Milillo Tammy M, Gardella Joseph A
Department of Chemistry, University at Buffalo, State University of New York, Buffalo, New York 14260, USA.
Anal Chem. 2008 Jul 1;80(13):4896-905. doi: 10.1021/ac702640v. Epub 2008 Jun 7.
Ordinary kriging and inverse distance weighted (IDW) are two interpolation methods for spatial analysis of data and are commonly used to analyze macroscopic spatial data in the fields of remote sensing, geography, and geology. In this study, these two interpolation techniques were compared and used to analyze microscopic chemical images created from time of flight-secondary ion mass spectrometry images from a patterned polymer sample of fluorocarbon (C(x)F(y)) and poly(aminopropyl siloxane) (APS, a.k.a. siloxane). Data was eliminated from the original high-resolution data set by successive random removal, and the image file was interpolated and reconstructed with a random subset of points using both methods. The statistical validity of the reconstructed image was determined by both standard geographic information system (GIS) validation statistics and evaluating the resolution across an image boundary using ASTM depth and image resolution methodology. The results show that both ordinary kriging and IDW techniques can be used to accurately reconstruct an image using substantially fewer sample points than the original data set. Ordinary kriging performed better than the IDW technique, resulting in fewer errors in predicted intensities and greater retention of original image features. The size of the data set required for the most accurate reconstruction of the original image is directly related to the autocorrelation present within the data set. When 10% of the original siloxane data set was used for an ordinary kriging interpolation, the resulting image still retained the characteristic gridlike pattern. The C(x)F(y) data set exhibited stronger spatial correlation, resulting in reconstruction of the image with only 1% of the original data set. The removal of data points does result in a loss of image resolution; however, the resolution loss is not directly related to the percentage of sample points removed.
普通克里金法和反距离加权法（IDW）是用于数据空间分析的两种插值方法，常用于遥感、地理和地质领域的宏观空间数据分析。在本研究中，对这两种插值技术进行了比较，并用于分析由氟碳（C(x)F(y)）和聚（氨丙基硅氧烷）（APS，又名硅氧烷）图案化聚合物样品的飞行时间二次离子质谱图像生成的微观化学图像。通过连续随机去除从原始高分辨率数据集中剔除数据，并使用这两种方法用随机选取的点子集对图像文件进行插值和重建。通过标准地理信息系统（GIS）验证统计以及使用ASTM深度和图像分辨率方法评估图像边界的分辨率来确定重建图像的统计有效性。结果表明，普通克里金法和IDW技术都可用于使用比原始数据集少得多的采样点来准确重建图像。普通克里金法的性能优于IDW技术，预测强度的误差更少，原始图像特征保留得更多。最准确重建原始图像所需的数据集大小与数据集中存在的自相关直接相关。当使用原始硅氧烷数据集的10%进行普通克里金插值时，所得图像仍保留特征性的网格状图案。C(x)F(y)数据集表现出更强的空间相关性，仅用原始数据集的1%就能重建图像。数据点的去除确实会导致图像分辨率的损失；然而，分辨率损失与去除的采样点百分比没有直接关系。
