Sensor Array Technique Research Articles

One-component dual-mode sensor array for identification and quantification of biothiols

Biothiol analysis is significant to health assessment and early detection of potential diseases. Considering practical requirements, simple and rapid identification and determination of biothiols are still a great challenge due to the similar structures. Fortunately, the recently emerging colorimetric sensor array technique makes such a task possible. Herein, one-component dual-mode sensor array consisting of carbon dots (CDs) and Ag nanoparticles (AgNPs) system was designed for identification and quantification of biothiols. The identification principle is based on the inner filter effect (IFE) and different binding constants. Due to the IFE between CDs and AgNPs, the fluorescence of CDs was quenched, but recovered again after addition of biothiols because of the binding of biothiols with AgNPs. Significantly, the fluorescence recovered in varying degree due to the different binding constants of biothiols to AgNPs. Meanwhile, the absorbance of the system decreased and the color of the solution deepened. Therefore, the CDs-AgNPs system with fluorescence and absorbance response was used as the single sensing unit and generated the cross-responsive signal for different biothiols. The sensor array achieved 100% accuracy in identifying biothiols and biothiol mixtures. Moreover, the rapid quantification of biothiols in serum samples was also achieved by RGB-based smartphone colorimetry. The way to construct one component sensor array with dual mode signal outputs tremendously saves cost and time, providing a powerful tool for the identification of different biothiols. In addition, the rapid quantification of biothiols in serum samples based on RGB-based smartphone colorimetry demonstrated its powerful application prospects in disease diagnosis.

Quantitative and qualitative detection of target heavy metals using anti-interference colorimetric sensor Array combined with near-infrared spectroscopy

An anti-interference colorimetric sensor array (CSA) technique was developed for the qualitative and quantitative detection of target heavy metals in corn oil. This method involves a binding mechanism that triggers changes in atomic energy levels and visible color changes. A custom-built olfactory visualization device was employed to gather spectral data, revealing distinct CSA color difference patterns. Subsequently, three pattern recognition algorithms were used to create an identification model for the target heavy metals. The results showed that the ACO-KNN (Ant Colony Optimization-K-Nearest Neighbor) model outperformed the other models, achieving accuracy rates of 90.28% and 89.58% for the calibration and prediction sets, respectively. The ACO-PLS (Partial Least Square) model was more stable with the lowest root mean square error of prediction (RMSEP), which were 0.1730 and 0.1180, respectively. The limit of detection (LOD) and quantification (LOQ) of Pb and Hg were (0.3, 0.6, 1.1 and 2.2) x 10−3 mg/L, respectively.

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH−, CO32−, PO43−, NH3, ClO−, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001–1.0 mol L−1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864–0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

Nanozyme Inhibited Sensor Array for Biothiol Detection and Disease Discrimination Based on Metal Ion-Doped Carbon Dots.

Developing highly active and sensitive nanozymes for biothiol analysis is of vital significance due to their essential roles in disease diagnosis. Herein, two metal ion-doped carbon dots (M-CDs) with high peroxidase-like activity were designed and prepared for biothiol detection and identification through the colorimetric sensor array technique. The two M-CDs can strongly catalyze the decomposition of H2O2, accompanied by color changes of 3,3',5,5'-tetramethylbenzidine (TMB) from colorless to blue, indicating peroxidase-mimicking activities of M-CDs. Compared with pure carbon dots (CDs), M-CDs exhibited enhanced peroxidase-like activity owing to the synergistic effect between metal ions and CDs. However, due to the strong binding affinity between biothiols and metal ions, the catalytic activities of M-CDs could be inhibited by different biothiols to diverse degrees. Therefore, using TMB as a chromogenic substrate in the presence of H2O2, the developed colorimetric sensor array can form differential fingerprints for the three most important biothiols (i.e., cysteine (Cys), homocysteine (Hcy), and glutathione (GSH)), which can be accurately discriminated through pattern recognition methods (i.e., hierarchical clustering analysis (HCA) and principal component analysis (PCA)) with a detection limit of 5 nM. Moreover, the recognition of a single biothiol with various concentrations and biothiol mixtures was also realized. Furthermore, actual samples such as cells and sera can also be well distinguished by the as-fabricated sensor array, demonstrating its potential in disease diagnosis.

Application of colorimetric sensor array combined with visible near-infrared spectroscopy for the matcha classification

Matcha tea powder is considered as an integral part of a healthy diet due to its enormous health benefits. In the current study, visible near-infrared (Vis-NIR) and colorimetric sensor array (CSA) techniques are applied to identify the matcha grades. The color-sensitive dyes reacted with their volatile compounds and the information was recorded by Vis-NIR spectroscopy, namely Vis-NIR-CSA. Specifically, three linear and three nonlinear classification models were compared, yielding the optimal identification rate by the back-propagation artificial neural network (BPANN) model with 99% and 98% in the calibration and prediction sets, respectively. The results indicated the sensor combined with the BPANN model could be applied satisfactorily in identification of different matcha grades. Additionally, the variations in volatile compounds between different matcha grades and eight characteristic volatile compounds were identified, which verified the sensor identification results. This study provided a scientific and novel method for the stability of matcha quality in production.

