In this study two approaches to predict the total alkalinity (expressed as mg L(-1)HCO(3)(-)) of an anaerobic digester are examined: firstly, software sensors based on multiple linear regression algorithms using data from pH, redox potential and electrical conductivity and secondly, near infrared reflectance spectroscopy (NIRS). Of the software sensors, the model using data from all three probes but a smaller dataset using total alkalinity values below 6000 mg L(-1)HCO(3)(-) produced the best calibration model (R(2)=0.76 and root mean square error of prediction (RMSEP) of 969 mg L(-1)HCO(3)(-)). When validated with new data, the NIRS method produced the best model (R(2)=0.87 RMSEP=1230 mg L(-1)HCO(3)(-)). The NIRS sensor correlated better with new data (R(2)=0.54). In conclusion, this study has developed new and improved algorithms for monitoring total alkalinity within anaerobic digestion systems which will facilitate real-time optimisation of methane production.