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.

Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions.

This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods-including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)-were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time.

Explanatory predictive model for COVID-19 severity risk employing machine learning, shapley addition, and LIME.

The rapid spread of SARS-CoV-2 threatens global public health and impedes the operation of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 infection and examine its risk factors. To produce more effective treatment options and vaccines, it is still necessary to investigate biomarkers and immune responses in order to gain a deeper understanding of disease pathophysiology. This study aims to determine how cytokines influence the severity of SARS-CoV-2 infection. We measured the plasma levels of 48 cytokines in the blood of 87 participants in the COVID-19 study. Several Classifiers were trained and evaluated using Machine Learning and Deep Learning to complete missing data, generate synthetic data, and fill in any gaps. To examine the relationship between cytokine storm and COVID-19 severity in patients, the Shapley additive explanation (SHAP) and the LIME (Local Interpretable Model-agnostic Explanations) model were applied. Individuals with severe SARS-CoV-2 infection had elevated plasma levels of VEGF-A, MIP-1b, and IL-17. RANTES and TNF were associated with healthy individuals, whereas IL-27, IL-9, IL-12p40, and MCP-3 were associated with non-Severity. These findings suggest that these cytokines may promote the development of novel preventive and therapeutic pathways for disease management. In this study, the use of artificial intelligence is intended to support clinical diagnoses of patients to determine how each cytokine may be responsible for the severity of COVID-19, which could lead to the identification of several cytokines that could aid in treatment decision-making and vaccine development.

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.

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.

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.

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.

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.

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.

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.

In vivo anti-inflammatory response and bioactive compounds' profile of polyphenolic extracts from edible Argan oil (Argania spinosa L.), obtained by two extraction methods.

The present work examined and assessed the in vivo anti-inflammatory effect of polyphenolic extracts from Moroccan edible Argan oils (Argania spinosa L.), extracted by two extraction processes: Hand pressing and mechanical pressing. Chemical properties, such as acidity, peroxide index, ultraviolet indices, total polyphenols composition, fatty acid composition, tocopherol composition, phenolic profiling, and sterol composition were studied. Then, the anti-inflammatory potential was determined by applying carrageenan, an induced paw edema test in rats. The results revealed an anti-inflammatory effect of edible Argan oil and indicated a higher efficiency of hand-pressed oil compared to mechanical-pressed oil, supporting its traditional use in human health, related to pain and inflammations. The chemical composition of these oils was evaluated, and total polyphenols, tocopherol composition, and some phenolic compounds were found highly concentrated in the hand-pressed oil. PRACTICAL APPLICATIONS: The present study highlights and compares the in vivo anti-inflammatory effect of polyphenolic compounds, extracted from Argan oil by two processes (hand and mechanical extraction). The study demonstrated the better quality of hand-pressed oil over mechanically pressed, supporting the traditional uses of this oil in treating several inflammations and pain-related situations. Moreover, the edible Argan oil may be introduced as a regular diet and food ingredient.

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).

Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils.

This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H3O+, NO+ and O2+). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification.

[Development and validation of an assay method of the paraphenylene diamine by gas chromatography-mass spectrometry]. / Développement et validation d'une méthode de dosage de la paraphénylène diamine par chromatographie gazeuse couplée à la spectrométrie de masse.

Paraphenylenediamine is an aromatic amine used as a hair dye; it is responsible for poisoning characterized by respiratory distress involving life-threatening. The objective of this work is the development and validation of an assay of para-phenylenediamine in the whole blood. The method is based on the determination of paraphenylene diamine in whole blood by gas chromatography-mass spectrometry after liquid-liquid extraction and derivatization. The validation protocol has included the study of the recovery factor of extraction, the measurement range, accurency, repetability and intermediate precision. The calibration curve was linear between 98 and 1350 µg/L (r = 0.999), the limit of detection and quantification were 37 µg/L and 63 µg/L respectively. The accuracy were 94.7%. Coefficients of variation were (2.3/6.8/9.7%) for repeatability and (4.4/8.7/9.8%) for intermediate precision. The method is suitable for quantification of PPD in acute poisoning situations. A method for the determination of the paraphenylene diamine in the whole blood by gas chromatography coupled to mass spectrometry was developed. The validation of the method showed good linearity, good accuracy and low limit of quantification.

Determination of fluoride in tap water in Morocco using a direct electrochemical method.

This study aimed to analyze the fluoride concentration in tap drinking water in different cities of Morocco using an electrochemical ion-selective method. Three samples were collected from each thirteen selected cities in the period between March and May 2011. The median value of fluoride was 0.94 mg/L (0.21-2.97). High fluoride concentrations (>0.8 mg/L) were found in sixteen cities. Very high values were found in phosphate regions such as Khouribga which is known to be an endemic dental fluorosis area. This study has shown that the concentration of fluoride in drinking water exceeds the limit especially in phosphate regions.

Solid-state characterization and impurities determination of fluconazol generic products marketed in Morocco.

In this paper, we report the results of quality control based in physicochemical characterization and impurities determination of three samples of fluconazole drug substances marketed in Morocco. These samples were supplied by different pharmaceuticals companies. The sample A, as the discovered product, was supplied by Pfizer, while samples B and C (generics), were manufactured by two different Indian industries. Solid-state characterization of the three samples was realized with different physicochemical methods as: X-ray powder diffraction, Fourier-transformation infrared spectroscopy, differential scanning calorimetry. High performance liquid chromatography was used to quantify the impurities in the different samples. The results from the physicochemical methods cited above, showed difference in polymorph structure of the three drug substances. Sample A consisted in pure polymorph III, sample B consisted in pure polymorph II, sample C consisted in a mixture of fluconazole Form III, form II and the monohydrate. This result was confirmed by differential scanning calorimetry. Also it was demonstrated that solvents used during the re-crystallization step were among the origins of these differences in the structure form. On the other hand, the result of the stability study under humidity and temperature showed that fluconazole polymorphic transformation could be owed to the no compliance with the conditions of storage. The HPLC analysis of these compounds showed the presence of specific impurities for each polymorphic form, and a possible relationship could be exist between impurities and crystalline form of fluconazole.

Transfer of drug dissolution testing by statistical approaches: Case study.

The analytical transfer is a complete process that consists in transferring an analytical procedure from a sending laboratory to a receiving laboratory. After having experimentally demonstrated that also masters the procedure in order to avoid problems in the future. Method of transfers is now commonplace during the life cycle of analytical method in the pharmaceutical industry. No official guideline exists for a transfer methodology in pharmaceutical analysis and the regulatory word of transfer is more ambiguous than for validation. Therefore, in this study, Gauge repeatability and reproducibility (R&R) studies associated with other multivariate statistics appropriates were successfully applied for the transfer of the dissolution test of diclofenac sodium as a case study from a sending laboratory A (accredited laboratory) to a receiving laboratory B. The HPLC method for the determination of the percent release of diclofenac sodium in solid pharmaceutical forms (one is the discovered product and another generic) was validated using accuracy profile (total error) in the sender laboratory A. The results showed that the receiver laboratory B masters the test dissolution process, using the same HPLC analytical procedure developed in laboratory A. In conclusion, if the sender used the total error to validate its analytical method, dissolution test can be successfully transferred without mastering the analytical method validation by receiving laboratory B and the pharmaceutical analysis method state should be maintained to ensure the same reliable results in the receiving laboratory.