title: Simplified method for the screening of technological maturity of red grape and total phenolic compounds of red grape skin: application of the characteristic vector method to near-infrared spectra.
abstract: Characteristic vector analysis has been applied to near-infrared spectra to extract the main spectral information from hyperspectral images. For this purpose, 3, 6, 9, and 12 characteristic vectors have been used to reconstruct the spectra, and root-mean-square errors (RMSEs) have been calculated to measure the differences between characteristic vector reconstructed spectra (CVRS) and hyperspectral imaging spectra (HIS). RMSE values obtained were 0.0049, 0.0018, 0.0012, and 0.0012 [log(1/R) units] for spectra allocated into the validation set, for 3, 6, 9, and 12 characteristic vectors, respectively. After that, calibration models have been developed and validated using the different groups of CVRS to predict skin total phenolic concentration, sugar concentration, titratable acidity, and pH by modified partial least-squares (MPLS) regression. The obtained results have been compared to those previously obtained from HIS. The models developed from the CVRS reconstructed from 12 characteristic vectors present similar values of coefficients of determination (RSQ) and standard errors of prediction (SEP) than the models developed from HIS. RSQ and SEP were 0.84 and 1.13 mg g(-1) of skin grape (expressed as gallic acid equivalents), 0.93 and 2.26 °Brix, 0.97 and 3.87 g L(-1) (expressed as tartaric acid equivalents), and 0.91 and 0.14 for skin total phenolic concentration, sugar concentration, titratable acidity, and pH, respectively, for the models developed from the CVRS reconstructed from 12 characteristic vectors.
authors.affiliation: †Food Colour and Quality Laboratory, Department of Nutrition and Food Science, Facultad de Farmacia, Universidad de Sevilla, 41012 Sevilla, Spain.
authors.forename: Julio
authors.initials: J
authors.lastname: Nogales-Bueno
authors.affiliation: ‡Laboratory of Colour La Rioja, Universidad de La Rioja, 26006 Logroño, Spain.
authors.forename: Fernando
authors.initials: F
authors.lastname: Ayala
authors.affiliation: †Food Colour and Quality Laboratory, Department of Nutrition and Food Science, Facultad de Farmacia, Universidad de Sevilla, 41012 Sevilla, Spain.
authors.forename: José Miguel
authors.initials: JM
authors.lastname: Hernández-Hierro
authors.affiliation: †Food Colour and Quality Laboratory, Department of Nutrition and Food Science, Facultad de Farmacia, Universidad de Sevilla, 41012 Sevilla, Spain.
authors.forename: Francisco José
authors.initials: FJ
authors.lastname: Rodríguez-Pulido
authors.affiliation: ‡Laboratory of Colour La Rioja, Universidad de La Rioja, 26006 Logroño, Spain.
authors.forename: José Federico
authors.initials: JF
authors.lastname: Echávarri
authors.affiliation: †Food Colour and Quality Laboratory, Department of Nutrition and Food Science, Facultad de Farmacia, Universidad de Sevilla, 41012 Sevilla, Spain.
authors.forename: Francisco José
authors.initials: FJ
authors.lastname: Heredia
mesh_terms: D005638:Fruit / Q000737:chemistry / Q000254:growth & development*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D010636:Phenols / Q000032:analysis*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D027843:Vitis / Q000737:chemistry* / Q000254:growth & development
chemical_list: D010636:Phenols
keywords: characteristic vector analysis; chemometrics; grape maturity; hyperspectral imaging; near-infrared spectroscopy