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Part 1: Document Description
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Citation |
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Title: |
Dataset for non-infused aroma-based quality identification of Gambung green tea using electronic nose |
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Identification Number: |
doi:10.34820/FK2/NNAL9K |
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Distributor: |
Telkom University Dataverse |
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Date of Distribution: |
2023-10-02 |
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Version: |
1 |
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Bibliographic Citation: |
Wijaya, Dedy Rahman; Wijaya, Dedy Rahman, 2023, "Dataset for non-infused aroma-based quality identification of Gambung green tea using electronic nose", https://doi.org/10.34820/FK2/NNAL9K, Telkom University Dataverse, V1, UNF:6:HIrxUS/O2lfpkjEvq/3zgA== [fileUNF] |
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Citation |
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Title: |
Dataset for non-infused aroma-based quality identification of Gambung green tea using electronic nose |
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Identification Number: |
doi:10.34820/FK2/NNAL9K |
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Authoring Entity: |
Wijaya, Dedy Rahman (Fakultas Ilmu Terapan - Applied Information Systems) |
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Wijaya, Dedy Rahman (Fakultas Ilmu Terapan - Applied Information Systems) |
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Distributor: |
Telkom University Dataverse |
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Access Authority: |
Wijaya, Dedy Rahman |
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Depositor: |
Wijaya, Dedy Rahman |
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Date of Deposit: |
2023-06-07 |
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Study Scope |
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Keywords: |
Agricultural Sciences, Computer and Information Science, Non-infused, aroma, Gambung green tea, e-nose, machine learning, hyperparameter optimization |
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Abstract: |
This experiment focuses on the analysis of green tea aroma using a set of gas sensors. Specifically, the gas sensors selected for this research are the TGS822, TGS2602, TGS2620, MQ138, MQ5, and MQ3. The experiment involved testing a total of 78 different tea samples (chops), with each sample being observed three times. To conduct the experiment, a tea chamber was utilized, capable of accommodating 125 grams of dry green tea. The tea chamber was connected to a sensor chamber through a hose and intake micro air pump. During data acquisition, air from the tea chamber flowed into the sensor chamber for a duration of 60 seconds. Once the airflow from the tea sample was complete, the gas sensors recorded the aroma data for 60 seconds, resulting in 60 data records. These records were then saved into a CSV file for further processing and labeling. The labeling process involved referencing the Indonesian National Standard (SNI) 3945:2016, which defines the quality parameters for green tea according to ISO 11287 Green tea - definition and basic requirements. The SNI 3945:2016 standard specifies both special requirements and general requirements for green tea quality assessment. Special requirements encompass aspects such as water content, soluble ash, ash alkalinity, crude fiber, polyphenols, metal contamination, and microbial contamination. On the other hand, general requirements cover the physical and organoleptic characteristics of the tea, including dryness, steeping water, and steeping dregs. To evaluate the quality of Gambung green tea, an organoleptic test was conducted by a tea tester. The results of this test were used to label the data set obtained from the e-nose. The data set had two labels: quality standard ("good" and "quality defect") for the discrete classification task, and organoleptic score, which combined ratings for dry appearance, brew color, taste, aroma, and dregs of brewing, for the continuous regression task. In summary, this study aimed to predict the quality standard and organoleptic score of green tea samples using gas sensor data. The gas sensors were selected based on their suitability for analyzing the tea's aroma. The labeled data set, obtained through experimentation and organoleptic testing, would serve as the basis for training models for classification and regression tasks. |
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Notes: |
This data set contains several columns as follows: 1. Sampling_id: describes chop/sample id 2. MQ3: response of MQ3 gas sensor 3. TGS822: response of TGS822 gas sensor 4. TGS2602: response of TGS2602 gas sensor 5. MQ5: response of MQ5 gas sensor 6. MQ138: response of MQ138 gas sensor 7. TGS2620: response of TGS2620 gas sensor 8. Score: organoleptic score for continuous label 9. Class: green tea quality (“good” or “defect”) for discrete label |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
CC0 Waiver |
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Other Study Description Materials |
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File Description--f10520 |
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File: gambung_green_tea_78_chops.tab |
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Notes: |
UNF:6:HIrxUS/O2lfpkjEvq/3zgA== |
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List of Variables: | |
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Variables |
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f10520 Location: |
Variable Format: character Notes: UNF:6:DlNbSQwLEV86GYwxLaH5MA== |
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f10520 Location: |
Summary Statistics: Max. 893.0; Valid 14040.0; Min. 0.0; Mean 809.769230769231; StDev 33.85146269066717 Variable Format: numeric Notes: UNF:6:AjMVikKnP7TrfDaveHRQtA== |
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f10520 Location: |
Summary Statistics: StDev 14.532351126764494; Min. 0.0; Max. 329.0; Mean 289.9786324786322; Valid 14040.0 Variable Format: numeric Notes: UNF:6:dHDqZwskg1cIDE+/KZZ++g== |
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f10520 Location: |
Summary Statistics: Valid 14040.0; StDev 15.321886469741242; Max. 353.0; Mean 294.8872507122509; Min. 0.0 Variable Format: numeric Notes: UNF:6:E64cj8YoMLH4gmmTPRL1cA== |
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f10520 Location: |
Summary Statistics: Min. 0.0; Mean 415.7396723646727; StDev 27.245208946424583; Valid 14040.0; Max. 597.0; Variable Format: numeric Notes: UNF:6:NQXmgk26NSKDsWbctJmEfA== |
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f10520 Location: |
Summary Statistics: Min. 0.0; Max. 942.0; Valid 14040.0; Mean 884.3279914529904; StDev 44.3598407314248; Variable Format: numeric Notes: UNF:6:3HWdT+hbvPXKnGD5Z0mFQg== |
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f10520 Location: |
Summary Statistics: Mean 330.64665242165273; StDev 16.376400201433565; Min. 203.0; Valid 14040.0; Max. 408.0; Variable Format: numeric Notes: UNF:6:O+NkThS5XSpKywB8ss9ASw== |
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f10520 Location: |
Summary Statistics: Valid 14040.0; Mean 47.98589743589743; Min. 40.8; Max. 58.0; StDev 3.2515740263097346 Variable Format: numeric Notes: UNF:6:CZI0+DGbMHyWhE4zF/tKRA== |
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f10520 Location: |
Variable Format: character Notes: UNF:6:La5W4b+FYOhpBz4RgLekNg== |