{"title":"通过迁移学习和拉曼集成推进中国冷杉中全纤维素含量的预测","authors":"Wenli Gao, Ying Guan, Huahong Huang, Shengquan Liu, Shengjie Ling, Liang Zhou","doi":"10.1007/s10570-024-06033-1","DOIUrl":null,"url":null,"abstract":"<p>Holocellulose, a term encompassing both cellulose and hemicellulose, constitutes a crucial component of plant cell walls. Traditional wet chemistry methods (WCMs) for measuring holocellulose content have been criticized for their environmental unfriendliness and low efficiency. In the southern part of China, Chinese fir plantations play a significant role as a resource for wood, paper, and bioenergy. This study proposes the use of Raman signals, along with various algorithms, to predict the holocellulose content of Chinese fir as an alternative to traditional WCMs. The results indicate the successful development of a reliable predictive model by carefully selecting the most suitable internal standard peak and algorithm. Furthermore, transfer learning is demonstrated to enhance the accuracy and efficiency of the model. Consequently, the establishment of such predictive models is recommended for consideration in similar endeavors aiming to be a complementary method to WCMs.</p>","PeriodicalId":511,"journal":{"name":"Cellulose","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing holocellulose content prediction in Chinese fir via transfer learning and Raman integration\",\"authors\":\"Wenli Gao, Ying Guan, Huahong Huang, Shengquan Liu, Shengjie Ling, Liang Zhou\",\"doi\":\"10.1007/s10570-024-06033-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Holocellulose, a term encompassing both cellulose and hemicellulose, constitutes a crucial component of plant cell walls. Traditional wet chemistry methods (WCMs) for measuring holocellulose content have been criticized for their environmental unfriendliness and low efficiency. In the southern part of China, Chinese fir plantations play a significant role as a resource for wood, paper, and bioenergy. This study proposes the use of Raman signals, along with various algorithms, to predict the holocellulose content of Chinese fir as an alternative to traditional WCMs. The results indicate the successful development of a reliable predictive model by carefully selecting the most suitable internal standard peak and algorithm. Furthermore, transfer learning is demonstrated to enhance the accuracy and efficiency of the model. Consequently, the establishment of such predictive models is recommended for consideration in similar endeavors aiming to be a complementary method to WCMs.</p>\",\"PeriodicalId\":511,\"journal\":{\"name\":\"Cellulose\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cellulose\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1007/s10570-024-06033-1\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, PAPER & WOOD\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cellulose","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s10570-024-06033-1","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, PAPER & WOOD","Score":null,"Total":0}
Advancing holocellulose content prediction in Chinese fir via transfer learning and Raman integration
Holocellulose, a term encompassing both cellulose and hemicellulose, constitutes a crucial component of plant cell walls. Traditional wet chemistry methods (WCMs) for measuring holocellulose content have been criticized for their environmental unfriendliness and low efficiency. In the southern part of China, Chinese fir plantations play a significant role as a resource for wood, paper, and bioenergy. This study proposes the use of Raman signals, along with various algorithms, to predict the holocellulose content of Chinese fir as an alternative to traditional WCMs. The results indicate the successful development of a reliable predictive model by carefully selecting the most suitable internal standard peak and algorithm. Furthermore, transfer learning is demonstrated to enhance the accuracy and efficiency of the model. Consequently, the establishment of such predictive models is recommended for consideration in similar endeavors aiming to be a complementary method to WCMs.
期刊介绍:
Cellulose is an international journal devoted to the dissemination of research and scientific and technological progress in the field of cellulose and related naturally occurring polymers. The journal is concerned with the pure and applied science of cellulose and related materials, and also with the development of relevant new technologies. This includes the chemistry, biochemistry, physics and materials science of cellulose and its sources, including wood and other biomass resources, and their derivatives. Coverage extends to the conversion of these polymers and resources into manufactured goods, such as pulp, paper, textiles, and manufactured as well natural fibers, and to the chemistry of materials used in their processing. Cellulose publishes review articles, research papers, and technical notes.