Non-Destructive Prediction of Moisture and Fat Content in Cocoa Beans Using Near-Infrared Spectroscopy and Multivariate Regression Models
Bibliographic record
Abstract
Presented paper aimed to apply the near infrared spectroscopy (NIRS) for rapid, nondestructive and simultaneous prediction of moisture and fat content on intact cocoa beans.Near infrared spectral data were acquired using a portable NIRS instrument (PSD NIRS i16, Universitas Syiah Kuala) in the wavelength range of 1000-2500 nm.Actual moisture and fat contents were measured and determined by means of thermo-gravimetry and soxhlet methods respectively.Spectral data were enhanced and corrected using standard normal variate (SNV) and de-trending order 2 (Dt-2) methods.Prediction models, used to determine the moisture and fat contents were established using partial least square regression (PLSR) and principal component regression (PCR).The results showed that moisture content and fat content can be predicted non-destructively and simultaneously using NIRS with maximum prediction index (RPD) are 2.16 for moisture content and 2.39 for fat content respectively which can be categorized as good model performances.Moreover, SNV found to be the ebst spectra correction method in predicting both moisture and fat contents.
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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) | 0.000 | 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 | 0.001 |
| 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".