Texture analysis of Red Delicious fruit: Towards multiple measurements on individual fruit

Phenolic Profiling of Five Different Australian Grown Apples

Apples (Malus domestica) are one of the most widely grown and consumed fruits in the world that contain abundant phenolic compounds that possess remarkable antioxidant potential. The current study characterised phenolic compounds from five different varieties of Australian grown apples (Royal Gala, Pink Lady, Red Delicious, Fuji and Smitten) using LC-ESI-QTOF-MS/MS and quantified through HPLC-PDA. The phenolic content and antioxidant potential were determined using various assays. Red Delicious had the highest total phenolic (121.78 ± 3.45 mg/g fw) and total flavonoid content (101.23 ± 3.75 mg/g fw) among the five apple samples. In LC-ESI-QTOF-MS/MS analysis, a total of 97 different phenolic compounds were characterised in five apple samples, including Royal Gala (37), Pink Lady (54), Red Delicious (17), Fuji (67) and Smitten (46). In the HPLC quantification, phenolic acid (chlorogenic acid, 15.69 ± 0.09 mg/g fw) and flavonoid (quercetin, 18.96 ± 0.08 mg/g fw) were most abundant in Royal Gala. The obtained results highlight the importance of Australian apple varieties as a rich source of functional compounds with potential bioactivity.

Comparing conventional Descriptive Analysis and Napping®-UFP against physiochemical measurements: a case study using apples

BACKGROUND

The extensive time and cost associated with conventional sensory profiling methods has spurred sensory researchers to develop rapid method alternatives, such as Napping® with Ultra-Flash Profiling (UFP). Napping®-UFP generates sensory maps by requiring untrained panellists to separate samples based on perceived sensory similarities. Evaluations of this method have been restrained to manufactured/formulated food models, and predominantly structured on comparisons against the conventional descriptive method. The present study aims to extend the validation of Napping®-UFP (N = 72) to natural biological products; and to evaluate this method against Descriptive Analysis (DA; N = 8) with physiochemical measurements as an additional evaluative criterion.

RESULTS

The results revealed that sample configurations generated by DA and Napping®-UFP were not significantly correlated (RV = 0.425, P = 0.077); however, they were both correlated with the product map generated based on the instrumental measures (P < 0.05). The finding also noted that sample characterisations from DA and Napping®-UFP were driven by different sensory attributes, indicating potential structural differences between these two methods in configuring samples.

CONCLUSIONS

Overall, these findings lent support for the extended use of Napping®-UFP for evaluations of natural biological products. Although DA was shown to be a better method for establishing sensory-instrumental relationships, Napping®-UFP exhibited strengths in generating informative sample configurations based on holistic perception of products. © 2017 Society of Chemical Industry.