100 and 1450 nm, was used for on-line, in situ fermentation monitoring by placing a bifurcated fibre bundle inside a fermentation vessel. Non-linearities in the spectral response were complicated by the combined effects of spectral variations in the OH vibrational bands due to temperature fluctuations and pH variation. As the fermentation proceeds interferences from other absorbing molecules and cell masses increase at an exponential rate. The feasibility of accurately predicting both glucose and ethanol concentrations simultaneously during a fermentation process were assessed by applying Partial Least Squares (PLS) and Artificial Neural Networks (ANN) to the NIR spectral data. For a 5% glucose fermentation, a PLS model was able to predict "online" concentrations with standard errors of prediction (SEP) of 0.19% for glucose and 0.11% for ethanol, Three separate on-line fermentation experiments were performed in order to determine the possibility of using a model developed from one experiment to predict the concentrations of another experiment. PLS models produced an average SEP of 0.21% when used to predict different fermentation experiments. The ANN algorithm produced an average SEP of 0.13% on the same data.