Comparison of principal component regression (PCR) and partial least square regression (PLSR) modeling methods for quantifying polyethylene (PE) in recycled polypropylene (rPP) with near-infrared spectrometry (NIR)-Reference-Cited by-同舟云学术

Comparison of principal component regression (PCR) and partial least square regression (PLSR) modeling methods for quantifying polyethylene (PE) in recycled polypropylene (rPP) with near-infrared spectrometry (NIR)

Published:2024-01-29 Issue: Volume: Page:1-8
ISSN:1023-666X
Container-title:International Journal of Polymer Analysis and Characterization
language:en
Short-container-title:International Journal of Polymer Analysis and Characterization

Author:

Wang Pixiang¹,Zhan Ke²,Wang Xueqi²,Peng Yucheng²,Peng Haixin³,Wang Yifen³,Liu Shaoyang¹

Affiliation:

1. Center for Materials and Manufacturing Sciences, Departments of Chemistry and Physics, Troy University, Troy, Alabama, USA

2. College of Forestry, Wildlife and Environment, Auburn University, Auburn, Alabama, USA

3. Department of Biosystems Engineering, Auburn University, Auburn, Alabama, USA

Funder

National Institute of Standards and Technology, U.S. Department of Commerce

Publisher

Informa UK Limited

Subject

Polymers and Plastics,General Chemical Engineering,Analytical Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/1023666X.2024.2306428

Reference22 articles.

1. Production, use, and fate of all plastics ever made

2. Polypropylene as a promising plastic: A review;Maddah H. A.;Am. J. Polym. Sci.,2016

3. Waste Polypropylene Plastic Recycling toward Climate Change Mitigation and Circular Economy: Energy, Environmental, and Technoeconomic Perspectives

4. Luijsterburg, B. 2015. Mechanical Recycling of Plastic Packaging Waste. Chemical Engineering and Chemistry. Eindhoven, Netherlands: Technische Universiteit Eindhoven.

5. Karian, H. 2003. Handbook of Polypropylene and Polypropylene Composites, Revised and Expanded. New York, NY: Marcel Dekker, Inc.