BackgroundThis study aimed to evaluate the remineralizing effect of sodium trimetaphosphate (STMP) combined with fluoride varnish on enamel subsurface lesions in vitro.MethodsFifty human enamel samples were randomly assigned to five groups (n = 10): sound enamel (S), demineralized enamel (SL), 5% NaF, 2.5% STMP/NaF, and 5% STMP/NaF. Remineralization was performed using a pH-cycling model. Surface and subsurface changes were analyzed using SEM, EDX, and FTIR. Data were analyzed using ANOVA with Bonferroni post hoc tests (p < 0.05).ResultsThe 5% STMP/NaF group showed significantly higher fluoride content and Ca/P ratios compared to other groups. FTIR analysis demonstrated enhanced phosphate band intensity and reduced carbonate substitution.ConclusionThe addition of 5% STMP to fluoride varnish enhances enamel remineralization at both surface and subsurface levels.

High-value Moroccan honeys, including Daghmous, Thyme, Zakoum, and Jujube, are highly appreciated for their nutritional and therapeutic properties but are increasingly vulnerable to adulteration with artificial honey syrups. This study investigates, for the first time, the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric tools for the authentication and adulteration assessment of these premium Moroccan honeys. Samples were adulterated with 5–50% (w/w) artificial honey prepared from sucrose and glucose syrups to simulate realistic fraud scenarios. Spectral datasets were analyzed using Principal Component Analysis (PCA), Partial Least Squares–Discriminant Analysis (PLS-DA), and Partial Least Squares Regression (PLSR). Generalized Least Squares Weighting (GLSW) preprocessing significantly improved spectral discrimination and model performance. PCA enabled clear separation between pure and adulterated samples, while PLS-DA achieved high sensitivity and specificity for classification. Quantitative prediction of adulteration levels by PLSR showed excellent performance, with coefficients of determination (R²) above 0.99 and prediction errors below 2%. The proposed strategy combines global screening models with honey-specific quantitative models, providing a rapid, non-destructive, and cost-effective approach for the authentication and quality control of high-priced Moroccan honeys.

This work presents an innovative methodology for identifying instances of honey adulteration by utilizing the Vision Transformer (ViT) model and thermal imaging techniques to assess and classify honey samples. Conventional techniques employed for the identification of honey adulteration are characterized by protracted processing durations and frequently exhibit limited sensitivity. Thermal imaging is a distinctive benefit as it enables the identification of temperature fluctuations within honey samples, hence facilitating the assessment of disparities in sugar composition, moisture levels, and the existence of adulterants. Thermal imaging technique offers a notable advantage in the detection of adulterants, as it may reveal temperature variations within honey samples caused by differences in sugar composition, moisture levels, and other adulterating substances. To establish a dependable method for classifying honey, we gathered an extensive dataset comprising thermal pictures of 9 unadulterated honey samples, as well as 84 honey samples that were contaminated at varying levels ranging from 1

This study evaluated three Raman instruments (benchtop Senterra II, portable Rigaku, and portable MIRA XTR), with different laser wavelengths, for detecting and quantifying argan oil adulteration. The data analysis followed a tiered chemometric approach: first employing Principal Component Analysis for preliminary data exploration and visualization, then applying Partial Least Squares-Discriminant Analysis (PLS-DA) to distinguish authentic from adulterated samples and to identify specific adulterants, and finally developing Partial Least Squares Regression (PLSR) models to precisely estimate adulteration levels. The PLS-DA models enabled the identification of the adulterant, while the PLSR models successfully quantified adulterant levels down to 5 % with high accuracy. Comparative analysis revealed that all instruments were effective for screening purposes, with the portable MIRA XTR demonstrating a somewhat superior performance in both classification accuracy and quantification precision. To our knowledge, this is the first direct comparison of benchtop and portable Raman instruments for the qualitative and quantitative analysis of argan oil.

Honey is a complex natural product valued for its nutritional and therapeutic properties, including antioxidant, antimicrobial, and anti-inflammatory effects. Its composition varies with botanical and geographical origin, making authenticity assurance essential. However, increasing adulteration, including the addition of low-cost sweeteners and mislabeling of origin, continues to undermine consumer trust and distort global markets. Rapid and reliable analytical approaches are therefore critical for effective quality control. This review provides a comprehensive overview of spectroscopic techniques for honey authentication and adulteration detection, including Ultraviolet-Visible spectroscopy, Near- and Mid-Infrared spectroscopy, Raman spectroscopy, Nuclear Magnetic Resonance spectroscopy, fluorescence spectroscopy, Hyperspectral Imaging, Laser-Induced Breakdown Spectroscopy, portable and field-deployable spectroscopic devices. These techniques rely on characteristic spectral fingerprints that capture subtle compositional variations. When coupled with multivariate chemometric tools, they enable efficient extraction of meaningful information from complex datasets, enhancing both classification and prediction performance. Representative studies demonstrate that techniques such as Near-Infrared spectroscopy combined with Partial Least Squares Discriminant Analysis achieve classification accuracies above 95%, while Raman spectroscopy with Partial Least Squares Regression enables quantification of sugar adulteration at low levels with high accuracy (R & sup2; > 0.98). The spectroscopic-chemometric approaches offer powerful, rapid, and non-destructive solutions for ensuring honey authenticity and market integrity.

