The effect of flow rate, accelerometer location and temperature in acoustic chemometrics on liquid flow: Spectral changes and robustness of the prediction models

Prediction of chemical composition of flowing liquids using passive acoustic measurements and multivariate regression (acoustic chemometrics) has been reported as a promising in-line measurement method. However, the passive acoustic measurement results are also affected directly or indirectly by other factors than composition of the liquid, i.e. physical conditions of the flow and equipment/pipe properties. The present study focuses on the effects of flow rate, accelerometer location and temperature on the acoustic spectra and prediction of composition of liquids. The studied liquids were two-component mixtures of sucrose and water, and three-component mixtures of ethanol, sucrose and water. Multivariate models were estimated using both local and global calibration on full spectra, and augmented frequency and amplitude matrices derived from full spectra. Flow rate and accelerometer location had the most pronounced effect on acoustic spectra and prediction results from recalibrated local models. Temperature had a minor effect on the acoustic spectra and prediction results. The prediction error for determination of ethanol, sucrose and water increased with increasing flow rate. Changes in flow rate resulted in considerable spectral variations, causing the resultant local calibration model to perform poorly predicting the new samples taken at other flow conditions. Global models performed well on prediction of liquid composition at all studied flow and temperature levels. The global models, however, needed higher number of PLS factors and led to higher prediction errors compared to local models. Using the augmented frequency and amplitude matrices in PLS/PPLS global regression models led to higher prediction errors compared to full spectra models. However, the augmented frequency and amplitude models were more parsimonious (4–6 PLS factors) compared to the full spectra models (10–12 PLS factors).