RESUMO
The composition and fine structure of pectins found in plant cell walls are heterogeneous, with striking differences, depending on their source, and this eventually determines their functional and technological properties. The aim of this study was to extract and determine the chemical composition and physicochemical properties of pectins from different sources: passion fruit peel, orange pomace, and soy hull. Pectin extraction was performed with heated hydrochloric acid solution, followed by precipitation with 96% ethanol. Extraction yield, chemical composition, molar mass, physicochemical properties (fat absorption capacity, cation exchange capacity, water holding capacity, and antioxidant activity) of pectin were measured. Pectin extraction efficiency was higher for passion fruit peel and orange pomace (15.71 and 17.96%, respectively). Soy hull had low pectin extraction (5.66%). Galacturonic acid content was 23.21% for passion fruit peel pectin and 16.01% for orange pomace pectin. Water holding capacity, fat absorption capacity, and cation-binding capacity present in pectin extracted from passion fruit peel were higher, suggesting this poorly investigated product could be used as thickening and emulsifying agents in food preparations. Phenolic compounds with antioxidant capacity provide pectins with additional properties and expand their industrial use.
RESUMO
This study represents a rapid and non-destructive approach based on mid-infrared (MIR) spectroscopy, time domain nuclear magnetic resonance (TD-NMR), and machine learning classification models (ML) for monitoring soluble pectin content (SPC) changes in orange juice. Current reference methods of SPC in orange juice are laborious, requiring several extractions with successive adjustments hindering rapid process intervention. 109 fresh orange juices samples, representing different harvests, were analysed using MIR, TD-NMR and reference method. Unsupervised algorithms were applied for natural clustering of MIR and TD-NMR data in two groups. Analyses of variance of the two MIR and TD-NMR datasets show that only the MIR groups were different at 95% confidence for SPC average values. This approach allows build classification models based on MIR data achieving 85% and 89% of accuracy. Results demonstrate that MIR/ML can be a suitable strategy for the quick assessment of SPC trends in orange juices.
Assuntos
Citrus sinensis/química , Sucos de Frutas e Vegetais/análise , Aprendizado de Máquina , Pectinas/química , Citrus sinensis/metabolismo , Análise por Conglomerados , Humanos , Espectroscopia de Ressonância Magnética , Pectinas/metabolismo , Análise de Componente Principal , Espectrofotometria InfravermelhoRESUMO
Este trabalho avaliou a utilização de resíduo de goiaba (casca e semente) na formulação de barras de cereais com qualidade nutricional, fonte de fibras e boa aceitabilidade sensorial. Foram formuladas quatro barras de cereais contendo proporções crescentes de resíduos de goiaba em substituição à aveia, flocos de arroz e gergelim (B15 % - 15 % de resíduos nos ingredientes secos, B30 % - 30 % de resíduos nos ingredientes secos, B50 % - 50 % de resíduos nos ingredientes secos e o Padrão sem resíduos). As formulações foram analisadas quanto aos teores de umidade, cinzas, proteínas, lipídeos, carboidratos totais e fibra alimentar. Foi realizada a Análise Sensorial por meio de testes de aceitação e de ordenação pela preferência. As formulações apresentaram em média 10,93 % de umidade, 60,55 % de carboidratos, 9,62 % de lipídeos, 8,41 % de proteínas e 1,38 % de cinzas. A adição de resíduos às barras de cereais aumentou o teor de fibras e apresentou aceitabilidade satisfatória em todos os atributos sensoriais sem influência significativa(p ≥ 0,05) da proporção de resíduos, exceto na textura; B50 % demonstrou as menores médias. Não houve preferência por formulações específicas. A adição de resíduos proporcionou produto com qualidade nutricional, incremento de fibras alimentares e aceitabilidade sensorial, que contribuem para valorização de partes do fruto desperdiçadas pelas agroindústrias...