Impact of Pre-Processing Methods for the Identification of the Botanical Origin of Honey Based Upon Isotopic and Elemental Profiles

Abstract

Botanical differentiation of four floral honey types based on isotopic and elemental content coupled with partial least squares—discriminant analysis (PLS-DA) was performed in the present work. In order to enhance the discrimination potential of this approach, an advanced pre-processing step was applied. For data pre-processing, a comparison between unsupervised (principal component analysis - PCA) and supervised (PLS-DA) methods was performed to achieve the highest classification rate of the developed models. Based on the prediction rate of the final models, it was assumed that the most efficient data pre-processing method corresponds to the selection of relevant features by PLS-DA. The statistical models allowed the differentiation of individual classes with percentages between 82% and 100%. Thus, the model accuracies for the recognition of each honey category were 100% (sunflower), 92% (acacia), 90% (colza) and 82% (linden). The applied variable selection, performed through PLS-DA, led to a significant improvement of the models in the cross-validation evaluation (i.e., for the sunflower honey samples, the true positive rate increased from 66% to 83%).

Keywords:

• botanical differentiation
• elemental content
• honey
• partial least squares—discriminant analysis (PLS-DA)
• principal component analysis (PCA)
• stable isotopes

Additional information

Funding

This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS–UEFISCDI, project number PN-III-P4-ID-PCE-2020-0644 (Contract no. 7PCE/2021), within PNCDI III. A. R. Hategan acknowledges the financial support received from Babeş-Bolyai University through the special scholarship for scientific activity for the academic year 2021–2022.