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Objective This study aims on the one hand to study the physicochemical data of olive oils collected from six regions of Morocco during two consecutive years 2020 and 2021. Methods Fourier transform infrared spectroscopy (FTIR) was used in this study as an emerging analytical technique to express a unique "fingerprint". A preliminary processing of the ATR-FTIR spectral data was performed by preprocessing algorithm in order to reduce the noise, the effect of signal variation and minimize the effects of light scattering to extract the maximum analytical information from the spectra. A multivariate statistical procedure based on principal component analysis (PCA) coupled with linear discriminant analysis (LDA) as well as partial least squares discriminant analysis (PLS-DA) were developed to provide a powerful classification approach. Results Based on principal component analysis (PCA), six clusters were identified. The application of PCA-LDA and PLS-DA procedures demonstrate a powerful capacity in predicting the geographic origin of olive oils, this capacity is showed by the high value of CCR varying between 84.09% and 100%. Conclusion The suggested procedure has given reliable results for the classification of olive oils according to their geographical origin, with advantages such as being fast, inexpensive and not requiring any prior separation process. Highlight The performance of this approach is significantly faster and possesses a higher degree of selectivity and sensitivity. The Implementation of this technique for routine analysis of olive oil would save significant time, resources and solvents.

Background Recent technological progress has bolstered efforts to bring personalized medicine from theory into clinical practice. However, progress in areas such as therapeutic drug monitoring (TDM) has remained somewhat stagnant. In drugs with well-known dose-response relationships, TDM can enhance patient outcomes and reduce health care costs. Traditional monitoring methods such as chromatography-based or immunoassay techniques are limited by their higher costs and slow turnaround times, making them unsuitable for real-time or onsite analysis. Objective In this work, we propose the use of a fast, direct, and simple approach using Fourier transform infrared spectroscopy (FT-IR) combined with chemometric techniques for the therapeutic monitoring of valproic acid (VPA). Method In this context, a database of FT-IR spectra was constructed from human plasma samples containing various concentrations of VPA; these samples were characterized by the reference method (immunoassay technique) to determine the VPA contents. The FT-IR spectra were processed by two chemometric regression methods: partial least-squares regression (PLS) and support vector regression (SVR). Results The results provide good evidence for the effectiveness of the combination of FT-IR spectroscopy and SVR modeling for estimating VPA in human plasma. SVR models showed better predictive abilities than PLS models in terms of root-mean-square error of calibration and prediction RMSEC, RMSEP, R2Cal, R2Pred, and residual predictive deviation (RPD). Conclusions This analytical tool offers potential for real-time TDM in the clinical setting. Highlights FTIR spectroscopy was evaluated for the first time to predict VPA in human plasma for TDM. Two regressions were evaluated to predict VPA in human plasma, and the best-performing model was obtained using nonlinear SVR.

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.

Background Morocco is an important world producer and consumer of several varieties of date palm. In fact, the discrimination between varieties remains difficult and requires the use of complex and high-cost techniques. Objective We evaluated in this work the potential of mid-IR (MIR) spectroscopy and chemometric models to discriminate eight date palm varieties. Method Four chemometric models were applied for the analysis of the spectral data, including principal-component analysis (PCA), support-vector machine discriminant analysis (SVM-DA), linear discriminant analysis (LDA), and partial-least-squares (PLS) analysis. MIR spectroscopic data were recorded from the wavenumber range 4000–600 cm−1, with a spectral resolution of 4 cm−1. Results The discriminant analysis was performed by LDA and SVM-DA with a 100% correct classification rate for the date mesocarp. PLS analysis was applied as a complementary chemometric tool aimed at quantifying moisture content; the validation of this model shows a good predictive capacity with a regression coefficient of 84% and a root-mean-square error of cross-validation of 0.50. Conclusions The present study clearly demonstrates that MIR spectroscopy combined with chemometric approaches constitutes a promising analytical method to classify date palms according to their varietal origin and to establish a regression model for predicting moisture content. Highlights An alternative analytical method to discriminate date palm cultivars by FTIR-attenuated total reflection spectroscopy coupled with chemometric approaches is described.

