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...
| Main Authors: | , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.11939/8976 https://www.sciencedirect.com/science/article/abs/pii/S0925521424001157 |
| _version_ | 1855492598630711296 |
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| author | Serna-Escolano, Vicente Giménez, María J. Zapata, Pedro J. Cubero, Sergio Blasco, José Munera, Sandra |
| author_browse | Blasco, José Cubero, Sergio Giménez, María J. Munera, Sandra Serna-Escolano, Vicente Zapata, Pedro J. |
| author_facet | Serna-Escolano, Vicente Giménez, María J. Zapata, Pedro J. Cubero, Sergio Blasco, José Munera, Sandra |
| author_sort | Serna-Escolano, Vicente |
| collection | ReDivia |
| description | 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. |
| format | Artículo |
| id | ReDivia8976 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia89762025-04-25T14:49:41Z Non-destructive assessment of ’Fino’ lemon quality through ripening using NIRS and chemometric analysis Serna-Escolano, Vicente Giménez, María J. Zapata, Pedro J. Cubero, Sergio Blasco, José Munera, Sandra Non-destructive Chemometrics Q01 Food science and technology Citrus Quality Spectroscopy 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. 2024-09-06T11:45:12Z 2024-09-06T11:45:12Z 2024 article publishedVersion Serna-Escolano, V., Giménez, M. J., Zapata, P. J., Cubero, S., Blasco, J., & Munera, S. (2024). Non-destructive assessment of'Fino'lemon quality through ripening using NIRS and chemometric analysis. Postharvest Biology and Technology, 212, 112870. 0925-5214 https://hdl.handle.net/20.500.11939/8976 10.1016/j.postharvbio.2024.112870 https://www.sciencedirect.com/science/article/abs/pii/S0925521424001157 en This work was partially funded by projects GVA-IVIA 52204 and GVA-PROMETEO CIPROM/2021/014. Sandra Munera thanks the postdoctoral contract Juan de la Cierva-Formaci´on (FJC2021–047786-I) cofunded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR info:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52204/ES/Tecnología inteligente para una agricultura digital, sostenible y precisa en la comunitat valenciana/AgrIntel·ligència-CV Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess Elsevier electronico |
| spellingShingle | Non-destructive Chemometrics Q01 Food science and technology Citrus Quality Spectroscopy Serna-Escolano, Vicente Giménez, María J. Zapata, Pedro J. Cubero, Sergio Blasco, José Munera, Sandra Non-destructive assessment of ’Fino’ lemon quality through ripening using NIRS and chemometric analysis |
| title | Non-destructive assessment of ’Fino’ lemon quality through ripening using NIRS and chemometric analysis |
| title_full | Non-destructive assessment of ’Fino’ lemon quality through ripening using NIRS and chemometric analysis |
| title_fullStr | Non-destructive assessment of ’Fino’ lemon quality through ripening using NIRS and chemometric analysis |
| title_full_unstemmed | Non-destructive assessment of ’Fino’ lemon quality through ripening using NIRS and chemometric analysis |
| title_short | Non-destructive assessment of ’Fino’ lemon quality through ripening using NIRS and chemometric analysis |
| title_sort | non destructive assessment of fino lemon quality through ripening using nirs and chemometric analysis |
| topic | Non-destructive Chemometrics Q01 Food science and technology Citrus Quality Spectroscopy |
| url | https://hdl.handle.net/20.500.11939/8976 https://www.sciencedirect.com/science/article/abs/pii/S0925521424001157 |
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