## 1 Introduction
In the literature, there are also reports that have used artificial intelligence (e.g., machine learning algorithms) in order to process the infrared spectral data to obtain a differentiation model capable to discriminate between five Chinese kinds of honey [11] or samples having a protected geographical indication [15]. An application for honey authentication using PLS-DA was realized for botanical differentiation of four varieties of honey by using the isotopic and elemental content data [16]. It was proved that the discrimination potential of the approach can be increased by applying an advanced pre-processing step. The selection of the most appropriate data pre-processing has been realized by comparison between two methods, namely, principal component analysis (PCA) (unsupervised) and partial least squares-discriminant analysis (PLS-DA) (supervised). Thus, considering the final models' prediction rate, it was shown that an efficient data pre-processing can be realized by using the most relevant features selected by PLD-DA. In these conditions, percentages between 82% and 100% can be obtained for discrimination of individual classes by using the statistical models (model accuracies for each honey category was: 82%--linden, 90%--colza, 92%--acacia, and 100%--sunflower). Artificial neural networks (ANNs) were also applied for food authentication investigation [17]. In this study, it was tested the possibility to use isotopic and elemental data and ANNs models for discriminations of various honey types (acacia, linden, honeydew, colza, allium verum, coriander, sunflower, thyme, raspberry, lavender, and chestnut) from Romania (50 samples) and France (46 samples). It was proved that by involving this approach, a prediction model for geographical recognition can be developed with an accuracy of 96%. The potential of ATR-FTIR diamond in discrimination of the geographical and botanical origins of different honey types from Romania and France has been also tested in correlation with a supervised classification method, namely, soft independent modeling class analogy (SIMCA) [18]. In this work, it was proved that a spectral range between 880 and 1600 cm\({}^{-1}\) can be successfully used for botanical and geographical investigations if pre-processing data treatment (second derivative) and \"autoscale\" processing are applied to experimental spectra before the statistical classification. It was also shown that despite the most significant discriminators for both investigations are from the same spectral region, the discrimination power is 10 times lower for geographical recognition than for the floral origin identification. These results are also sustained by the interclass distances, which are quite modest for geographical classification, suggesting the reliability of this approach for honey floral discrimination. However, it was also demonstrated that the geographical differentiation inside a single class of honey variety is possible by using this approach due to the fact that a correct classification with a significant interclass distance was obtained. A prediction model based on ATR-FTIR spectral data and PLS-DA statistical method has been recently developed for botanical and geographical origin differentiation, and also for harvesting year discrimination [10]. Thus, considering this approach, 83% of the investigated samples have been correctly assigned in relation to their botanical origin when four honey varieties were employed, while, for geographical discrimination, 91% of the Transylvanian samples and 85% of the samples produced in other regions were successfully identified. Moreover, for harvest recognition, true positive rates of 95% and 97% have been obtained for the 2020 and 2021 harvesting years, allowing a correct assignment of 105 out of 109 honey samples [19]. This study proposes a new approach for the development of prediction models able to classify 73 honey samples belonging to seven different botanical origins.