Mid-infrared spectroscopy and chemometrics in the detection of adulteration in chia oil (Salvia hispanica L) and α-linolenic acid content prediction

Chia oil has high commercial value due to polyunsaturated fatty acids (PUFAs), especially α-linolenic acid (ALA), and suffers from tampering. Traditional adulterant detection in oils applies gas chromatography, but this approach has disadvantages such as time consumption. The development of fast analytical methods like infrared spectroscopy is important to detect oil fraud. The study aims to employ mid-infrared (FTIR) and chemometrics to detect adulteration in chia oil. Chia oil was extracted by cold pressing and adulterated with sunflower, corn, and soybean oils. FTIR-ATR spectra were obtained using a Fourier transform infrared spectrophotometer and horizontal attenuated reflectance accessory (HATR). Partial least square (PLS) models were adjusted to predict the adulteration content in chia oil and to predict the fatty acid content, including ALA. Gas chromatography was the reference method for the fatty acid content, and the adulteration content was known. The model obtained for adulteration content in chia oil had a high predictive capacity with r² = 0.9868 for the prediction set and a low limit of detection (1.47%) and limit of quantification (4.40%). The models for fatty acid content also had good prediction capabilities (0.90 < r², RMSE <21 mg g⁻¹, RSD <6.5%, LOD <12 mg g⁻¹, and LOQ <36 mg g⁻¹). The results indicate that it is possible to quantify fraud in chia oil even using different adulterants when analyzing FTIR-ATR spectra in tandem with PLS. The proposed method is an important, fast, low-cost alternative for monitoring adulterations in vegetable oils.