Emprego de espectroscopia de infravermelho médio e quimiometria na detecção de adulteração em óleo de chia (Salvia hispânica L.)

• Ano de defesa: 2024
• Autor(a) principal: Aguiar, Tainara Rodrigues de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Campo Mourao; Medianeira; Brasil; Programa de Pós-Graduação em Tecnologia de Alimentos; UTFPR
• Palavras-chave em Português: Alimentos funcionais; Alimentos - Adulteração e inspeção; Quimiometria; Análise espectral; Functional foods; Food adulteration and inspection; Chemometrics; Spectrum analysis; CNPQ::CIENCIAS AGRARIAS::CIENCIA E TECNOLOGIA DE ALIMENTOS; Tecnologia de Alimentos
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/34885

Chia oil is rich in polyunsaturated fatty acids, primarily linolenic acid (ω3 or ALA) and linoleic acid (ω6), which can only be obtained through diet. Due to its higher commercial value than other edible oils, high demand, and lack of regulation, this oil has become a target for food fraud. Traditional techniques like gas chromatography (GC) are highly effective for identifying food fraud, especially in oils and fats. However, GC is costly and time-consuming, requiring extensive sample preparation, specialized expertise, and reagent consumption. Therefore, developing new techniques for rapid, simple, cost-effective analyses without sample disposal is always welcomed. This study aims to employ mid-infrared (FTIR) and chemometrics to detect adulteration in chia oil. The obtained data were processed using principal component analysis (PCA) and partial least squares regression (PLS). 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 with a horizontal attenuated total reflectance (HATR) accessory. PLS models were adjusted to predict the level of adulteration in chia oil and to predict the fatty acid content, including ALA. Gas chromatography was the reference method for fatty acid content, and the level of adulteration was known. The model obtained for the level of adulteration in chia oil showed high predictive capability with r² = 0.9868 for the prediction set and low detection limit (1.47%) and quantification limit (4.40%). Models for fatty acid content also showed good predictive capability (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, by analyzing FTIR-ATR spectra together with PLS. The proposed method is an important, rapid, cost-effective alternative for monitoring adulterations in vegetable oils.