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Olfaction

Olfaction

The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...

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Olfactory analysis of oolong tea sensory quality using composite nano-colorimetric sensor array.

Hao Lin¹, Kexin Zhang², Jilong Guo²

¹School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Stunt Talent Laboratory, Bamatea Co., Ltd, Quanzhou 362000, PR China.

Food Research International (Ottawa, Ont.)

|September 4, 2024

Summary

A new colorimetric sensor array (CSA) accurately detects volatile organic compounds (VOCs) in oolong tea. Integrated with a support vector machine (SVM) model, it precisely predicts sensory quality, flavor intensity, and grade.

Keywords:

Binding mechanism Colorimetric sensor array Multivariate data analysis Oolong tea Sensory quality

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Area of Science:

Analytical Chemistry
Food Science
Sensor Technology

Background:

Oolong tea's sensory quality is crucial and linked to its aroma profile.
Accurate and rapid evaluation of oolong tea's sensory quality remains a challenge.
Volatile organic compounds (VOCs) are key indicators of tea aroma and quality.

Purpose of the Study:

To develop a novel colorimetric sensor array (CSA) for detecting VOCs in oolong tea.
To explore the charge transfer mechanism underlying color changes in the CSA dyes.
To optimize the CSA for enhanced sensitivity and stability in quality assessment.

Main Methods:

Development and modification of a novel colorimetric sensor array (CSA).
Investigation of the binding mechanism between colorimetric dyes and VOCs.
Optimization of CSA for improved sensitivity and stability.
Comparison of linear and non-linear classification models, including support vector machine (SVM).

Main Results:

The optimized CSA demonstrated improved sensitivity (17.1-234.9%) and stability (8.7-33.3%).
The support vector machine (SVM) model achieved high accuracy in classification.
The SVM model successfully identified different flavor intensities (100%) and grades (95.83%) of oolong tea.

Conclusions:

The novel CSA effectively detects VOCs for oolong tea quality assessment.
The CSA integrated with the SVM model shows significant potential for predicting sensory quality.
This method offers a rapid and accurate approach to oolong tea quality evaluation.