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Hyperspectral imaging for evaluating impact damage to mango according to changes in quality attributes

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journal contribution
posted on 2018-11-01, 00:00 authored by Duohua Xu, Huaiwen Wang, Hongwei Ji, Xiaochuan Zhang, Yanan WangYanan Wang, Zhe Zhang, Hongfei Zheng
Evaluation of impact damage to mango (Mangifera indica Linn) as a result of dropping from three different heights, namely, 0.5, 1.0 and 1.5 m, was conducted by hyperspectral imaging (HSI). Reflectance spectra in the 900⁻1700 nm region were used to develop prediction models for pulp firmness (PF), total soluble solids (TSS), titratable acidity (TA) and chroma (∆b*) by a partial least squares (PLS) regression algorithm. The results showed that the changes in the mangoes' quality attributes, which were also reflected in the spectra, had a strong relationship with dropping height. The best predictive performance measured by coefficient of determination (R²) and root mean square errors of prediction (RMSEP) values were: 0.84 and 31.6 g for PF, 0.9 and 0.49 oBrix for TSS, 0.65 and 0.1% for TA, 0.94 and 0.96 for chroma, respectively. Classification of the degree of impact damage to mango achieved an accuracy of more than 77.8% according to ripening index (RPI). The results show the potential of HSI to evaluate impact damage to mango by combining with changes in quality attributes.

History

Journal

Sensors

Volume

18

Issue

11

Article number

3920

Pagination

1 - 14

Publisher

MDPI

Location

Basel, Switzerland

eISSN

1424-8220

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2018, the authors