Journal
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
Volume 63, Issue 17, Pages 4284-4290Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jf505870s
Keywords
characteristic vector analysis; chemometrics; grape maturity; hyperspectral imaging; near-infrared spectroscopy
Funding
- Spanish Ministry of Economy and Competitiveness (MINECO) [BES-2012-060192, AGL2014-58486-C2]
- Department of Financial Economics and Operations Management (VPPI-US), Universidad de Sevilla
- Junta de Andalucia [P10-AGR6331]
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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 degrees 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.
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