Etude chimique des graines du Gilletiodendron glandulosum (Portères) J. Léonard, Césalpiniacées

Jaeger Paul, Ucciani E., Busson F. Etude chimique des graines du Gilletiodendron glandulosum (Portères) J. Léonard, Césalpiniacées. In: Journal d'agriculture tropicale et de botanique appliquée, vol. 11, n°8-9, Août-septembre 1964. pp. 250-258

Composición en ácidos grasos de los aceites de semillas de especies mediterráneas

In the context of research into the lipids of higher plant, the fatty acid composition of eleven species is reported. These species belong to nine families, and most of them are common in the Mediterranean flora. Oils from some plants (Arctium tomentosum, Hypericum perforatum, Linaria vulgaris. Pulicaria dysenterica, Smilax áspera) were partially hydrolyzed, and contained free fatty acids. Arctium tomentosum seed oil and, to a lesser extent, Smilax áspera seed oil, showed a trans absorption band in their IR spectra. Small quantities of waxes were present in Pulicaria dysenterica and Silybum marianum seed oil. The fatty acid composition of the oils from these plants approaches levels found in food oils as well as industrial oils. Three species showed high levels of their predominant fatty acid: Alliaria petiolata (erucic acid, 41.5%), Linaria vulgaris and Pulicaria dysenterica (linoleic acid, 70 and 72% respectively). In the seed oil of Arctium tomentosum four fatty acids were not identified: one of them could be 3(t), 9(c), 12(c)- octadecatrienoic acid. But the most complex composition was that of Atriplex hortensis oil, which contained thirty three fatty acids, of which fifteen were unknown.En el marco de investigaciones sobre los lípidos de las plantas superiores, se presenta la composición en ácidos grasos de once especies. Estas especies pertenecen a nueve familias diferentes y la mayoría son frecuentes en la flora mediterránea. Numerosos aceites (Arctium tomentosum, Hypericum perforatum,Linaria vulgaris, Pulicaria dysenterica y Smilax áspera) contienen glicéridos parciales y ácidos grasos libres. El aceite de Arctium tomentosum, y en menor grado, el de Smilax áspera muestran una banda espectral trans en espectrometría IR. En los aceites de Pulicaria dysenterica y de Silybum marianum pequeñas cantidades de ceras han sido puestas de manifiesto. Las composiciones en ácidos grasos se aproximan a las de los aceites alimenticios e industriales conocidos. Tres especies contienen un ácido graso útil a un nivel elevado: Alliaria petiolata (41,5% de ácido erúcico). Linaria vulgaris y Pulicaria dysenterica (respectivamente 70 y 72% de ácido linoléico). En el aceite de Arctium tomentosum cuatro ácidos grasos menores no han sido identificados: uno de ellos podría ser el octadeca-3(t), 9(c), 12(c) trienóico. Pero se alcanza el máximo de complejidad con el aceite de Atriplex hortensis con treinta y tres ácidos grasos de los cuales quince son desconocidos

A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

The classification of seismo-volcanic signals is performed manually at La Soufrière Volcano, which is time consuming and can be biased by subjectivity of the operator. We propose here a machine-learning-based model for classification of these signals, to handle large datasets and provide objective and reproducible results. To describe the properties of the signals, we used 104 statistical, entropy, and shape descriptor features computed from the time waveform, the spectrum, and the cepstrum. First, we trained a random forest classifier with a dataset provided by the Observatoire Volcanologique et Sismologique de Guadeloupe that consisted of 845 labeled events that were recorded from 2013 to 2018: 542 volcano-tectonic (VT); 217 Nested; and 86 long period (LP). We obtained an overalll accuracy of 72%. We determined that the VT class includes a variety of signals that cover the VT, Nested and LP classes. After visual inspection of the waveforms and spectral characteristics of the data set, we introduced two new classes: Hybrid and Tornillo. A new random forest classifier was trained with this new information, and we obtained a much better overall accuracy of 82%. The model is very good for recognition of all event classes, except Hybrid events (67% accuracy, 70% precision). Hybrid events are often considered to be a mix of VT and LP events. This can be explained by the nature of this class and the physical processes that include both fracturing and resonating components with different modal frequencies. By analyzing the feature weights and by training a model with the most important features, we show that a subset of the 14 best features is sufficient to obtain a performance that is close to that of the model with the whole feature set. However, these best features are different from the 13 best features obtained for another volcano in Peru, with only one feature common to both sets of best features. Therefore, the model is not universal and it must be trained for each volcano, or it is too specific to the one station used here