Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation

Abstract

The Amazon tree Aniba rosaeodora Ducke (rosewood) provides an essential oil valuable for the perfume industry, but after decades of predatory extraction it is at risk of extinction. The extraction of the essential oil from wood implies the cutting of the tree, and then the study of oil extracted from the leaves is important as a sustainable alternative. The goal of this study was to test the applicability of Raman spectroscopy and Partial Least Square Discriminant Analysis (PLS-DA) as means to classify the essential oil extracted from different parties (wood, leaves and branches) of the Brazilian tree A. rosaeodora. For the development of classification models, the Raman spectra were split into two sets: training and test. The value of the limit that separates the classes was calculated based on the distribution of samples of training. This value was calculated in a manner that the classes are divided with a lower probability of incorrect classification for future estimates. The best model presented sensitivity and specificity of 100%, predictive accuracy and efficiency of 100%. These results give an overall vision of the behavior of the model, but do not give information about individual samples; in this case, the confidence interval for each sample of classification was also calculated using the resampling bootstrap technique. The methodology developed have the potential to be an alternative for standard procedures used for oil analysis and it can be employed as screening method, since it is fast, non-destructive and robust.

Keywords: Chemometric; Raman spectroscopy; Reliability; Rosewood oil; Uncertainty estimation.