Utilização da espectroscopia de fluorescência na determinação de qualidade e processos oxidativos em óleos vegetais comestíveis
Arquivos
Data
2023-11-10
Autores
Lopes, Carla Regina Borges 
Orientadores
Courrol, Lilia Coronato
Tipo
Tese de doutorado
Título da Revista
ISSN da Revista
Título de Volume
Resumo
O controle de qualidade de óleos vegetais é essencial para garantir a segurança alimentar, permitindo identificar oxidação avançada, detectar adulterações e otimizar o tempo de prateleira. Contribui para prevenção do desperdício e redução de riscos à saúde, sendo crucial para a indústria alimentícia. Neste estudo, exploramos o potencial da espectroscopia de fluorescência, em estado estacionário e resolvida no tempo, para a identificação de óleos vegetais comestíveis, como soja, canola e milho. Investigamos também sua capacidade de detectar adulteração em óleos orgânicos de soja pela adição de óleo de soja convencional, e de indicar estágios avançados de oxidação lipídica. Um aspecto notável deste estudo é a análise direta, que dispensa a necessidade de diluição ou procedimentos prévios de preparo de amostra. Isso resulta em redução de custos, tempo de análise, geração de resíduos e riscos de contaminação ambiental. Na caracterização da fluorescência dos óleos de soja, canola e milho, a excitação em 340 nm foi a mais adequada, uma vez que minimiza as diferenças entre óleos do mesmo tipo de diferentes fabricantes e, ao mesmo tempo, maximiza as diferenças entre os tipos de óleo estudados, permitindo sua classificação com precisão de 100% pelo modelo de Redes Neurais Artificiais. A adulteração do óleo de soja orgânico com óleo de soja convencional resultou no aumento da intensidade de emissão em aproximadamente 433 nm com amostras excitadas em 340 nm. Em contrapartida, a excitação em 500 nm resultou em comportamento inverso, com redução na intensidade de emissão em aproximadamente 545 nm. As alterações na fluorescência de óleos de soja oxidados sob diversas condições foi estudada. Amostras termo-oxidadas excitadas em 340 nm apresentaram supressão da fluorescência em aproximadamente 430 nm devido ao avanço da oxidação, deslocamento hipsocrômico do pico de emissão e redução na emissão na região relacionada à fluorescência das clorofilas (cerca de 660 nm). Maiores temperaturas resultaram em avanço mais significativo da oxidação, evidenciado por alterações nos espectros de FTIR e em mudanças mais expressivas nas características de fluorescência. Na oxidação fotossensibilizada, a exposição à luz em câmara de foto-oxidação acelerada durante 10 dias resultou em aumentos significativos nos indicadores de oxidação, superando os limites máximos estabelecidos. Esse aumento foi mais pronunciado em amostras armazenadas em frascos incolores, sugerindo que essas embalagens não garantem a estabilidade oxidativa de óleos vegetais expostos à luz. A oxidação fotossensibilizada levou à supressão da fluorescência da clorofila e ao deslocamento hipsocrômico na região de emissão entre 380 e 620 nm, quando excitada em 373 nm. Finalmente, nossa análise revelou que a permanência em câmara de oxidação durante 10 dias resultou em aumentos comparáveis nos índices de peróxido de óleos de soja em relação ao aquecimento a temperaturas médias de 180 °C por 60 minutos. No entanto, o aquecimento provocou um aumento expressivo nos valores de p-anisidina, que estão relacionados a produtos de oxidação secundários. Utilizamos Hierarchical Cluster Analysis (HCA) e Principal Component Analysis (PCA) para demonstrar a diferenciação das amostras em diferentes estágios de oxidação, com base nos dados de emissão. Além disso, o modelo Multivariate Adaptive Regression Splines (MARS) foi capaz de classificar as amostras de acordo com o estágio de oxidação com precisão de 100%.
