###### Abstract
They have proven effective in solving classification problems in the biomedical field according to measured vibrational spectra [18; 19; 20]. Several types of classification software have been developed and optimized, and they are now available to support researchers in properly addressing the problem of attributing unknown spectra to a suitable class. One of such software is \"Orange\" ([[https://orangedatamining.com/](https://orangedatamining.com/)]([https://orangedatamining.com/](https://orangedatamining.com/))), which is freely available and contains many classification algorithms [21]. Some popular and efficient algorithms included in Orange are CN2 rule induction (CN2-RI), logistic regression (LR), classification tree (CT), support vector Machine (SVM), k nearest neighbours (kNN), and neural network (NN). The CN2_RI algorithm is a classification technique designed for the efficient induction of simple and comprehensible rules of form, \"if cond then predict class\" [22]. The CN2_RI algorithm generates, according to an iterative process, a list of rules for classifying samples [23]. In particular, first the algorithm sequentially searches for reliable rules that allow us to correctly classify a large number of samples of the dataset. The reliability of a given rule is estimated with a proper evaluation function [24]. Then, the samples covered by this rule are removed from the dataset, whereas the remaining samples are successively classified by other rules. The process eventually stops if all samples are classified and no more rules can be found [23]. Recently, the CN2_RI model was used to predict the risk level of cervical and ovarian cancer in association to stress [25], as well as to predict the severity of obstructive sleep apnoea syndrome [26], although the classification of vibrational spectra by this technique has not yet been reported in the literature. LR is a binary classification model capable of providing the probability that an unknown sample belongs to one of two classes. During the training step, all selected variables, x, which characterize a sample are appropriately summarized to contribute to a new variable, z. In particular, the coefficients linking the variable z to the variables x are properly determined so that the values of the variable z approximate the values of 0 and 1 for the two classes, respectively. Next, the z values for the training samples are fitted with a sigmoid function (ranging between 0 and 1). By computing the sigmoid function of z (thatis, a weighted sum of the input features), we obtain a probability (between 0 and 1) of an observation belonging to one of the two classes. Then, for the prediction of an unknown sample, first, the z value should be computed (using the previously determined coefficients) before it is entered into the sigmoid function: the probability of belonging to one of the two classes is established [17]. The LR model was used for the classification of new analogues of drugs at a high risk of being abused, belonging to the class of hallucinogenic amphetamines, based on their FTIR spectra [27]. [PERSON]. [PERSON] et al. reported that a LR model can discriminate between healthy controls and Alzheimer's patients with a precision of 98% when the input for the model combines data from both Raman and FTIR spectra measured for cerebral spinal fluid [28]. The CT algorithm classifies data according to a hierarchical model composed of decision rules that are applied recursively to the variables in order to separate the dataset into single-class subsets [29]. The decision rules are found according to a tree structure, which consists of a root node, branches, internal nodes, and leaf nodes. The root node identifies a spectral feature that allows for the division of data into classes in the best possible way. The branches that originate from the root node report the decision rules regarding the value of the spectral features that separate the whole dataset into subsets according to the classes.