Comparison of the performance of laser-induced breakdown spectroscopy and color, visible, near-infrared and mid-infrared spectroscopy in the prediction of various soil properties
Bibliographic record
Abstract
Spectroscopy can predict soil chemical properties; thus, it is complementary to traditional laboratory analysis, which is more costly and time-consuming. The objective of this thesis was to evaluate the ability of seven spectroscopic instruments to predict nine soil chemical properties: available phosphorus (P), exchangeable potassium (K), calcium (Ca), magnesium (Mg) and aluminum (Al), buffer pH (BpH), pH, soil organic matter (SOM) and cation exchange capacity (CEC). In total, 798 air dried and compressed soil samples, representing different agro-climatic conditions across Québec (Canada), were analyzed with these instruments, which have variable resolution, spectral range and optics. For instance, visible (Vis) spectra were collected with RGB bands from a digital microscope (Vis-1) and a visible spectrometer that scanned wavelengths from 425 – 725 nm (Vis-2). The visible and near-infrared (Vis-NIR) spectra was collected from the range of 350 – 2,200 nm (Vis-NIR-1) with low-resolution field equipment and from 350– 2,500 nm (Vis-NIR-2) with a high-resolution laboratory scanner. Mid-infrared (MIR) spectra were collected from 5,500 – 11,000 nm (MIR-1) with a custom portable diffuse reflectance infrared Fourier-transform (DRIFT) spectrometer and with a benchtop attenuated total reflectance Fourier-transform infrared (ATR FTIR) spectrometer covering 2,500 – 17,000 nm (MIR-2). Finally, laser-induced breakdown spectroscopy (LIBS) spectra were acquired with the LaserAg technology developed and owned by Logiag (Chateauguay, Quebec, Canada). Performances of instruments, spectral ranges and spectral resolutions were compared using partial least squares regression (PLSR) with relevant test statistics, such as the root mean squared error of prediction (RMSEP), the coefficient of determination (R2) for the linear regression between measured and predicted values and the ratio of performance to interquartile distance (RPIQ). The best fit lines between measured and predicted values of P, K, Mg, Ca, pH, BpH and SOM were obtained with the LIBS spectra, while Vis-NIR-1 gave the best prediction for Al and Vis-NIR-2 gave the best prediction of CEC. Overall, the prediction was “excellent” for Ca, “good” for Mg, Al, SOM and CEC, “moderate” for P, pH and Bph and “poor” for K. Prediction MAEs of models for K (Vis-NIR-2), Ca (Vis-NIR-1, Vis-NIR-2, MIR-2, LIBS), Al (Vis-NIR-1, Vis-NIR-2, LIBS), BpH (all intruments except Vis-1) and CEC (all intruments) respected soil laboratory analysis standards
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".