Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique

Maamor, H. N.; Rashid, Farzana; Zakaria, N. Z. I.; Zakaria, Ammar; Kamarudin, Kamarulzaman; Jaafar, Mohd Saleh; Shakaff, Ali Yeon Md; Subari, Norazian; Yusuf, N.; Ismail, S. W. M.; Khashman, Adnan

doi:10.1109/iced.2014.7015812

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…With appropriate sensing front-ends, the application of spike-based neuromorphic gustatory sensors can be extended to determine the chemical composition of liquids. More recently, implementation of e-noses in conjunction with e-tongues have been reported for applications such as food quality assessment [ 99 , 100 , 101 , 102 ]. Based on similar concepts, correlation of neuromorphic gustation and olfaction can expose numerous research avenues for future work.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems

Vanarse

Osseiran

Rassau

2017

Sensors

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The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses.

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Section: Conclusion and Future Researchmentioning

confidence: 99%

An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems

Vanarse

Osseiran

Rassau

2017

Sensors

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“…The use of potentiometric sensors in combination with principal component analysis (PCA) is an alternative in the classification and identification of samples [ 23 ]. Other techniques that can be applied are: LDA [ 24 ], canonical correlation analysis (CCA) [ 25 ], support vector machine (SVM), probabilistic neural network (PNN) and k-nearest neighbour (KNN) [ 26 ], discriminant function analysis (DFA) [ 27 ], cluster analysis (CA), artificial neural networks (ANN), partial least squares (PLS), principal component regression (PCR) [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Voltammetric E-Tongue for Honey Adulteration Detection

Ciursa

Oroian

2021

Sensors

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The aim of this study is to establish the usefulness of an electronic tongue based on cyclic voltammetry e-tongue using five working electrodes (gold, silver, copper, platinum and glass) in honey adulteration detection. Authentic honey samples of different botanical origin (acacia, tilia, sunflower, polyfloral and raspberry) were adulterated with agave, maple, inverted sugar, corn and rice syrups in percentages of 5%, 10%, 20% and 50%. The silver and copper electrodes provided the clearest voltammograms, the differences between authentic and adulterated honey samples being highlighted by the maximum current intensity. The electronic tongue results have been correlated with physicochemical parameters (pH, free acidity, hydroxymethylfurfural content—5 HMF and electrical conductivity—EC). Using statistical methods such as Linear discriminant analysis (LDA) and Support vector machines (SVM), an accuracy of 94.87% and 100% respectively was obtained in the calibration step and 89.65% and 100% respectively in the validation step. The PLS-R (Partial Least Squares Regression) model (constructed from the minimum and maximum current intensity obtained for all electrodes) was used in physicochemical parameters prediction; EC reached the highest regression coefficients (0.840 in the calibration step and 0.842 in the validation step, respectively), being followed by pH (0.704 in the calibration step and 0.516 in the validation step, respectively).

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Voltammetric Electrode Array Optimization for Black Tea Discrimination Using Computational Intelligence Approach

et al. 2021

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Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique

Cited by 5 publications

References 19 publications

An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems

An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems

Voltammetric E-Tongue for Honey Adulteration Detection

Voltammetric Electrode Array Optimization for Black Tea Discrimination Using Computational Intelligence Approach

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