One of the most important challenges in the authentication of olive oil is the determination of the geographical origin of virgin olive oil. In this work, we evaluated the capacity of two spectroscopic techniques, UV-Visible and ATR-FTMIR, coupled with chemometric tools to determine the geographical origin of olive oils. These analytical approaches have been applied to samples that have been collected during the period of olive oil production, in the Moroccan region of Beni Mellal-Khenifra. To develop a rapid analysis tool capable of authenticating the geographical origin of virgin olive oils from five geographical areas of the Moroccan region of Beni Mellal-Khenifra, UV-Visible and ATR-FTMIR spectral data were processed by chemometric algorithms. PCA was applied on the spectral data set to represent the data in a very small space, and then discrimination methods were applied on the principal components synthesized by the PCA. The application of the PCA-LDA method on the spectral data of UV-Visible and ATR-FTMIR shows a good ability to classify olive oils according to their geographical origin with a percentage of correct classification that represents 90.24% and 85.87%, respectively, and the processing of the spectral data of UV-Visible and ATR-FTMIR by PCA-SVM allows differentiating correctly between five olive oils with a correct classification rate of 100% and 97.56, respectively. This study demonstrated the feasibility of UV-Visible and ATR-FTMIR fingerprinting (routine technique) for the geographical classification of olive oils in the Moroccan region of Beni Mellal-Khenifra. Such developed methods can be proposed as alternative and complementary methods to authenticate the geographical origin of virgin olive oil.
Background: AIDS is a complicated disease, and the underlying complication makes a total cure impossible. This demands the vigorous need of suitable anti-HIV agents. Styrylquinoline, a quinolone derivative emerged as a potent HIV-IN inhibitor. Objective: construct an easily transferable and reproducible model that relates the biological activities of styrylquinoline derivatives to their molecular descriptors. Method: 2D Quantitative structure–activity relationship (QSAR) studies were carried out on a series of 36 styrylquinoline derivatives. Results: The technique of recursive feature elimination with random forests was used to select the descriptors rich in information regarding biological activity. The selected descriptors were used in QSAR studies based on multiple linear regression (MLR) and multiple nonlinear regression (MNLR). The performance of models was evaluated by internal and external validations. The values of R_pred^2 and Q_LOO^2for the MLR model are 0.814 and 0.713 respectively, while the MNLR model has R_pred^2 and Q_LOO^2values of 0.810 and 0.699 respectively. Conclusion: The information obtained from 2D-QSAR models will aid in gaining a better understanding of the structural requirements for creating novel HIV-IN inhibitors.
This study was aimed at building a robust quantitative structure–activity relationship (QSAR) to predict the anti-proliferate activity of 1,3,4-thiadiazole derivatives against the A549 lung cancer cell lines. The semi-empirical PM7 parametrization approach was used to optimize the complete set of 1,3,4-thiadiazole derivatives and various classes of molecular descriptors have been calculated. We built models using Fisher score and the best subset selection for feature selection, and the final model was developed using the multiple linear regression technique, all in accordance with the rigorous Organization for Economic Co-operation and Development (OECD) requirements. Furthermore, various internationally agreed severe validation parameters were used to validate the model. Overall, our established model for quick prediction should be relevant to new, untested, or not yet produced compounds that fall within the applicability domain (AD) of the model. The drug-likeness properties of the 10 compounds with the greatest activity value were also calculated using Lipinski's rule properties. Keywords: QSAR, Thiadiazole derivatives, A549, PM7, OECD
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