The determination of moisture in tobacco by guided microwave spectroscopy and multivariate calibration

The feasibility of using a general calibration model based on guided microw ave spectroscopic (GMS) data for the determination of moisture in various t obacco types is described. Several calibration methods were used on this da ta; multivariate linear regression (MLR), partial least square (PLS), polyn omial PLS (n-PLS) and multi-layer feed-forward artificial neural networks ( MLF) and the results are compared. Promising results (approximately 2% root mean squared validation error, for moisture levels between roughly 10 and 50%) were obtained. There was no significant difference between PLS and MLF predictions. It was found that the method is sensitive to the weight of th e sample used, and that best results were obtained when weight is included as a variable, whereas the position of the sample in the field had little e ffect. The paper suggests several experimental adaptations to improve the d ata and thus increases the predictive ability of models based on this data. (C) 2001 Elsevier Science B.V. All rights reserved.