Geo-Tracing of Black Pepper Using Metal Oxide Semiconductor (MOS) Gas Sensors Array

Lee, Hui En; Mercer, Zehnder J. A.; Ng, Sing Muk; Shafiei, Mahnaz; Chua, Hong Siang

doi:10.1109/jsen.2020.2981602

Cited by 13 publications

(2 citation statements)

References 5 publications

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“…This study utilized the MOS gas sensors module, sampling process, and feature extraction method practiced by Lee et al in their studies [8][9][10][11]. Sensor response is defined as the change in sensor output voltage (voltage across an external load resistor) due to the change in resistance of the sensing material in the sensor.…”

Section: Methodology 21 Preparation Storage and Sampling Of Ricementioning

confidence: 99%

Product differentiation of Sarawak premium rice using MOS gas sensors: comparison between transient and frequency response

Jee,

Chua,

Lee

2024

futech

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In Malaysia, rice, ranked as the third most crucial crop, faces challenges due to domestic consumption outpacing production, resulting in increased instances of rice adulteration. This underscores the imperative of maintaining integrity and quality standards across the entire supply chain. This study uses an electronic nose, comprising four metal oxide semiconductor (MOS) gas sensors, and employing temperature modulation, Principal Component Analysis (PCA) and supervised machine learning (classification models) to distinguish rice varieties such as Bario, Bajong, Borneo Fragrant, Biris, and Jasmine. The study evaluated 30 classifiers based on their classification and validation accuracy. Sensor data was first extracted from the transient response of sensors output voltage, yielding a 12-dimension dataset with response times of 30 s, 50 s, and 95 s. Classification models trained from this dataset achieved classification (training) accuracy of up to 100% and validation accuracy of up to 96%, where the best performing models are subspace discriminant and kernel naïve bayes classifiers. An attempt was also made to analyze the sensor data frequency response for rice classification. Comparison between the prediction results in the transient and frequency domains showed that transient response is better suited for the classification of rice.

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Section: Methodology 21 Preparation Storage and Sampling Of Ricementioning

confidence: 99%

Product differentiation of Sarawak premium rice using MOS gas sensors: comparison between transient and frequency response

Jee,

Chua,

Lee

2024

futech

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“…Sarawak pepper samples are compared with the Indian ground black pepper sample for species adulteration determination. Different VOCs in the black pepper was determined by utilizing the geo-tracing module built with Metal-oxide Semiconductor (MOS) gas sensor array [14]. Totally 200 counts of black pepper samples are obtained from India and Malaysia.…”

Section: A Related Workmentioning

confidence: 99%

Development of a Smart Sensor Array for Adulteration Detection in Black Pepper Seeds using Machine Learning

Natarajan¹,

Ponnusamy²

2023

IJACSA

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Black pepper is an expensive commodity with a high risk of adulteration. Ground papaya seed is the main adulterant in pepper because it cannot be discriminated visually. There are few destructive methods. Since pepper is costlier, nondestructive method of adulteration is must but it is challenging one. The existing non-destructive method uses costlier equipment, bulky, involve laboratory-based testing, time consuming in the process. To overcome the above issues, this article presents the development of Non-destructive E-nose gas sensor for pepper adulteration detection. This system determines the VOC in a controlled environment. The proposed system utilizes MQ2 and MQ3 gas sensor arrays to identify Volatile Organic Compounds present in pepper seeds to discriminate adulterant and non-adulterant sample. The sensor data are utilized to perform the qualitative analysis to determine the adulteration using a support vector machine learning algorithm. The proposed sensor system with Support Vector Machine learning algorithm outperforms in comparison with existing methods with 100% classification accuracy. Conclusion: The developed gas sensor system is connected to the internet via the IoT application model to show results on the web pages and enables access by the authenticated user from anywhere. Client server model with MQTT protocol is used for developing IoT application.

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Geo-tracing of Coffee Beans Using Metal Oxide Semiconductor Gas Sensors Array

Nyaut,

Lee,

Mercer

et al. 2023

Proceedings in Technology Transfer

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Geo-Tracing of Black Pepper Using Metal Oxide Semiconductor (MOS) Gas Sensors Array

Cited by 13 publications

References 5 publications

Product differentiation of Sarawak premium rice using MOS gas sensors: comparison between transient and frequency response

Product differentiation of Sarawak premium rice using MOS gas sensors: comparison between transient and frequency response

Development of a Smart Sensor Array for Adulteration Detection in Black Pepper Seeds using Machine Learning

Geo-tracing of Coffee Beans Using Metal Oxide Semiconductor Gas Sensors Array

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