Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends-Reference-Cited by-同舟云学术

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Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends

Published:2024-04 Issue: Volume:310 Page:123910
ISSN:1386-1425
Container-title:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
language:en
Short-container-title:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Author:

Barea-Sepúlveda Marta^ORCID,Calle José Luis P.,Ferreiro-González Marta,Palma Miguel

Publisher

Elsevier BV

Subject

Spectroscopy,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference45 articles.

1. ASTM International. ASTM Standard D87: Standard Test Method for Melting Point of Petroleum Wax (Cooling Curve). https://www.astm.org/d0087-09r18.html.

2. ASTM International. ASTM Standard D938: Standard Test Method for Congealing Point of Petroleum Waxes, Including Petrolatum. https://www.astm.org/d0938-12r17.html.

3. ASTM International. ASTM Standard D1321: Standard Test Method for Needle Penetration of Petroleum Waxes. https://www.astm.org/d1321-16a.html.

4. ASTM International. ASTM Standard D1833: Standard Test Method for Odor of Petroleum Wax https://www.astm.org/d1833-87r17.html.

5. ASTM International. ASTM Standard D156: Standard Test Method for Saybolt Color of Petroleum Products (Saybolt Chromometer Method). https://www.astm.org/d0156-15.html.

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