Characterization of cannabis varieties and the intrinsic and extrinsic factors affecting cannabis germination and seedling establishment: A descriptive review

Langa, Sabeliwe; Magwaza, Lembe Samukelo; Mditshwa, Asanda; Tesfay, Samson Zeray

doi:10.1016/j.indcrop.2023.117861

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Preprint1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 152 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Hybrid Adaptive Multiple Intelligence System (HybridAMIS) for classifying cannabis leaf diseases using deep learning ensembles

Sriprateep,

Khonjun,

Pitakaso

et al. 2024

Smart Agricultural Technology

View full text Add to dashboard Cite

Hybrid Adaptive Multiple Intelligence System (HybridAMIS) for classifying cannabis leaf diseases using deep learning ensembles

Sriprateep,

Khonjun,

Pitakaso

et al. 2024

Smart Agricultural Technology

View full text Add to dashboard Cite

Chemical profiling and clustering of various dried cannabis flowers revealed by volatilomics and chemometric processing

Janta,

Vimolmangkang

2024

Preprint

View full text Add to dashboard Cite

Cannabis flower scent is one of the key characteristics of the cannabis plant. The diverse scents impact user experiences and offer medicinal benefits. These scents originate from volatile compounds, particularly terpenes and terpenoids. This study characterized the volatile profile of 19 different dried cannabis flowers using gas chromatography-mass spectrometry coupled with headspace-solid phase microextraction (HS-SPME-GC-MS). A total of 75 compounds were identified, including alcohols, aldehyde, benzenes, esters, ketone, monoterpenes, monoterpenoids, sesquiterpenes and sesquiterpenoids. Cluster analysis was able to group the 19 cannabis cultivars into five clusters based on volatile chemotypes using chemometric techniques of hierarchical cluster analysis (HCA) and principal component analysis (PCA). Potential discriminant markers of each cultivar were then analyzed using a supervised partial least squares discriminant analysis (PLS-DA) verified through Variable Importance in Projection values (VIP), identifying twenty discriminant markers. In addition, the correlations among 75 volatile compounds were also obtained. The findings of this study provide a valuable database of single cannabis cultivars, useful for identifying individual strains and verifying their quality. Clustering the cultivars by volatile chemotype can be used for classification of cannabis in the market. The results of this study are expected to be a starting point for further cannabis breeding programs to expand knowledge of this plant. Furthermore, the proposed method is applicable to other aroma plants in the future

show abstract

Characterization of cannabis varieties and the intrinsic and extrinsic factors affecting cannabis germination and seedling establishment: A descriptive review

Cited by 2 publications

References 152 publications

Hybrid Adaptive Multiple Intelligence System (HybridAMIS) for classifying cannabis leaf diseases using deep learning ensembles

Hybrid Adaptive Multiple Intelligence System (HybridAMIS) for classifying cannabis leaf diseases using deep learning ensembles

Chemical profiling and clustering of various dried cannabis flowers revealed by volatilomics and chemometric processing

Contact Info

Product

Resources

About