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.

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.

Chemical Sensor Array Modeling: Application to Resistive Based Chemo Sensors

2014 ◽  
Vol 605 ◽  
pp. 15-18
Author(s):  
Abdelaziz Abbas ◽  
Ahcene Bouabdallah
Keyword(s):  
Mathematical Models ◽  
Differential Form ◽  
Sensor Array ◽  
Chemical Sensor ◽  
Sensor Arrays ◽  
Gas Mixture ◽  
State Equations ◽  
Dynamical Equilibrium ◽  
Chemical Sensor Array ◽  
Intensive Quantities

The aim of paper is to develop analytical mathematical models that describe the thermo dynamical equilibrium of resistive chemical sensor arrays /mixture of vapors multi-system. By using the Gibbs Duhem formalism, state equations in differential form, that the variations of intensive quantities (e.g. sensors partial sensitivity) as function of the gas mixture components concentrations and sensor array parameters describe, have been developed. Moreover, the responses of the sensor arrays as function of gas mixture components concentrations were modeled.

scholarly journals A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2845 ◽  
Author(s):  
Chi-Hsiang Huang ◽  
Chian Zeng ◽  
Yi-Chia Wang ◽  
Hsin-Yi Peng ◽  
Chia-Sheng Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Diagnostic Accuracy ◽  
Sensor Array ◽  
Chemical Sensor ◽  
External Validation ◽  
Machine Learning Technique ◽  
Linear Discriminant ◽  
Chemical Sensor Array ◽  
Learning Technique

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.

Energy saving technology for sintering of black bricks from high-siliceous clay

MRS Advances ◽  
2020 ◽  
Vol 5 (61) ◽  
pp. 3123-3131
Author(s):  
Mario Flores Nicolas ◽  
Marina Vlasova ◽  
Pedro Antonio Márquez Aguilar ◽  
Mykola Kakazey ◽  
Marcos Mauricio Chávez Cano ◽  
...  
Keyword(s):  
Low Temperature ◽  
Oxygen Deficiency ◽  
Glass Ceramics ◽  
Total Content ◽  
Ceramic Materials ◽  
Ternary Mixtures ◽  
High Porosity ◽  
Reducing Conditions ◽  
Binary And Ternary Mixtures ◽  
Dark Color

AbstractThe low-temperature synthesis of bricks prepared from high-siliceous clays by the method of plastic molding of blanks was used. For the preparation of brick blanks, binary and ternary mixtures of high-siliceous clays, black sand, and bottle glass cullet were used. Gray-black low-porosity and high-porosity ceramics was obtained by sintering under conditions of oxygen deficiency. It has been established that to initiate plastic in mixtures containing high-siliceous clay, it is necessary to add montmorillonite/bentonite additives, carry out low-temperature sintering, and introduce low-melting glass additives with a melting point ranging from 750 to 800 °C. The performed investigations have shown that the sintering of mixtures with a total content of iron oxide of about 5 wt% under reducing conditions at Tsint. = 800°C for 8 h leads to the formation of glass ceramics consisting of quartz, feldspars, and a phase. The main sources of the appearance of a dark color is the formation of [Fe3+O4]4- and [Fe3+O6]9- anions in the composition of the glass phase and feldspars. By changing the contents of clay, sand, and glass in sintering, it is possible to obtain two types of ceramic materials: (a) in the form of building bricks and (b) in the form of porous fillers.

Properties of Refrigerants from Cubic Equations of State

2008 ◽  
Vol 59 (5) ◽  
Author(s):  
Viorel Feroiu ◽  
Dan Geana ◽  
Catinca Secuianu
Keyword(s):  
Experimental Data ◽  
Vapor Pressure ◽  
Equations Of State ◽  
Ternary Mixtures ◽  
Volumetric Properties ◽  
Mixing Rules ◽  
Saturation Curve ◽  
Equilibrium Thermodynamic ◽  
Liquid Equilibrium ◽  
Binary And Ternary Mixtures

Vapour � liquid equilibrium, thermodynamic and volumetric properties were predicted for three pure hydrofluorocarbons: difluoromethane (R32), pentafluoroethane (R125) and 1,1,1,2 � tetrafluoroethane (R134a) as well as for binary and ternary mixtures of these refrigerants. Three cubic equations of state GEOS3C, SRK (Soave � Redlich � Kwong) and PR (Peng � Robinson) were used. A wide comparison with literature experimental data was made. For the refrigerant mixtures, classical van der Waals mixing rules without interaction parameters were used. The GEOS3C equation, with three parameters estimated by matching several points on the saturation curve (vapor pressure and corresponding liquid volumes), compares favorably to other equations in literature, being simple enough for applications.