Quality control of vegetable oils is essential to ensure food safety, allowing for the identification of advanced oxidation, detection of adulterations, and optimization of shelf life. It contributes to the prevention of waste and reduction of health risks, making it crucial for the food industry. In this study, we explore the potential of fluorescence spectroscopy, both in steady-state and time-resolved states, for the identification of edible vegetable oils such as soybean, canola, and corn. We also investigate its ability to detect adulteration in organic soybean oils by adding conventional soybean oil and to indicate advanced stages of lipid oxidation. A notable aspect of this study is the direct analysis, eliminating the need for sample dilution or prior preparation procedures. This results in cost reduction, analysis time, waste generation, and environmental contamination risks. In the fluorescence characterization of soybean, canola, and corn oils, excitation at 340 nm was most suitable, minimizing differences between oils of the same type from different manufacturers while maximizing differences between the studied oil types, allowing for accurate classification by the Artificial Neural Networks model at a precision rate of 100%. Adulteration of organic soybean oil with conventional soybean oil resulted in increased emission intensity at approximately 433 nm when excited at 340 nm. In contrast, excitation at 500 nm resulted in the opposite behavior, with reduced emission intensity at approximately 545 nm. Changes in the fluorescence of oxidized soybean oils under various conditions were studied. Thermally oxidized samples excited at 340 nm showed fluorescence suppression at approximately 430 nm due to oxidation advancement, hypsochromic shift of the emission peak, and reduction in emission in the region related to chlorophyll fluorescence (around 660 nm). Higher temperatures led to more significant oxidation advancement, evidenced by changes in FTIR spectra and more pronounced alterations in fluorescence characteristics.In photosensitized oxidation, exposure to light in an accelerated photo-oxidation chamber for 10 days resulted in significant increases in oxidation indicators, surpassing established maximum limits. This increase was more pronounced in samples stored in colorless bottles, suggesting that these packaging materials do not ensure oxidative stability of vegetable oils exposed to light. Photosensitized oxidation led to the suppression of chlorophyll fluorescence and hypsochromic shift in the emission region between 380 and 620 nm when excited at 373 nm. Finally, our analysis revealed that staying in an oxidation chamber for 10 days resulted in comparable increases in soybean oil peroxide indices compared to heating at average temperatures of 180 °C for 60 minutes. However, heating caused a significant increase in p-anisidine values, which are related to secondary oxidation products. We used Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) to demonstrate sample differentiation at different oxidation stages based on emission data. Additionally, the Multivariate Adaptive Regression Splines (MARS) model accurately classified samples according to the oxidation stage with a precision of 100%.
Quality control of vegetable oils is essential to ensure food safety, allowing for the identification of advanced oxidation, detection of adulterations, and optimization of shelf life. It contributes to the prevention of waste and reduction of health risks, making it crucial for the food industry. In this study, we explore the potential of fluorescence spectroscopy, both in steady-state and time-resolved states, for the identification of edible vegetable oils such as soybean, canola, and corn. We also investigate its ability to detect adulteration in organic soybean oils by adding conventional soybean oil and to indicate advanced stages of lipid oxidation. A notable aspect of this study is the direct analysis, eliminating the need for sample dilution or prior preparation procedures. This results in cost reduction, analysis time, waste generation, and environmental contamination risks. In the fluorescence characterization of soybean, canola, and corn oils, excitation at 340 nm was most suitable, minimizing differences between oils of the same type from different manufacturers while maximizing differences between the studied oil types, allowing for accurate classification by the Artificial Neural Networks model at a precision rate of 100%. Adulteration of organic soybean oil with conventional soybean oil resulted in increased emission intensity at approximately 433 nm when excited at 340 nm. In contrast, excitation at 500 nm resulted in the opposite behavior, with reduced emission intensity at approximately 545 nm. Changes in the fluorescence of oxidized soybean oils under various conditions were studied. Thermally oxidized samples excited at 340 nm showed fluorescence suppression at approximately 430 nm due to oxidation advancement, hypsochromic shift of the emission peak, and reduction in emission in the region related to chlorophyll fluorescence (around 660 nm). Higher temperatures led to more significant oxidation advancement, evidenced by changes in FTIR spectra and more pronounced alterations in fluorescence characteristics.In photosensitized oxidation, exposure to light in an accelerated photo-oxidation chamber for 10 days resulted in significant increases in oxidation indicators, surpassing established maximum limits. This increase was more pronounced in samples stored in colorless bottles, suggesting that these packaging materials do not ensure oxidative stability of vegetable oils exposed to light. Photosensitized oxidation led to the suppression of chlorophyll fluorescence and hypsochromic shift in the emission region between 380 and 620 nm when excited at 373 nm. Finally, our analysis revealed that staying in an oxidation chamber for 10 days resulted in comparable increases in soybean oil peroxide indices compared to heating at average temperatures of 180 °C for 60 minutes. However, heating caused a significant increase in p-anisidine values, which are related to secondary oxidation products. We used Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) to demonstrate sample differentiation at different oxidation stages based on emission data. Additionally, the Multivariate Adaptive Regression Splines (MARS) model accurately classified samples according to the oxidation stage with a precision of 100%.