Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging

By Sandra Munera, José Manuel Amigo, José Blasco, Sergio Cubero, P. Talens, Nuria Aleixos. 2017

78 citations recorded by the source corpus.

Abstract Visible–near-infrared (450–1040 nm) hyperspectral reflectance imaging was studied in order to assess the internal physicochemical properties and sensory perception of ‘Big Top’ and ‘Magique’ nectarines (Prunus persica L. Batsch var. nucipersica) (yellow and white-flesh cultivar, respectively) during ripening using the Ripening Index (RPI) and the Internal Quality Index (IQI). Hyperspectral images of the intact fruits were acquired during the ripeness under controlled conditions, and their physicochemical properties (flesh firmness, total soluble solids, titratable acidity and flesh colour) were analysed. IQI and RPI were used to relate the spectral information obtained from nectarines with the physicochemical properties and the sensory perception of their maturity using Partial Least Square (PLS) regression with proper variable selection. Optimal results were obtained with R2 values higher than 0.87 for the two indices and the two cultivars. The ripeness of each fruit could be visualised by projecting the PLS models of the IQI on the pixels of the fruits in the images, showing great potential for further monitoring of the evolution of intact nectarine ripeness in industrial setups.

Subfields: Analytical Chemistry, Biochemistry, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/81bab9d9d163912989a3a8d35e42a6c55bc69be3

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In Analytical Chemistry

1. Sergio Cubero
2. José Blasco
3. P. Talens
4. Sandra Munera
5. Nuria Aleixos
6. José Manuel Amigo
7. Da‐Wen Sun

In Biomedical Engineering

1. José Blasco
2. P. Talens
3. Sandra Munera
4. Nuria Aleixos
5. Sergio Cubero
6. Da‐Wen Sun

Citations in the corpus

• Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines (2019, methodology): “This technology was also used to monitor the ripeness of two cultivars of nectarines (Munera et al., 2017) and to discriminate between similar cultivars with precision (Munera et al.”