Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.
Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.
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DOI：
10.1021/cn200035u
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发表时间：
2011-06-07
影响因子：
5
通讯作者：
Wightman, R. Mark
中科院分区：
文献类型：
--
作者：
Keithley, Richard B.;Wightman, R. Mark
关键词：
主成分回归是一种多变量数据分析方法，通常用于预测体内快速扫描循环伏安法测量的神经化学物质浓度。这个数学过程可以快速地与当今的计算机编程语言一起使用。在这里，我们评估了几种方法，可用于评估和改善多元浓度测定。计算的回归向量的循环伏安表示被证明是一个有价值的工具，在确定计算的多元模型是否是化学上适当的。Cook距离的使用成功地识别了包含在体内快速扫描循环伏安法训练集内的离群值。这项工作还提出了第一个直接解释的残差颜色图，并证明了预测多巴胺浓度的峰位移的影响。最后，一个单一的连续测量的较小增量的单独的分析不能连接在一起，没有实质性的错误，由于电极漂移的预测神经化学物质的浓度。总之，这些工具允许构建更稳健的多变量校准模型，并提供第一种方法来评估由于缺乏体内标准而本质上不可能验证的程序的预测能力。
Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.
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影响因子：
64.8
作者：
Flagel SB;Clark JJ;Robinson TE;Mayo L;Czuj A;Willuhn I;Akers CA;Clinton SM;Phillips PE;Akil H
通讯作者：
Akil H
影响因子：
3
作者：
KAWAGOE, KT;ZIMMERMAN, JB;WIGHTMAN, RM
通讯作者：
WIGHTMAN, RM
影响因子：
4.2
作者：
Heien, MLAV;Phillips, PEM;Wightman, RM
通讯作者：
Wightman, RM
影响因子：
48
作者：
Clark, Jeremy J.;Sandberg, Stefan G.;Wanat, Matthew J.;Gan, Jerylin O.;Horne, Eric A.;Hart, Andrew S.;Akers, Christina A.;Parker, Jones G.;Willuhn, Ingo;Martinez, Vicente;Evans, Scott B.;Stella, Nephi;Phillips, Paul E. M.
通讯作者：
Phillips, Paul E. M.
影响因子：
13.1
作者：
Daszykowski, Michal;Walczak, Beata
通讯作者：
Walczak, Beata