文献类型：期刊文献

中文题名：中草药释香型刨花板特征挥发性有机化合物的指纹图谱分析

英文题名：Fingerprint Analysis of Characteristic Volatile Organic Compounds in Fragrance-Scented Particleboards with Added Chinese Herbal Medicine

作者：李善明[1] 樊正强[1] 黄成福[2] 何伟[2] 赵海誉[3] 文天国[4] 彭立民[1]

第一作者：李善明

机构：[1]中国林业科学研究院木材工业研究所,北京100091;[2]广东艾高智能家居有限公司,广东佛山528051;[3]中国中医科学院中药研究所,北京100700;[4]云南新泽兴人造板有限公司,云南昆明650499

年份：2022

卷号：36

期号：4

起止页码：38-44

中文期刊名：木材科学与技术

外文期刊名：Chinese Journal of Wood Science and Technology

收录：CSTPCD;;北大核心:【北大核心2020】；

基金：中央级公益性科研院所基本科研业务费专项资金“释香型无醛实木复合地板制造关键技术与产业化应用”(CAFYBB2022ZB003)。

语种：中文

中文关键词：气相色谱-离子迁移谱;中草药释香型刨花板;挥发性有机化合物;指纹图谱

外文关键词：gas chromatography-ion mobility spectrometry(GC-IMS);fragrance-scented particleboards with added Chinese herbal medicine;volatile organic compounds(VOCs);fingerprint

分类号：TS653;S781

摘要：应用气相色谱-离子迁移谱(GC-IMS)采集添加中草药的释香型刨花板(简称释香板)的挥发性香气指纹信息,并进行主成分分析和“最近邻”分析,评价释香板的香气指纹信息,确定其与普通刨花板的差异和辨别方法。结果表明:GC-IMS共鉴定出释香板37种挥发性有机化合物(VOCs),主要VOCs为醛类、烯类、酮类和酯类化合物。释香板的特征VOCs为芳樟醇、石竹烯氧化物和樟脑。通过指纹图谱、主成分分析和“最近邻”指纹分析,可区分释香板和普通板的VOCs差异,GC-IMS可为快速判别释香板提供理论依据和数据支持。
In this study,the gas chromatography-ion mobility spectrometry(GC-IMS)was employed to collect the fingerprint information of volatile organic compounds(VOCs)of fragrance-scented particleboards added with the Chinese herbal medicine.The detailed information,i.e.,the fragrance fingerprint,the difference and degree of differentiation of the fragrance-scented particleboards,and the control group were evaluated by the principal component analysis and the nearest-neighbor analysis.There are 37 kinds of VOCs identified by GC-IMS in fragrance-scented particleboards,among which aldehydes,alkenes,ketones,and esters are the main VOCs.Linalool,caryophyllene oxide,and camphor are the characteristic VOCs in the fragrance-scented particleboards.The fingerprint information,the principal component analysis,and the nearest-neighbor analysis diagrams can distinguish the difference of VOCs between the control group and fragrance-scented particleboards.The results show that GC-IMS provides theoretical basis and data supporting the rapid identification of scented particleboards with the Chinese herbal medicine.