Measurement and differentiation of banana juice scent using an electronic nose FF-2A

Nomura, Motoo; Osada, Erika; Tokita, Tsuyoshi; Iwamoto, Takeo; Manome, Yoshinobu

doi:10.7717/peerj.10638

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…In [21], an e-nose called FF-2A is used to detect the differences between several banana juice brands. The details of the sensor used are not stated but have been calibrated and standardised with nine ingredients, namely hydrogen sulfide, methyl mercaptan, ammonia, trimethylamine, propionic acid, butyraldehyde, butyl acetate, toluene, and heptane.…”

Section: Related Workmentioning

confidence: 99%

“…The selection of the sensors is based on the analysis of gases contained in human axillary odour, where human odour contains active compounds called volatile organic compounds (VOCs) [22] then we look for correlations of sensors that can detect each of these VOCs. Then, based on the analysis of previous studies, especially in [3,[14][15][16][17] which discussed the use of sensors for the detection of some plant products, we chose 8 MOS sensors that can detect the content between human axillary odour and food material aroma. The 8 sensors, each has its function to retrieve data on specific gases, as shown in Table 2.…”

Section: Electronic Nosementioning

confidence: 99%

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Electronic Nose Signals for Analysing Similarity of Male and Female Axillary Odour to Food Material Aroma

2022

IJIES

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Human axillary odour is a unique discussion because it stores various useful information, including health, food quality analysis, and the similarity to the smell of the food eaten which can be done using an electronic nose (enose). However, rarely for research use an e-nose to determine the similarity of human axillary odour to food material odours. This research aimed to determine the similarity between male and female odours based on food material odours, such as garlic, shallot, and red chilli using an e-nose. We propose a method using an electronic nose with 8 Taguchi gas sensor (TGS) and 1 digital & humidity (DHT) sensor, then processing the resulting data using the fast fourier transform (FFT) smoothing method. Sensor data that have anomalies are removed by the quantile method. The standardization process is carried out to the signal data and feature extraction is processed with mean, standard deviation, and minimum value. This study found that the proposed method can produce a fairly good cluster of aroma food materials, faithfully getting the highest accuracy of 96.67 % using support vector classifier (SVC) and k-nearest neighbour (KNN) with each best parameter. We also found that the smell of human armpit sweat generally has a lot of similarity with food materials, specifically that male has the highest similarity to the shallot, while female has the highest level of similarity to garlic. In contrast, red chilli has the lowest level of similarity with human armpit odour.

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Section: Related Workmentioning

confidence: 99%