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The most common form of incorporating by-products from the fruit and vegetable processing into foods is flour, which can be used in various bakery products. This study aimed to optimize gluten-free (GF) muffin formulations, containing pineapple peel flour (PPF), banana peel flour (BPF), and pumpkin seed flour (PSF) using a mixture design approach, and to evaluate the physicochemical characteristics and the acceptability. The addition of PSF led to an increase in the L* value, hue angle and chroma of muffin's crust and crumb, whereas BPF decreased these parameters.The PPF determined higher total and insoluble fiber contents. The muffins with PPF and BPF had higher moisture content, whereas PSF increased the lipid content, thus reducing the firmness. The addition of BPF and PSF increased the springiness. The formulations with PPF (F1) and PPF + PSF (F5) were the most consumers' acceptance, with interesting nutritional characteristics, allowing diversification of GF products. Practical applicationsThe pineapple peel flour, banana peel flour, and pumpkin seed flour can be used in the production of muffins, as a way of using by-products, leading to the diversification of products for the celiac individuals and meeting the demand of consumers for gluten-free products, with an improvement in the nutritional value. The muffin formulations made with PPF had higher insoluble fiber and total fiber contents, and was well accepted, showing the consumers' acceptance for fiber-enriched products.

A produção de farinhas é uma forma de aproveitar partes de frutas e hortaliças que são descartadas e uma alternativa na elaboração de produtos alimentícios livres de glúten, juntamente com a farinha de arroz. Objetivou-se avaliar as características físicas e químicas da farinha de arroz (FA), farinha de casca de abacaxi (FCA), farinha de casca de banana (FCB) e farinha de semente de abóbora (FSA). A FA foi adquirida no comércio. As cascas de banana e abacaxi foram secas em desidratadora de bandejas e as sementes de abóbora torradas em fogão industrial, em seguida moídas e analisadas. A FCB apresentou menor valor L*, ou seja, mais escura, além de menor ângulo hue e maior valor a*, resultado contrário foi observado na FA. A FCB apresentou maior teor de umidade (9,91%), seguida da FA (7,95%), FCA (5,67%) e FSA (4,11%). A FSA apresentou alto teor de lipídeos (35,41%). As FCA e FCB tiveram os maiores teores de fibras solúveis (5,25% e 6,20%, respectivamente) e a FCA o maior teor de fibra insolúvel (45,06%). Não foi detectada atividade de inibição de tripsina nas farinhas. As FCA e FCB tiveram os maiores teores de fenólicos totais, logo maior atividade antioxidante. A FSA apresentou entre o conjunto de seus fenólicos, compostos com maior poder antioxidante, devido ao maior índice PAOXI. Conclui-se que as FCA, FCB e FSA apresentaram alto teor de fibras, fenólicos totais e atividade antioxidante, sendo uma opção para o enriquecimento de alimentos.

The use of yield prediction maps is an important tool for the delineation of within-field management zones. Vegetation indices based on crop reflectance are of potential use in the attainment of this objective. There are different types of vegetation indices based on crop reflectance, the most commonly used of which is the NDVI (normalized difference vegetation index). NDVI values are reported to have good correlation with several vegetation parameters including the ability to predict yield. The field research was conducted in two commercial farms of processing tomato crop, Cantillana and Enviciados. An NDVI prediction map developed through ordinary kriging technique was used for guided sampling of processing tomato yield. Yield was studied and related with NDVI, and finally a prediction map of crop yield for the entire plot was generated using two geostatistical methodologies (ordinary and regression kriging). Finally, a comparison was made between the yield obtained at validation points and the yield values according to the prediction maps. The most precise yield maps were obtained with the regression kriging methodology with RRMSE values of 14% and 17% in Cantillana and Enviciados, respectively, using the NDVI as predictor. The coefficient of correlation between NDVI and yield was correlated in the point samples taken in the two locations, with values of 0.71 and 0.67 in Cantillana and Enviciados, respectively. The results suggest that the use of a massive sampling parameter such as NDVI is a good indicator of the distribution of within-field yield variation.

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