A systematized review on the applications of hyperspectral imaging for quality control of potatoes
Fecha
2024Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
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10.1007/s11540-024-09702-7
Resumen
The application of hyperspectral imaging (HSI) has gained signifcant importance in
the past decade, particulary in the context of food analysis, including potatoes. However, the current literature lacks a comprehensive systematic review of the application of this technique in potato cultivation. Therefore, the aim of this work was to
conduct a systematized review by analysing the most relevant ...
[++]
The application of hyperspectral imaging (HSI) has gained signifcant importance in
the past decade, particulary in the context of food analysis, including potatoes. However, the current literature lacks a comprehensive systematic review of the application of this technique in potato cultivation. Therefore, the aim of this work was to
conduct a systematized review by analysing the most relevant compounds, diseases
and stress factors in potatoes using hyperspectral imaging. For this purpose, scientifc studies were retrieved through a systematic keyword search in Web of Science
and Scopus databases. Studies were only included in the review if they provided
at least one set of quantitative data. As a result, a total of 52 unique studies were
included in the review. Eligible studies were assigned an in-house developed quality
scale identifying them as high, medium or low risk. In most cases the studies were
rated as low risk. Finally, a comprehensive overview of the HSI applications in potatoes was performed. It has been observed that most of the selected studies obtained
better results using linear methods. In addition, a meta-analysis of studies based on
regression and classifcation was attempted but was not possible as not enough studies were found for a specifc variable. [--]
Materias
Food quality control,
Hyperspectral imaging,
Machine learning,
Nondestructive techniques,
Potatoes,
Solanum tuberosum L.,
Systematized review
Editor
Springer
Publicado en
Potato Research 2024, 1-23
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Versión del editor
Entidades Financiadoras
The funding of this work
has been covered by the Ministry of Science and Innovation (Spain) project: PID2019-109790RR-C22
and the predoctoral grant (PRE2020-094533) associated to it. The Open Access funding was provided by
Universidad Pública de Navarra.