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The development of green and environmentally friendly analytical methods for agri-food products is an essential element to be treated by green analytical chemistry. In this study, UV-Visible spectroscopy, combined with a mathematical and statistical or chemometrics algorithm, has been developed to monitor honey quality. Partial Least Squares Regression (PLS-R) and Support Vector Machine Learning Regression (SVM-R) showed an adequate quantification of the percentage of impurity. The use of these models demonstrates a high ability to predict the quality of honey. R-square’s high value shows this ability, and the low value of root mean square error of calibration and cross-validation (RMSECV, RMSEC). The results indicate that UV-Visible spectroscopy allied with the Chemometrics algorithms can provide a quick, non-destructive, green, and reliable method to control the quality and predict honey’s adulteration level.

The development of sustainable and environmentally friendly analytical methods for agri-food products and the modification of reference methods is an essential issue to be treated in green analytical chemistry. The potential application of non-destructive spectroscopic techniques with chemometrics tools to achieve these principles are examined in this work. In this study a new sustainable analytical approach based on the use of fluorescence spectroscopy and multivariate analysis methods of Machine-Learning(Support Vector Machine regression) and chemometrics (Partial Least Square regression) have been developed to control the quality of virgin olive oils in Morocco according to their shelf life. The spectral data of 45 samples were first analyzed by principal component analysis method (PCA), the PCA method shows an important classification of the three groups of olive oil according to their shelf life. The use of the regression methods SVM and PLS shows a high ability to predict the quality of olive oils, this ability is shown by the high value of R-square and the low value of root mean square error of calibration and crossvalidation (RMSEC, RMSECV), the validation of these models by cross-validation shows the potential of this sustainable analytical approach in the determination of the quality of virgin olive oils.

Lavender, widely cultivated in the Mediterranean region, produces essential oil known for its significant biological activities and is a key component of the perfume industry due to its high levels of Linalool and Linalyl acetate, along with low Camphor content, which contributes to its high cost. However, the market is plagued by adulterated lavender oil, often mixed with cheaper alternatives such as eucalyptus and rosemary. Current detection methods, primarily gas chromatography, are expensive, time‐consuming and often fail to detect low levels of adulteration. To address these limitations, this study examines the use of mid‐infrared spectroscopy for the detection and prediction of adulteration levels. A set of 105 samples, comprising pure lavender oil and adulterated lavender oil, was prepared in the laboratory. Principal component analysis (PCA), hierarchical clustering ascending (HCA) and K‐means clustering were applied to the FT‐MIR results for qualitative analysis to effectively discriminate between authentic and adulterated essential oils. For quantitative analysis, partial least squares regression (PLSR) was used to develop accurate calibration models for predicting the percentage of adulteration. The results from PCA, HCA and K‐means demonstrated the efficacy of these techniques in detecting adulteration, even at low levels (2%). Calibration models were developed using the PLSR method with different spectral preprocessing techniques to predict the percentage of adulteration, with results indicating that models generated on the raw data and those using MSC (multiplicative signal correction) pre‐processing are optimal. In addition, the use of interval‐partial least squares (IPLS) variable selection techniques (Forward, Backward) improved the predictive accuracy of the models developed by reducing the number of wavelengths used. [ABSTRACT FROM AUTHOR]

Eucalyptus essential oil is widely valued for its therapeutic properties and extensive commercial applications, with its chemical composition significantly influenced by species variety, geographical origin, and environmental conditions. This study aims to develop a reliable method for identifying the geographical origin and variety of eucalyptus oil samples through the application of advanced analytical techniques combined with chemometric methods. Essential oils from Eucalyptus globulus and Eucalyptus camaldulensis were analyzed using Gas Chromatography–Flame Ionization Detection (GC–FID) and Fourier Transform Infrared (FTIR) Spectroscopy. Chemometric analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (O2PLS-DA) and Hierarchical Cluster Analysis (HCA), were utilized to classify the oils based on their volatile compound profiles. Notably, O2PLS-DA was applied directly to the raw FTIR data without additional spectral processing, showcasing its robustness in handling unprocessed data. For geographical origin determination, the GC–FID model achieved a Correct Classification Rate (CCR) of 100%, with 100% specificity and 100% sensitivity for both calibration and validation sets. FTIR spectroscopy achieved a CCR of 100%, specificity of 100%, and sensitivity of 100% for the calibration set, while the validation set yielded a CCR of 95.83%, specificity of 99.02%, and sensitivity of 94.44%. In contrast, the analysis based on species variety demonstrated 100% accuracy across all metrics CCR, specificity, and sensitivity—for both calibration and validation using both techniques. These findings underscore the effectiveness of volatile and infrared spectroscopy profiling for quality control and authentication, providing robust tools for ensuring the consistency and reliability of eucalyptus essential oils in various industrial and therapeutic applications. [ABSTRACT FROM AUTHOR]

