Non-Invasive NIR Spectroscopy for Precise Water Content Determination in Sumatran Coffee Beans
Bibliographic record
Abstract
The amount of water in roasted coffee beans can lead to fat oxidation, shortening their shelf life and affecting both the grinding procedure and the distribution of particle sizes in the coffee powder.In this work, the ability of NIR spectroscopy to ascertain the water content of Sumatran-roasted coffee beans will be evaluated non-invasively.The Thermo Nicolet Antaris TM II NIRS device collected NIR spectrum data for wavelengths between 1000 and 2500 nm, which has a high response to water content.The reference water content is calculated using the gravimetric method.Pre-processing techniques used to generate the calibration model include multiplicative scatter correction (MSC) to eliminate optical path differences, the Savitzky-Golay 9-point smoothing window, firstorder derivative (SG-1 st D) to smooth the spectra, and a combination of MSC and SG-1 st D (MSC+SG-1 st D).According to research findings, the calibration model employing the MSC+SG-1 st D pre-processing techniques provides the most accurate prediction of the water content of Sumatran roasted coffee beans with Rp 2 = 0.995 and RMSEP = 0.003%.This finding will significantly impact the coffee industry, guarantee high-quality roasted coffee beans, and increase competitiveness in international markets.This is due to the ability of the NIR spectroscopy method to provide water content results quickly compared to conventional methods that require a relatively long time to carry out measurements, even though it gives accurate results if done carefully and using wellcalibrated equipment.Additionally, this study advances our understanding of how to use NIR spectroscopy to evaluate roasted coffee quality and extends its applicability to Sumatran coffee, which is renowned for its distinctive qualities.
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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".