This paper presents a novel automatic pattern recognition system for the classification of herbal substances, which comprises the analysis of chemical data obtained from three analytical techniques such as Thin Layer Chromatography (TLC), Gas Chromatography (GC) and Ultraviolet Spectrometry (UV), composed of the following stages. First, a preprocessing stage takes place that ranges from the TLC plate image conversion into a spectrum to the normalization and alignment of spectral data for all techniques. Then, a hierarchical clustering procedure is applied for each technique with the goal of discovering groups or classes that provide evidence concerning the different existing types. Next, an entropy-based template selection step for each group was introduced to exclude the less significant samples, thus allowing to improve the quality of the training set for each technique. In this manner, each class is now described by a set of key prototypes that allows the field expert to have a more accurate characterization and understanding of the phenomenon. Moreover, an improvement of the computational complexity for training and prediction tasks of the Support Vector Machines (SVM) is also achieved. Finally, a SVM classifier is trained for each technique. The experiments conducted show the validity of the proposal, showing an improvement of the classification results on each technique.

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The goal of this paper is the development of a multivariate calibration method for the quantitative determination of petroleum hydrocarbons in water and waste water by using FT-IR spectroscopy and PLS as a regression method to improve the results attained at the present time through the univariate standard method. In order to evaluate the performance of the regression model, four experimental responses obtained from an independent validation set prepared with spiked samples were examined: Root mean square error of prediction (RMSEP), average recovery, standard deviation, and relative standard deviation. In order to compare final results, the univariate model was developed together with the multivariate approach. The results show that the multivariate calibration method outperforms the univariate standard method. The accuracy of the results, capability of detection, and the high index of recovery obtained show that a multivariate calibration approach for the determination of petroleum hydrocarbons in water and waste water by means of IR spectroscopy can be seen as a very promising option to improve the current univariate standard method.

For many applications, a straightforward representation of objects is by multi-dimensional arrays e.g. signals. However, there are only a few classification tools which make a proper use of this complex structure to obtain a better discrimination between classes. Moreover, they do not take into account context information that can also be very beneficial in the classification process. Such is the case of multidimensional continuous data, where there is a connectivity between the points in all directions, a particular (differentiating) shape in the surface of each class of objects. The dissimilarity representation has been recently proposed as a tool for the classification of multi-way data, such that the multi-dimensional structure of objects can be considered in their dissimilarities. In this paper, we introduce a dissimilarity measure for continuous multi-way data and a new kernel for gradient computation. It allows taking the connectivity between the measurement points into account, using the information on how the shape of the surface varies in all directions. Experiments show the suitability of this measure for classifying continuous multi-way data.

Functional data analysis has been a novel option for representing images, since the continuous nature of images is preserved. Image representation using functional data provides significant advantages, being the appreciable reduction of the dimensionality one of the most significant. This paper gives a detailed description of the entire imaging process using the proposed approach. As an example, the representation of iris images through functional data for recognition tasks was used. The paper presents experiments and results of applying this approach to the recognition of iris images, demonstrating its effectiveness.