Enhanced detection of pennyroyal essential oil adulteration using FTIR spectroscopy and chemometrics.

Introduction: The essential oil of pennyroyal has a number of bioactive properties, including antiviral, antifungal, and anti-inflammatory effects. Accurate, rapid, and non-destructive assessment of its quality is therefore a major challenge for the pharmaceutical industry. Conventional methods, such as gas chromatography, although effective, are time-consuming, destructive, and require the use of costly and polluting reagents. In contrast, mid-infrared spectroscopy, a non-invasive analytical method, provides spectral data while preserving sample integrity. Methods: In this work, Fourier transform infrared spectroscopy (FT-IR) was coupled with machine learning techniques to detect and quantify the adulteration of pennyroyal essential oil with spearmint essential oil at different levels. Principal component analysis (PCA) was used to explore the samples and better understand the structure of the data. Partial least squares regression (PLSR) was then used to predict the levels of adulteration. Variable selection strategies, such as Interval PLS (iPLS) in forward and reverse modes, were applied to improve model performance and interpretability. Results: By identifying the most informative spectral regions, these methods improved the accuracy and resilience of the predictive models. The detrend pre-processing method produced the most efficient model, with an RPD of 8.29, an RMSEt of 1.25, and an R Discussion: These findings demonstrate how infrared spectroscopy and machine learning may be successfully combined to quickly, sustainably, and accurately assess the quality of pennyroyal essential oil, offering a powerful alternative to traditional chromatographic techniques for industry-level quality control.