Abstract
The feasibility of using a general calibration model based on guided microwave spectroscopic (GMS) data for the determination of moisture in various tobacco types is described. Several calibration methods were used on this data; multivariate linear regression (MLR), partial least square (PLS), polynomial 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 the 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 effect. The paper suggests several experimental adaptations to improve the data and thus increases the predictive ability of models based on this data.
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