Assessment of non-destructive spectroscopy and chemometrics tools for the development of green analytical methods to determine the shelf-life of olive oils

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

Trace Metal Elements in Different Categories of Drinking Water by Exploratory Analysis

The objective of this work was to assess the quality of different categories of water intended for human consumption through monitoring and quantification of metallic trace elements. Chosen study matrix was made up of tap water, bottled Moroccan water, bottled foreign water and finally surface water from Beni Mellal area: Ain Asserdoune and Bouyakoub. Four trace elements were studied namely: As, Cd, Cr and Pb. The assays were carried out using inductively coupled plasma technique equipped with Atomic Emission Spectrometer (ICP-AES). Dissimilarities between waters and correlations between metallic trace elements were carried out by Principal Component Analysis. According to the analytical results, Arsenic (As) would be much more present in foreign waters with an average value of 6.33µg/L followed by Moroccan surface water category with an average value of 6.191 µg/L. Cadmium (Cd) was also more present in Moroccan surface water category. Chromium (Cr) was much more present in Moroccan waters especially in natural water category with an average value of 45.65 µg/L followed by tap water with of 44.875 µg/L value. Lead (Pb) was much more present in Moroccan waters compared to foreign waters. Analysis results allow us to locate the different samples analyzed in relation to Moroccan drinking water standard and that of World Health Organization on the one hand and to classify different types of water according to their concentrations of metallic elements on the other hand

The development of green analytical methods to monitor adulteration in honey by UV-visible spectroscopy and chemometrics models

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

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools

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

Chemometric Analysis of UV-Visible Spectral Fingerprints for the Discrimination and Quantification of Clinical Anthracycline Drug Preparation Used in Oncology

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

Evaluation of the Capability of Horizontal ATR-FTMIR and UV-Visible Spectroscopy in the Discrimination of Virgin Olive Oils from the Moroccan Region of Beni Mellal-Khenifra

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

Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy

International audienceThe aim of the present study was to evaluate the potential of handheld near-infrared (NIR) and benchtop mid-infrared (MIR) spectroscopy for the rapid prediction of antioxidant capacity, dry matter, and total phenolic contents in cayenne pepper (Capsicum annuum ‘Cayenne’). Using NIR spectroscopy, the best-performing model for dry matter had an R2pred = 0.74, RMSEP = 0.38%, and RPD of 2.02, exceeding the best results previously reported in the literature. This was also the first study to predict dry matter content from the mid-infrared spectra, although with lower accuracy (R2pred = 0.54; RMSEP = 0.51%, RPD 1.51). The models for antioxidant capacity and total phenolic content did not perform well using NIR or MIR spectroscopy (RPD values < 1.5), indicating that further optimization is required in this area. Application of support vector regression (SVR) generally gave poorer results compared to partial least squares regression (PLSR). NIR spectroscopy may be useful for in-field measurement of dry matter in the chili crop as a proxy measure for fruit maturity. However, the lower accuracy of MIR spectroscopy is likely to limit its use in this crop

Prediction of phytochemical constituents in cayenne pepper using MIR and NIR spectroscopy

The aim of the present study was to evaluate the potential of handheld near-infrared (NIR) and benchtop mid-infrared (MIR) spectroscopy for the rapid prediction of antioxidant capacity, dry matter, and total phenolic contents in cayenne pepper (Capsicum annuum ‘Cayenne’). Using NIR spectroscopy, the best-performing model for dry matter had an R2pred = 0.74, RMSEP = 0.38%, and RPD of 2.02, exceeding the best results previously reported in the literature. This was also the first study to predict dry matter content from the mid-infrared spectra, although with lower accuracy (R2pred = 0.54; RMSEP = 0.51%, RPD 1.51). The models for antioxidant capacity and total phenolic content did not perform well using NIR or MIR spectroscopy (RPD values < 1.5), indicating that further optimization is required in this area. Application of support vector regression (SVR) generally gave poorer results compared to partial least squares regression (PLSR). NIR spectroscopy may be useful for in-field measurement of dry matter in the chili crop as a proxy measure for fruit maturity. However, the lower accuracy of MIR spectroscopy is likely to limit its use in this crop