Establishing factors of coffee cultivation for geographical identification using multi elemental profiling
Coffee has been identified as a priority crop due to its rise in demand as a daily commodity, and the Philippines is one of the countries that have the ideal climatic and soil conditions to produce and grow a wide variety of coffee. To curb the increase in fraudulent practices, analytical techniques...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2022
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_chem/19 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1023&context=etdb_chem |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Coffee has been identified as a priority crop due to its rise in demand as a daily commodity, and the Philippines is one of the countries that have the ideal climatic and soil conditions to produce and grow a wide variety of coffee. To curb the increase in fraudulent practices, analytical techniques have been utilized to determine the authenticity of coffee, with an emphasis on their origin. Unroasted coffee beans were acquired from open call respondents as well as participants of the Philippine Coffee Quality Competition (PCQC) were collected for analysis. More than fifty (50) coffee bean samples were prepared and analyzed using a portable X-ray Fluorescence Spectrometer. The multi elemental profile of each coffee bean was subjected to the Random Forest algorithm to determine whether there are significant factors that contribute to the fingerprinting and traceability of coffee beans. All three contributing factors, ‘Region’, ‘Fermentation’, ‘Elevation’ and visually showcased clustering, with outliers present in all cases. The factor, ‘Region,’ yielded the best classification and had the lowest percent error. The other two factors, although displaying clustering properties similar to the first factor, ‘Region,’ unfortunately had more misclassifications with higher percent errors. Even so, the significance of these clustering proves that with more contributing factors that affect the elemental profile, traceability and identification of coffee beans becomes more difficult to replicate, and thus multi elemental profiling can be effective as a geographical indicator. |
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