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

Journal of Analytical Methods in Chemistry

Cheikh

et al. 2020

Self Cite

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.

Section: Methodsmentioning

confidence: 99%

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

Elhamdaoui

Journal of Analytical Methods in Chemistry

Cheikh

et al. 2020

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“…is evolution is also explained by the increasing attention to very complex authentication issues such as geographical origin or agricultural techniques, thus increasing the need for innovative analytical approaches [6,14]. Among these methods, we find spectroscopic methods [15], such as infrared spectroscopy, UV-Visible, Raman, and fluorescence spectroscopy [12,16,17]. Food analysis using spectroscopic techniques has become very common and widespread since these methods are extremely fast, inexpensive, nondestructive, and have no negative impact on the environment [18,19].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Classification Methods for Determining the Geographical Origin of Raw Milk Using Vibrational Spectroscopy

Journal of Spectroscopy

Mamad

Elhamdaoui

et al. 2021

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One of the significant challenges in the food industry is the determination of the geographical origin, since products from different regions can lead to great variance in raw milk. Therefore, monitoring the origin of raw milk has become very relevant for producers and consumers worldwide. In this exploratory study, midinfrared spectroscopy combined with machine learning classification methods was investigated as a rapid and nondestructive method for the classification of milk according to its geographical origin. The curse of dimensionality makes some classification methods struggle to train efficient models. Thus, principal component analysis (PCA) has been applied to create a smaller set of features. The application of machine learning methods such as PLS-DA, PCA-LDA, SVM, and PCA-SVM demonstrates that the best results are obtained using PLS-DA, PCA-LDA, and PCA-SVM methods which show a correct classification rate (CCR) of 100% for PLS-DA and PCA-LDA and 94.95% for PCA-SVM, whereas the application of SVM without feature extraction gives a low CCR of 66.67%. These findings demonstrate that FT-MIR spectroscopy, combined with machine learning methods, is an efficient and suitable approach to classify the geographical origins of raw milk.

“…For this reason, the development of fast and environmentally friendly methods constitutes a great challenge in the field of analytical chemistry. Recently, new technology has been emerged to satisfy this demand, and these methods are based on the combination of nondestructive spectroscopic methods with the recognition algorithms such as PLS-DA, PLS, and support vector machine for the classification between groups of individuals and the quantification of their content in a given matrix according to spectral data [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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

BioMed Research International

Adade

Mefetah

et al. 2021

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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.