Quality Control and Authentication of Argan Oils: Application of Advanced Analytical Techniques.

In addition to the nutritional and therapeutic benefits, Argan oil is praised for its unique bio-ecological and botanic interest. It has been used for centuries to treat cardiovascular issues, diabetes, and skin infections, as well as for its anti-inflammatory and antiproliferative properties. Argan oil is widely commercialized as a result of these characteristics. However, falsifiers deliberately blend Argan oil with cheaper vegetable oils to make economic profits. This reduces the quality and might result in health issues for consumers. Analytical techniques that are rapid, precise, and accurate are employed to monitor its quality, safety, and authenticity. This review provides a comprehensive overview of studies on the quality assessment of Moroccan Argan oil using both untargeted and targeted approaches. To extract relevant information on quality and adulteration, the analytical data are coupled with chemometric techniques.

Current Applications of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in Pharmaceutical Analysis: Review.

The present review encompasses various applications of multivariate curve resolution- alternating least squares (MCR-ALS) as a promising data handling, which is issued by analytical techniques in pharmaceutics. It involves different sections starting from a concise theory of MCR-ALS and four detailed applications in drugs analysis. Dissolution, stability, polymorphism, and quantification are the main four detailed applications. The data generated by analytical techniques associated with MCR-ALS deals accurately with different challenges compared to other chemometric tools. For each reviewed purpose, it was explained how MCR-ALS was applied and detailed information was given. Different approaches were introduced to overcome challenges that limit the use of MCR-ALS efficiently in pharmaceutical mixture were also discussed.

Bioassay-guided isolation of anti-inflammatory and antinociceptive metabolites among three Moroccan Juniperus leaves extract supported with in vitro enzyme inhibitory assays.

ETHNOPHARMACOLOGICAL RELEVANCE: Herbs of the genus Juniperus (family Cupressaceae) have been commonly used in ancestral folk medicine known as "Al'Araar" for treatment of rheumatism, diabetes, inflammation, pain, and fever. Bioassay-guided isolation of bioactives from medicinal plants is recognized as a potential approach for the discovery of novel drug candidates. In particular, non-addictive painkillers are of special interest among herbal phytochemicals. AIM OF THE STUDY: The current study aimed to assess the safety of J. thurifera, J. phoenicea, and J. oxycedrus aqueous extracts in oral treatments; validating the traditionally reported anti-inflammatory and analgesic effects. Further phytochemical investigations, especially for the most bioactive species, may lead to isolation of bioactive metabolites responsible for such bioactivities supported with in vitro enzyme inhibition assays. MATERIALS AND METHODS: Firstly, the acute toxicity study was investigated following the OECD Guidelines. Then, the antinociceptive, and anti-inflammatory bioactivities were evaluated based on chemical and mechanical trauma assays and investigated their underlying mechanisms. The most active J. thurifera n-butanol fraction was subjected to chromatographic studies for isolating the major anti-inflammatory metabolites. Moreover, several enzymatic inhibition assays (e.g., 5-lipoxygenase, protease, elastase, collagenase, and tyrosinase) were assessed for the crude extracts and isolated compounds. RESULTS: The results showed that acute oral administration of the extracts (300-500 mg/kg, p. o.) inhibited both mechanically and chemically triggered inflammatory edema in mice (up to 70% in case of J. thurifera) with a dose-dependent antinociceptive (tail flick) and anti-inflammatory pain (formalin assay) activities. This effect was partially mediated by naloxone inhibition of the opioid receptor (2 mg/kg, i. p.). In addition, 3-methoxy gallic acid (1), quercetin (2), kaempferol (3), and ellagic acid (4) were successfully identified being involved most likely in J. thurifera extract bioactivities. Nevertheless, quercetin was found to be the most potent against 5-LOX, tyrosinase, and protease with IC50 of 1.52 ± 0.01, 192.90 ± 6.20, and 399 ± 9.05 µM, respectively. CONCLUSION: J. thurifera extract with its major metabolites are prospective drug candidates for inflammatory pain supported with inhibition of inflammatory enzymes. Interestingly, antagonism of opioid and non-opioid receptors is potentially involved.

