Evidence map›Paper›PMID 40218606›Full record

ArticleSensors (Basel, Switzerland)2025

Coffee Bean Characterization Using Terahertz Sensing.

Dook van Mechelen, Daan Meulendijks, Milan Koumans

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dook van MechelenDepartment of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.ORCID 0009-0005-5567-9318
Daan MeulendijksDepartment of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
Milan KoumansDepartment of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coffee bean sorting is currently based primarily on visual appearance and near-infrared techniques that probe the bean's skin. However, sorting based on compositional differences has significant potential to optimize the roasting process. We present a novel coffee bean sorting method using terahertz (THz) spectroscopy, which effectively penetrates both green and roasted beans. Our findings show that the optical properties of coffee beans at THz frequencies are primarily governed by internal moisture levels. To demonstrate industrial feasibility, we implement a robot-guided THz sensing system capable of scanning beds of beans for automated sorting. More broadly, our results confirm the potential of THz technology for moisture content analysis across various applications.

Indexed as

coffee bean sortingmoisture level of coffee beansoptical properties of coffee beansTHz spectroscopy

Identifiers

PMID40218606
PMCPMC11991485

What OpenQuestion holds

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Registered trials

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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.