Over the past decade, Raman spectroscopy and chemometrics have been extensively utilized in the food industry for the research and development of new products but have failed to establish a strong foothold in quality control and the assessment of food items. To bridge this gap, we introduce a novel application of Raman spectroscopy capable of swiftly identifying free fatty acids (FFAs) in cooking oil and quantifying adulteration. This advanced method was validated using camellia oil, a highly esteemed cooking oil in China and various Asian countries known for its nutritional richness and diverse culinary applications. With its growing popularity among high-end food consumers in Asia, camellia oil has increasingly become a target for adulteration, causing dissatisfaction among both consumers and genuine producers. In this study, we employed Raman spectroscopy to characterize the FFA profiles of cooking oil samples, complemented by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) for sample categorization and adulteration detection in camellia oil. By segregating camellia oil from other vegetable oils and differentiating genuine from adulterated samples using the partial least squares (PLS) method, we achieved a high determination coefficient (R²) of over 0.98 and a low root mean square error of prediction (RMSEP) of less than 1.45%. These findings offer a robust predictive model for rapid camellia oil adulteration assessment, potentially augmenting traditional qualitative tests and streamlining sampling procedures in the food industry.

Camellia oil, rich in monounsaturated fatty acids, squalene, tocopherols, and polyphenols, is highly valued for its nutritional benefits. However, its high market value and regional variations have led to frequent adulteration, highlighting the need for rapid, non-destructive methods for authentication, geographical traceability, and quality assessment. This study employed portable Raman spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) and Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) to differentiate camellia oil from other edible oils and evaluate its thermal and photo-oxidative stability. PLS-DA, based on VIP-selected spectral variables, effectively distinguished camellia oil, with Raman bands near 1250 cm⁻¹ and 1650 cm⁻¹ contributing significantly. A unique peak at 1525 cm⁻¹, observed in samples from Gongcheng, Guangxi, was associated with carotenoids and served as a potential marker for geographical traceability. MCR-ALS modeling revealed significant reductions in the 1650 cm⁻¹ and 1525 cm⁻¹ peaks when temperatures exceeded 150 °C, indicating degradation of unsaturated fatty acids and carotenoids. Under UV exposure, the 1525 cm⁻¹ peak declined sharply and nearly disappeared after 24 h, suggesting rapid carotenoid degradation via photooxidation. Extended UV treatment also affected the 1650 cm⁻¹ peak and led to oxidative product accumulation. Overall, this study demonstrates the feasibility of integrating Raman spectroscopy with chemometric analysis for efficient oil classification, traceability, and stability monitoring, offering a valuable tool for food quality control and market supervision.

To address the limitations of a traditional Fourier-transform infrared (FTIR) spectrometer, including its bulky size, high cost, and unsuitability for on-site industrial detection, this study developed a Fourier-transform near-infrared (FT-NIR) absorption testing system utilizing Micro-Electro-Mechanical System (MEMS) technology for detecting trace water content in ethylene glycol. The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. To enhance predictive accuracy of water trace, spectral data were preprocessed using smoothing combined with first-derivative processing, and optimal selection of absorption wavelength feature was performed using interval Partial Least Squares (iPLS). Cross-batch external validation demonstrated a Limit of Detection (LOD) of 0.026% with 95% confidence which satisfies the rapid screening requirements for water exceedances (>0.1%) in industrial applications. These findings provide a robust technical foundation for developing handheld, in situ water detection devices.