Related Experiment Video
Updated: May 30, 2026
06:50
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
Published on: November 8, 2019
[The influence of reference data noise on the NIR prediction results]
Sheng Yao1, Guo-feng Wu, Shu-ke Zhou
1College of Material Science and Technology, Beijing Forestry University, Beijing 100083, China. yao.sh@163.com
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|August 2, 2011
Summary
Noise in reference data significantly impacts Near-Infrared (NIR) calibration models for hemicelluloses in acacia wood. Cleaner data improves accuracy, but regression methods offer better performance with noisier datasets.
Area of Science:
- Wood Chemistry
- Analytical Chemistry
- Spectroscopy
Background:
- Accurate quantification of hemicelluloses in acacia spp. wood is crucial for biomass utilization.
- Near-Infrared (NIR) spectroscopy is a promising technique for rapid chemical analysis.
- The reliability of NIR calibration models depends heavily on the quality of reference data.
Purpose of the Study:
- To investigate the impact of noise in reference data on NIR calibration models.
- To evaluate the performance of NIR models with varying levels of data noise.
- To compare different approaches for building NIR models using noisy data.
Main Methods:
- Case study using hemicelluloses content in acacia spp. wood.
- Development and evaluation of Near-Infrared (NIR) calibration models.
- Analysis of the influence of varying levels of noise in reference data.
- Application of regression mathematics methods for model building.
Main Results:
- The accuracy of NIR calibration models is demonstrably affected by noise in the reference data.
- Models built with less noisy data generally yield better results.
- Regression mathematics methods can enhance NIR model performance when dealing with larger amounts of noise compared to using primary reference data.
Conclusions:
- Data quality is a critical factor for the successful implementation of NIR spectroscopy in wood analysis.
- Strategies to mitigate the effects of noisy reference data are essential for robust NIR model development.
- Regression techniques offer a viable solution for improving NIR calibration accuracy in the presence of data noise.
Related Concept Videos
01:14NMR Spectrometers: Resolution and Error Correction
When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
01:04Random Error
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
01:17Chemical Shift: Internal References and Solvent Effects
In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
01:20Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...