Instrumental acoustic-mechanical measures of crispness in apples. (March 2015)

• Record Type: Journal Article
• Authors: Piazza, L.; Giovenzana, V.
• Is Part Of: Food research international. Volume 69(2015)
• Page Start: 209
• Page End: 215
• Publication Date: 2015-03
• Subjects: Apple -- Crispness evaluation -- Acoustic-mechanical analysis -- Chewing sound -- Juiciness -- PCA; Food -- Analysis -- Periodicals; Food industry and trade -- Periodicals; Food industry and trade -- Canada -- Periodicals; Food Technology -- Periodicals; Food -- Periodicals; Food-Processing Industry -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Industrie et commerce -- Canada -- Périodiques; Aliments -- Recherche -- Périodiques; Food industry and trade; Canada; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.foodres.2014.12.041
• Languages: English
• ISSNs: 0963-9969

Abstract

Abstract: Texture is a quality attribute closely related to the structural properties of the apple cellular tissue and is claimed to be the most important aspect affecting consumer acceptability apart from taste. Instrumental and human-based assessment of apples crispness are presented in this paper. A commercially available acoustic AED detector, interfaced with a TA.XT.plus Texture Analyzer, was used to collect both the acoustic emissions recorded during the instrumental mechanical penetration test, and the acoustic emissions resulting from the first bite of an apple flash, which were accomplished by 10 subjects with random dental state. Seven commercial cultivars of apples with different textural characteristics (Fuji, Golden Delicious, Granny Smith, Pink Lady, Renetta Canada, Royal Gala, and Stark) were analyzed. In order to measure the apple juice content, the expressible fluid released from the apple flash during compression was also quantified. Merging distinctive parameters taken from the mechanical signals and simultaneously recorded acoustic traces allowed apples to be clustered based on their crispness attributes using principal component analysis (PCA), a qualitative approach for multivariate data set. Results showed that sounds emitted during the human biting could not be assumed as a capable predictor of the sensory attribute of crispness, while apples can be efficiently distinguished for crispness by means of coupled acoustic and mechanical texture analysis. Finally, the inclusion of juiciness in the acoustic-mechanical PCA data set did not significantly increase the efficiency in cluster separation in terms of the texture crispness property. Highlights: Acoustic-mechanical analysis was applied to found criteria for crispness evaluation. Comparison between consumer perceptions and instrumental measurements were assessed. Multivariate analysis was applied to verify crispness classification performance. The expressible fluid buckled under compression was measured. Analytical tool to meet crispness requirements by the consumers was applied.