Development of predictive sensory models to screen textural attributes of new selections from an apple breeding program
Citation
Bejaei, M., Cliff, M.A., & Stanich, K. (2019, July). Development of predictive sensory models to screen textural attributes of new selections from an apple breeding program. 13th Pangborn Sensory Science Symposium, Edinburg, UK.
Plain language summary
Sensory attributes of apple texture are key factors affecting the fruit quality perception and consumers’ acceptance. Researchers in apple breeding programs screen sensory characteristics of thousands of apple selections annually to select cultivars with superior qualities. It is an extremely labour intensive practice, and limited number of fruits are available at earlier stages of selection. The development of predictive models to estimate sensory textural attributes would assist in the screening of new apple selections with desirable traits and maximize the use of resources. The models for hardness and crispness were simple (1- and 2-variables) and accounted for a high proportion of variance (R2 = 85-94%). The predictability and validity of all models (especially the skin toughness model) were improved in Experiment 2. Validations of the hardness and crispness models were successful (R2 = 79-94%) and provided textural estimates with small standard errors. This meant that the models were practical/appropriate for identifying fruit with superior textural attributes as part of an apple breeding program.
Abstract
Sensory attributes of apple texture are key factors affecting the fruit quality perception and consumers’ acceptance. Researchers in apple breeding programs screen sensory characteristics of hundreds of apple cultivars annually to select cultivars with superior qualities. It is an extremely labour intensive practice, and limited number of fruits are available at earlier stages of selection. The development of predictive sensory models using instrumental measurements could maximize the use of resources and provide a preliminary prediction of sensory characteristics which would be very valuable in screening new selections. Two experiments were conducted to develop cross-validated sensory models to predict textural attributes of apple using instrumental measurements.
Semi-trained sensory panels (n = 10) evaluated four textural attributes (crispness, hardness, juiciness, skin toughness) in two experiments. In the first experiment, firmness determinations from three instruments (Sinclair iQ™ Firmness Tester, Aweta Acoustic Firmness Sensor, Mohr Digi-Test-2) and compositional analyses (titratable acidity, soluble solids concentration, absorbed juice) were performed on nine apple cultivars. Data were collected in two years. Linear and non-linear multiple regression models were developed to predict the textural attributes using data from the second year, and cross-validated using data from the first year. In the second experiment, firmness determinations from the Texture Analyzer (TA.XTPlus) and compositional analyses, were performed on 12 apple cultivars. Assessments were repeated twice, so that the prediction power of the developed models could be cross-validated.
The models for hardness and crispness were simple (1- and 2-variables) and accounted for a high proportion of variance (R2 = 85-91%), while those for juiciness and skin toughness were more complex. Cross-validations of the hardness and crispness models were successful (R2 = 79-89%) and provided textural estimates with small standard errors. This meant that the models were practical/appropriate for identifying fruit with superior textural attributes as part of an apple breeding program.