Data set for Gambung green tea aroma using on electronic nose

Area of Science:

• Food Science
• Sensory Science
• Analytical Chemistry

Background:

• Electronic nose (e-nose) technology is increasingly applied in food and medical fields for sensory analysis.
• E-nose systems often integrate machine learning for predicting multiple sensory attributes.
• Processing e-nose signals is crucial for reliable prediction model generalization.

Purpose of the Study:

• To classify the quality of dry green tea samples.
• To predict the organoleptic score of dry green tea.
• To address the challenge of detecting weak aromas from dry tea using e-nose technology.

Main Methods:

• Utilized six gas sensors to analyze green tea aroma.
• Tested 78 dry green tea samples, with each sample observed three times.
• Collected aroma data via a tea chamber connected to a sensor chamber, recording for 60 seconds after a 60-second air flow.

Main Results:

• Data was labeled according to Indonesian National Standard (SNI) 3945:2016 for green tea quality.
• An organoleptic test classified samples as 'good' or 'quality defect' and provided continuous scores.
• The dataset serves as a resource for e-nose research in categorizing and identifying organoleptic ratings for dry green tea.

Conclusions:

• The developed dataset enables deeper understanding of dry green tea quality.
• Facilitates further integration of e-nose technology within the tea industry.
• Highlights the potential of e-nose for analyzing challenging dry tea samples.