A fast and sample cleanup approach for fluoxetine in human plasma was developed using protein precipitation coupled with LC–MS-MS. Samples were treated with methanol prior to LC–MS-MS analysis. Chromatographic separation was performed on a reverse phase column with an isocratic mobile phase of methanol and 10 mM ammonium formate pH acidified with formic acid (80:20, v/v) at a flow rate of 0.2 mL/min. The run time was 4 min. Mass parameters were optimized to monitor transitions at m/z [M + H]+ 310 > > 148 for fluoxetine and m/z [M + H]+ 315.1 > > 153 for fluoxetine-d5 as an internal standard. The lower limit of quantification and the dynamic range were 0.25 and 0.25–50 ng/mL, respectively. Linearity was good for intra-day and inter-day validations (R2 = 0.999). The matrix effect was acceptable with CV% < 15 and accuracy% < 15. The hemolytic effect was negligible. Fluoxetine was stable in human plasma for 48 h at room temperature (25 °C), for 12 months frozen at −25 °C, for 48 h in an auto-sampler at 6 °C, and for three freeze/thaw cycles. The validated method was applied in a pharmacokinetic study to determine the concentration of fluoxetine in plasma samples. The study provides a fast and simple bioanalytical method for routine analysis and may be particularly useful for bioequivalence studies. The method was successfully applied to a pharmacokinetic study of fixed-dose fluoxetine in nine healthy volunteers. [ABSTRACT FROM AUTHOR]

This study explores the migration of Bisphenol A (BPA) from plastic packaging to Moroccan extra virgin olive oil (EVOO), a key Mediterranean diet component. Assessing the impact of plastic containers on olive oil quality and safety, the research investigates the influence of olive oil acidity on BPA release using an HPLC-UV method. Despite BPA's known environmental and health risks, detectable levels persist in human samples, emphasizing the need for strict control. Examining 30 olive oil samples from the Beni Mellal-Khenifra region stored in plastic bottles, the study revealed BPA levels above the daily intake limits set by EFSA in its latest version (0.2 ng/kg body weight per day), reaching (0.238 ppm) in polycarbonate (PC) bottles and (0.144 ppm) in polyethylene (PE) bottles. It advocates for controlled packaging and highlights the delicate balance between convenience and consumer well-being. The findings call for informed choices in production and storage practices, urging increased scrutiny of food packaging for enhanced public health and regulatory measures. [ABSTRACT FROM AUTHOR]

Background: Monitoring impurities in drug products is a principal requirement of pharmaceutical regulatory authorities all over the world to ensure drug safety. For this reason, there is a great need for analytical QC of dugs products. Objective: In this study, a simple, efficient, and direct HPLC method was developed for the determination of three impurities of diclofenac. Methods: The HPLC method was developed using a mobile phase which consisted of an HPLC grade mixture, acetonitrile-0.01M phosphoric acid adjusted to pH 2.3 (1 þ 3, by volume). Results: The separation was performed in 15 min. The calibration curves of the three impurities were linear; the correlation coefficients were 0.999 at concentrations of 0.00015-0.003 mg/mL. Conclusion: The validation of this method shows that it meets all validation criteria. This shows the reliability of this method for the routine control of diclofenac impurities. Highlights: The validation of a robust HPLC method for the determination of diclofenac impurities is of great importance for the pharmaceutical industry to control its products. [ABSTRACT FROM AUTHOR]

