Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) Models
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 acquir...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
|
| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2023/25/bioconf_icosia2023_06007.pdf |
| _version_ | 1797353048410947584 |
|---|---|
| author | Dharmawan Agus Evi Masithoh Rudiati Zuhrotul Amanah Hanim |
| author_facet | Dharmawan Agus Evi Masithoh Rudiati Zuhrotul Amanah Hanim |
| author_sort | Dharmawan Agus |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T13:25:32Z |
| format | Article |
| id | doaj.art-6ea90da5a6c94f0bae3665a5326d040f |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-03-08T13:25:32Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-6ea90da5a6c94f0bae3665a5326d040f2024-01-17T14:58:04ZengEDP SciencesBIO Web of Conferences2117-44582023-01-01800600710.1051/bioconf/20238006007bioconf_icosia2023_06007Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) ModelsDharmawan Agus0Evi Masithoh Rudiati1Zuhrotul Amanah Hanim2Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada UniversityDepartment of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada UniversityDepartment of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Gadjah Mada UniversityA 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.https://www.bio-conferences.org/articles/bioconf/pdf/2023/25/bioconf_icosia2023_06007.pdf |
| spellingShingle | Dharmawan Agus Evi Masithoh Rudiati Zuhrotul Amanah Hanim Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) Models BIO Web of Conferences |
| title | Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) Models |
| title_full | Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) Models |
| title_fullStr | Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) Models |
| title_full_unstemmed | Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) Models |
| title_short | Performance of A Portable NIR Spectrometer to Distinguish Coffee Species Based on Qualitative Chemometric and Artificial Neural Network (ANN) Models |
| title_sort | performance of a portable nir spectrometer to distinguish coffee species based on qualitative chemometric and artificial neural network ann models |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2023/25/bioconf_icosia2023_06007.pdf |
| work_keys_str_mv | AT dharmawanagus performanceofaportablenirspectrometertodistinguishcoffeespeciesbasedonqualitativechemometricandartificialneuralnetworkannmodels AT evimasithohrudiati performanceofaportablenirspectrometertodistinguishcoffeespeciesbasedonqualitativechemometricandartificialneuralnetworkannmodels AT zuhrotulamanahhanim performanceofaportablenirspectrometertodistinguishcoffeespeciesbasedonqualitativechemometricandartificialneuralnetworkannmodels |