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: Degawan, Ivan Joseph K., Gaite, Beatrice Cristina B.
Format: text
Language:English
Published: Animo Repository 2022
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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
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spelling oai:animorepository.dlsu.edu.ph:etdb_chem-10232023-01-04T01:11:46Z Establishing factors of coffee cultivation for geographical identification using multi elemental profiling Degawan, Ivan Joseph K. Gaite, Beatrice Cristina B. 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. 2022-12-14T08:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/etdb_chem/19 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1023&context=etdb_chem Chemistry Bachelor's Theses English Animo Repository Coffee—Philippines—Identification Coffee—Philippines—Analysis Biochemistry Chemistry
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Coffee—Philippines—Identification
Coffee—Philippines—Analysis
Biochemistry
Chemistry
spellingShingle Coffee—Philippines—Identification
Coffee—Philippines—Analysis
Biochemistry
Chemistry
Degawan, Ivan Joseph K.
Gaite, Beatrice Cristina B.
Establishing factors of coffee cultivation for geographical identification using multi elemental profiling
description 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.
format text
author Degawan, Ivan Joseph K.
Gaite, Beatrice Cristina B.
author_facet Degawan, Ivan Joseph K.
Gaite, Beatrice Cristina B.
author_sort Degawan, Ivan Joseph K.
title Establishing factors of coffee cultivation for geographical identification using multi elemental profiling
title_short Establishing factors of coffee cultivation for geographical identification using multi elemental profiling
title_full Establishing factors of coffee cultivation for geographical identification using multi elemental profiling
title_fullStr Establishing factors of coffee cultivation for geographical identification using multi elemental profiling
title_full_unstemmed Establishing factors of coffee cultivation for geographical identification using multi elemental profiling
title_sort establishing factors of coffee cultivation for geographical identification using multi elemental profiling
publisher Animo Repository
publishDate 2022
url 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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