化学计量学
转化(遗传学)
化学
模式识别(心理学)
近红外光谱
航程(航空)
水准点(测量)
随机变量
分割
人工智能
数据处理
生物系统
算法
计算机科学
统计
光学
数学
色谱法
随机变量
材料科学
地理
复合材料
物理
操作系统
基因
生物
生物化学
大地测量学
作者
Yiming Bi,Kailong Yuan,Weiqiang Xiao,Jizhong Wu,Chunyun Shi,Junfeng Xia,Guixin Chu,Guangxin Zhang,Zhou Guang-hong
标识
DOI:10.1016/j.aca.2016.01.010
摘要
Pre-processing of near-infrared (NIR) spectral data has become a necessary part of chemometrics modeling and is widely used in many practical applications. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve subsequent qualitative or quantitative analysis. Herein, a localized version of standard normal variate (SNV) is proposed, in which the correction parameters are estimated from local spectral areas. The method of determining the optimal spectral segmentation is also presented. Compared with full range methods, the local method demonstrates advantages in spectral linearity correction, model interpretation and prediction accuracy. Several benchmark NIR data sets were studied in our experiments; the proposed method achieved comparable performance against proven full range methods, with the reduction of prediction errors being statistically significant in many cases.
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