New GrapefruitMilk Permeate Beverage Academic Article uri icon


  • 2015 Wiley Periodicals, Inc. A variety of fruit beverage formulations using demineralized milk permeates hydrolyzed with -galactosidase and mixed with concentrated grapefruit juice, sugar and pectin were evaluated for sensory criteria (appearance, aroma, mouthfeel, taste and after taste) and measured physiochemical (PC) parameters. The total sensory scores (TSS) and PC parameters of accepted beverages were comparable for most grapefruit drinks. The newly formulated beverages had very low lactose and sodium (<1%) without any astringency and salty taste. A regression model relating the TSS of the beverages and six effective PC parameters (Brix, lightness, color value of a, viscosity, sedimentation and pH) was formulated. The resulting regression model provides consistent predictions of TSS for the new beverages without having to impanel a new set of tasters to reassess the TSS properties of the new formulations. The model of TSS had an R2 value of 94% and provided the trend in TSS for the increases in the formulation parameters. Practical Applications: Milk permeate (a low-cost by-product of the ultrafiltration) is a rich source of growth factors such as essential amino acids. Demineralized and dried milk permeate (DDMP) is a salt-free additive with numerous nutrients and a long shelf life. Varying amounts of DDMP (hydrolyzed with -galactosidase), concentrated grapefruit juice (full of antioxidants, vitamins and minerals), sugar and high methoxyl pectin were mixed to determine appealing physiochemical (PC) parameters and to evaluate sensory criteria (appearance, odor, mouthfeel, taste and aftertaste) after refining and pasteurization. The optimal combinations of raw materials resulted in pleasant beverages (free of lactose) that had total sensory scores (TSS) and PC parameters completely comparable with the scores and parameters of fresh grapefruit drink. A regression model relating TSS to important PC parameters yielded predicted TSS of the beverage with a high degree of accuracy. This model has a potential to predict TSS of a formulated beverage without performing the tedious and time-consuming taste panel process.

published proceedings

  • Journal of Food Process Engineering

author list (cited authors)

  • Rahimi, M., KalbasiAshtari, A., Labbafi, M., Longnecker, M., & Khodayian, F.

citation count

  • 5

complete list of authors

  • Rahimi, Monireh||Kalbasi‐Ashtari, Ahmad||Labbafi, Mohsen||Longnecker, Michael||Khodayian, Fereidoon

publication date

  • February 2017