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Sensors 2011, 11(6), 6109-6124; doi:10.3390/s110606109

Optimization of NIR Spectral Data Management for Quality Control of Grape Bunches during On-Vine Ripening

1
Centro de Investigación y Formación Agraria de ‘‘Cabra-Priego”, Instituto de Investigación y Formación Agraria y Pesquera (IFAPA), Consejería de Agricultura y Pesca, Junta de Andalucía, Cabra, Spain
2
Department of Animal Production, University of Cordoba, Campus Rabanales, 14071 Cordoba, Spain
3
Department of Bromatology and Food Technology, University of Cordoba, Campus Rabanales, 14071 Cordoba, Spain
*
Authors to whom correspondence should be addressed.
Received: 10 May 2011 / Revised: 30 May 2011 / Accepted: 31 May 2011 / Published: 7 June 2011
(This article belongs to the Section Chemical Sensors)
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Abstract

NIR spectroscopy was used as a non-destructive technique for the assessment of chemical changes in the main internal quality properties of wine grapes (Vitis vinifera L.) during on-vine ripening and at harvest. A total of 363 samples from 25 white and red grape varieties were used to construct quality-prediction models based on reference data and on NIR spectral data obtained using a commercially-available diode-array spectrophotometer (380–1,700 nm). The feasibility of testing bunches of intact grapes was investigated and compared with the more traditional must-based method. Two regression approaches (MPLS and LOCAL algorithms) were tested for the quantification of changes in soluble solid content (SSC), reducing sugar content, pH-value, titratable acidity, tartaric acid, malic acid and potassium content. Cross-validation results indicated that NIRS technology provided excellent precision for sugar-related parameters (r2 = 0.94 for SSC and reducing sugar content) and good precision for acidity-related parameters (r2 ranging between 0.73 and 0.87) for the bunch-analysis mode assayed using MPLS regression. At validation level, comparison of LOCAL and MPLS algorithms showed that the non-linear strategy improved the predictive capacity of the models for all study parameters, with particularly good results for acidity-related parameters and potassium content. View Full-Text
Keywords: NIR spectroscopy; quality parameters; on-vine; bunch analysis NIR spectroscopy; quality parameters; on-vine; bunch analysis
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

González-Caballero, V.; Pérez-Marín, D.; López, M.-I.; Sánchez, M.-T. Optimization of NIR Spectral Data Management for Quality Control of Grape Bunches during On-Vine Ripening. Sensors 2011, 11, 6109-6124.

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