Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation

- The foundation for a chemosensor to monitor solid-state fermentation (SSF) was set.
- Enzyme activities and protein concentration in aqueous extracts were monitored.
- Two chemometric methods were assessed to monitor SSF from near-infrared (NIR) data.
- Artificial neural networks (ANNs) were set up to predict biochemical attributes.
- The third derivative of spectral data improved the prediction quality of the ANNs.

This work aimed to establish a chemometric technique for quantifying amylase and protease activities as well as protein concentration in aqueous extracts of Rhizopus microsporus var. oligosporus obtained via solid-state fermentation (SSF). The kinetics of four agro-industrial wastes (wheat bran, soybean meal, type II wheat flour and sugarcane bagasse) were studied for 144 h, along with two different sets of their ternary mixtures, at a constant fermentation time of 120 h, to obtain primary data (biochemical parameters as well as near-infrared (NIR) spectral data). Then, models such as artificial neural network (ANN) and partial least squares (PLS) were calibrated to predict biochemical parameters using the spectral data. Primary data and three methods of preprocessing data - first, second and third derivatives - were assessed as inputs for both chemometric tools. The third derivative, that is, spectral pre-processing plus an optimized ANN, showed the least relative errors (<8.3% ± 10.5%). The third-derivative spectrum was found to be suitable as the ANN input data for monitoring amylase and protease activities and protein concentration in the SSF under study. The proposed methodology can serve as a foundation for at-line sensor development and decrease the time and cost of bioprocess development using Rhizopus microsporus var. oligosporus.

144