Non-destructive assessment of ’Fino’ lemon quality through ripening using NIRS and chemometric analysis
The lemon industry has the challenge of providing fruits with high-quality standards worldwide. Replacing the subjective fruit quality assessment methods with objective and non-destructive techniques. Total soluble solids (TSS) and titratable acidity (TA) have been revealed as important ripening mar...
| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.11939/8976 https://www.sciencedirect.com/science/article/abs/pii/S0925521424001157 |
| Sumario: | The lemon industry has the challenge of providing fruits with high-quality standards worldwide. Replacing the subjective fruit quality assessment methods with objective and non-destructive techniques. Total soluble solids (TSS) and titratable acidity (TA) have been revealed as important ripening markers in lemons. Therefore, this study proposes, for the first time, using near-infra-red spectroscopy (NIRS) as a rapid and non-destructive alternative to evaluate these quality traits in 'Fino' lemons (Citrus limon L. Burm) during ripeness. NIR spectra (950–1700 nm) of intact lemons collected from two different orchards at three ripening stages were acquired, while standard destructive methods were used to determine TSS and TA in the juice of each fruit. The prediction of the quality parameters was carried out using partial least squares regression (PLS-R) models. Three approaches were followed to validate the models: internal, external, and recalibrated external validation. The results following the first approach presented a good predictive performance for both quality parameters (TSS: R2 = 0.84, RMSEP = 0.42 and RPD = 2.5; TA: R2= 0.72, RMSEP = 0.45 and RPD = 2.0). When the external validation was performed, the best results were obtained for the TSS prediction using recalibrated models, maintaining good predictive performance accuracy (R2 = 0.74 and 0.67, RMSEP = 0.42 and 0.58, and RPD = 2.4 and 1.7). Regarding distinguishing different origins, models based on partial least squares discriminant analysis (PLS-DA) were externally validated, achieving 66.4% correct classification, respectively. Thus, applying NIR technology in the lemon fruit packinghouses is a promising alternative to improve fruit management and meet consumer demands. |
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