Signal Smoothing with PLS Regression

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy

BACKGROUND

Guayule (Parthenium argentatum A. Gray), a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to replace the current labor-intensive and cost-inefficient method for quantifying rubber and resin contents. Near-infrared (NIR) spectroscopy is a promising technique that simplifies and speeds up the quantification procedure without losing precision. In this study, two spectral instruments were used to rapidly quantify resin and rubber contents in 315 ground samples harvested from a guayule germplasm collection grown under different irrigation conditions at Maricopa, AZ. The effects of eight different pretreatment approaches on improving prediction models using partial least squares regression (PLSR) were investigated and compared. Important characteristic wavelengths that contribute to prominent absorbance peaks were identified.

RESULTS

Using two different NIR devices, ASD FieldSpec^®3 performed better than Polychromix Phazir™ in improving R² and residual predicative deviation (RPD) values of PLSR models. Compared to the models based on full-range spectra (750-2500 nm), using a subset of wavelengths (1100-2400 nm) with high sensitivity to guayule rubber and resin contents could lead to better prediction accuracy. The prediction power of the models for quantifying resin content was better than rubber content.

CONCLUSIONS

In summary, the calibrated PLSR models for resin and rubber contents were successfully developed for a diverse guayule germplasm collection and were applied to roughly screen samples in a low-cost and efficient way. This improved efficiency could enable breeders to rapidly screen large guayule populations to identify cultivars that are high in rubber and resin contents.