Special Issue "Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches".

The globalization of the food market has created a pressing need for food producers to meet the ever-increasing demands of consumers while ensuring adherence to stringent food safety and quality standards [...].

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches.

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Tensor methods in data analysis of chromatography/mass spectroscopy-based plant metabolomics.

Plant metabolomics is an important research area in plant science. Chemometrics is a useful tool for plant metabolomic data analysis and processing. Among them, high-order chemometrics represented by tensor modeling provides a new and promising technical method for the analysis of complex multi-way plant metabolomics data. This paper systematically reviews different tensor methods widely applied to the analysis of complex plant metabolomic data. The advantages and disadvantages as well as the latest methodological advances of tensor models are reviewed and summarized. At the same time, application of different tensor methods in solving plant science problems are also reviewed and discussed. The reviewed applications of tensor methods in plant metabolomics cover a wide range of important plant science topics including plant gene mutation and phenotype, plant disease and resistance, plant pharmacology and nutrition analysis, and plant products ingredient characterization and quality evaluation. It is evident from the review that tensor methods significantly promote the automated and intelligent process of plant metabolomics analysis and profoundly affect the paradigm of plant science research. To the best of our knowledge, this is the first review to systematically summarize the tensor analysis methods in plant metabolomic data analysis.

Secondary-metabolites fingerprinting of Argania spinosa kernels using liquid chromatography-mass spectrometry and chemometrics, for metabolite identification and quantification as well as for geographic classification.

Argan (Argania spinosa L.) fruit kernels' composition has been poorly studied and received less research intensity than the resulting Argan oil. The Moroccan Argan kernels contain a wealth of metabolites and can be investigated for nutritional and health aspects as well as for economic benefits. Ultra-Performance Liquid Chromatography Mass Spectrometry (UPLC-MS) was employed to trace the geographical origin of Argan kernels based on secondary-metabolite profiles. One-hundred and twenty Argan fruit kernels from five regions ('Agadir', 'Ait-Baha' 'Essaouira', 'Tiznit' and 'Taroudant') were studied. Characterization and quantification of 36 secondary metabolites (33 polyphenolic and 3 non-phenolic) were achieved. Those metabolites are highly influenced by the geographic origin. Then, the untargeted UPLC-MS fingerprint was decomposed by metabolomic data handling tools, such as multivariate curve resolution alternating least squares (MCR-ALS) and XCMS. The two resulting data matrices were pretreated and prepared separately by chemometric tools and then two data fusion strategies (low- and mid-levels) were applied on them. The four data sets were comparatively investigated. Principal component analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Soft Independent Modeling of Class Analogies (SIMCA) were used to classify samples. The exploration or classification models demonstrated a good ability to discriminate and classify the samples in the geographical-origin based classes. Summarized, the developed fingerprints and their metabolomics-based data handling successfully allowed geographical traceability evaluation of Moroccan Argan kernels.

Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics.

Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H3O+, NO+, O2+ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.

Multi-Way Analysis Coupled with Near-Infrared Spectroscopy in Food Industry: Models and Applications.

Near-infrared spectroscopy (NIRS) is a fast and powerful analytical tool in the food industry. As an advanced chemometrics tool, multi-way analysis shows great potential for solving a wide range of food problems and analyzing complex spectroscopic data. This paper describes the representative multi-way models which were used for analyzing NIRS data, as well as the advances, advantages and limitations of different multi-way models. The applications of multi-way analysis in NIRS for the food industry in terms of food process control, quality evaluation and fraud, identification and classification, prediction and quantification, and image analysis are also reviewed. It is evident from this report that multi-way analysis is presently an attractive tool for modeling complex NIRS data in the food industry while its full potential is far from reached. The combination of multi-way analysis with NIRS will be a promising practice for turning food data information into operational knowledge, conducting reliable food analyses and improving our understanding about food systems and food processes. To the best of our knowledge, this is the first paper that systematically reports the advances on models and applications of multi-way analysis in NIRS for the food industry.

The food plant Silybum marianum (L.) Gaertn.: Phytochemistry, Ethnopharmacology and clinical evidence.

