Article(id=1322924462148034947, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924314793750542, articleNumber=null, orderNo=null, doi=10.14067/j.cnki.1673-923x.2026.10.021, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1761753600000, receivedDateStr=2025-10-30, revisedDate=1767456000000, revisedDateStr=2026-01-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339370524, onlineDateStr=2026-10-30, pubDate=1792857600000, pubDateStr=2026-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339370524, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339370523, creator=13701087609, updateTime=1793339370523, updator=13701087609, issue=Issue{id=1322924314793750542, tenantId=1146029695717560320, journalId=1322258771877134531, year='2026', volume='46', issue='10', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='1792857600000', pubDateStr='2026-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793339335392, creator='13701087609', updateTime=1793344604640, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322946415693017274, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924314793750542, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322946415693017275, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924314793750542, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=231, endPage=243, ext={EN=ArticleExt(id=1322924462307418500, articleId=1322924462148034947, tenantId=1146029695717560320, journalId=1322258771877134531, language=EN, title=NIR model transfer for wood density across tree species based on stacked autoencoder, columnId=null, journalTitle=Journal of Central South University of Forestry & Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=
【Objective】 Wood density, as a key parameter for evaluating wood quality, is a focal point in both wood trading and processing. The development of efficient, non-destructive, and cross-species detection technologies for wood density is of great importance for the exploitation of forestry resources and the efficient utilization of wood.
【Method】 This study selected two typical hardwood species from northeast China, specifically Manchurian walnut (Juglans mandshurica) and Manchurian ash (Fraxinus mandshurica), as research subjects to establish a near-infrared (NIR) spectral transfer model for cross-species wood density prediction. A stable predictive feature selection (SPFS) algorithm was designed to score and rank spectral bands. The elbow method was applied to determine the optimal number of bands and to select common feature bands. A stacked autoencoder (SAE) was introduced as a feature transformation mechanism. Combined with an artificial backpropagation (BP) neural network prediction model, a new cross-species spectral model transfer method was constructed.
【Result】 Following the transfer of the Juglans mandshurica model based on SAE, the coefficient of determination (R2) for the Fraxinus mandshurica prediction set rose from 0.172 7 to 0.827 1, and its root mean square error of prediction (RMSEP) fell from 0.180 1 to 0.049 3 g/cm3. In the opposite transfer direction, applying SAE to the Fraxinus mandshurica model improved the R2 for the Juglans mandshurica prediction set from 0.000 9 to 0.884 9, and reduced the RMSEP from 0.187 8 to 0.032 9 g/cm3. These results indicate that the SAE-based method achieves substantial improvements in both R2 and RMSEP metrics when compared to traditional techniques such as piecewise direct standardization and slope/bias correction algorithms.
【Conclusion】 The findings of this study demonstrate that the SAE-based NIR transfer model is effective for cross-species applications, allowing for the rapid and efficient prediction of wood density in different tree species. This approach promotes model sharing across species and offers both a theoretical basis and technical support for improving the universality of NIR models.
, authors=Yiwei Li, Yuanda Qi, Chengwu Chen, Zijian Qin, Chao Sun, Hao Sun, Yaoxiang Li, authorsList=Yiwei Li, Yuanda Qi, Chengwu Chen, Zijian Qin, Chao Sun, Hao Sun, Yaoxiang Li, authorCompany=null, correspAuthors=Yaoxiang Li, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2026 Journal of Central South University of Forestry & Technology, copyrightOwner=Journal of Central South University of Forestry & Technology, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322924462680711557, articleId=1322924462148034947, tenantId=1146029695717560320, journalId=1322258771877134531, language=CN, title=基于SAE的跨树种木材密度NIR模型传递, columnId=1322924330723713471, journalTitle=中南林业科技大学学报, columnName=林业工程, runingTitle=null, highlight=null, articleAbstract=
【目的】 木材密度作为木材品质评价的核心参数，在木材交易和木材加工中受到重点关注。开发高效无损的跨树种木材密度检测技术对林业资源开发和木材高效利用具有重要意义。
【方法】 以东北地区典型硬阔胡桃楸Juglans mandshurica和水曲柳Fraxinus mandshurica为研究对象，构建跨树种木材密度近红外光谱传递模型。通过稳定预测特征选择算法（SPFS）得到各个波段的得分排序，基于肘部法则确定波段数目并选出共同波段，引入堆栈自编码器（SAE）作为特征转换机制，结合人工BP神经网络预测模型构建跨树种光谱模型传递新方法。
【结果】 胡桃楸模型经过SAE传递后，水曲柳预测集决定系数R2从0.172 7提高到0.827 1，预测集均方根误差RMSEP从0.180 1下降到0.049 3 g/cm3；水曲柳模型经SAE传递后，胡桃楸预测集决定系数R2从0.000 9提高到0.884 9，RMSEP从0.187 8下降到0.032 9 g/cm3。与传统方法分段直接校正和斜率/截距校正算法相比，SAE在R2与RMSEP指标上都有大幅度的提升。
【结论】 基于SAE的近红外传递模型可以实现不同树种木材密度的快速高效预测，实现模型在不同树种间的共享，为近红外模型的普适性提供了理论基础与技术支撑。
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李易伟（liyiwei@nefu.edu.cn），硕士研究生。
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李易伟（liyiwei@nefu.edu.cn），硕士研究生。
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