Honey adulteration poses a huge challenge with considerable health and economic consequences, underscoring the necessity for effective and precise quality evaluation techniques. This research introduces a novel approach for classifying levels of honey adulteration through thermal imaging and Artificial Intelligence (AI). Traditional detection methods are frequently marked by protracted processing durations, elevated expenses, and restricted sensitivity. To mitigate these constraints, a dataset of thermal images was compiled from 15 pure honey samples and 69 adulterated samples including glucose syrup at amounts between 1% and 30%. An adaptable AI model was created to categorize various honey types, attaining elevated accuracy, sensitivity, and specificity across different levels of adulteration. The model achieved a precision and specificity of 100% for pure honey and 1% adulteration, demonstrating strong performance at higher adulteration levels (0.98 and 0.97 for 3% and 5% adulteration, respectively). This methodology offers significant benefits, such as swift identification and versatility across various honey varieties. The results indicate that the integration of thermal imaging and AI can improve quality control in the honey sector, providing a dependable method for verifying the authenticity and safety of natural bee products. This approach facilitates enhanced quality assurance methods and bolsters consumer confidence in honey products.

The research suggests a novel approach for the determination of honey adulteration through the Vision Transformer (ViT) model and thermography techniques in honey sample analysis and grading. Honey adulteration, being a natural and highly prized dietetic food item, is a major economic and health hazard. Conventional procedures for the determination of honey adulteration require long processing time and are not as sensitive. Thermal imaging constitutes a distinctive benefit in that it permits temperature difference determination among honey samples and hence facilitates the determination of differences in sugar, moisture, and adulterants. Thermal imaging technique offers a great advantage in adulterant detection since it has the potential to detect temperature variations in honey samples as a result of differences in sugar content, moisture levels, and other adulterants. To find a trustworthy method for honey classification, we gathered a large dataset of thermal images of 9 pure honey samples, and 45 honey samples adulterated at various levels ranging from 1% to 20% during their cooling processes. The dataset was employed to train and fine-tune the model in this work. The findings indicated that the model achieved a level of accuracy at 99.9% with sensitivity of 99.5% and specificity of 100%. The finding of the current study provides the proof to establish the effectiveness of thermal image analysis using Transformers as a capable instrument for the prompt and accurate detection of instances of honey adulteration. The above-mentioned approach presents a likely useful method of implementing quality control policies in the honey industry such that authenticity and safety of this valuable organic resource is ensured.

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.

Honey, a natural product generated from organic sources, is widely recognized for its revered reputation. Nevertheless, honey is susceptible to adulteration, a situation that has substantial consequences for both the well-being of the general population and the financial well-being of a country. Conventional approaches for detecting honey adulteration are often associated with extensive time requirements and restricted sensitivity. This paper presents a novel approach to address the aforementioned issue by employing Convolutional Neural Networks (CNNs) for the classification of honey samples based on thermal images. The use of thermal imaging technique offers a significant advantage in detecting adulterants, as it can reveal differences in temperature in honey samples caused by variations in sugar composition, moisture levels, and other substances used for adulteration. To establish a meticulous approach to categorizing honey, a thorough dataset comprising thermal images of authentic and tainted honey samples was collected. Several state-of-the-art Convolutional Neural Network (CNN) models were trained and optimized using the dataset that was gathered. Within this set of models, there exist pre-trained models such as InceptionV3, Xception, VGG19, and ResNet that have exhibited exceptional performance, achieving classification accuracies ranging from 88% to 98%. Furthermore, we have implemented a more streamlined and less complex convolutional neural network (CNN) model, outperforming comparable models with an outstanding accuracy rate of 99%. This simplification offers not only the sole advantage of the model, but it also concurrently offers a more efficient solution in terms of resources and time. This approach offers a viable way to implement quality control measures in the honey business, so guaranteeing the genuineness and safety of this valuable organic commodity.

