LAUSR

In the process of converting food‐processing by‐products to value‐added ingredients, fine grained control of the raw materials, enzymes and process conditions ensures the best possible yield and economic return. However, when raw material batches lack good characterization and contain high batch variation, online or at‐line monitoring of the enzymatic reactions would be beneficial. We investigate the potential of deep neural networks in predicting the future state of enzymatic hydrolysis as described by Fourier‐transform infrared spectra of the hydrolysates. Combined with predictions of average molecular weight, this provides a flexible and transparent tool for process monitoring and control, enabling proactive adaption of process parameters.