Quantitative measurements of anaerobic digestion process parameters using near infrared spectroscopy and local calibration models

The performance of local calibration models for quantitative measurements of ammonium and acetate on samples from an anaerobic digestion process was examined. The local calibration methods used were locally weighted regression (LWR) and multi-layer partial least squares (ML-PLS) regression. The results of these two methods were compared to each other and to the results from the global partial least squares (PLS) model regression as well. For ammonium, both the Local methods performed excellently in comparison with global PLS models. However, the results from the 150 LWR models regressed for ammonium also showed that the accuracy can be highly dependent on the different combination alternatives for model parameter settings and pre-processing alternatives. For this reason, a number of distance measures were evaluated as local subset selection methods in ML-PLS. The benefits of an optimised layer structure and the iterative approach in ML-PLS were also evaluated for ammonium. This showed that some benefits can be obtained by optimising the layer structure, at least in the sense that the number of layers can be reduced,and that there can be a significant advantage in using an iterative approach in the selection of the local subset of calibration data. The local calibration methods were also evaluated for acetate but, in this case, the benefits compared to global PLS calibration models were fairly insignificant with ML-PLS and none at all with LWR.