Implementation of an electronic nose for classification of synthetic flavors

Abstract

Classification and identification of synthetic flavor become routine activities in the flavor and food industry due to its application. As a modern olfactory technology, electronic nose (e-nose) has the possibility to be applied in these activities. This study aimed to evaluate an e-nose for classifying synthetic flavors. In this study, an e-nose was designed with an array of gases sensors as the main sensing component and principal component analysis (PCA) for the pattern recognition software. This research was started with preparation of the hardware, continued with preparation of sample, data collection, and analysis. There were nine samples of synthetic flavors with different aroma, namely: grapes, strawberry, mocha, pandanus, mango, jackfruit, orange, melon, and durian. The data collection process includes three stages, i.e. flushing, collecting, and purging of 2 min, 3 min, 2 min respectively. These sensor responses were then analyzed for forming aroma patterns. Four pre-treatment methods were applied for the aroma pattern formation: absolute data, normalize of absolute data, relative data, and normalize of relative data. With the PCA for evaluation, the results showed that the absolute data treatment provided the best results, indicated from the distribution of aroma patterns that were grouped according to the type of samples.

Reliable e-nose for air toxicity monitoring by filter diagonalization method

“…To explain the method, let us consider a complex unidimensional signal cn=c(nτ) with values along with equidistant time intervals nτ with n = 0, 1,…, N-1. The FDM aims to represent cn as a sum of damped sinusoids, as shown in (2), where 𝜔 𝑘 = 2𝜋𝑓 𝑘 − 𝑗𝛾 𝑘 are the complex frequencies of the signal, including the damping factor, and dk are the corresponding amplitudes. To solve (1), the FDM associates a correlation function described by the Hamiltonian operator 𝛺 ̂, which has complex eigenvalues {𝜔 𝑘 }, thus cn can be further transformed into (3):…”

“…Viejo et al [1] implemented an e-nose to assess the aroma profiles in beer and automate the industrial quality inspection process. Similarly, Radi et al [2] developed an e-nose for classifying odors from synthetic flavors such as grapes, strawberry, mango, and orange. To alert about rancidity, Xu et al [3] introduced an e-nose monitoring the changes of pecans during storage.…”

Design of sample display system on electronic nose for synthetic flavor classification

“…The flavor industry generally has a team of panelists to measure the organoleptic quality of the product. Although the panelists have good adaptability, conventional sensory testing has some weaknesses [3]. The human olfactory system is generally subjective because susceptible to subject's physical and psychological state [3].…”

“…Although the panelists have good adaptability, conventional sensory testing has some weaknesses [3]. The human olfactory system is generally subjective because susceptible to subject's physical and psychological state [3]. The sensory test will also be difficult to quantify, impacting the difficulty of obtaining a consistent quality assessment.…”

Performance Analysis of MOS Sensors on Electronic Nose for Synthetic Flavor Classification

“…Research related to electronic nose applications based on MQ sensors and TGS sensors for the identification of synthetic flavors is still very limited. In 2021, [18] used an electronic nose to classify synthetic flavors, but very little information has been obtained regarding the gas sensor used in the electronic nose series, which is the type of gas sensor. Therefore, the purpose of this study was to compare the performance of the MQ and TGS gas sensors on the electronic nose for the classification of synthetic flavors.…”