Search

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]

Essential Oil (EO) extracted from Rosemary is known for its therapeutic, antifungal, stimulant and antibacterial effects. This study aimed to detect and quantify the adulteration of Rosemary essential oil with different percentages of eucalyptus essential oil, using two analytical techniques: gas chromatography with Flame Ionization Detection (GC-FID) and Fourier Transform Mid-infrared spectroscopy (FT-MIR), combined with chemometric tools such as Principal Component Analysis (PCA), Partial Least Squares regression (PLS-R) and support vector regression (SVR). The use of PCA on the results obtained from GC-FID and FT-MIR indicates the possibility of categorizing the data into two distinct groups: adulterated essential oil and non-adulterated essential oil. However, it is noted that GC-FID can only detect adulteration starting from 40%, while spectroscopy is capable of detecting lower percentages of adulteration. The use of PLS-R and SVR calibration models for adulteration quantification demonstrates high performance capabilities for both techniques (GC-FID and FT-MIR), as indicated by high R2 correlation coefficients indicating good fit, with lower root mean square error (RMSE) values demonstrating predictive accuracy. The results suggest that FT-MIR spectroscopy is preferable to GC-FID for the quantification and discrimination of adulterated essential oils. FT-MIR spectroscopy is considered superior to GC-FID due to its non-destructiveness, speed and lack of sample preparation. [ABSTRACT FROM AUTHOR]