Response Surfaces Method and Artificial Intelligence Approaches for Modeling the Effects of Environmental Factors on Chlorophyll a in

RSM artificial intelligence algorithms chlorophyll a condition optimization

Journal

Microorganisms
ISSN: 2076-2607
Titre abrégé: Microorganisms
Pays: Switzerland
ID NLM: 101625893

Informations de publication

Date de publication:
25 Jul 2023
Historique:
received: 04 07 2023
revised: 13 07 2023
accepted: 19 07 2023
medline: 26 8 2023
pubmed: 26 8 2023
entrez: 26 8 2023
Statut: epublish

Résumé

This study reported the condition optimization for chlorophyll a (

Identifiants

pubmed: 37630435
pii: microorganisms11081875
doi: 10.3390/microorganisms11081875
pmc: PMC10458309
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Linlin Zhang (L)

College of Chemistry and Environment Engineering, Shandong University of Science & Technology, Qingdao 266510, China.

Jie Liu (J)

College of Chemistry and Environment Engineering, Shandong University of Science & Technology, Qingdao 266510, China.

Xin Shen (X)

College of Chemistry and Environment Engineering, Shandong University of Science & Technology, Qingdao 266510, China.

Shuangwei Li (S)

College of Chemistry and Environment Engineering, Shandong University of Science & Technology, Qingdao 266510, China.

Wenfang Li (W)

College of Chemistry and Environment Engineering, Shandong University of Science & Technology, Qingdao 266510, China.

Xinfeng Xiao (X)

College of Chemistry and Environment Engineering, Shandong University of Science & Technology, Qingdao 266510, China.

Classifications MeSH