Abstract. The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data. However, the learning algorithm of the SOM is sensitive to the presence of noise and outliers as we will show in this paper. Due to the influence of the outliers in the learning process, some neurons (prototypes) of the ordered map get located far from the majority of data, and therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the learning algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust SOM (RSOM). We will illustrate our technique on synthetic and real data sets.
The interaction between Florfenicol (FF), Hydroxypropyl-β-cyclodextrin (HPβCD), and Chitosan (CH) has been studied in aqueous solution and in solid state, using 3 preparation methods (Evaporation, Lyophilization, Spray Drying) for HPβCD and only Spray Drying for Chitosan. The phase solubility study shows that the complex is formed with 1:1 stoichiometry and 181,4 M -1 as the association constant. The analysis with Differential Scanning Calorimetry (DSC) together with Scanning Electron Microscopy (SEM) micrographs evidenced the formation of inclusion complexes, mainly with the product prepared by spray drying. Studies in vitro showed that FF solubility was improved almost to double, with a better dissolution profile exhibited by the product prepared by spray drying.
Learning the structure of real world data is difficult both to recognize and describe. The structure may contain high dimensional clusters that are related in complex ways. Furthermore, real data sets may contain several outliers.Vector quantization techniques has been successfully applied as a data mining tool. In particular the Neural Gas (NG) is a variant of the Self Organizing Map (SOM) where the neighborhoods are adaptively defined during training through the ranking order of the distance of prototypes from the given training sample.Unfortunately, the learning algorithm of the NG is sensitive to the presence of outliers as we will show in this paper. Due to the influence of the outliers in the learning process, the topology of the employed network does not conserve the topology of the manifold of the data which is presented.In this paper, we propose to robustify the learning algorithm where the parameter estimation process is resistant to the presence of outliers in the data. We call this algorithm Robust Neural Gas (RNG). We will illustrate our technique on synthetic and real data sets.
Avocado oil is considered a highly prized food due to its nutritional contribution. On the other hand, Aristotelia chilensis (Molina) Stuntz (Elaeocarpaceae), common name “maqui”, is an endemic fruit in Chile, well known for its exceptional antioxidant properties. In general, maqui by-products such as leaves are considered as waste. Thus, maqui leaves extracts were used to improve the stability of vegetable oils, particularly avocado oil. Hence, avocado oil was fortified with two extracts (ethyl ether and methanol) obtained of maqui leaves and exposed to 120 °C for 386 h in an oven. The results showed a high content of monounsaturated fatty acids (69.46%, mainly oleic acid), followed by polyunsaturated fatty acids (16.41%, mainly linoleic acid) and finally saturated fatty acids (14.13%). The concentration of the total phenolic compounds in the pure oil, ethyl ether and methanol maqui leaves extracts were 45.8, 83.7, and 4100.9 ppm, respectively. In addition, the antioxidant activity was 5091.6 and 19,452.5 µmol Trolox eq/g for the ethyl ether and methanol extracts, respectively. The secondary degradation compounds showed significant differences between the fortified and non-fortified samples after 144 h and the TG/DTG analysis showed a significant increment of 7 °C in the degradation temperature (Tonset) of avocado oil fortified with the methanol extract when compared to the non-fortified oil and fortified oil with ethyl ether extract. After heating for 336 h, fortified oil with methanol extract reached the limit percentages of polar compounds, while pure oil reached it in a shorter time, i.e., 240 h. Based on the results, avocado oil can be protected with natural additives such as extracts obtained from maqui leaves, leading to an increase in its thermo-oxidative stability.
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