e present study aimed to characterize the nutraceutical properties and the antimicrobial e ect of Moroccan Spirulina (Arthrospira platensis). e nutritional composition was evaluated, including water content, crude protein, total carbohydrates, lipids, phenolic composition, macro-and micromineral content, ber content, and energy value. en, the microbiological analysis and antioxidant activity were measured. e antimicrobial activity was evaluated using the minimum inhibitory concentration method on bacteria and fungi. Moroccan Spirulina contained a large amount of protein (76.65 ± 0.15%), followed by carbohydrates (6.46 ± 0.32%), minerals (20.91 ± 0.88%), crude ber (4.07 ± 1.42%), lipids (2.45 ± 0.82%), ash (14.56 ± 0.74), and twenty phenolic acids being identi ed and quanti ed. Moreover, avonoid and phenolic contents were present at 15.60 ± 2.74 mg RE/g dw and 4.19 ± 0.21 mg GAE/g dw, respectively. Microbiological risk assessment indicated that this product is safe to be consumed as a human food product. e antioxidant activity was higher in the methanolic fraction (23 mg TE/g dw) (DPPH).

No abstract

In this work Fourier Transform Infrared Spectroscopy (FTIR) was selected as a reliable, fast and non-destructive technique to record spectroscopic fingerprints of Moroccan Protected Geographical Indication (PGI) Argan oils. Classification and discrimination according to their five geographical origins (Ait-Baha, Agadir, Essaouira, Tiznit and Taroudant) was performed. A total of 120 PGI Argan oil samples were collected during four harvest seasons between 2011 and 2014.First, several physicochemical parameters were measured, i.e. free acidity, peroxide value, spectrophotometric indices, fatty acid composition, tocopherols and sterols content. Secondly, FTIR fingerprints were recorded for all samples. The data was subjected to Principal Component Analysis (PCA) for visualization and to reveal differences between samples. Classification models were developed by Partial Least Squares Discriminant Analysis (PLS-DA). Mathematical data pre-treatments were applied to improve the performance of the multivariate classification models. The results obtained, based on both the chemical composition and the spectroscopic fingerprints, indicate that PCA plots were able to distinguish the five sample classes. PLS-DA models based on either chemical composition or FTIR spectra gave a good prediction and an accurate discrimination between the samples from different regions.The proposed approach with the FTIR spectra provided reliable results to classify the Moroccan PGI Argan oils from different regions in a rapid, inexpensive way requiring no prior separation procedure.