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Non-Destructive VIS/NIR Reflectance Spectrometry for Red Wine Grape Analysis

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dc.contributor.advisor Brown, Ralph
dc.contributor.author Fadock, Michael
dc.date.accessioned 2011-08-04T13:22:03Z
dc.date.available 2011-08-04T13:22:03Z
dc.date.copyright 2011-07
dc.date.created 2011-07-06
dc.date.issued 2011-08-04
dc.identifier.uri http://hdl.handle.net/10214/2827
dc.description.abstract A novel non-destructive method of grape berry analysis is presented that uses reflected light to predict berry composition. The reflectance spectrum was collected using a diode array spectrometer (350 to 850 nm) over the 2009 and 2010 growing seasons. Partial least squares regression (PLS) and support vector machine regression (SVMR) generated calibrations between reflected light and composition for five berry components, total soluble solids (°Brix), titratable acidity (TA), pH, total phenols, and anthocyanins. Standard methods of analysis for the components were employed and characterized for error. Decomposition of the reflectance data was performed by principal component analysis (PCA) and independent component analysis (ICA). Regression models were constructed using 10x10 fold cross validated PLS and SVM models subject to smoothing, differentiation, and normalization pretreatments. All generated models were validated on the alternate season using two model selection strategies: minimum root mean squared error of prediction (RMSEP), and the "oneSE" heuristic. PCA/ICA decomposition demonstrated consistent features in the long VIS wavelengths and NIR region. The features are consistent across seasons. 2009 was generally more variable, possibly due to cold weather affects. RMSEP and R2 statistics of models indicate that PLS °Brix, pH, and TA models are well predicted for 2009 and 2010. SVM was marginally better. The R2 values of the PLS °Brix, pH, and TA models for 2009 and 2010 respectively were: 0.84, 0.58, 0.56 and: 0.89, 0.81, 0.58. 2010 °Brix models were suitable for rough screening. Optimal pretreatments were SG smoothing and relative normalization. Anthocyanins were well predicted in 2009, R2 0.65, but not in 2010, R2 0.15. Phenols were not well predicted in either year, R2 0.15-0.25. Validation demonstrated that °Brix, pH, and TA models from 2009 transferred to 2010 with fair results, R2 0.70, 0.72, 0.31. Models generated using 2010 reflectance data did not generate models that could predict 2009 data. It is hypothesized that weather events present in 2009 and not in 2010 allowed for a forward calibration transfer, and prevented the reverse calibration transfer. Heuristic selection was superior to minimum RMSEP for transfer, indicating some overfitting in the minimum RMSEP models. The results are demonstrative of a reflectance-composition relationship in the VIS-NIR region for °Brix, pH, and TA requiring additional study and development of further calibrations. en_US
dc.language.iso en en_US
dc.subject PLS en_US
dc.subject SVM en_US
dc.subject regression en_US
dc.subject reflectance en_US
dc.subject multivariate en_US
dc.subject cross validation en_US
dc.subject feature selection en_US
dc.subject calibration en_US
dc.subject °Brix en_US
dc.subject pH en_US
dc.subject titratable acidity en_US
dc.subject total phenols en_US
dc.subject anthocyanins en_US
dc.title Non-Destructive VIS/NIR Reflectance Spectrometry for Red Wine Grape Analysis en_US
dc.type Thesis en_US
dc.degree.programme Engineering en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Engineering en_US
dc.rights.license All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.


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