Fluorescence Quenching Sensor Arrays for the Discrimination of Nitroaromatic Vapors

2018 ◽  
pp. 58-93
Author(s):  
Nico Bolse ◽  
Anne Habermehl ◽  
Carsten Eschenbaum ◽  
Uli Lemmer
Keyword(s):  
Fluorescence Quenching ◽  
Sensor Arrays ◽  
Principal Component ◽  
Future Research ◽  
Sensor Systems ◽  
Vapor Detection ◽  
Electronic Noses ◽  
Theoretical Concepts ◽  
Linear Discriminant ◽  
Practical Applications

Fluorescence quenching is a promising technique for chemical sensing applications such as the detection of explosive trace vapors. Nitroaromatic explosives are one of the primary targets for this approach enabling ultra-low detection limits down to sub parts-per-billion in air. Many studies, however, focus on enhanced sensor sensitivity, whereas practical applications often require the identification and quantification of detected species. Electronic noses and efficient sensor systems are a promising solution to address this challenge. The authors review recent approaches and trends for explosive trace vapor detection and discuss theoretical concepts for fluorescence quenching as well as target analytes, sensor materials, and fabrication techniques. Statistical learning techniques such as principal component analysis and linear discriminant analysis, sensor systems, and camera-based read-out strategies are in the focus of the chapter. The authors conclude with recommendations and solutions for the elaborated challenges and with visions on future research directions.

scholarly journals Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

2021 ◽  
Vol 8 (5) ◽  
pp. 74
Author(s):  
Tatiana Kuchmenko ◽  
Anastasiia Shuba ◽  
Ruslan Umarkhanov ◽  
Anton Chernitskiy
Keyword(s):  
Gaseous Phase ◽  
Sensor Arrays ◽  
Principal Component ◽  
Clinical Score ◽  
Hydrocarbon Chain ◽  
Test Sample ◽  
Bromocresol Green ◽  
Portable Devices ◽  
Linear Discriminant ◽  
Nasal Secretions

The paper demonstrates a new approach to identify healthy calves (“healthy”) and naturally occurring infectious bronchopneumonia (“sick”) calves by analysis of the gaseous phase over nasal secretions using 16 piezoelectric sensors in two portable devices. Samples of nasal secretions were obtained from 50 red-motley Holstein calves aged 14–42 days. Calves were subjected to rectal temperature measurements, clinical score according to the Wisconsin respiratory scoring chart, thoracic auscultation, and radiography (Carestream DR, New York, USA). Of the 50 calves, we included samples from 40 (20 “healthy” and 20 “sick”) in the training sample. The remaining ten calves (five “healthy” and five “sick”) were included in the test sample. It was possible to divide calves into “healthy” and “sick” groups according to the output data of the sensor arrays (maximum sensor signals and calculated parameters Ai/j) using the principal component linear discriminant analysis (PCA–LDA) with an accuracy of 100%. The adequacy of the PCA–LDA model was verified on a test sample. It was found that data of sensors with films of carbon nanotubes, zirconium nitrate, hydroxyapatite, methyl orange, bromocresol green, and Triton X-100 had the most significance for dividing samples into groups. The differences in the composition of the gaseous phase over the samples of nasal secretions for such a classification could be explained by the appearance or change in the concentrations of ketones, alcohols, organic carboxylic acids, aldehydes, amines, including cyclic amines or those with a branched hydrocarbon chain.

scholarly journals Identification of Mint Scents Using a QCM Based E-Nose

2020 ◽  
Author(s):  
Salih Okur ◽  
Mohammed Sarheed ◽  
Robert Huber ◽  
Zejun Zhang ◽  
Lars Heinke ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
K Nearest Neighbor ◽  
Efficient Manner ◽  
Mentha Spicata ◽  
Electronic Noses ◽  
Linear Discriminant ◽  
Nepeta Cataria ◽  
Metal Oxide Nanoparticle ◽  
Nanoparticle Dispersions

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal-Organic Frame as Cu(BPDC), and 7 different polymer films including PVA, PEDOT: PSS, PFO, SB, SW, SG, PB were used for functionalizing of QCM sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models including Principal Component Analy-sis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

Applications of Machine Learning in Real-Time Tumor Localization

2012 ◽  
pp. 419-431
Author(s):  
Ruijiang Li ◽  
Steve B. Jiang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Tumor Localization ◽  
Linear Discriminant ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Applications Of Machine Learning