This work aims to investigate the potential of mid-infrared spectroscopy (MIR) and chemometrics algorithms for the determination of geographical origin and detection of adulteration of Moroccan saffron samples. First, the determination of the geographical origin of five saffron varieties was analyzed by linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA). As a result, the developed models correctly classified saffron samples in a subset of external validation with 100% predictive ability. Next, partial least squares regression (PLS-R) was conducted to estimate the amount of adulterants (safflower) in the saffron samples. A good performance was found with Coefficient of Determination (R2) between 0.97 and 0.99. Compared to other techniques, the main advantage of the proposed methods are non-destructive, fast and sensitive which allows to achieve very precise and accurate results.

In clinical treatment, the analytical quality assessment of the delivery of chemotherapeutic preparations is required to guarantee the patient’s safety regarding the dose and most importantly the appropriate anticancer drug. On its own, the development of rapid analytical methods allowing both qualitative and quantitative control of the formulation of prepared solutions could significantly enhance the hospital’s workflow, reducing costs, and potentially providing optimal patient care. UV-visible spectroscopy is a nondestructive, fast, and economical technique for molecular characterization of samples. A discrimination and quantification study of three chemotherapeutic drugs doxorubicin, daunorubicin, and epirubicin was conducted, using clinically relevant concentration ranges prepared in 0.9% NaCl solutions. The application of the partial least square discriminant analysis PLS-DA method on the UV-visible spectral data shows a perfect discrimination of the three drugs with a sensitivity and specificity of 100%. The use of partial least square regression PLS shows high quantification performance of these molecules in solution represented by the low value of root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSCECV) on the one hand and the high value of R -square on the other hand. This study demonstrated the viability of UV-visible fingerprinting (routine approach) coupled with chemometric tools for the classification and quantification of chemotherapeutic drugs during clinical preparation.

Adulteration detection of argan oil is one of the main objective to control its quality and to ensure customer protection. The aim of this study is to develop an automat tool based on the combination of spectroscopic method with mathematical and statistical algorithm to detect the percentages of adulteration of argan oil with commercial oil by. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), support vector machine learning regression (SVMR), partial least square regression (PLSR). The result obtained can discriminate and quantify the percentage of adulteration with commercial oil. This technologies could be successfully applied to the detection of adulteration of argan oil.

One of the most important challenges in the authentication of olive oil is the determination of the geographical origin of virgin olive oil. In this work, we evaluated the capacity of two spectroscopic techniques, UV-Visible and ATR-FTMIR, coupled with chemometric tools to determine the geographical origin of olive oils. These analytical approaches have been applied to samples that have been collected during the period of olive oil production, in the Moroccan region of Beni Mellal-Khenifra. To develop a rapid analysis tool capable of authenticating the geographical origin of virgin olive oils from five geographical areas of the Moroccan region of Beni Mellal-Khenifra, UV-Visible and ATR-FTMIR spectral data were processed by chemometric algorithms. PCA was applied on the spectral data set to represent the data in a very small space, and then discrimination methods were applied on the principal components synthesized by the PCA. The application of the PCA-LDA method on the spectral data of UV-Visible and ATR-FTMIR shows a good ability to classify olive oils according to their geographical origin with a percentage of correct classification that represents 90.24% and 85.87%, respectively, and the processing of the spectral data of UV-Visible and ATR-FTMIR by PCA-SVM allows differentiating correctly between five olive oils with a correct classification rate of 100% and 97.56, respectively. This study demonstrated the feasibility of UV-Visible and ATR-FTMIR fingerprinting (routine technique) for the geographical classification of olive oils in the Moroccan region of Beni Mellal-Khenifra. Such developed methods can be proposed as alternative and complementary methods to authenticate the geographical origin of virgin olive oil.

In this study, the Fourier transform mid-infrared (FT-MIR) spectroscopy technique combined with chemometrics methods was used to monitor adulteration of honey with sugar syrup. Spectral data were recorded from a wavenumber region of 4000–600 cm−1, with a spectral resolution of 4 cm−1. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for qualitative analysis to discriminate between adulterated and nonadulterated honey. For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. The use of PCA shows that the first two principal components account for 96% of the total variability. PCA and HCA allow classifying the dataset into two groups: adulterated and unadulterated honey. The use of the PLS-R and SVM-R calibration models for the quantification of adulteration shows high-performance capabilities represented by a high value of correlation coefficients R2 greater than 98% and 95% with lower values of root mean square error (RMSE) less than 1.12 and 1.85 using PLS-R and SVM-R, respectively. Our results indicate that FT-MIR spectroscopy combined with chemometrics techniques can be used successfully as a simple, rapid, and nondestructive method for the quantification and discrimination of adulterated honey.

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.