Near infrared (NIR) spectroscopy is a potentially valuable tool for estimating the process parameters of anaerobic digestion (AD) in agricultural biogas plants. In addition to precision and accuracy, the evaluation of model robustness versus changes in the feedstock composition and process stages are needed to implement this analytical technology into common practice. This paper reports the first step of a global modelling approach, addressing the need for increased calibration robustness for the estimation of the process parameters volatile solids (VS), ammonium (NH4–N), total inorganic carbon (TIC), total volatile fatty acids (VFA), acetic acid and propionic acid in the fresh matter (FM) of digester sludge. Spectra from samples in different training sets, varying with respect to their origin and feedstock composition, were assessed using partial least square regression. The comparison of the offline calibration results among the training sets revealed that an increase in the heterogeneity of the sample matrices did not result in a relevant performance loss of the NIR models. With a root mean square error of cross-validation (RMSECV) of 4.0 g kg−1 and 0.16 g kg−1 FM, VS and NH4–N exhibited the highest potential for estimation via NIR spectroscopy. While the strong X–Y relationship for the structurally NIR-inactive TIC (RMSECV = 0.80 g kg−1 FM) indicated a satisfactory screening potential, further study is required before application. The volatile fatty acid parameters, which are useful for detecting short-term changes of the AD, did not result in good NIR models. However, there appears to be a realistic potential for a global VFA model with an estimation error of 0.9 g kg−1 FM, which may support the use of NIR-based rapid screening of the dynamics of the acidity level in digester sludge.
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