Argan oil, a rare and luxury oil, is often adulterated with cheaper vegetable oils to make profits. Therefore, in this study, the potential of Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy, along with chemometrics, for the rapid identification and quantification of argan oil adulteration, was investigated. First, the authentication of pure and adulterated samples was visually explored by Principal Component Analysis. MIR and NIR spectra allowed an excellent distinction between pure oil samples. Next, Partial Least Squares - Discriminant Analysis (PLS-DA) modelling was applied to discriminate between pure and adulterated argan oils. PLS-DA classification figures of merit, in terms of sensitivity, specificity, and accuracy, were very good for both NIR and MIR datasets. Finally, Partial Least Squares regression was used to model and predict the level of adulterant. The developed models showed a good performance, with RMSE values below 1.7% and coefficients of determination higher than 98% for both techniques.

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.

This study aims to quantify ciprofloxacin in commercial tablets with varying excipient compositions using Fourier Transform Near-Infrared Spectroscopy (FT-NIR) and chemometric models: Partial Least Squares (PLS) and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Matrix variation, arising from differences in excipient compositions among the tablets, can impact quantification accuracy. We discuss this phenomenon, emphasizing potential issues introduced by varying certain excipients and its importance in reliable ciprofloxacin quantification. We evaluated the performance of PLS and MCR-ALS models independently on two sets of tablets, each containing the same drug substance but different excipients. The statistical results revealed promising results with PLS prediction error of 0.38% w/w of the first set and 0.47% w/w of the second set, while MCR-ALS achieved prediction errors of 0.67% w/w of the first set and 1.76% w/w of the second set. To address the challenge of matrix variation, we developed single models for PLS and MCR-ALS using a dataset combining both first and second sets. The PLS single model demonstrated a prediction error of 4.3% w/w and a relative error of 6.41% w/w, while the MCR-ALS single model showed a prediction error of 1.88% w/w and a relative error of 1.29% w/w. We then assessed the performance of the single PLS and MCR-ALS models developed based on the combination of the first and the second set in quantifying ciprofloxacin in various commercial tablet brands containing new excipients. The PLS model achieved a prediction error ranging between 6.2% w/w and 8.39% w/w, with relative errors varied between 8.53% w/w and 12.82% w/w. On the other hand, the MCR-ALS model had a prediction error between 1.11% w/w and 2.66% w/w, and the relative errors ranging from 0.8% to 1.74% w/w.

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.

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.

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.

Diesel control by routine methods involves the use of heavy techniques that are generally costly and hazardous. Moreover, these techniques require using several reagents that are harmful for the environment. Methods for rapid and accurate characterization of petroleum derivatives properties are needed to ensure the quality of these essential products. Herein, we tested the application of Fourier transform medium infrared spectroscopy (FT-MIR) for the rapid characterization of key performance-related diesel properties. The proposed methodology, based on the use of Partial Least Square Regression (PLSR), led to accurate predictions of ten properties of interest, namely, the viscosity, density, color, flash point, conductivity, cetane number, cold filter plugging point, pour point, 40% of the distillation fraction and water content. The prediction models yield high correlation coefficients between the observed and the predicted responses (around 0.90) and satisfactory cross-validation and prediction error (RMSEP and RMSECV) values. Therefore, the proposed approach that uses the medium infrared spectroscopy (MIR) for quality indicators estimation can be highly recommended as an accurate, eco-friendly, rapid, and reliable solution.

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, ɣ-tocopherol, δ-tocopherol, acidity, stigma-8-22-dien-3β-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.

In order to achieve a better understanding of the shelf-life behavior of extra virgin Argan oils (EVAO) during storage, the influences of storage periods, roasting process and packaging materials were studied. Those oils were extracted from roasted and unroasted kernels. The EVAO shelf life assessment was made by determining chemical properties (acidity, peroxide value, specific absorbances K232 and K270, tocopherol content, fatty-acids and sterol composition, and oxidative stability index) and by FTIR spectra. Sixty EVAO samples (30 roasted and 30 unroasted) were evaluated after production and then were packed in two glass bottle types (dark and clear), which resulted in 120 samples. They were stored under realistic storage conditions (ambient temperature) for two successive years and analysed 6-monthly. Chemometric data analysis was applied to study the shelf-life influence. PCA and PLS-DA, on either the chemical data or the FTIR spectra, allowed the discrimination between fresh and oxidized oils. The oil shelf-life was predicted by means of PLS regression. Thus, the time of storage after which the oil loses its extra virgin quality could be predicted. Finally, the potential of FTIR fingerprinting to quantify four physicochemical properties (i.e. acidity, PV, K232 and K270) during EVAO storage was established using PLS regression.

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.

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.