Optimizing dehydration of apples Malus Domestica with fructo-oligosaccharide incorporation

Abstract

The objective of the present study was to study the effect of the variables of the osmotic dehydration process on sliced Fuji apples (Malus domestica) using a 2 x 3 2 factorial design. The variables studied in the apple slices were the pretreatment (blanching or acidification), the temperatures (30, 45 and 60ºC) T4 (45ºC and 40% m/v) and T7 (60ºC and 40%m/v), using the acidification presented responses within the recommended standards and FOS were validated by the repetition. and the FOS concentration (40%, 50% and 60% m/v) of the osmotic solution. There was no difference among the pretreatments for the water activity and titratable acidity. The slices pre-treated by the acidification presented less enzymatic browning (greatest luminosity L* value) combined with a greater soluble solid contents (thus, this treatment was selected). Treatments

Physico-Chemical and Sensory Quality of Oven-Dried and Dehydrator-Dried Apples of the Starkrimson, Golden Delicious and Florina Cultivars

“…In this study, the sugar/acid ratio decreased in dried apple chip samples as follows: G1 (36.19) > S1 (33.33) > G2 (29.57) Iordănescu et al [53] reported TSS values ranging between 13.0 and 15.3 • Brix for 'Starkrimson' apples, between 10.6 and 14.2 • Brix for apples from the 'Florina' cultivar and 12.2-15.8 • Brix for 'Golden Delicious' apples. The total solids content for three apple cultivars determined by Egea et al [60] varied from 13.78 to 17.61%, while Butkeviciute et al [6] reported that the soluble solids content of ten different apple cultivars ranged between 12.40 and 14.00%.…”

Estudo da fermentação alcoólica do extrato aquoso da polpa do butiá (Butia eriospatha (Martius) Beccari): Otimização pela metodologia de superfície de resposta

“…Either insufficient or exaggerated quantities may reflect negatively on the fermentation process and affect the yield (CESAR et al, 1987). To increase efficiency in determining the optimal solution to this problem, various statistical techniques have been proposed, particularly planning and multifactorial approaches; among these, response surface methodology (RSM) has distinguished itself by enabling satisfactory data to be obtained with fewer experiments (ROSA et al, 2011;BOTARO;BATISTUTI, 2011, EGEA et al, 2012. RSM is an efficient tool to optimize the properties of products and is based on simultaneous variations in several factors (independent variables) selected for their influence on the properties of the process (dependent variables or responses).…”