Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue

2004 ◽  
Vol 525 (1) ◽  
pp. 133-139 ◽  
Author(s):  
S. Buratti ◽  
S. Benedetti ◽  
M. Scampicchio ◽  
E.C. Pangerod
Keyword(s):  
Electronic Nose ◽  
Electronic Tongue

Sensor Fusion of Electronic Nose and Electronic Tongue for Classification of Orthosiphon stamineus

2011 ◽  
Vol 9 (2) ◽  
pp. 837-840 ◽  
Author(s):  
A. Zakaria ◽  
A. Y. M. Shakaff ◽  
A. H. Adom ◽  
M. N. Ahmad ◽  
A. R. Shaari ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Electronic Nose ◽  
Electronic Tongue ◽  
Orthosiphon Stamineus

Electronic nose and electronic tongue integration for improved classification of clinical and food samples

2000 ◽  
Vol 64 (1-3) ◽  
pp. 15-21 ◽  
Author(s):  
Corrado Di Natale ◽  
Roberto Paolesse ◽  
Antonella Macagnano ◽  
Alessandro Mantini ◽  
Arnaldo D'Amico ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Electronic Tongue ◽  
Food Samples

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

scholarly journals Application of Artificial Intelligence in Food Industry—a Guideline

2021 ◽  
Author(s):  
Nidhi Rajesh Mavani ◽  
Jarinah Mohd Ali ◽  
Suhaili Othman ◽  
M. A. Hussain ◽  
Haslaniza Hashim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Food Industry ◽  
Near Infrared ◽  
Vision System ◽  
Electronic Tongue ◽  
World Population ◽  
Computer Vision System ◽  
The Past

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

Intelligent Classification of Cocoa Bean using E-nose

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 28-35
Author(s):  
Nur Amanda Nazli ◽  
Muhammad Sharfi Najib ◽  
Suhaimi Mohd Daud ◽  
Mujahid Mohammad
Keyword(s):  
Electronic Nose ◽  
Chemical Sensors ◽  
Classification Accuracy ◽  
Electronic Device ◽  
Raw Material ◽  
Cocoa Bean ◽  
Cocoa Beans ◽  
Microsoft Excel ◽  
Specificity And Sensitivity

Cocoa bean (Theobrama cacao) is an essential raw material in the manufacture of chocolate, and their classification is crucial for the synthesis of good chocolate flavour. Cocoa beans appear to be very similar to one another when visualised. Hence, an electronic device named the electronic nose (E-Nose) is used to classify the odor of cocoa beans to give the best cocoa bean quality. E-nose is a set of an array of chemical sensors used to sense the gas vapours produced by the cocoa bean and the raw data collected was kept in Microsoft Excel, and the classification took place in Octave. They then underwent normalisation technique to increase classification accuracy, and their features were extracted using mean calculation. The features were classified using CBR, and the similarity value is obtained. The results show that CBR's classification accuracy, specificity and sensitivity are all 100%.

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