## 4 Discussion  
### Models Based on Statistical Methods  
In this work, the possibility of developing classification models able to detect subtle differences between very similar honey classes (i.e., fir and honeydew honey) has been prospected. In this regard, new recognition models for the individual differentiation of fir, chestnut, coriander, sunflower, honeydew, and raspberry honey samples were developed using the PLS-DA statistical method. For the development of the botanical differentiation models, a pre-processing treatment consisting in the application of the 1 st derivative followed by an autoscale step was performed on the raw experimental data. For each individual botanical differentiation of the honey samples, a first classification model was conducted by applying PLS-DA on the entire FTIR fingerprint range. Then, a model-based variable selection using PLS was performed with the aim of identifying the spectral points that have the highest discrimination potential. Taking into consideration only those features, a final PLS-DA model was built with respect to each desired botanical origin assessment. The first discrimination model was developed to differentiate the chestnut honey from the rest of the samples. In this regard, two groups were compared, one formed by six chestnut samples while the second one contained the remaining honey samples (fir, polyfloral, coriander, sunflower, honeydew, and raspberry). The constructed PLS-DA model conducted to the correct assessment of 98.7% of the chestnut samples when the entire spectral range was used; only one honeydew sample was misclassified and wrongly attributed to the chestnut group. The variable reduction step had as result a diminishing of data dimension from 1246 variables to 377. In this case, this step did not have any effect on the further improvement of the model prediction, with the model accuracy remaining the same (98.6%). The model built to discriminate coriander honey from the rest of the samples conducted to the correct classification of 82% of the samples when the entire fingerprint region  
Figure 2: Experimental ATRFTIR spectra of the honey samples distribution. was taken into account. When the model-based feature selection was applied, it was observed that there are 377 significant variables that mainly contributed to the coriander honey classification. Therefore, a second model for the coriander honey differentiation was conducted only on these markers, though the data dimensionality reduction did not conduct to an improvement of the model prediction. In this case, just three coriander samples were correctly classified, while one fir, three honeydew, one polyfloral, and three raspberry samples were wrongly assigned to the coriander botanical class (Figure 3c). The PLS-DA model developed for the fir honey discrimination had a correct prediction rate of 72.6% when the entire fingerprint spectral range was employed for the model construction. The variable selection step allowed a drastic decrease in the dimensionality of the input data, namely out of 1246, 35 were selected as meaningful features. The subsequent model built only on this reduced data set had an accuracy of 75.3%. In this case, from 8 fir samples, 5 were correctly attributed, while the other 15 samples from the remaining group  
Figure 3: PLS-DA analysis—predicted Y values in cross-validation, for each of the investigated botanical differentiations: (**a**) chestnut, (**b**) coriander, (**c**) fir, (**d**) honeydew, (**e**) raspberry, and (**f**) sunflower. were wrongly assigned to the fir group. Misassignments were expected in this classification taking into account the existing similarities between fir and honeybee samples. Thus, the 15 misclassified samples attributed to the fir group had the following distribution: honeybee (8), raspberry (4), chestnut (2), and polyfloral (1).