Abstract Advanced predictive methods for wine age prediction: Part I – A comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods
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首页> 外文期刊>Talanta: The International Journal of Pure and Applied Analytical Chemistry >Advanced predictive methods for wine age prediction: Part I – A comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods
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Advanced predictive methods for wine age prediction: Part I – A comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods

机译：葡萄酒年龄预测的高级预测方法：第I部分 - 基于变量选择，惩罚回归，潜在变量和基于树的集合方法的单块回归方法的比较研究

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Abstract In this paper we test and compare advanced predictive approaches for estimating wine age in the context of the production of a high quality fortified wine – Madeira Wine. We consider four different data sets, namely, volatile, polyphenols, organic acids and the UV–vis spectra. Each one of these data sets contain chemical information of a different nature and present diverse data structures, namely a different dimensionality, level of collinearity and degree of sparsity. These different aspects may imply the use of different modelling approaches in order to better explore the data set's information content, namely their predictive potential for wine age. This happens to be so, because different regression methods have different prior assumptions regarding the predictors, response variable(s) and the data generating mechanism, which may or may not find good adherence to the case study under analysis. In order to cover a wide range of modelling domains, we have incorporated in this work methods belonging to four very distinct classes of approaches that cover most applications found in practice: linear regression with variable selection, penalized regression, latent variables regression and tree-based ensemble methods. We have also developed a rigorous comparison framework based on a double Monte Carlo cross-validation scheme, in order to perform the relative assessment of the performance of the various methods. Upon comparison, models built using the polyphenols and volatile composition data sets led to better wine age predictions, showing lower errors under testing conditions. Furthermore, the results obtained for the polyphenols data set suggest a m
机译：<！[cdata [ 抽象 在本文中，我们测试并比较在高品质强化葡萄酒 - 马德拉葡萄酒生产中估算葡萄酒时代的高级预测方法。我们考虑四种不同的数据集，即挥发性，多酚，有机酸和UV-Vis光谱。这些数据集中的每一个都包含不同性质的化学信息，并呈现不同的数据结构，即不同的维度，相连程度和稀疏程度。这些不同的方面可能意味着使用不同的建模方法，以便更好地探索数据集的信息内容，即它们的葡萄酒年龄的预测潜力。这恰好是如此，因为不同的回归方法具有关于预测器，响应变量和数据生成机制的不同的现有假设，其可能是或可能无法在分析下找到良好的粘附性研究。为了涵盖各种建模域，我们已在本工作方法中纳入属于四种非常不同的方法，该方法涵盖了实践中的大多数应用程序：具有可变选择，惩罚回归，潜在变量回归和基于树的线性回归合奏方法。我们还基于双蒙特卡罗交叉验证方案开发了一种严格的比较框架，以便执行各种方法性能的相对评估。在比较时，使用多酚和挥发性组成数据组建造的模型导致更好的葡萄酒年龄预测，在测试条件下显示出较低的误差。此外，对于多酚数据集获得的结果表明了一个m

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