scholarly journals DATA ANALYSIS FOR CHEMICAL SENSOR ARRAYS

2006 ◽  
pp. 147-169 ◽  
Author(s):  
Corrado Di Natale ◽  
Eugenio Martinelli ◽  
Giorgio Pennazza ◽  
Andrea Orsini ◽  
Marco Santonico
Keyword(s):  
Data Analysis ◽  
Chemical Sensor ◽  
Sensor Arrays

Neural-network-based data analysis for chemical sensor arrays

1995 ◽  
Author(s):  
Sherif Hashem ◽  
Paul E. Keller ◽  
Richard T. Kouzes ◽  
Lars J. Kangas
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Chemical Sensor ◽  
Sensor Arrays

scholarly journals Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2547 ◽  
Author(s):  
Tuo Gao ◽  
Yongchen Wang ◽  
Chengwu Zhang ◽  
Zachariah A. Pittman ◽  
Alexandra M. Oliveira ◽  
...  
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Single Type ◽  
Multivariate Techniques ◽  
Integrated Sensor ◽  
Resistance Change ◽  
Linear Discriminant ◽  
Lift Off

Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (ΔR/R0), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products.

ODOR MIXTURES AND CHEMOSENSORY ADAPTATION IN GAS SENSOR ARRAYS

2003 ◽  
Vol 12 (01) ◽  
pp. 1-16 ◽  
Author(s):  
RICARDO GUTIERREZ-OSUNA ◽  
NILESH U. POWAR
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Ternary Mixtures ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Chemical Sensor Array ◽  
Binary And Ternary Mixtures ◽  
Olfactory Systems ◽  
Odor Mixtures ◽  
Input Adaptation

Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.

Differentiation of vapor mixture with chemical sensor arrays

2015 ◽  
Author(s):  
Chulki Kim ◽  
Youngmo Jung ◽  
Hi Gyu Moon ◽  
Ji Eun Lee ◽  
Bum Ju Shin ◽  
...  
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Vapor Mixture
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