ETHNOPHARMACOLOGICAL RELEVANCE: Silybum marianum (L.) Gaertn. or Milk thistle is a medicinal plant native to Northern Africa, Southern Europe, Southern Russia and Anatolia. It also grows in South Australia, North and South America. In traditional knowledge, people have used S. marianum for liver disorders such as hepatitis, liver cirrhosis and gallbladder diseases. The main active compound of the plant seeds is silymarin, which is the most commonly used herbal supplement in the United States for liver problems. Nowadays, S. marianum products are available as capsules, powders, and extracts. AIM OF STUDY: The aim of our study is to draw a more comprehensive overview of the traditional heritage, pharmacological benefits and chemical fingerprint of S. marianum extracts and metabolites; as well as their metabolism and bioavailability. MATERIALS AND METHODS: An extensive literature search has been conducted using relavant keywords and papers with rationale methodology and robust data were selected and discussed. Studies involving S. marianum or its main active ingredients with regards to hepatoprotective, antidiabetic, cardiovascular protection, anticancer and antimicrobial activities as well as the clinical trials performed on the plant, were discussed here. RESULTS: S. marianum was subjected to thousands of ethnopharmacological, experimental and clinical investigations. Although, the plant is available for use as a dietary supplement, the FDA did not yet approve its use for cancer therapy. Nowadays, clinical investigations are in progress where a global evidence of its real efficiency is needed. CONCLUSION: S. marianum is a worldwide used herb with unlimited number of investigations focusing on its benefits and properties, however, little is known about its clinical efficiency. Moreover, few studies have discussed its metabolism, pharmacokinetics and bioavailability, so that all future studies on S. marianum should focus on such areas.

In Vitro & In Vivo Anti-Hyperglycemic Potential of Saponins Cake and Argan Oil from Argania spinosa.

The Argan tree (Argania spinosa. L) is an evergreen tree endemic of southwestern Morocco. For centuries, various formulations have been used to treat several illnesses including diabetes. However, scientific results supporting these actions are needed. Hence, Argan fruit products (i.e., cake byproducts (saponins extract) and hand pressed Argan oil) were tested for their in-vitro anti-hyperglycemic activity, using α-glucosidase and α-amylase assays. The in-vivo anti-hyperglycemic activity was evaluated in a model of alloxan-induced diabetic mice. The diabetic animals were orally administered 100 mg/kg body weight of aqueous saponins cake extract and 3 mL/kg of Argan oil, respectively, to evaluate the anti-hyperglycemic effect. The blood glucose concentration and body weight of the experimental animals were monitored for 30 days. The chemical properties and composition of the Argan oil were assessed including acidity, peroxides, K232, K270, fatty acids, sterols, tocopherols, total polyphenols, and phenolic compounds. The saponins cake extract produced a significant reduction in blood glucose concentration in diabetic mice, which was better than the Argan oil. This decrease was equivalent to that detected in mice treated with metformin after 2-4 weeks. Moreover, the saponins cake extract showed a strong inhibitory action on α-amylase and α-glucosidase, which is also higher than that of Argan oil.

Biochar Improves Soil-Tomato Plant, Tomato Production, and Economic Benefits under Reduced Nitrogen Application in Northwestern China.

