Classification of select functional dietary fiber ingredients based on quantitative properties and latent qualitative criteria

Abstract

AbstractFunctional dietary fiber ingredient (FDFI) functionality can depend on the fibers’ chemistry, composition, size, botanical origin, and microstructure. However, such claims have never been generalized for a broad range of fibers in one study before. To support these claims, 23 FDFI were characterized based on 11 physicochemical, physical, and compositional property measurements: Water‐ and oil‐holding capacity (WHC and OHC), water absorption and solubility indices (WAI and WSI), flour‐swelling potential (FSP), particle size distribution (D10, D50, and D90 values), and soluble, insoluble, and total dietary fiber content. Multivariate statistical techniques were employed to partition fiber ingredients into functional categories based on these quantitative data, and scanning electron microscopy was used to examine the microstructure of the FDFI. Strong correlations (p < 0.05) were found among many of the physicochemical properties measured, and five categories based on quantitative physicochemical functionality, size, and fiber composition were ultimately found. Distinct patterns emerged between these quantitative partitions and the latent microstructure features and botanical origins of the FDFI. These results can be combined into one intuitive summary of FDFI functionality based on the described quantitative and qualitative observations. Such summaries are useful for ingredient suppliers or product developers with limited resources to infer the general functionality, structure, and food applications utility of their materials based on a subset of the information provided here.Practical ApplicationThe quantitative and qualitative relationships among a range of commercially available functional dietary fiber ingredients are documented. Industry may utilize this information to predict the general functionality of their ingredients based on a subset of the information provided here by assuming that the same relative relationships will exist. This can save time during the ingredient screening process, either for product developers looking to optimize a formulation or for ingredient suppliers doing new ingredient applications testing.