Recently, machine learning has gained great popularity in many aspects of radiation therapy. In this chapter, the authors will demonstrate the applications of various machine learning techniques in the context of real-time tumor localization in lung cancer radiotherapy. These cover a wide range of well established machine learning techniques, including principal component analysis, linear discriminant analysis, artificial neural networks, and support vector machine, etc. Respiratory gating, as a special case of tumor localization, will also be discussed. The chapter will demonstrate how domain specific knowledge and prior information can be useful in achieving more accurate and robust tumor localization. Future research directions in machine learning that can further improve the accuracy for tumor localization are also discussed.

scholarly journals Low-Power Detection of Food Preservatives by a Novel Nanowire-Based Sensor Array

Foods ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 226 ◽  
Author(s):  
Dario Zappa
Keyword(s):  
Metal Oxide ◽  
Shelf Life ◽  
Sensor Array ◽  
Sensor Arrays ◽  
Principal Component ◽  
Industrial Applications ◽  
Food Preservatives ◽  
Electronic Noses ◽  
Long Time ◽  
Sensors Array

Food preservatives are compounds that are used for the treatment of food to improve the shelf life. In the food industry, it is necessary to monitor all processes for both safety and quality of the product. An electronic nose (or e-nose) is a biomimetic olfactory system that could find numerous industrial applications, including food quality control. Commercial electronic noses are based on sensor arrays composed by a combination of different sensors, which include conductometric metal oxide devices. Metal oxide nanowires are considered among the most promising materials for the fabrication of novel sensing devices, which can enhance the overall performances of e-noses in food applications. The present work reports the fabrication of a novel sensor array based on SnO2, CuO, and WO3 nanowires deposited on top of μHPs provided by ams Sensor Solutions Germany GmbH. The array was tested for the discrimination of four typical compounds added to food products or used for their treatment to increase the shelf life: ethanol, acetone, nitrogen dioxide, and ozone. Results are very promising; the sensors array was able to operate for a long time, consuming less than 50 mW for each single sensor, and principal component analysis (PCA) confirmed that the device was able to discriminate between different compounds.

scholarly journals An Improved Comparison of Chemometric Analyses for the Identification of Acids and Bases With Colorimetric Sensor Arrays

2018 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Michael James Kangas ◽  
Christina L Wilson ◽  
Raychelle M Burks ◽  
Jordyn Atwater ◽  
Rachel M Lukowicz ◽  
...  
Keyword(s):  
Image Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Sensor Arrays ◽  
Principal Component ◽  
Colorimetric Sensor ◽  
Support Vector ◽  
Chemometric Methods ◽  
Data Set ◽  
Linear Discriminant

Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods were compared for the task of classifying 631 solutions (0.1 to 3 M) of acetic acid, malonic acid, lysine, and ammonia using an eight sensor colorimetric array. PCA and LDA (linear discriminant analysis) were effective for visualizing the dataset. For classification, linear discriminant analysis (LDA), (k nearest neighbors) KNN, (soft independent modelling by class analogy) SIMCA, recursive partitioning and regression trees (RPART), and hit quality index (HQI) were very effective with each method classifying compounds with over 90% correct assignments. Support vector machines (SVM) and partial least squares – discriminant analysis (PLS-DA) struggled with ~85 and 39% correct assignments, respectively. Additional mathematical treatments of the data set, such as incrementally increasing the exponents, did not improve the performance of LDA and KNN. The literature precedence indicates that the most common methods for analyzing colorimetric arrays are PCA, LDA, HCA, and KNN. To our knowledge, this is the first report of comparing and contrasting several more diverse chemometric methods to analyze the same colorimetric array data.

scholarly journals Integration of Wavelet and Recurrence Quantification Analysis in Emotion Recognition of Bilinguals

2019 ◽  
Vol 7 (1) ◽  
pp. 35-45
Author(s):  
Ateke Goshvarpour ◽  
Atefeh Goshvarpour ◽  
Ataollah Abbasi
Keyword(s):  
Discriminant Analysis ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Feature Space ◽  
Affective States ◽  
Linear Discriminant ◽  
Practical Applications ◽  
Human Computer Interfaces ◽  
Recurrence Quantification ◽  
Quantification Analysis

Background: This study offers a robust framework for the classification of autonomic signals into five affective states during the picture viewing. To this end, the following emotion categories studied: five classes of the arousal-valence plane (5C), three classes of arousal (3A), and three categories of valence (3V). For the first time, the linguality information also incorporated into the recognition procedure. Precisely, the main objective of this paper was to present a fundamental approach for evaluating and classifying the emotions of monolingual and bilingual college students. Methods: Utilizing the nonlinear dynamics, the recurrence quantification measures of the wavelet coefficients extracted. To optimize the feature space, different feature selection approaches, including generalized discriminant analysis (GDA), principal component analysis (PCA), kernel PCA, and linear discriminant analysis (LDA), were examined. Finally, considering linguality information, the classification was performed using a probabilistic neural network (PNN). Results: Using LDA and the PNN, the highest recognition rates of 95.51%, 95.7%, and 95.98% were attained for the 5C, 3A, and 3V, respectively. Considering the linguality information, a further improvement of the classification rates accomplished. Conclusion: The proposed methodology can provide a valuable tool for discriminating affective states in practical applications within the area of human-computer interfaces.

