Application of Boosting Partial Least Square Regression to Soft-sensor Modeling for NIRS Determination of Alcohol Degree of Beer

Soft sensors have been widely used in industrial process,the core of which is the establishment of a reliable soft-sensor model. Conventionally the application of soft-sensor is based on the establishment of a single mathematical model,which is often difficult to achieve at the required accuracy and robustness. Base on the idea of ensemble from machine learning,the algorithm of boosting ensemble partial least square regression (boosting-PLS) was proposed and applied to soft-sensor modelling,which was used in NIRS determination of alcohol degree of beer. As shown by experimental results,the soft-sensor model established on the base of the algorithm of boosting-PLS was proved to be accurate and robust and suitable especially for soft-sensor involving high-dimensional spectral data.