4.5 Article

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometric analysis for detection and quantification of adulteration in lavender and citronella essential oils

期刊

PHYTOCHEMICAL ANALYSIS
卷 32, 期 6, 页码 907-920

出版社

WILEY
DOI: 10.1002/pca.3034

关键词

attenuated total reflectance– Fourier transform infrared (ATR– FTIR); citronella; counterfeits; Cymbopogon winterianus Jowitt; Lavandula angustifolia Miller; lavender; multivariate statistical analysis; partial least‐ squares (PLS) regression; principal component analysis (PCA)

资金

  1. University of Modena
  2. Fondazione Cassa di Risparmio di Modena [E54I19002010007]
  3. Reggio Emilia

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This study aimed to detect counterfeit essential oils using mid-infrared spectroscopy coupled with chemometrics. The results showed that the developed method was robust in quantifying counterfeits, offering a research advance in phytochemistry and providing an easily applicable method for essential oil quality assessment.
Introduction The growing consumer interest in naturals led to an increased application of essential oils (EOs). The market outbreak induced the intensification of EO adulterations, which could affect their quality. Objectives Nowadays, little is known about the illegal practice of adulteration of EOs with vegetable oils. Therefore, the application of mid-infrared spectroscopy coupled with chemometrics was proposed for the detection of EO counterfeits. Materials and methods Two EOs, three seed oils, and their mixtures were selected to build the adulteration model. EO-adulterant mixtures for model calibration and validation were analyzed by attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral data were analyzed with principal component analysis (PCA) and partial least-squares (PLS) regression. Results PCA allowed the discrimination of the EO and adulterant percentages by explaining 97.47% of the total spectral variance with two principal components. A PLS regression model was generated with three factors explaining 97.73% and 99.69% of the total variance in X and Y, respectively. The root mean square error of calibration and the root mean square error of cross-validation were 0.918 and 1.049, respectively. The root mean square error of prediction value obtained from the external validation set was 1.588 and the coefficients of determination R-CAL(2) and R-CV(2) were 0.997 and 0.996, respectively. Conclusions The results highlighted the robustness of the developed method in quantifying counterfeits in the range from 0 to 50% of adulterants, disregarding the type of EO and adulterant employed. The present work offers a research advance and makes an important impact in phytochemistry, revealing an easily applicable method for EO quality assessment.

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