Catalytic hydrogenation of binary and ternary mixtures of unsaturated substances in the liquid phase on platinum

1979 ◽  
Vol 44 (8) ◽  
pp. 2378-2383 ◽  
Author(s):  
Libor Červený ◽  
Radka Junová ◽  
Vlastimil Růžička
Keyword(s):  
Liquid Phase ◽  
Binary Mixtures ◽  
Silica Gel ◽  
Catalytic Hydrogenation ◽  
Ternary Systems ◽  
Ternary Mixtures ◽  
Unsaturated Alcohol ◽  
Solvent Concentration ◽  
Binary And Ternary Mixtures ◽  
Hydrogenation Selectivity

Hydrogenation of olefinic substrates in binary and ternary mixtures using 5% Pt on silica gel as the catalyst was studied in normal conditions in the liquid phase with methanol or cyclohexane or in solvent-free systems. The effect of the solvent concentration on the selectivity of hydrogenation of the unsaturated alcohol-olefin binary mixtures was investigated. In ternary systems of unsaturated substrates, the effect of each of the substrates on the selectivity of hydrogenation of the remaining two substances was examined. Another system was found in which a jump change of the hydrogenation selectivity occurred on the vanishing of the fastest reacting substance.

scholarly journals How to Improve Physico-Chemical Properties of Silk Fibroin Materials for Biomedical Applications?—Blending and Cross-Linking of Silk Fibroin—A Review

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1510
Author(s):  
Sylwia Grabska-Zielińska ◽  
Alina Sionkowska
Keyword(s):  
Silk Fibroin ◽  
Biomedical Applications ◽  
Magnetic Particles ◽  
Chemical Properties ◽  
Ternary Mixtures ◽  
Cross Linking ◽  
Advantages And Disadvantages ◽  
Physico Chemical ◽  
Binary And Ternary Mixtures ◽  
To Receive

This review supplies a report on fresh advances in the field of silk fibroin (SF) biopolymer and its blends with biopolymers as new biomaterials. The review also includes a subsection about silk fibroin mixtures with synthetic polymers. Silk fibroin is commonly used to receive biomaterials. However, the materials based on pure polymer present low mechanical parameters, and high enzymatic degradation rate. These properties can be problematic for tissue engineering applications. An increased interest in two- and three-component mixtures and chemically cross-linked materials has been observed due to their improved physico-chemical properties. These materials can be attractive and desirable for both academic, and, industrial attention because they expose improvements in properties required in the biomedical field. The structure, forms, methods of preparation, and some physico-chemical properties of silk fibroin are discussed in this review. Detailed examples are also given from scientific reports and practical experiments. The most common biopolymers: collagen (Coll), chitosan (CTS), alginate (AL), and hyaluronic acid (HA) are discussed as components of silk fibroin-based mixtures. Examples of binary and ternary mixtures, composites with the addition of magnetic particles, hydroxyapatite or titanium dioxide are also included and given. Additionally, the advantages and disadvantages of chemical, physical, and enzymatic cross-linking were demonstrated.

DECISION MAKING BY VARIABLE CONTRIBUTION IN DISCRIMINANT, LOGIT, AND REGRESSION ANALYSES

2004 ◽  
Vol 03 (02) ◽  
pp. 265-279 ◽  
Author(s):  
STAN LIPOVETSKY ◽  
MICHAEL CONKLIN
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Statistical Models ◽  
Regression Analyses ◽  
Discriminant Functions ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Variable Contribution ◽  
Linear Aggregate

Comparative contribution of predictors in multivariate statistical models is widely used for decision making on the importance of the variables for the aims of analysis and prediction. However, the analysis can be made difficult because of the predictors' multicollinearity that distorts estimates for coefficients in the linear aggregate. To solve the problem of the robust evaluation of the predictors' contribution, we apply the Shapley Value regression analysis that provides consistent results in the presence of multicollinearity both for regression and discriminant functions. We also show how the linear discriminant function can be constructed as a multiple regression, and how the logistic regression can be approximated by linear regression that helps to obtain the variables contribution in the linear aggregate.

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