Modified robust continuum regression by net analyte signal to improve prediction performance for data with outliers

2011 ◽  
Vol 107 (2) ◽  
pp. 333-342 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Qing-Bo Li ◽  
Guang-Jun Zhang
Keyword(s):  
Prediction Performance ◽  
Net Analyte Signal ◽  
Analyte Signal ◽  
Continuum Regression

Net analyte signal with floating reference theory in non-invasive blood glucose sensing by near-infrared spectroscopy

2012 ◽  
Vol 10 (8) ◽  
pp. 083002-83005 ◽  
Author(s):  
Wanjie Zhang Wanjie Zhang ◽  
Rong Liu Rong Liu ◽  
Wen Zhang Wen Zhang ◽  
Jiaxiang Zheng Jiaxiang Zheng ◽  
Kexin Xu Kexin Xu
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Glucose Sensing ◽  
Non Invasive ◽  
Net Analyte Signal ◽  
Analyte Signal ◽  
Reference Theory

Selection of a calibration sample subset by a semi-supervised method

2018 ◽  
Vol 26 (2) ◽  
pp. 87-94 ◽  
Author(s):  
Zhonghai He ◽  
Zhenhe Ma ◽  
Mengchao Li ◽  
Yang Zhou
Keyword(s):  
Selection Procedure ◽  
Calibration Model ◽  
Validation Test ◽  
Net Analyte Signal ◽  
Analyte Signal ◽  
Calibration Sample ◽  
Supervised Selection ◽  
Reference Measurements ◽  
Representative Samples ◽  
Selection Of

For spectroscopic measurements, representative samples are needed in the course of building a calibration model to guarantee accurate predictions. The most widely used selection method is the Kennard-Stone method, which can be used before a reference measurement is done. In this paper, a method termed semi-supervised selection is presented to determine whether a sample should be added to the calibration set. The selection procedure has two steps. First, part of the population of samples is selected using the Kennard-Stone method, and their concentrations are measured. Second, another part of the population of samples is selected based on the scalar value distribution of the net analyte signal. If the net analyte signal of a sample is distinctive compared to the existing net analyte signal values, then the sample is added to the calibration set. The analyte of interest in the sample is then measured so that the sample can be used as a calibration sample. By a validation test, it is shown that the presented method is more efficient than random selection and Kennard-Stone selection. As a result, both the time and the money spent on reference measurements are saved.

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