Chemicals with high manufacturing volumes, such as bisphenol A (BPA), find widespread use in thermal paper, epoxy resins, and polycarbonate plastics. However, due to its endocrine-disrupting properties, several consumer items have restricted or banned its use. As a result, BPA has been replaced by other chemical substances with similar structures in consumer goods. This study aims to enhance and validate a chromatographic method for detecting BPA in baby bottles using chemometrics tools. A Box-Behnken experimental design was employed to optimize the laboratory conditions for the maximum release of BPA through forced degradation. The study utilized three parameters as independent variables, including incubation temperature (35 °C, 40 °C, and 60 °C), time (12 hours, 24 hours, and 36 hours), and solvent acetic acid concentration (2 %, 3 %, and 4 %). A second-order polynomial model was proposed, and a total of 15 experiments with two replicates were conducted to determine the optimal extraction conditions. The high-performance liquid chromatography (HPLC) partitioning method was used to determine peak areas of the different extracts, serving as responses. By applying the individual desirability approach, the optimal extraction conditions were found to be a temperature of 35 °C, an extraction time of 36 hours, and an acetic acid concentration of 4 % (pH = 2.72). The method was validated according to the conditions recommended by the International Conference on Harmonization (ICH) and SFSTP (Société Française des Sciences et Techniques Pharmaceutiques), ensuring high linearity, accuracy, precision, sensitivity, and robustness compared to existing liquid chromatographic methods. The improved method was applied to assess BPA levels in commercially available baby bottles, with 3 out of 7 samples testing positive for BPA, ranging from 0.615 ppb to 3.802 ppb. Notably, the enhanced method demonstrated a 12 % increase in yield compared to the ISO EN 14350-2:2004 method. This study provides a reliable and effective approach for detecting BPA in baby bottles, addressing critical concerns related to consumer safety and regulatory compliance. [ABSTRACT FROM AUTHOR]

Artemisinin-based combination therapy (ACTs) has become the primary first-line treatment for mild falciparum malaria in the majority of African countries. A fixed-dose combination of amodiaquine and artesunate is commonly employed to enhance treatment compliance and achieve successful malaria outcomes. In this study, a specific, accurate, linear, precise, and repeatable method was optimized, verified, and applied for the simultaneous estimation of artesunate and amodiaquine HCl in a commercially available artesunate-amodiaquine tablet with a dosage of 100 mg/270 mg. The optimization process involved two steps. Firstly, the top three were carefully selected out of seven characteristics influencing the separation. These key elements required fine-tuning, namely the column type, ion pair, and the residual amount of acetonitrile (ACN) remaining after elution. In the second step, a Box-Behnken experimental design, coupled with Derrenguer's desirability approach, was utilized to identify the ideal target conditions. The optimized method demonstrated excellent specificity, accuracy, linearity, precision, and repeatability, allowing for the reliable simultaneous estimation of artesunate and amodiaquine HCl in the artesunate-amodiaquine tablet. This method offers a valuable tool for quality control and dosage determination in the pharmaceutical industry. By employing advanced experimental techniques and focusing on critical parameters, the study contributes to analytical methodologies in malaria treatment. [ABSTRACT FROM AUTHOR]

Verification of sterilization number of medical devices is one of the main objectives to control its quality and ensure the protection of patients. The objective of this study is to investigate the capability of an automatic tool based on the combination of mid-infrared spectroscopy with a mathematical and statistical algorithm to estimate the number of sterilization cycles of medical pipes. Data analysis is performed by three main methods of pattern recognition: principal component analysis (PCA), support vector learning regression (SVR), and partial least squares regression (PLSR). The result obtained shows high performance capabilities for the prediction of sterilization cycle number. These technologies could be successfully applied in hospitals to ensure the quality of medical devices. [ABSTRACT FROM AUTHOR]

BACKGROUND: Argan oil is one of the purest and rarest oils in the world, so that the addition of any further product is strictly prohibited by international regulations. Consequently, it is necessary to establish reliable analytical methods to ensure its authenticity. In this study, three multivariate approaches have been developed and validated using fluorescence, UV–visible, and attenuated total reflectance Fourier transform mid‐infrared (FT‐MIR) spectroscopies. RESULTS: The application of a partial least squares discriminant analysis model showed an accuracy of 100%. The quantification of adulteration have been evaluated using partial least squares (PLS) regression. The PLS model developed from fluorescence spectroscopy provided the best results for the calibration and cross‐validation sets, as it showed the highest R2 (0.99) and the lowest root mean square error of calibration and cross‐validation (0.55, 0.79). The external validation of the three multivariate approaches by the accuracy profile shows that these approaches guarantee reliable and valid results of 0.5–32%, 7–32%, and 10–32% using fluorescence, FT‐MIR and UV–visible spectroscopies respectively. CONCLUSION: This study confirmed the feasibility of using spectroscopic sensors (routine technique) for rapid determination of argan oil falsification. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]