An overview of non-destructive technologies for postharvest quality assessment in horticultural crops

Date published

2025

Free to read from

2025-05-09

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Taylor & Francis

Department

Type

Article

ISSN

1462-0316

Format

Citation

O’Brien C, Alamar MC. (2025) An overview of non-destructive technologies for postharvest quality assessment in horticultural crops. The Journal of Horticultural Science and Biotechnology, Available online 14 April 2025

Abstract

Artificial intelligence and machine vision are increasingly popular within food supply chains for automated decision making in quality grading and disease identification. There are many types of data that these models can be trained on, and choosing which information is needed is a critical factor in minimising both food loss and cost, while maximising the impact on food quality. Non-destructive technologies give information about crop phenotypes (e.g. external colour, oil content, sweetness) without damaging the crop, allowing a greater and more representative proportion the stored food to be analysed. These non-destructive technologies use different methods to analyse the product, each with different intrinsic capabilities and limitations. Therefore, choosing which technology is most appropriate for each application is a complex and costly decision. This mini-review summarises the physical and chemical basis of how some popular non-destructive technologies function, and how these different methods give unique advantages and limitations. The most popular technologies summarised include Red-Green-Blue (RGB) imaging, visible and near-infrared spectroscopy, and vibrometry. We also review technologies that are growing in popularity, including X-ray imaging, ultraviolet spectroscopy, and magnetic resonance imaging.

Description

Software Description

Software Language

Github

Keywords

Spectroscopy, vibrometry, imaging, food loss, crop quality

DOI

Rights

Attribution 4.0 International

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Relationships

Resources

Funder/s

This work was funded by Orchard House Foods Ltd. andCranfield University through the Cranfield IndustrialPartnership PhD Scheme.