文献类型：期刊文献

英文题名：Multimodal recognition strategy for genuine and counterfeit Qi-nan agarwood based on data fusion and machine learning

作者：Zhu, Jianyi[1,2] Ma, Sheng[1] Yan, Tingting[1] Li, Gaiyun[1] Chen, Yuan[1]

第一作者：Zhu, Jianyi

通信作者：Chen, Y[1]

机构：[1]Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China;[2]Beijing Forestry Univ, MOE Key Lab Wooden Mat Sci & Applicat, Beijing 100083, Peoples R China

年份：2026

卷号：450

外文期刊名：SENSORS AND ACTUATORS B-CHEMICAL

收录：;EI(收录号：20255019683352);Scopus(收录号：2-s2.0-105024067062);WOS:【SCI-EXPANDED(收录号:WOS:001639002100001)】；

基金：This work was financially supported by the National Key Research and Development Program of China (2022YFD2200805).

语种：英文

外文关键词：Agarwood; E -nose; Chemometrics; Data fusion

摘要：Owing to its distinctive aroma and high economic value, Qi-nan agarwood is in high demand. However, its limited availability has resulted in rampant forgery, highlighting the urgent necessity for rapid, nondestructive, and intelligent authentication techniques. This study proposes a multimodal recognition framework employing an intermediate data fusion strategy for the accurate discrimination between genuine and counterfeit specimens. Initially, texture features were extracted from sample images utilizing discrete wavelet transform (DWT). Subsequently, odor signals were captured using an electronic nose (E-nose). In unimodal classification endeavors, multiple machine learning algorithms were applied to the E-nose data, achieving classification accuracies surpassing 90 %, with logistic regression (LR) demonstrating the optimal performance. Additionally, a decision-level data fusion model amalgamating E-nose odor features and DWT-derived texture features was established, achieving a classification accuracy of 100 %, thus outperforming the performance of all unimodal models. Further, the volatile compounds were validated by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) to verify the differences in the odor components of genuine and counterfeit samples. This study presents a novel, efficient, and practical methodology for the reliable authentication of Qi-nan agarwood, offering extensive potential applications in quality assurance and market regulation.