scholarly journals Salivary spectrum evaluation with Raman’s Spectrometer

2021 ◽  
Vol 5 (3) ◽  
pp. 126-130
Author(s):  
Kumar Sagar ◽  
◽  
Sonal Vahanwala ◽  
Murali Chilakapati ◽  
Mandavi Waghmare ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Normal Saline ◽  
Principal Component ◽  
Future Research ◽  
Tobacco Products ◽  
Male And Female ◽  
Linear Discriminant ◽  
Unstimulated Saliva ◽  
Spectrum Evaluation

Background: Biofluids possess a lot of hidden informations. Along with serum, saliva beholds great potential which needs to be discovered. Certain physical properties of saliva need to be studied closely and thus can be put into use for various diagnostic purposes. Aims and Objectives: To find difference between male and female saliva through spectrometry. Design: 20 systemically sound subjects were selected and explained about the study. The subjects should not be consuming any medication or tobacco products. Materials and Methodology: Subjects were asked to collect unstimulated saliva in the disposable vials. The samples were then centrifuged with the help of speed vacuum concentrator. Then with the help of Gilson’s pipette some centrifuge sample were taken and normal saline was added and was left to dry. With the help of Raman’s Spectrometer 585nm the spectra was recorded and each sample were assessed 5 times. And then raw data was processed. Result and Conclusion: PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis) and Average Spectra are derived. The LDA accuracy of our study was above 70% which is good for future research purposes as anything above 60% can be considered as important breakthrough.

scholarly journals Identification of Mint Scents Using a QCM Based E-Nose

Chemosensors ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 31
Author(s):  
Salih Okur ◽  
Mohammed Sarheed ◽  
Robert Huber ◽  
Zejun Zhang ◽  
Lars Heinke ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
K Nearest Neighbor ◽  
Efficient Manner ◽  
Mentha Spicata ◽  
Electronic Noses ◽  
Linear Discriminant ◽  
Nepeta Cataria ◽  
Metal Oxide Nanoparticle ◽  
Nanoparticle Dispersions

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

scholarly journals A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm

2021 ◽  
Vol 11 (5) ◽  
pp. 2251
Author(s):  
Shu-Chuan Chu ◽  
Zhongjie Zhuang ◽  
Junbao Li ◽  
Jeng-Shyang Pan
Keyword(s):  
Affine Transformation ◽  
Transfer Functions ◽  
Hyperspectral Image ◽  
Optimization Problems ◽  
Principal Component ◽  
Search Space ◽  
Linear Discriminant ◽  
Practical Applications ◽  
De Algorithm ◽  
Generalized Differential

QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

scholarly journals The Potential of the Synthetic Strigolactone Analogue GR24 for the Maintenance of Photosynthesis and Yield in Winter Wheat under Drought: Investigations on the Mechanisms of Action and Delivery Modes

Plants ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1223
Author(s):  
Mojde Sedaghat ◽  
Yahya Emam ◽  
Ali Mokhtassi-Bidgoli ◽  
Saeid Hazrati ◽  
Claudio Lovisolo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Foliar Application ◽  
Mechanisms Of Action ◽  
Bioactive Molecules ◽  
Future Research ◽  
Antioxidant Enzyme Activities ◽  
Synthetic Analog ◽  
Practical Applications ◽  
New Family ◽  
Harsh Environmental Condition

Strigolactones (SLs) have been implicated in many plant biological and physiological processes, including the responses to abiotic stresses such as drought, in concert with other phytohormones. While it is now clear that exogenous SLs may help plants to survive in harsh environmental condition, the best, most effective protocols for treatment have not been defined yet, and the mechanisms of action are far from being fully understood. In the set of experiments reported here, we contrasted two application methods for treatment with a synthetic analog of SL, GR24. A number of morphometric, physiological and biochemical parameters were measured following foliar application of GR24 or application in the residual irrigation water in winter wheat plants under irrigated and drought stress conditions. Depending on the concentration and the method of GR24 application, differentiated photosynthesis and transpiration rate, stomatal conductance, leaf water potential, antioxidant enzyme activities and yield in drought conditions were observed. We present evidence that different methods of GR24 application led to increased photosynthesis and yield under stress by a combination of drought tolerance and escape factors, which should be considered for future research exploring the potential of this new family of bioactive molecules for practical applications.

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