The tomato is an important economic crop that is a main ingredient of some prepared food as well as a focus of the agricultural industry. Optimizing nitrogen (N) fertilizers is essential for sustainable agricultural development, while the excessive use of N fertilizers leads to environmental and food production problems. As a soil amendment, biochar has been widely used to improve soil quality and crop yield. However, little information is available on the effects of biochar and N fertilizer reduction on tomato plant, soil characteristics in tomato cultivation and tomato production. In this study, a greenhouse experiment was carried out in Yangling, Shaanxi province, China, including four biochar levels (0, 30, 50, and 70 t ha-1) under drip irrigation and four N application rates (170, 190, 210, and 250 kg ha-1). The results showed that adding too much biochar (e.g., 70 t ha-1) and reducing N fertilizer too far (e.g., by 32%) will not lead to satisfactory results in terms of tomato growth, tomato yield and quality, and economic benefits. Biochar addition could significantly enhance microbial abundance, enzyme activity, and tomato growth compared with non‒biochar treatments when reducing the amount of applied N fertilizer by 16% or 24% (N2 and N3). From the perspectives of tomato yield, tomato quality (sugar‒acid ratio and vitamin C (VC) content), and economic benefits, optimal application rate of biochar and N fertilizer based on the silty clay loam soil of northwest China under drip irrigation is proposed, respectively. The proposal is based on both multidimensional nonlinear regression models and a comparison with experimental treatments. For example, biochar addition at 50 t ha-1 and reducing N fertilizer by 24% achieved the greatest tomato yield. Compared with non-biochar treatment under the corresponding N fertilizer level, soil enzyme activity (urease, phosphatase, and catalase), microbial abundance (bacteria, fungi, and actinomycetes), leaf gas exchange parameters (gs, Pn, and Tr), and biomass increased on average by 88.76%, 7.49%, 43.23%, and 39.67%, respectively. Based on a comprehensive consideration of tomato yield, VC content, sugar‒acid ratio, and economic benefits, 35 t ha-1 biochar and 200 kg ha-1 N fertilizer is the recommended combination of biochar and nitrogen fertilizer for local farmers.

UPLC-PDA-ESI-QTOF-MS/MS and GC-MS analysis of Iranian Dracocephalum moldavica L.

Dracocephalum moldavica L. is a significant component in the Iranian food basket. This study aimed to investigate the bioactive compounds and biological activities of different extracts obtained from D. moldavica aerial parts. From the aerial parts, a crude methanolic (MeOH) extract and its four sub-fractions, that is, petroleum ether (Pet), ethyl acetate (EtOAc), n-butanol (n-BuOH), and aqueous (water) extracts were obtained. The total phenolic and flavonoid contents as well as the antioxidant and cytotoxic activities of the extracts were determined. Moreover, the phytochemical profiles of the essential oil (EO) and of those extracts with the highest antioxidant activity measured by GC/MS and UPLC-PDA-ESI-QTOF-MS/MS. Results showed that the highest concentrations of phenols and flavonoids as well as the most potent antioxidant potential according to the DPPH method were determined in the EtOAc and MeOH extracts with IC50 values of 22.0 and 34.4 µg.ml-1, respectively. Quantitative analysis of these extracts was subsequently performed by UPLC-PDA-ESI-QTOF-MS/MS. Both extracts contained mainly rosmarinic acid, caffeic acid, and 2-hydroxycinnamic acid, which may be responsible for their high antioxidant activity. Moreover, none of the extracts showed cytotoxic effects against MCF7, SW48, and a normal cell line of mouse embryonic fibroblast cells (NIH/3T3) in the tested concentrations (up to 400 µg.ml-1). Additionally, GC-MS analysis showed that oxygenated monoterpenes (55.4%) were the main constituents of the EO of D. moldavica.

New insights into the Argan oil categories characterization: Chemical descriptors, FTIR fingerprints, and chemometric approaches.

The characterization of Argan oils to classify them in three categories ('Extra Virgin', 'Virgin' and 'Lower quality') was evaluated. A total of 120 Moroccan Argan oils samples from the Taroudant Argan forest was investigated. The free acidity, peroxide value, spectrophotometric indices (K232 and K270), fatty acids, sterols, and tocopherol contents were assessed. The samples were also scanned by FTIR spectroscopy. The Principal Component Analysis (PCA) and four classification methods, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modelling of Class Analogy (SIMCA), K-nearest Neighbors (KNN), and Support Vector Machines (SVM), were applied on both the chemical and spectral data. Besides the conventional chemical profiling, FTIR spectra were evaluated for their feasibility as a rapid non-invasive approach for classifying and predicting the oil quality categories. The most important variables for differentiating the oil categories were identified as K232, peroxide value, É£-tocopherol, δ-tocopherol, acidity, stigma-8-22-dien-3ß-ol, stearic acid (C18:0) and linoleic acid (C18:2) and could be used as quality indicators. Eight chemical descriptors or key features from the FTIR spectra (selected by interval-PLS) could also be established as indicators of quality and freshness of Argan oils.

