Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines
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Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines. / Munera, Sandra; Blasco, José; Amigo, Jose M.; Cubero, Sergio; Talens, Pau; Aleixos, Nuria.
In: Biosystems Engineering, Vol. 182, 2019, p. 54-64.Research output: Contribution to journal › Journal article › Research › peer-review
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T1 - Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines
AU - Munera, Sandra
AU - Blasco, José
AU - Amigo, Jose M.
AU - Cubero, Sergio
AU - Talens, Pau
AU - Aleixos, Nuria
PY - 2019
Y1 - 2019
N2 - The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. ‘Big Top’ (yellow flesh) and ‘Magique’ (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630–900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split pit of the ‘Big Top’ cultivar. Accuracies of 95.7% and 94.6% were achieved in the classification of the ‘Big Top’ and ‘Magique’ cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R 2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.
AB - The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. ‘Big Top’ (yellow flesh) and ‘Magique’ (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630–900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split pit of the ‘Big Top’ cultivar. Accuracies of 95.7% and 94.6% were achieved in the classification of the ‘Big Top’ and ‘Magique’ cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R 2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.
KW - Computer vision
KW - Hyperspectral imaging
KW - Internal quality
KW - Ripeness
KW - Split pit
KW - Stone fruit
U2 - 10.1016/j.biosystemseng.2019.04.001
DO - 10.1016/j.biosystemseng.2019.04.001
M3 - Journal article
AN - SCOPUS:85064228358
VL - 182
SP - 54
EP - 64
JO - Biosystems Engineering
JF - Biosystems Engineering
SN - 1537-5110
ER -
ID: 217996301