[Show abstract][Hide abstract] ABSTRACT: We present a new method to detect the presence of the hollow heart, an internal disorder of the potato tubers, using hyperspectral imaging technology in the infrared region. A set of 468 hyperspectral
cubes of images has been acquired from Agria variety potatoes, that have been cut later to check the presence of a hollow
heart. We developed several experiments to recognize hollow heart potatoes using different Artificial Intelligence and Image
Processing techniques. The results show that Support Vector Machines (SVM) achieve an accuracy of 89.1% of correct classification.
This is an automatic and non-destructive approach, and it could be integrated into other machine vision developments.
[Show abstract][Hide abstract] ABSTRACT: We present a benchmarking framework to design multi spectral systems working in the NIR range for multiple purposes. This framework is composed of a hyperspectral imaging hardware and an ad-hoc software that performs pattern recognition experiments (image acquisition, segmentation, feature extraction, feature selection, classification and evaluation steps) comparing different algorithms in every step. For each experiment, we obtain a solution using a generic hyper spectral system, but we also obtain enough data to design a specific multi-spectral system in order to decrease the overall execution time. This improvement is based in the feature se lection step, that provides the most relevant wavelengths for the problem. The framework has been tested for detecting internal and external features in potatoes, determining the origin of honey, and studying fecundity parameters in hen eggs.
[Show abstract][Hide abstract] ABSTRACT: The study of biology and population dynamics of fish species requires the estimation of fecundity parameters in individual
fish in many fisheries laboratories. The traditional procedure used in fisheries research is to classify and count the oocytes
manually on a subsample of known weight of the ovary, and to measure few oocytes under a binocular microscope. With an adequate
interactive tool, this process might be done on a computer. However, in both cases the task is very time consuming, with the
obvious consequence that fecundity studies are not conducted routinely. In this work we develop a computer vision system for
the classification of oocytes using texture features in histological images. The system is structured in three stages: 1)
extraction of the oocyte from the original image; 2) calculation of a texture feature vector for each oocyte; and 3) classification
of the oocytes using this feature vector. A statistical evaluation of the proposed system is presented and discussed.
[Show abstract][Hide abstract] ABSTRACT: The common scab is a skin disease of the potato tubers that decreases the quality of the product and influences significantly the price. We present an objective and non-destructive method to detect the common scab on potato tubers using an experimental hyperspectral imaging system. A supervised pattern recognition experiment has been performed in order to select the best subset of bands and classification algorithm for the problem. Support Vector Machines (SVM) and Random Forest classifiers have been used. We map the amount of common scab in a potato tuber by classifying each pixel in its hyperspectral cube. The result is the percentage of the surface affected by common scab. Our system achieves a 97.1% of accuracy with the SVM classifier.
[Show abstract][Hide abstract] ABSTRACT: This paper has a double objective. The first goal was to develop an authentication system to differentiate between traditional orujo alcoholic distillates with and without a certified brand of origin (CBO). Owing to their low price and quality, samples without a CBO can be used as substrates for falsification of genuine CBO ones. The second objective was to perform a comparison of the abilities of the different chemometric procedures employed for this classification. The classification was performed on the basis of the chemical information contained in the metal composition of the orujo distillates. Eight metals determined by electrothermal atomic absorption spectrometry and inductively coupled plasma optical emission spectrometry were considered (Ca, Cd, Cr, Cu, K, Mg, Na and Ni). After the appropriate pretreatment, the data were processed using different chemometric techniques. In the first stage, principal component analysis and cluster analysis were employed to reveal the latent structure contained in the data. Once it had been demonstrated that a relation exists between the metal composition and the raw materials, and not between the metal composition and the distillation systems employed for the orujo production, the second step consisted in the comparative application of different supervised pattern recognition procedures (such as linear discriminant analysis, K-nearest neighbours, soft independent modelling of class analogy, UNEQ and different artificial neural network approaches, including multilayer feed-forward, support vector machines, learning vector quantization and probabilistic neural networks). The results showed the different capabilities of the diverse classification techniques to discriminate between Galician orujo samples. The best results were those provided by probabilistic neural networks, in which the correct recognition abilities for CBO classes and without CBO classes were 98.6 +/- 3.1 and 98.0 +/- 4.5%; the prediction results were 87.7 +/- 3.3 and 86.2 +/- 5.0%, respectively. The usefulness of chemical metal analysis in combination with chemometric techniques to develop a classification procedure to authenticate Galician CBO orujo samples is demonstrated.
No preview · Article · Jul 2010 · Analytical and Bioanalytical Chemistry