In-vivo classification of THC and CBD contents in cannabis plants using hand-handled Raman spectrometer
Bibliographic record
Abstract
We present an innovative approach to the in-vivo classification of THC/CBD-rich cannabis plants through a novel miniaturization strategy for Raman spectroscopy. By developing compact Raman spectrometers that utilize patented technology based on cheap non-stabilized laser diodes, densely-packed optics, and small pixel size sensors without cooling, the study achieves performance comparable to more expensive, research-grade systems. This miniaturization is facilitated by real-time calibration of Raman shift and intensity using a built-in reference channel. The miniRaman spectrometer effectively records high-quality Raman spectra of fresh cannabis and its products without the need for sample or environment preparation, identifying characteristic peaks of primary phytocannabinoids such as THC, CBD, and CGB and avoiding time-consuming HPLC analysis. Through spectral deconvolution and chemometrics, quantitative analysis becomes possible, significantly reducing the influence of fluorescence for more precise analysis [1]. The application of this technology allows for the identification of THC or CBD-rich plants with a high accuracy rate of 92%, demonstrating the potential of Raman spectroscopy aided by machine learning for rapid, non-destructive cannabis classification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".