| Autor: |
Rima Hayati, Agus Arip Munawar, Endang Lukitaningsih, Nanda Earlia, Taufiq Karma, Rinaldi Idroes |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Case Studies in Chemical and Environmental Engineering, Vol 9, Iss , Pp 100552- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-0164 |
| DOI: |
10.1016/j.cscee.2023.100552 |
| Popis: |
Since Indonesia is the world's largest producer of coconuts, it is necessary to classify them according to their chemical profiles. The examination of coconut endosperm from 13 districts in Aceh's coastal plantations was done using the NIRS. Chemometric analysis of the NIRS spectra revealed that smoothing, the 1st derivative, and SNV preprocessing of the PCA produced improved visualization outcomes. The discrimination study revealed that the PCA-LDA and PCA-SVM produced accurate results that were satisfactory, with 100% accuracy for each. The effectiveness of combinations of PCA-LDA and PCA-SVM was examined in this article as being helpful for enhancing classification accuracy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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