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首页> 外文期刊>Journal of applied statistics >Aggregated functional data model for near-infrared spectroscopy calibration and prediction
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Aggregated functional data model for near-infrared spectroscopy calibration and prediction

机译：用于近红外光谱校准和预测的汇总功能数据模型

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Calibration and prediction for NIR spectroscopy data are performed based on a functional interpretation of the Beer-Lambert formula. Considering that, for each chemical sample, the resulting spectrum is a continuous curve obtained as the summation of overlapped absorption spectra from each analyte plus a Gaussian error, we assume that each individual spectrum can be expanded as a linear combination of B-splines basis. Calibration is then performed using two procedures for estimating the individual ana-lytes' curves: basis smoothing and smoothing splines. Prediction is done by minimizing the square error of prediction. To assess the variance of the predicted values, we use a leave-one-out jackknife technique. Departures from the standard error models are discussed through a simulation study, in particular, how correlated errors impact on the calibration step and consequently on the analytes' concentration prediction. Finally, the performance of our methodology is demonstrated through the analysis of two publicly available datasets.
机译：基于Beer-Lambert公式的功能解释，对NIR光谱数据进行校准和预测。考虑到对于每种化学样品，所得光谱是一条连续曲线，该曲线是来自每种分析物的重叠吸收光谱的总和加上高斯误差，我们假设可以将各个光谱扩展为B样条曲线的线性组合。然后使用两种估计单个分析物曲线的程序进行校准：基础平滑和样条平滑。通过最小化预测的平方误差来完成预测。为了评估预测值的方差，我们使用了留一刀的折刀技术。通过仿真研究讨论了偏离标准误差模型的问题，特别是相关误差如何影响校准步骤，进而影响分析物的浓度预测。最后，通过对两个公开可用的数据集的分析证明了我们方法的性能。

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