Honghong Wang¹, Zhixin Xiong^1,*, Yunchao Hu¹, Zhijian Liu¹, Long Liang²

Journal of Renewable Materials, Vol.11, No.6, pp. 2713-2727, 2023, DOI:10.32604/jrm.2023.025817

Abstract This study aims to realize the sharing of near-infrared analysis models of lignin and holocellulose content in pulp wood on two different batches of spectrometers and proposes a combined algorithm of SPA-DS, MCUVE-DS and SiPLS-DS. The Successive Projection Algorithm (SPA), the Monte-Carlo of Uninformative Variable Elimination (MCUVE) and the Synergy Interval Partial Least Squares (SiPLS) algorithms are respectively used to reduce the adverse effects of redundant information in the transmission process of the full spectrum DS algorithm model. These three algorithms can improve model transfer accuracy and efficiency and reduce the manpower and material consumption required for modeling. These results… More >

Simon Crase^1,2, Benjamin Hall², Suresh N. Thennadil^3,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1945-1965, 2021, DOI:10.32604/cmc.2021.018517

Abstract Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the specific characteristics of IR and… More >

Safwat A. Aly¹, David Zurakowski², Penny Glass³, Kami Skurow-Todd⁴, Richard A. Jonas⁵, Mary T. Donofrio⁴

Congenital Heart Disease, Vol.12, No.2, pp. 188-195, 2017, DOI:10.1111/chd.12426

Abstract Importance: There are no well-established noninvasive biomarkers for identifying patients at risk for poor outcome after surgery for congenital heart disease. Few studies have assessed prognostic accuracy of cerebral tissue oxygenation index (cTOI) measured by near infrared spectroscopy (NIRS).
Objective: To assess the utility of noninvasive NIRS monitoring as a predictor of outcomes after neonatal cardiac surgery through measurement of cTOI. To examine the utility of noninvasive NIRS monitoring in combination with lactate concentration and inotropic score in prediction of outcomes after neonatal cardiac surgery.
Design: Prospective longitudinal cohort study.
Setting: Operating room and cardiac intensive care unit, Children’s National… More >