A Comparison of Consumer Responses Using Paper and Digital Ballots for Eating Quality Assessment of Beef Steaks

A consumer study was conducted to determine whether consumers scored beef palatability traits differently on paper versus digital ballots. Beef subprimals representing 4 treatments with inherent variation in eating quality were collected: USDA Select eye of round aged 7 d postmortem, USDA Select strip loin aged 7 d postmortem, USDA Choice tenderloin aged 21 d postmortem, and USDA Prime strip loin aged 21 d postmortem. Accessory muscles, external fat, and connective tissue were removed from subprimals. Muscles were fabricated into 2.5-cm steaks and further divided into 2 equal halves for consumer testing. Consumers (n = 360) evaluated 8 samples divided into 2 blocks representing the 2 ballot types. Within each ballot block, Select longissimus lumborum samples were always served in the first and fifth position, followed by the remaining 3 treatments served in a randomized order among the latter 3 positions. Consumers rated each steak sample for tenderness, juiciness, flavor liking, and overall liking from 0 to 100 on either a paper or a digital ballot and then rated the paired steak halves on the opposite ballot during the second block of sample testing. Ballot type influenced (P < 0.02) all traits, as consumers scored traits greater (P < 0.05) on paper compared with digital ballots, regardless of treatment. The magnitude of differences between ballot types was much smaller than the magnitude of differences between cut treatments, which also differed (P < 0.01). The smallest margin between ballot type was observed for tenderness (1.8 points); juiciness, flavor liking, and overall liking all differed by 3.4 points. Independent studies could and have utilized digital ballots without concern, as consumers sorted samples by treatment in the current study similarly, regardless of ballot type. However, researchers should consider ballot type for their sensory studies, especially if data will be added to a collective data set.