This work includes the evaluation of 168 samples of raspberries ‘Glen Lyon’, representing whole maturation period, by colorimetric and near infrared imaging techniques, as well as the quantification of total phenols, total anthocyanins and antioxidant activity by chemical methods. Samples showed significant differences depending on the maturation stage using CIELAB colour parameters and total anthocyanins content. The application of partial least squares regression allowed predicting the chemical features from image analysis data, with coefficients of determination (R2) up to 0.75. The best prediction for total anthocyanins including colorimetric data was observed. The proposed methodology can be used as a reference method for assessing important quality attributes of raspberries. Moreover, it is useful, rapid and accurate automatic inspection method.
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This work was supported by Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía (PE11-AGR-7843). Moreover, the authors want to thank Surexport Cía. Agrícola S.L. for supplying the samples and collaborate with the Color y Calidad de Alimentos research group. Francisco J. Rodríguez-Pulido also thanks VPPI-Universidad de Sevilla for a postdoctoral grant. Finally, we are indebted to the staff of Biology Service (SGI, Universidad de Sevilla) for the technical assistance.
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Rodríguez-Pulido, F.J., Gil-Vicente, M., Gordillo, B. et al. Measurement of ripening of raspberries (Rubus idaeus L) by near infrared and colorimetric imaging techniques. J Food Sci Technol 54, 2797–2803 (2017) doi:10.1007/s13197-017-2716-3
- Raspberry ‘Glen Lyon’ (Rubus idaeus L)
- Image analysis
- Hyperspectral image analysis
- Partial least squares regression