Development and implementation of an affordable high-throughput imaging system for phenotyping enzymatic browning in apples
Enzymatic browning (EB) substantially affects the visual quality and marketability of fresh-cut apples. This study aimed to develop an affordable high-throughput imaging system for phenotyping EB in apples. Browning was quantified using four CIELab-derived indices; a browning Index (BI), the difference in BI (∆BI), a normalized CIE color difference (∆E*); and a CIEDE2000 color difference (∆E 00 ) at multiple time points post-cutting to evaluate browning speed (S EB ) and intensity (I EB ) in 142 apple cultivars, including commercial and traditional Spanish cultivars from germplasm collections. The image-based system has demonstrated high accuracy and practical relevance, overcoming limitations associated with traditional colorimeter-based approaches. A wide phenotypic range was observed, in which elite reference cultivars fell within a narrow band at the lower end of the range. Measurements taken at 30 min post-cutting were found to be nearly equivalent to those at 60 min, allowing to optimize the phenotyping protocol without compromising precision. EB has been shown to be an inherently stable trait, though different year effects were noted, particularly for BI and ∆BI. Among the indices evaluated, ∆E 00 proved less effective for cultivar differentiation, whereas ∆BI showed the highest discriminant capacity and strongest correlation with visual browning, making it the most suitable index for phenotyping purposes. These findings provide a robust methodological basis for screening low-browning apple genotypes, establish a classification framework for EB expression levels, and highlight the potential of underutilized traditional cultivars in developing improved fresh-cut apple products. • Affordable system allows large-scale screening of enzymatic browning (EB) in fruit. • EB is a stable trait within cultivars but influenced by seasonal conditions. • ∆BI index shows highest accuracy for phenotyping enzymatic browning. • 30-minute classifications match 60-minute ones, streamlining the protocol. • Preparation time is minimal, allowing efficient large-scale, multi-season phenotyping.
Authors
- Patricia Irisarri Sarto (ORCID: https://orcid.org/0000-0003-0773-5133)
- P. Errea (ORCID: https://orcid.org/0000-0002-2889-1728)
- Luis Gonzaga Santesteban (ORCID: https://orcid.org/0000-0001-6924-6744)
- Sara Crespo‐Martínez (ORCID: https://orcid.org/0000-0002-4176-9866)
- C. Miranda (ORCID: https://orcid.org/0000-0001-7217-0859)
- Ana Piña (ORCID: https://orcid.org/0000-0002-4988-9467)
- Francisco Javier Bielsa (ORCID: https://orcid.org/0000-0001-5852-6804)
- Lucie Castel
- Néréa Iturmendi (ORCID: https://orcid.org/0000-0001-7680-9365)
- Jorge Urrestarazu (ORCID: https://orcid.org/0000-0003-0849-6939)
- Haizea Romeo
Institutions
- Universidad Pública de Navarra (UPNA) (ES)
- Centro de Investigación y Tecnología Agroalimentaria de Aragón (ES)
Publication Details
- Journal
- Postharvest Biology and Technology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1016/j.postharvbio.2025.114066
- Primary Topic
- Postharvest Quality and Shelf Life Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Universidad Pública de Navarra
- Gobierno de Aragón
- European Regional Development Fund
- Agencia Estatal de Investigación