Publication:
A systematized review on the applications of hyperspectral imaging for quality control of potatoes

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Date

2024

Authors

Peraza Alemán, Carlos Miguel
Rubio Padilla, Niuton

Director

Publisher

Springer
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109790RR-C22/ES/

Abstract

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.

Keywords

Food quality control, Hyperspectral imaging, Machine learning, Nondestructive techniques, Potatoes, Solanum tuberosum L., Systematized review

Department

Ingeniería / Ingeniaritza / Institute on Innovation and Sustainable Development in Food Chain - ISFOOD

Faculty/School

Degree

Doctorate program

Editor version

Funding entities

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.

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