Incorporating ad hoc spectral pre-processing strategies and a sufficiently large number of samples is necessary to cover the high variability observed in industrial processes due to measurement changes, seasonal variability and feedstock changes. An insufficient number of samples can increase the possibility of spectral artefacts interfering with NIRS fingerprints , thereby reducing the predictive power when modeling certain ingredients and nutrients. In our study, this was noted, e.g., for predicted fiber from soybean (Fig. ). This finding emphasizes the need to include large sample sizes in industrial settings. Such operational expenditures in model building and maintenance are needed to attain the ability to predict valuable process information.