Tian, Y等:Quantification of Cannabinoid Content in Cannabis
被阅读 1215 次
2015-11-13
Quantification of Cannabinoid Content in Cannabis
作者:Tian, Y (Tian, Y.)[ 1 ] ; Zhang, F (Zhang, F.)[ 1 ] ; Jia, K (Jia, K.)[ 2 ] ; Wen, M (Wen, M.)[ 1 ] ; Yuan, C (Yuan, Ch.)[ 1 ]
JOURNAL OF APPLIED SPECTROSCOPY
卷: 82  期: 4  页: 628-633
DOI: 10.1007/s10812-015-0155-3
出版年: SEP 2015
 
摘要
Cannabis is an economically important plant that is used in many fields, in addition to being the most commonly consumed illicit drug worldwide. Monitoring the spatial distribution of cannabis cultivation and judging whether it is drug- or fiber-type cannabis is critical for governments and international communities to understand the scale of the illegal drug trade. The aim of this study was to investigate whether the cannabinoids content in cannabis could be spectrally quantified using a spectrometer and to identify the optimal wavebands for quantifying the cannabinoid content. Spectral reflectance data of dried cannabis leaf samples and the cannabis canopy were measured in the laboratory and in the field, respectively. Correlation analysis and the stepwise multivariate regression method were used to select the optimal wavebands for cannabinoid content quantification based on the laboratory-measured spectral data. The results indicated that the delta-9-tetrahydrocannabinol (THC) content in cannabis leaves could be quantified using laboratory-measured spectral reflectance data and that the 695 nm band is the optimal band for THC content quantification. This study provides prerequisite information for designing spectral equipment to enable immediate quantification of THC content in cannabis and to discriminate drug- from fiber-type cannabis based on THC content quantification in the field.
 
通讯作者地址: Jia, K (通讯作者)
Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
[ 2 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China