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This paper aims to study the effect of different organic solvents on the extraction of pigments present in callus cultures of E. cinerascens.

Attempts have been made to extract pigments from callus cultures through tissue culture system as an alternative replacement for conventional plant cultivation as tissue culture provides unlimited supplies of plant samples. Callus of E. cinerascens was induced from stem explant cultured in Murashige and Skoog medium supplemented with combination of 0.5 mg/L 6-benzylaminopurine and 0.5 mg/L α-naphthaleneacetic acid maintained under photoperiod of 16 h light and 8 h dark. Fresh samples of the callus were harvested and dissolved in various types and concentrations of solvents such as 100 per cent acetone, 80 per cent acetone, 95 per cent ethanol, 100 per cent methanol and 90 per cent methanol. Each of the mixtures was directly centrifuged to get clear supernatant containing pigments of interest. The pigments were detected and subsequently quantified via two simple techniques, ultraviolet-visible (UV-Vis) spectrophotometer and thin layer chromatography (TLC).

UV-Vis spectrophotometer detected two families of pigments present in the callus cultures, namely, carotenoids (carotene and xanthophyll) and tetrapyrroles (chlorophyll a and b). Pigment contents in various solvent extractions were estimated using spectroscopic quantification equations established. Through TLC, spots were seen on the plates, and Rf values of each spots were assessed to indicate the possible existence of carotenoids and tetrapyrroles.

This preliminary study offers significant finding for further advance research related on natural pigments extracted from E. cinerascens that would provide profits in the future applications, especially in food industry, medicine, agriculture, etc.

This paper aims to propose an approach to automatically annotate a large corpus in Arabic dialect. This corpus is used in order to analyse sentiments of Arabic users on social medias. It focuses on the Algerian dialect, which is a sub-dialect of Maghrebi Arabic. Although Algerian is spoken by roughly 40 million speakers, few studies address the automated processing in general and the sentiment analysis in specific for Algerian.

The approach is based on the construction and use of a sentiment lexicon to automatically annotate a large corpus of Algerian text that is extracted from Facebook. Using this approach allow to significantly increase the size of the training corpus without calling the manual annotation. The annotated corpus is then vectorized using document embedding (doc2vec), which is an extension of word embeddings (word2vec). For sentiments classification, the authors used different classifiers such as support vector machines (SVM), Naive Bayes (NB) and logistic regression (LR).

The results suggest that NB and SVM classifiers generally led to the best results and MLP generally had the worst results. Further, the threshold that the authors use in selecting messages for the training set had a noticeable impact on recall and precision, with a threshold of 0.6 producing the best results. Using PV-DBOW led to slightly higher results than using PV-DM. Combining PV-DBOW and PV-DM representations led to slightly lower results than using PV-DBOW alone. The best results were obtained by the NB classifier with F1 up to 86.9 per cent.

The principal originality of this paper is to determine the right parameters for automatically annotating an Algerian dialect corpus. This annotation is based on a sentiment lexicon that was also constructed automatically.