Phenolic Compound Analysis and Pharmacological Screening of Vitex agnus-castus Functional Parts.

Vitex agnus-castus is a medicinal plant of the Verbenaceae family, widely used in traditional medicine. This study is aimed at investigating the functional variability of phenolic compounds in different parts (leaves, stems, flowers, roots, and seeds) of Vitex agnus-castus methanolic extracts and at assessing their in vitro antidiabetic, antioxidant, and antibacterial activities. The results of HPLC-DAD-QTOF-MS indicated the presence of 25 phenolic compounds with a remarkable variability between plant parts; high levels were registered in chlorogenic, vanillic, 3,4-dihydroxybenzoic, and 3-hydroxybenzoic acids; hesperidin; and luteolin. V. agnus castus fruits and stems presented higher antioxidant activities. The extracts inhibited the growth of five pathogenic bacteria with MIC values documented between 7.81 and 31.25 mg/mL. In vitro antihyperglycemic effect revealed higher effect in flowers (2921.84 µg/mL) and seeds (2992.75 µg/mL) against α-glucosidase and of leaves (2156.80 µg/mL) and roots (2357.30 µg/mL) against α-amylase. The findings of this showed that V. agnus castus is a promising source for antidiabetic bioactive compounds. However, further investigations regarding the evaluation of in vivo antidiabetic effects of these compounds are needed.

Classification of polymorphic forms of fluconazole in pharmaceuticals by FT-IR and FT-NIR spectroscopy.

The main goal of this work was to test the ability of vibrational spectroscopy techniques to differentiate between different polymorphic forms of fluconazole in pharmaceutical products. These are mostly manufactured with fluconazole as polymorphic form II and form III. These crystalline forms may undergo polymorphic transition during the manufacturing process or storage conditions. Therefore, it is important to have a method to monitor these changes to ensure the stability and efficacy of the drug. Each of FT-IR or FT-NIR spectra were associated to partial least squares-discriminant analysis (PLS-DA) for building classification models to distinguish between form II, form III and monohydrate form. The results has shown that combining either FT-IR or FT-NIR to PLS-DA has a high efficiency to classify various fluconazole polymorphs, with a high sensitivity and specificity. Finally, the selectivity of the PLS-DA models was tested by analyzing separately each of three following samples by FT-IR and FT-NIR: lactose monohydrate, which is an excipient mostly used for manufacturing fluconazole pharmaceutical products, itraconazole and miconazole. These two last compounds mimic potential contaminants and belong to the same class as fluconazole. Based on the plots of Hotelling's T² vs Q residuals, pure compounds of miconazole and itraconazole, that were analyzed separately, were significantly considered outliers and rejected. Furthermore, binary mixtures consist of fluconazole form-II and monohydrate form with different ratios were used to test the suitability of each technique FT-IR and FT-NIR with PLS-DA to detect minimum contaminant or polymorphic conversion from a polymorphic form to another using also the plots of Hotelling's T² vs Q residuals.

Recent advances in untargeted and targeted approaches applied in herbal-extracts and essential-oils fingerprinting - A review.

Herbal extracts and essential oils have been used over the centuries for their dietary, cosmetic and therapeutic properties. Quality control is needed to guarantee the safety and quality of these consumables. In this regard, fingerprinting techniques are important for inspection of the authenticity and for quality control. Analytical fingerprinting techniques provide signals related to the composition of a matrix (oil, plant extract, food…). The resulting fingerprint (spectrum or chromatogram) obtained for an untargeted or targeted approach is coupled to chemometric data processing, which may allow, for instance, the desired identification or discrimination of the sample considered. In this context, recent advances in untargeted/targeted fingerprinting approaches (especially chromatographic and spectroscopic) were described and their application in the taxonomic identification, classification and authentication of plants (medicinal) and essential oils discussed. An overview of the applications of untargeted/targeted fingerprinting techniques on herbal-extracts and essential-oils analysis, using different chemometric tools, has been included.

Comparative Study of Leaf and Rootstock Aqueous Extracts of Foeniculum vulgare on Chemical Profile and In Vitro Antioxidant and Antihyperglycemic Activities.

