The majority of the analyzed calculi from patients are composed of calcium oxalate (CaOx) monohydrate whewellite (Wh) and CaOx dihydrate wedellite (Wd). The urinary calculi were identified by chemical and morphological analysis based on106 urine samples from human voluntary. The Crystalluria made by an optical polarized light microscopy. The oxaluria and urinary calcium were determined by conventional volumetric assays. The aim of this paper was to develop a simple system to predict and classify the type of crystalluria using Artificial Neural Networks (ANNs) algorithm.

The objective of the present work is the evaluation of the nutritional and organoleptic properties of olive oils from oil mills in the rural commune of Tagzirt, area of Beni Mellal (center of Morocco) by a physicochemical characterization of their compositions. Fifteen samples of olive oils extracted from the Moroccan Picholine variety were collected from traditional oil mills. Physicochemical analyzes of free acidity, peroxide value, refractive index, density, K232, K270 and K, the chlorophyll content, the content of phenolic compounds, the α-tocopherol content and oleic acid proportion were conducted according to the standards of the International Olive Council (IOC). The results were used to classify the oils studied according to their quality standards. The data obtained confirm that the conditions of harvesting, crushing and storage of olive oils affect the quality of produced oil. Therefore, we must educate farmers on the importance of improving practices and cultivation techniques and the owners of oil mills as regards the storage, processing and storage of oils.

Thanks to its nutritional value, its cosmetic and therapeutic properties, virgin olive oil (VOO) is becoming as a food in the

The goal of this study is to develop a simple intelligent urolithiasis diagnosis system. The accuracy of the system was determined by comparing the recognition rates of the Artificial Neural Networks (ANNs)-, k-nearest Neighbor (kNN)-, and Support Vector Machines (SVM) algorithms. The results showed that the ANN model was superior to SVM and KNN models in prediction. We aimed through this work to classify the subjects in three classes according to the chemical concentrations of variables (Ca, Ox, pCaOx, Ca/ Ox) using and according to their clinical status. The ANN model, used to determine the first class that contains the subjects presenting their urine a calcium oxalate monohydrate (CaC2O4,H2O : whewellite (Wh)) crystal type. This ANN model reached a correct prediction rate of 85.3%. Using SVM- and KNN model the correct prediction rate reached 82.6% and 65.55% respectively. The second class contains the subjects presenting a calcium oxalate dihydrate (CaC2O4,2H2O wedellite (Wd)) crystal type. The ANN-, SVM- and KNN model reached a 93.4%-, 94.2%- and 77.25% correct prediction rate, respectively. In third class that corresponds to the subjects who have negative crystalluria (NC), ANN-, SVM- and KNN model reached a 91.7%-, 87.8%- and 69.77% correct prediction rate, respectively. Compared to SVM- and KNN models, the developed system using ANN model has allowed us to discriminate the subjects. This system is important in clinical laboratories since it could be a helpful tool for provide information about the development, formation of urinary stones crystals and the determination of their crystal type.

Therapeutic efficiency of virgin olive oil components, like antioxidants, has been proved. Moreover, the adulteration of virgin olive oil by the refined olive oil is a known fraudulent practice in non formal markets in some developing countries. In Morocco, there is a need of non expensive and fast tool to quantify the adulteration of virgin olive oil by the refined one. That is why we used a coupling between Fourier transform middle infrared spectroscopy, as a non expensive analysis technique, and Euclidean distance and hierarchical ascending cluster methods.
Virgin olive oil was extracted from ''Picholine'' cultivar olives in Tadla Azilal area, in Morocco. Fourier transform middle infrared spectroscopic parameters of prepared mixtures of virgin and refined olive oils have been used to determine the adulteration. The result of the Euclidean Distance concerning such an adulterated virgin olive oil has allowed the quantification of the adulteration percentage. The results of the hierarchical ascending cluster could provide a fast classification of virgin oil oils. Thanks to its rapidity and relatively low cost, coupling between middle infrared spectroscopy and chemometric methods would be an efficient tool to ensure authentication and traceability of virgin olive oil.