Partial least squares regression as a powerful tool for investigating large combinatorial polymer libraries.
Partial least squares regression as a powerful tool for investigating large combinatorial polymer libraries.
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DOI：
10.1002/sia.2969
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发表时间：
2009-02
影响因子：
1.7
通讯作者：
Alexander, Morgan R.
中科院分区：
文献类型：
--
作者：
Taylor, Michael;Urquhart, Andrew J.;Anderson, Daniel G.;Langer, Robert;Davies, Martyn C.;Alexander, Morgan R.
偏最小二乘（PLS）回归是表面科学中已建立的分析工具，特别是用于将多变量ToF-SIMS数据与单变量表面性质相关联。在这里，我们构建了一个PLS模型，使用ToF-SIMS和表面能数据从496共聚物微图案库。使用这个496共聚物库，我们研究如何改变用于构建PLS模型的样品数量影响的身份，最有影响力的离子中确定的回归向量。回归系数的大小不同，但离子结构和表面能之间的一般关系保持不变。如所预期的，如果从训练集中去除含有具有独特化学性质的单体的共聚物，则这些共聚物特有的二次离子不存在于回归向量中。在表面分析领域中，使用PLS来获得定量预测还没有被积极地探索。我们调查是否PLS模型获得可以用来预测的表面能的聚合物的训练集内外。该模型系统地低估了一组丙烯酸酯共聚物的表面能，这些共聚物是使用训练集共有的单体合成的，但组成不同。由训练集中未使用的单体合成的一组丙烯酸酯共聚物的预测非常差。当该模型被用来获得六个市售聚合物的预测值都接近训练集的平均表面能。该练习表明，PLS可能能够预测由训练集共有的单体合成的聚合物的表面能，证实了训练集反映待预测样品的化学性质的重要性。
Partial Least Squares (PLS) regression is an established analytical tool in surface science, particularly for relating multivariate ToF-SIMS data to a univariate surface property. Herein we construct a PLS model using ToF-SIMS and surface energy data from a 496 copolymer micro-patterned library. Using this 496 copolymer library we investigate how changing the number of samples used to construct the PLS model affects the identity of the most influential ions identified in the regression vector. The regression coefficients vary in magnitude, but the general relationship between ion structure and surface energy is maintained. As expected, if copolymers containing monomers with unique chemistries are removed from the training set, secondary ions specific to these copolymers are not present in the regression vector. The use of PLS to obtain quantitative predictions has not been actively explored in the surface analytical field. We investigate whether the PLS model obtained can be used to predict the surface energies of polymers within and outside of the training set. The model systematically underestimated the surface energy of a group of acrylate copolymers synthesised using monomers common to the training set, but in different compositions. The predictions for a group of acrylate copolymers that were synthesised from monomers not used in the training set were very poor. When the model was used to obtain predictions for six commercially available polymers the values obtained were all close to the mean surface energy of the training set. This exercise suggests that PLS may be able to predict the surface energy of polymers synthesised from monomers common to the training set, confirming the importance that the training set reflects the chemistry of the samples to be predicted.
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影响因子：
7.4
作者：
HAALAND, DM;THOMAS, EV
通讯作者：
THOMAS, EV
DOI：
10.1002/jbm.820281002
发表时间：
1994-10-01
期刊：
JOURNAL OF BIOMEDICAL MATERIALS RESEARCH
影响因子：
--
作者：
PEREZLUNA, VH;HORBETT, TA;RATNER, BD
通讯作者：
RATNER, BD
影响因子：
3.9
作者：
Shen, MC;Wagner, MS;Horbett, TA
通讯作者：
Horbett, TA
DOI：
10.1016/0169-7439(87)80096-5
发表时间：
1987-08-01
影响因子：
3.9
作者：
MANNE, R
通讯作者：
MANNE, R
影响因子：
15
作者：
Brocchini, S;James, K;Kohn, J
通讯作者：
Kohn, J