title: Optimization of NIR spectral data management for quality control of grape bunches during on-vine ripening.
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 (r(2) = 0.94 for SSC and reducing sugar content) and good precision for acidity-related parameters (r(2) 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.
authors.affiliation: 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. virginia.gonzalez.ext@juntadeandalucia.es
authors.forename: Virginia
authors.initials: V
authors.lastname: González-Caballero
authors.forename: Dolores
authors.initials: D
authors.lastname: Pérez-Marín
authors.forename: María-Isabel
authors.initials: MI
authors.lastname: López
authors.forename: María-Teresa
authors.initials: MT
authors.lastname: Sánchez
mesh_terms: D000383:Agriculture / Q000379:methods*; D000465:Algorithms; D002138:Calibration; D002241:Carbohydrates / Q000737:chemistry; D005638:Fruit / Q000737:chemistry; D006863:Hydrogen-Ion Concentration; D008293:Malates / Q000032:analysis; D011188:Potassium / Q000032:analysis; D011786:Quality Control; D012044:Regression Analysis; D015203:Reproducibility of Results; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013644:Tartrates / Q000032:analysis; D027843:Vitis / Q000502:physiology*
chemical_list: D002241:Carbohydrates; D008293:Malates; D013644:Tartrates; C030298:malic acid; D011188:Potassium; C029768:tartaric acid