MMSE-GC-MS Profiles Robusta Coffee Defects

Defective coffee beans can seriously hurt the flavor quality of Robusta coffee. This study takes a close look at the aroma-related chemical compounds found in twelve different types of defective green Robusta coffee beans from Bulukumba, Indonesia. To do this, researchers used a technique that extracts and concentrates these aroma compounds before analyzing them using monolithic material sorptive extraction coupled with gas chromatography-mass spectrometry (MMSE-GC-MS). A paper based on this work was published in the journal Food Chemistry.¹

What Makes Robusta Coffee Different from Arabica Coffee?

Robusta coffee tends to taste more bitter and slightly sour c0mpared to Arabica coffee. It also has more caffeine and a distinct sweet smell. Arabica, on the other hand, tends to taste sweeter and more acidic, largely because it has less caffeine than Robusta (about 1.2%) compared to Robusta's roughly 2.2%. When it comes to aroma, Robusta usually smells nutty and caramel-like, while Arabica often smells sweet. How Robusta coffee ultimately tastes is dependent on how the beans are processed and their overall quality.^1-3

What Causes Defects in Robusta Beans?

The quality of green Robusta coffee beans depends on the method of harvest as well as how they are handled afterward. When farmers use poor practices such as picking beans in a rushed or uneven way, fermenting them incorrectly, letting them get contaminated, or storing them badly, all of which potentially leading to defective beans. During sorting, these defects show up as things like black, sour, or moldy beans, foreign debris, insect damage, unripe beans, and leftover husk or parchment. Defective beans can ruin the coffee's quality, often giving it an unpleasant, grassy taste and making it look worse, which ultimately hurts the overall flavor.^4-6

How Did the Researchers Analyze the Aroma Compounds in Defective Coffee Beans, and What Did They Find?

The researchers used two statistical techniques to spot patterns in the aromas produced by different types of defective coffee beans, running each test three separate times to confirm the results were reliable. They then checked whether these patterns held up in real-world, unsorted Robusta coffee samples sourced from three different countries. In total, they identified 42 aroma-related compounds, falling into several chemical families. The first statistical technique explained close to 58% of the differences seen between samples, with the clearest divide being between actual coffee beans and non-bean material (like husks or foreign matter). The second technique grouped the samples into three main clusters based on their aroma profiles. Importantly, the key aroma markers the researchers identified could still be detected in the commercial-grade samples from all three countries, confirming their real-world relevance.¹

“MMSE-GC-MS,” write the authors of the paper,¹ “combined with multivariate analysis offers a sensitive, chemistry-informed framework for objective Robusta defect discrimination, complementing conventional visual inspection.”

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References

1. Bastian, F.; Pasilong, T. P. B.; Dirpan, A. et al. MMSE-GC-MS Volatile Organic Compound Profiling of Twelve Defect Categories in Robusta Coffee Beans: Chemical Markers and Multivariate Discrimination of Quality Defects. Food Chem. 2026, 523, 150212. DOI: 10.1016/j.foodchem.2026.150212
2. Campuzano-Duque, L. F.; Blair, M. W. Strategies for Robusta Coffee (Coffea canephora) Improvement as a New Crop in Colombia. Agriculture (Switzerland) 2022, 12 (10). DOI: 10.3390/agriculture12101576.
3. Wijaya, R. Y.; Tamrin, T.; Sugianti, C. Pengaruh Lama Fermentasi Menggunakan Mikroba Ghalkoff terhadap Perubahan Konsentrasi Kandungan Kafein Kopi Robusta Organik Kabupaten Lampung Barat. Jurnal Agricultural Biosystem Engineering 2023, 2 (3), 354–360. DOI: 10.23960/jabe.v2i3.7892
4. Chang, S. J.; Huang, C. Y. Deep Learning Model for the Inspection of Coffee Bean Defects. Appl. Sci.2021, 11 (17). DOI: 10.3390/app11178226
5. Gope, H. L.; Fukai, H. Ruhad, F. M. et al. Comparative Analysis of YOLO Models for Green Coffee Bean Detection and Defect Classification. Sci. Rep. 2024, 14 (1). DOI: 10.1038/s41598-024-78598-7
6. Zhang, D.; Gao, M.; Cai, Y. et al. Profiling Flavor Characteristics of Cold Brew Coffee with GC-MS, Electronic Nose and Tongue: Effect of Roasting Degrees and Freeze-Drying. J. Sci, Food Agric. 2024, 104 (10), 6139-6148. DOI: 10.1002/jsfa.13437