Metal oxide based surface acoustic wave sensors for fruits maturity detection

Hasanuddin, N.H.; Wahid, M. H. A.; Shahimin, M. M.; Hambali, N. A. M. Ahmad; Yusof, Nor Roshidah; Nazir, Nighat; Khairuddin, N.Z.; Azidin, M.A.M.

doi:10.1109/iced.2016.7804605

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…As the most crucial part, the sensor array has been made up of different kinds of gas sensors. Various studies have applied conductive polymer (CP) sensors [17,18], surface acoustic wave (SAW) sensors [19,20] and quartz crystal microbalance (QCM) sensors [21,22] in the E-nose system to detect the ripeness of fruit. Compared with these sensors, metal oxide semiconductor (MOS) sensors, which have the advantages of rapid response, high sensitivity and most importantly low cost, have also been widely used in E-nose applications [23].…”

Section: Introductionmentioning

confidence: 99%

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Wang

et al. 2019

Sensors

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Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

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Section: Introductionmentioning

confidence: 99%

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Wang

et al. 2019

Sensors

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“…Sa et al [4] Colour(RGB) and Near infrared(NIR) technique 4 Hasanuddin et al [9] Ehylene gas detection 5 Hasegawa et al [22] (Sic -FET) gas sensors for detecting ethylene gas 6 Mustaffa et al [7] Open Cv histogram 7…”

Section: Fig 4 Ullah Et Al[20]mentioning

confidence: 99%

“…Zawbaa et al [13] Scale invariant feature Transform(SIFT) 9 Lee et al [16] Direct colour mapping 10 Leekul et al [18] Rician Kfactors…”

Section: Fig 4 Ullah Et Al[20]mentioning

confidence: 99%

A Research on Fruit Maturity Detection Techniques

2019

IJITEE

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Fruits are the major source of food to humans. Damage in fruits can be of different types. Damage due to insects or damage during transportation are the most common types of damages caused. Fruits are also unfit for human consumption when they mature beyond permissible limit which can be termed as rotten. Fruit maturity detection is divided into different stages depending on type of fruit detection used. Various sensors and image processing methods are used for this purpose. The major stages involved in this process are pre-processing, detection using sensors and image processing. Spectroscopy has been a huge development in image processing. In later stages the results are classified or clustered according to the requirement. This paper presents a survey of the existing fruit maturity detection techniques.

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“…The speed of surface acoustic waves depends on the type of material used. The piezoelectric materials such as LiNbO 3 , LiTaO 3 , and quartz are widely used in SAW gas sensors [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Design Performance and Frequency Response Analysis of SAW-Based Sensor for Dichloromethane Gas Sensing Amidst the COVID-19

Kumar

Bhadu

2020

J. Vib. Eng. Technol.

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Background To avoid the risk of increase in the rate of human infection is a big challenge for healthcare staffs during the treatment of patients affected from corona virus disease-2019 (COVID-19). This work is intended to save COVID-19-infected patients from conveyed hazardous volatile gases (VOCs) by the gas stream. The VOCs are generally generated from the pathways of medical respiratory devices. Method Here, we present a surface acoustic wave-based sensor device for sensing dichloromethane (DCM) gas that can be connected with a gas pathway system used in medical devices. Dichloromethane is a volatile organic compound. Single-(polyisobutylene, PIB) and multi-(polyisobutylene/silicon nitride, PIB/Si 3 N 4) surface acoustic wave-based sensors are designed on LiNbO 3 piezoelectric substrate. The designed models patterned with interdigitated transducer (IDT) aluminum (Al) electrodes are used to analyze gas sensing behavior for dichloromethane (CH 2 Cl 2 , DCM) gas. Results The studies for DCM gas sensing are carried out for single-and multi-layer models using COMSOL Multi-physics software. The resonant frequency and displacement are also observed for 100 ppm gas concentration of DCM at room temperature. It has been found that multi-layer designed model shows higher sensitivity as compared to single layer (PIB/ LiNbO 3) model on exposing DCM gas. Conclusion The estimation of various parameters observed by the proposed module is also explained. It is observed that the sensitivity of sensor for detection of VOCs (generated in gas pathways in medical devices) is best suited in the present crisis of COVID-19 pandemic.

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Metal oxide based surface acoustic wave sensors for fruits maturity detection

Cited by 7 publications

References 7 publications

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

A Research on Fruit Maturity Detection Techniques

Design Performance and Frequency Response Analysis of SAW-Based Sensor for Dichloromethane Gas Sensing Amidst the COVID-19

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