## 3 Results
### Classification Models Built on Individual Spectroscopic Blocks  
In order to use the SPORT approach to build (and validate) the individual classification models for the MIR and the NIR data sets, each of the two matrices were pre-processed using the following pre-treatments: no pretreatment (raw data), standard normal variate  
Figure 1: Raw MIR (**a**) and NIR (**b**) spectra (after averaging replicated measurements) of the investigated samples. Comparison between the mean profiles of Senise (red) and adulterated (blue) samples: (**c**) MIR; (**d**) NIR. MIR/NIR: mid- and near-infrared spectroscopies. (SNV); [30], first derivative and second derivative (both calculated with a 15 points window and a third order polynomial) [31]. Since SO-PLS-LDA is a sequential model, it is important to specify the order of the blocks, which was the one reported above; moreover, mean centering was always used as a further pre-processing step. For the sake of comparison, PLS-DA analysis was also performed on the MIR and NIR data set after the individual application of the abovementioned pretreatments; consequently, 8 different models (four per data block) were calculated. The classification accuracy of the PLS-DA and SPORT models built on the MIR and the NIR data both for the training (in cross-validation) and the test sets are summarized in Table 2, together with their optimal complexity (the range of latent variables explored was 0-10). Here it should be stressed that for each data set, in the model selection stage, the number of latent variables (LVs) to be retained in each pre-processing block was selected as the one leading to the lowest classification error in a fivefold cross-validation procedure. Moreover, in the case of PLS-DA, the best pre-processing was also selected based on the maximum classification accuracy in cross-validation. Looking at the Table, it is possible to notice how the use of SPORT not only allows one to fuse different pre-processings into a single model, but also straightforwardly indicates which ones are most effective to deal with the problem at hand. Indeed, in the case of the MIR signals, only two of the four matrices corresponding to the different pre-treatments contributed with a non-zero number of latent variables to the classification model, i.e., the raw data and the second derivative (with one and four LVs, respectively). The SPORT model on the MIR data performed very well on the training set (around 92% correct classification rate for both classes) but showed a lower though still good predictive accuracy (82.8%, corresponding to five Senise samples and one adulterated sample misclassified) on the test individuals. The comparison of these results with those of the best individual PLS-DA model (which is the one built on data pretreated with the second derivative) indicates that the fusion of different preprocessing strategies could help improving the classification accuracy on the training data; however, in this case, the classification error on the test set is the same. On the other hand, the best model on NIR spectra was created selecting only one LV from the block preprocessed by the first derivative. This led to the perfect classification of all the training objects in cross-validation and an overall accuracy of 94.3% on the test set, corresponding to the misclassification of two adulterated samples. Obviously, since only the first derivative block was selected by SPORT, in the case of the NIR data the results are identical to those of the best PLS-DA model. Anyway, it should be stressed that, for both data sets, the use of SPORT provides comparable or better results without the need to choose the optimal pre-processing strategy.