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.

Background: Adulteration of camel milk powder with cheaper alternatives such as cow milk powder has become a growing concern, compromising both nutritional quality and consumer trust. Detecting such adulteration is critical for ensuring product authenticity, especially given the rising demand for camel milk in niche health markets. Objective: This study explores the application of Raman spectroscopy in conjunction with chemometric techniques for the detection and quantification of adulteration in camel milk powder with cow milk powder. Method: Camel milk powder was adulterated with cow milk powder across a range of concentrations from 0 to 50%. Raman spectra of these mixtures were analyzed using principal component analysis (PCA) for dimensionality reduction, followed by partial least-squares regression (PLSR) modeling with different spectral pretreatments (raw, Savitzky-Golay, gap derivative). Interval PLS (IPLS) in backward mode was applied to enhance variable selection. Results: PCA captured 99.6% of spectral variance. The raw PLSR model already showed strong predictive power (R2cv = 95.93%). Savitzky-Golay further boosted performance (R2 test = 99.47%), while the gap derivative achieved near-perfect prediction (R2 test = 99.94%, RMSEtest = 1.10). IPLS modeling significantly improved robustness, yielding high accuracy (R2 test = 98%) with fewer variables. Conclusions: The findings indicate that the integration of Raman spectroscopy with PCA, PLSR, and IPLS constitutes a robust, precise, and reliable approach for the detection of adulteration in camel milk powder. Highlights: The application of Raman spectroscopy coupled with chemometric modeling proves to be an efficient and robust analytical tool for quality control in the dairy industry, enabling the accurate detection of adulteration and ensuring the authenticity and safety of camel milk powder.

As global interest in camel milk rises because of its nutritional benefits, it is increasingly processed into powder to increase shelf life and transport. However, adulteration with cow milk powder poses a serious food safety issue. This study developed multivariate models using attenuated total reflectance infrared spectroscopy (ATR-IR) to detect adulteration in camel milk powder with cow milk powder. Partial least squares regression (PLSR) was applied after preprocessing, which included Savitzky–Golay (SG) smoothing (first and second derivatives) and multiplicative scatter correction (MSC), to quantify adulteration levels ranging from 0% (control) to 50% (w/w). Preprocessing techniques, including SG smoothing (first and second derivatives) and multiplicative scatter correction (MSC), enhanced model performance. Calibration achieved an R2 of ≥99.6% and RMSE <0.93, while cross-validation yielded an R2 of ≥99.3% and RMSE 98% and RMSE <1.8. Deviations remained low across all adulteration levels, even at higher concentrations (≈0.76–0.90 for 40%). These results demonstrate that ATR-FTIR spectroscopy combined with PLSR offers a rapid, nondestructive, and reliable method for detecting adulteration in camel milk powder, making it a valuable tool for quality control in the dairy industry.