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首页> 外文期刊>Acta Horticulturae >Comparison of robust modeling techniques on NIR spectra used to estimate grape quality.
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Comparison of robust modeling techniques on NIR spectra used to estimate grape quality.

机译：在用于估计葡萄品质的近红外光谱上强大的建模技术的比较。

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摘要

Multivariate calibration models are of critical importance for spectroscopic applications and so great efforts is placed in robust modelling. During the 2006 growing season, over 480 samples of wine grape berries Cabernet cultivar were scanned with a portable, hand-held NIR spectrometer and subsequently processed to determine degrees Brix using a digital refractometer. This study compares the performance of modelling techniques: partial least squares regression (PLSR) with or without spectral wavelength selection and external parameter ortogonalization (EPO) based on virtual standards. Models are performed on raw spectra, normalized spectra and spectra obtained after multiplicative scatter correction. PLSR with variable selection applied on normalized spectra was found as the best performing in terms of fitting (r2=0.72, RPD=1.64), with the minimum standard error of calibration and prediction on cross-validation 0.53 and 0.61 degrees Brix respectively.
机译：多元校准模型对于光谱学应用至关重要，因此在鲁棒建模中付出了巨大的努力。在2006年的生长季节中，使用便携式手持式NIR光谱仪扫描了480多种葡萄酒浆果的赤霞珠品种，随后使用数字折光仪进行处理，以测定白利糖度。这项研究比较了建模技术的性能：具有或不具有光谱波长选择的偏最小二乘回归（PLSR）和基于虚拟标准的外部参数正交化（EPO）。对原始光谱，归一化光谱和乘法散射校正后获得的光谱执行模型。发现在归一化光谱上应用变量选择的PLSR在拟合方面表现最佳（r 2 = 0.72，RPD = 1.64），校准和交叉验证的预测的最小标准误差为0.53和0.61度白利糖度。

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