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Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models

机译:减少用于成本效益的高光谱品质预测模型的样本数量

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摘要

Developing chemometric models from near-infrared (NIR) spectra requires the use of a representative calibration set of the entire population. Therefore, generally, the calibration procedure requires a large number of resources. For that reason, there is a great interest in identifying the most spectrally representative samples within a large population set. In this study, principal component and hierarchical clustering analyses have been compared for their ability to provide different representative calibration sets. The calibration sets generated have been used to control the technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars. Finally, the accuracy and precision of the models obtained with these calibration sets resulted from the application of the selection algorithms studied have been compared with each other and with the whole set of samples using an external validation set. Most of the standard errors of prediction (SEP) in external validation obtained from the reduced data sets were not significantly different from those obtained using the whole data set. Moreover, sample subsets resulting from hierarchical clustering analysis appear to produce slightly better results.
机译:从近红外线(NIR)光谱开发化学计量模型需要使用整个人口的代表性校准集。因此,通常,校准过程需要大量资源。因此,对识别大人口集中的最具光谱代表样本有很大兴趣。在本研究中,已经进行了主成分和分层聚类分析,以便提供其提供不同代表校准集的能力。产生的校准组已被用于控制红色和白色品种中葡萄皮的葡萄和总酚类化合物的技术成熟度。最后,利用这些校准组获得的模型的准确性和精度从研究所研究的选择算法产生,并且使用外部验证集进行了整组样本。从减少数据集获得的外部验证中的大多数标准误差(SEP)与使用整个数据集的数据集没有显着不同。此外,由分层聚类分析产生的样本子集似乎产生略微更好的结果。

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