Headspace GC/MS Imaging for Non-Destructive Hemp Determination
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Solution Overview
Problem
Existing methods for determining the THC content in cannabis samples are destructive and solvent-based, making them inefficient for differentiating between hemp and marijuana, which requires a non-destructive and non-solvent-based approach.
Innovation Solution
Utilizing a convolutional neural network (CNN) to analyze headspace-GC/MS data transformed into images based on retention time, scan range, and signal intensity, enabling accurate differentiation between hemp and marijuana without solvent extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If liquid extraction method is used to determine THC content, then measurement precision is improved, but the method becomes destructive and requires solvents
Solution Approach 1:
The patent replaces the mechanical/chemical extraction process with a headspace gas chromatography-mass spectrometry (GC-MS) analysis method. Instead of using solvents to extract THC from cannabis samples, the system analyzes volatile compounds directly from the sample headspace, eliminating the need for destructive liquid extraction and solvent-based processing while maintaining measurement precision.
Solution Approach 2:
The patent changes the analytical parameters from liquid phase extraction to gas phase analysis. By measuring volatile compounds in the headspace rather than extracting them into liquid solvents, the method achieves non-destructive sampling while maintaining the ability to accurately quantify THC content through GC-MS detection of characteristic mass spectra.
2Measurement precision
If traditional laboratory testing is used, then measurement precision is achieved, but testing time and productivity are reduced
Solution Approach 1:
The patent implements continuous automated analysis using headspace-GC-MS coupled with machine learning algorithms. The system continuously acquires mass spectra data and automatically processes it through trained neural networks, eliminating manual intervention and batch processing steps, thereby achieving both high measurement precision and rapid testing throughput.
Solution Approach 2:
The patent uses machine learning models to create digital representations and predictions of THC content based on patterns in mass spectra data. Instead of requiring physical extraction and manual analysis for each sample, the system uses trained algorithms to rapidly predict THC content from characteristic spectral signatures, dramatically increasing productivity while maintaining accuracy.
3Productivity
If headspace-GC/MS with machine learning is used, then productivity and non-destructive testing are improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary between the GC-MS instrument and the final THC content determination. The algorithms process the complex mass spectra data automatically, translating raw instrumental signals into accurate THC quantifications. This intermediary layer handles the computational complexity, allowing the physical instrumentation to remain relatively simple while achieving high productivity and accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Achieves high accuracy in distinguishing hemp from marijuana with over 99% accuracy, providing a rapid and non-destructive method for THC content determination.
Implementation Method 1
headspace-GC/MS analysis
Implementation Method 2
GC/MS data
Data Source
AI summary
System and methods for hemp determination of a cannabis sample are disclosed. The systems and methods may include obtaining headspace data of cannabis samples using a gas chromatography/mass spectrometer (GC/MS) device. The data may then be transformed into images based on retention time, scan range, and signal intensities in the data for assessment by a convolutional network to determine whether a cannabis sample is hemp or non-hemp.