Issue |
BIO Web Conf.
Volume 80, 2023
4th International Conference on Smart and Innovative Agriculture (ICoSIA 2023)
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Article Number | 06007 | |
Number of page(s) | 8 | |
Section | Smart and Precision Farming | |
DOI | https://doi.org/10.1051/bioconf/20238006007 | |
Published online | 14 December 2023 |
Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) Models
Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada University, Indonesia
A wide range of genetic cultivars of coffee and their characteristics determine consumer preference and increase industrial actors’ awareness of production and marketing. The primary objective of this study is to develop a method to distinguish coffee species based on spectral characteristics acquired from a portable near-infrared spectrometer. The performance of this spectrometer in addressing classification problems is evaluated by the classification accuracy obtained from qualitative chemometrics, such as PCA and LDA, and artificial neural networks (ANNs) models. In this study, the instrument was successfully used and gained moderate accuracy for discriminating two coffee species, Arabica and Robusta, from Temanggung and Toraja. The accuracy was fair and achieved greater than 75%. Therefore, the instrument can be implemented as it provides simple, real-time, and in-situ analyses and can reach reliable results.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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