Modelling and classification of apple textural attributes using sensory, instrumental and compositional analyses
Modelling and classification of apple textural attributes using sensory, instrumental and compositional analyses
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Full item details
- creativework.keywords - en
- Apples--Texture
- Apples--Sensory evaluation
- Apples--Composition
- Apples--Analysis
- creativework.keywords - fr
- Pomme--Texture
- Pomme--Analyse sensorielle
- Pomme--Composition
- Pomme--Analyse
- dc.contributor.author
- Bejaei, Masoumeh
- Stanich, Kareen
- Cliff, Margaret A.
- dc.date.accepted
- 2021-02-04
- dc.date.accessioned
- 2025-10-20T15:05:28Z
- dc.date.available
- 2025-10-20T15:05:28Z
- dc.date.issued
- 2021-02-10
- dc.date.submitted
- 2020-12-30
- dc.description.abstract - en
- Textural characteristics of fruit are important for their quality, storability, and consumer acceptance. While texture can be evaluated instrumentally or sensorially, instrumental measurements are preferred if they can be reliably related to human perception. The objectives of this research were to validate instrumental measurements with sensory determinations, develop a classification scheme to group apples by their textural characteristics, and create models to predict sensory attributes from instrumental and compositional analyses. The textural characteristics (crispness, hardness, juiciness, and skin toughness) of 12 apple cultivars were evaluated on new and established cultivars. Fruit was also evaluated using five instrumental measurements from TA.XTplus Texture Analyzer, and three compositional determinations. The experiment was repeated for analysis and validation purposes. Principal component (PC) analysis revealed that 95.88% of the variation in the instrumental determinations could be explained by two components (PC 1 and PC 2); which were highly correlated with flesh firmness and skin strength, respectively. Four textural groups of apples were identified, and the accuracy of classification was established at 94.44% by using linear discriminant analysis. The predictive models that were developed between the sensory and instrumental-compositional data explained more than 85% of the variation in the data for hardness and crispness, while models for juiciness and skin toughness were more complex. The work should assist industry personnel to reduce time-consuming and costly sensory testing, yet have an appreciation of the textural traits as perceived by the consumer.
- dc.description.fosrcfull - en
- This article belongs to the Special Issue: Instrument Analysis Applied in Food Science.
- dc.description.fosrcfull-fosrctranslation - fr
- Cet article fait partie du numéro spécial: Instrument Analysis Applied in Food Science.
- dc.identifier.citation
- Bejaei, M., Stanich, K., & Cliff, M. A. (2021). Modelling and classification of apple textural attributes using sensory, instrumental and compositional analyses. Foods, 10(2), Article 384. https://doi.org/10.3390/foods10020384
- dc.identifier.doi
- https://doi.org/10.3390/foods10020384
- dc.identifier.issn
- 2304-8158
- dc.identifier.uri
- https://open-science.canada.ca/handle/123456789/3971
- dc.language.iso
- en
- dc.publisher - en
- MDPI
- dc.publisher - fr
- MDPI
- dc.rights - en
- Creative Commons Attribution 4.0 International (CC BY 4.0)
- dc.rights - fr
- Creative Commons Attribution 4.0 International (CC BY 4.0)
- dc.rights.openaccesslevel - en
- Gold
- dc.rights.openaccesslevel - fr
- Or
- dc.rights.uri - en
- https://creativecommons.org/licenses/by/4.0/
- dc.rights.uri - fr
- https://creativecommons.org/licenses/by/4.0/deed.fr
- dc.subject - en
- Fruit crops
- dc.subject - fr
- Cultures fruitières
- dc.subject.en - en
- Fruit crops
- dc.subject.fr - fr
- Cultures fruitières
- dc.title - en
- Modelling and classification of apple textural attributes using sensory, instrumental and compositional analyses
- dc.type - en
- Article
- dc.type - fr
- Article
- local.acceptedmanuscript.articlenum
- 384
- local.article.journalissue
- 2
- local.article.journaltitle - en
- Foods
- local.article.journalvolume
- 10
- local.pagination
- 1-14
- local.peerreview - en
- Yes
- local.peerreview - fr
- Oui
- local.requestdoi - en
- No
- local.requestdoi - fr
- No
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