Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Growth & Conditions
2.2. Instrumentation & Parameters
2.3. Sample Preparation for LCMS Analysis
2.4. Statistical Analysis
3. Results
3.1. Principal Component Analysis of NIR Data
3.2. Partial Least Squares Discriminant Analysis (PLS-DA) Modelling
3.3. Partial Least Squares Regression (PLS-R), Support Vector Machine Regression (SVM-R) and XGBoost Regression (XGB-R) Modelling
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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High THCA 1 | Even Ratio 2 | |
---|---|---|
Sensitivity (Cal) 3 | 1.00 | 1.00 |
Specificity (Cal) | 1.00 | 1.00 |
Sensitivity (CV) 3 | 1.00 | 1.00 |
Specificity (CV) | 1.00 | 1.00 |
Sensitivity (Pred) 3 | 1.00 | 1.00 |
Specificity (Pred) | 1.00 | 1.00 |
Class. Err 4 (Cal) | 0.00 | 0.00 |
Class. Err (CV) | 0.00 | 0.00 |
Class. Err (Pred) | 0.00 | 0.00 |
RMSEC 5 | 0.15 | 0.15 |
RMSECV 6 | 0.15 | 0.15 |
RMSEP 7 | 0.12 | 0.12 |
Bias | 0.00 | 0.00 |
CV Bias | 0.00 | 0.00 |
Pred Bias | 0.01 | −0.01 |
R2Cal 8 | 0.83 | 0.83 |
R2CV | 0.82 | 0.82 |
R2Pred 9 | 0.79 | 0.79 |
Model | Figure Key | Region (nm) 1 | Scatter Correction 2 | Derivative 2a | N 3 | RMSEC 4 | R2Cal 5 | RMSECV 6 | R2CV 7 | RMSEP 8 | Pred Bias 9 | R2Pred 10 | RPD 11 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PLS-R | (a) | 950–1650 | DT, SNV and MC | 2, 2, 5 | 264 | 26.34 | 0.62 | 28.87 | 0.54 | 21.49 | −0.68 | 0.78 | 2.08 |
(b) | 950–1650 | DT, SNV and MC | 2, 2, 7 | 264 | 28.00 | 0.57 | 30.49 | 0.50 | 23.49 | −2.61 | 0.75 | 1.91 | |
(c) | 950–1650 | DT, SNV and MC | 2, 2, 3 | 264 | 26.41 | 0.62 | 30.93 | 0.48 | 23.34 | 0.77 | 0.73 | 1.93 | |
SVM-R | (a) | 950–1650 | DT, SNV and MC | 2, 2, 5 | 264 | 24.87 | 0.68 | 28.48 | 0.56 | 22.95 | −1.47 | 0.77 | 1.96 |
(b) | 950–1650 | DT, SNV and MC | 2, 2, 7 | 264 | 23.87 | 0.70 | 29.40 | 0.53 | 22.49 | −3.38 | 0.76 | 2.00 | |
(c) | 950–1650 | DT, SNV and MC | 2, 2, 3 | 264 | 25.11 | 0.68 | 30.34 | 0.51 | 24.87 | −1.80 | 0.74 | 1.81 | |
XGB-R | (a) | 950–1650 | DT, SNV and MC | 2, 2, 5 | 264 | 12.27 | 0.93 | 31.10 | 0.48 | 23.31 | −3.46 | 0.74 | 1.88 |
(b) | 950–1650 | DT, SNV and MC | 2, 2, 3 | 264 | 0.02 | 1.00 | 34.61 | 0.37 | 28.77 | 0.44 | 0.59 | 1.56 | |
(c) | 950–1650 | DT and MC | 2, 2, 5 | 264 | 0.25 | 1.00 | 34.09 | 0.38 | 23.02 | −1.64 | 0.74 | 1.95 |
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Tran, J.; Vassiliadis, S.; Elkins, A.C.; Cogan, N.O.O.; Rochfort, S.J. Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning. Sensors 2024, 24, 5081. https://doi.org/10.3390/s24165081
Tran J, Vassiliadis S, Elkins AC, Cogan NOO, Rochfort SJ. Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning. Sensors. 2024; 24(16):5081. https://doi.org/10.3390/s24165081
Chicago/Turabian StyleTran, Jonathan, Simone Vassiliadis, Aaron C. Elkins, Noel O. O. Cogan, and Simone J. Rochfort. 2024. "Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning" Sensors 24, no. 16: 5081. https://doi.org/10.3390/s24165081
APA StyleTran, J., Vassiliadis, S., Elkins, A. C., Cogan, N. O. O., & Rochfort, S. J. (2024). Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning. Sensors, 24(16), 5081. https://doi.org/10.3390/s24165081