Foeniculum vulgare is a medicinal plant used in Moroccan folk medicine to treat several diseases such as diabetes. The aim of this study was to determine the phenolic bioactive compounds and to evaluate the antioxidant and antihyperglycemic activities of Foeniculum vulgare leaf and rootstock extracts. Phenolic compounds of F. vulgare rootstock and leaf extracts were determined using HPLC-DAD-QTOFMS analysis. The antioxidant activity was evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+) radicals. Moreover, the in vitro antihyperglycemic effects were tested by measuring the inhibition of α-amylase and α-glucosidase activities. HPLC-DAD-QTOFMS analysis identified thirty-two phenolic components in both leaf and rootstock extracts. Caffeic acid, quinic acid, and chlorogenic acid were the major compounds of F. vulgare leaf extract (FVLE), while the main compound of F. vulgare rootstock extracts (FVRE) was quinic acid. In the DPPH assay, F. vulgare leaf extract showed important antioxidant activity (IC50 = 12.16 ± 0.02 µg/mL) than F. vulgare rootstock extract (IC50 = 34.36 ± 0.09 µg/mL). Moreover, fennel leaf extracts revealed also the most powerful antioxidant activity (IC50 = 22.95 ± 0.4 µg/mL) in the ABTS assay. The in vitro antihyperglycemic activity showed that F. vulgare rootstock extract exhibited a remarkable inhibitory capacity (IC50 = 194.30 ± 4.8 µg/mL) of α-amylase compared with F. vulgare leaf extract (IC50 = 1026.50 ± 6.5 µg/mL). Furthermore, the inhibition of α-glucosidase was more importantly with F. vulgare rootstock (IC50 of 165.90 ± 1.2 µg/mL) than F. vulgare leaf extracts (203.80 ± 1.3 µg/mL). The funding of this study showed that F. vulgare rootstock and leaf extracts presented several phenolic compounds and showed important antioxidant and antidiabetic effects. We suggest that the identified molecules are responsible for the obtained activities. However, further studies focusing on the isolation and the determination of antioxidant and antidiabetic effects of F. vulgare rootstock and leaf main compounds are required.

Discrimination of diesel fuels marketed in Morocco using FTIR, GC-MS analysis and chemometrics methods.

The purpose of this study was to perform a discrimination and classification of diesel samples from the four major suppliers of petroleum products in Morocco using Fourier Transform Infrared Spectroscopy (FTIR), Gas Chromatography coupled with Mass Spectrometry (GC-MS) and chemometrics tools. Eighty diesel samples were collected from different gas stations owned by the four biggest brands in the Moroccan market. Principal Component Analysis (PCA) was performed to depict the similarities between the samples and check the presence of outliers. Partial Least Squares Discriminant Analysis (PLS-DA) models were set up for the discrimination and the classification of the four groups of samples (i.e., diesel suppliers). The models proposed in this study, were characterized by good prediction abilities, especially the FTIR-PLSDA model that was characterized by 100% of accurate discrimination of the four groups. The approach of analysis showed that the FTIR spectra can provide a cheap and rapid means for the determination of the diesel origin and to ensure the traceability of diesel products marketed in Morocco with respect for the rules of the green chemistry.

Discrimination and Quantification of Moroccan Gasoline Adulteration with Diesel Using Fourier Transform Infrared Spectroscopy and Chemometric Tools.

In this work, transform-infrared spectroscopy (FTIR) was associated with chemometric tools, especially principal component analysis (PCA) and partial least squares regression (PLSR), to discriminate and quantify gasoline adulteration with diesel. The method is composed of a total of 100 mixtures were prepared, and then FTIR fingerprints were recorded for all samples. PCA was used to verify that mixtures can be distinguished from pure products and to check that there are no outliers. As a result of using just PC1 and PC2, more than 98% of the general variability was explained. The PLSR model based on infrared spectra has shown its capabilities to be suitable for predicting gasoline adulteration in the concentration range of 0 to 98% (w/w), with a high significant coefficient of determination (R² = 99.25%) and an acceptable calibration and prediction errors (root mean squared error of calibration = 0.63 and root mean square of external validation and/or prediction = 0.69).