ArticleMolecules (Basel, Switzerland)2024
The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee.
Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 13 citations in OpenAlex.
- Rapid Analysis of Caffeine, Protein and Trigonelline in Ugandan Arabica Coffee Using NIRS and Machine Learning Algorithms.Plants (Basel, Switzerland) · 2026Article
- Near-Infrared Spectroscopy-Based Discriminant Analysis for the Classification of Coffee Quality in Dry Parchment and Green Coffee.Molecules (Basel, Switzerland) · 2026Article
- Dual-frequency countercurrent ultrasonic-assisted extraction of the cold brew coffee and in situ real-time monitoring of extraction process.Ultrasonics sonochemistry · 2024Article
- The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades.Foods (Basel, Switzerland) · 2024Review
- The Impact of Sample Quantity, Traceability Scale, and Shelf Life on the Determination of the Near-Infrared Origin Traceability of Mung Beans.Foods (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.