Evaluation of Discrimination Performance in Case for Multiple Non-Discriminated Samples: Classification of Honeys by Fluorescent Fingerprinting

In this study we develop a variant of fluorescent sensor array technique based on addition of fluorophores to samples. A correct choice of fluorophores is critical for the successful application of the technique, which calls for the necessity of comparing different discrimination protocols. We used 36 honey samples from different sources to which various fluorophores were added (tris-(2,2′-bipyridyl) dichlororuthenium(II) (Ru(bpy)32+), zinc(II) 8-hydroxyquinoline-5-sulfonate (8-Ox-Zn), and thiazole orange in the presence of two types of deoxyribonucleic acid). The fluorescence spectra were obtained within 400–600 nm and treated by principal component analysis (PCA). No fluorophore allowed for the discrimination of all samples. To evaluate the discrimination performance of fluorophores, we introduced crossing number (CrN) calculated as the number of mutual intersections of confidence ellipses in the PCA scores plots, and relative position (RP) characterized by the pairwise mutual location of group centers and their most distant points. CrN and RP parameters correlated with each other, with total sensitivity (TS) calculated by Mahalanobis distances, and with the overall rating based on all metrics, with coefficients of correlation over 0.7. Most of the considered parameters gave the first place in the discrimination performance to Ru(bpy)32+ fluorophore.

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

BackgroundVentilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique.MethodsWe conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy.ResultsA total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement.ConclusionsThere was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

Rapid evaluation of chicken meat freshness using gas sensor array and signal analysis considering total volatile basic nitrogen

ABSTRACT Total volatile basic nitrogen (TVB-N) level rapid evaluation on chicken meat based on gas sensor array (GSA) technique was studied in this paper. GSA responses to chicken meat stored at 4°C were examined for 5 days. TVB-N content was synchronously measured by chemical examination. Principal component analysis (PCA) and non-linear double-layered cascaded serial stochastic resonance (DCSSR) were utilized for measurement data analysis. TVB-N examination results suggested that chicken meat stored for more than 3 days was not fresh. PCA showed poor discrimination abilities, while DCSSR signal-to-noise ratio (SNR) quantitatively characterized the freshness of all samples. Chicken meat TVB-N forecasting model was developed by non-linear fitting between SNR eigenvalues and TVB-N values. The predicting model was constructed. Validation experiment results demonstrated that the forecasting accuracy of the developed model reached 93.3%.

Sector sensor array technique for high conductivity materials imaging in magnetic induction tomography

BackgroundMagnetic induction tomography (MIT) is a tomographic imaging technique, which has potential applications in security, industry, and medicine. Typically, sensors form a closed structure around the object. However, the measurement cannot be achieved using a closed sensor array in the process of severe brain trauma nursing and the neurosurgery operation.ResultsThe new sector sensor array magnetic induction tomography (SMIT) system is developed to realize real-time monitoring in the treatment of the brain. The functions of the drive coil and the sensor coil are separated in this system. The detection sensitivity of the imaging region boundary is analyzed through simulation. The sensor array locates on the high detection-sensitivity area, and the low sensitivity detection area is reserved for operation and clinical equipment. The sensor array received the energy of the signal accounts for reach 90% of the total energy. The integrity measuring data are obtained using a rotating scan in the system. In the experiment, we analyze the effects that system parameters have on the quality of imaging, for example, the scan step size, the number of sensors, the coverage angle of the sensor array and the scan angle. The experiment result provides a reference for the SMIT system design under a particular condition. In the complete measurement, the SMIT system reconstructs the images of center goal and margin goal, and the actual images have high peak signal-to-noise ratio.ConclusionsThe SMIT system can rebuild the conductivity distribution of the imaging region using incomplete space. In rotation measurement, the system provides a working place for clinical care. The flexible design of the system based on the experiment result makes the different treatment for brain injury own matched SMIT equipment.

Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources.

An array of low-cost sensors was assembled and tested in a chamber environment wherein several pollutant mixtures were generated. The four classes of sources that were simulated were mobile emissions, biomass burning, natural gas emissions, and gasoline vapors. A two-step regression and classification method was developed and applied to the sensor data from this array. We first applied regression models to estimate the concentrations of several compounds and then classification models trained to use those estimates to identify the presence of each of those sources. The regression models that were used included forms of multiple linear regression, random forests, Gaussian process regression, and neural networks. The regression models with human-interpretable outputs were investigated to understand the utility of each sensor signal. The classification models that were trained included logistic regression, random forests, support vector machines, and neural networks. The best combination of models was determined by maximizing the F1 score on ten-fold cross-validation data. The highest F1 score, as calculated on testing data, was 0.72 and was produced by the combination of a multiple linear regression model utilizing the full array of sensors and a random forest classification model.

Improving the robustness of beamforming using asymmetric sensor arrays

Beamforming or phased array with an array of sensors is an advanced signal processing technique for directional signal transmission or reception. This directionality is achieved by phase shifts of received signals of each sensor for the constructive interference of wavefronts, resulting in the amplification of the signal from a particular direction. In this research, the use of an asymmetric sensor array is proposed to reduce the effects of “spatial aliasing,” which is typically encountered in the structural health monitoring practice when employing phased arrays. In this technique, a sensor array is asymmetrically and closely deployed for beamforming and for robust source localization. This sensor deployment has a great effect on reducing the spatial aliasing errors. Although many advanced signal processing algorithms have been developed in the past, an asymmetric sensor array proposed in this study is used to reduce the spatial aliasing error from the sensor deployment perspective. In order to demonstrate the proposed sensor array technique, several simulation and experimental investigations are carried out, and the performance comparison is made to demonstrate the superior robustness of the asymmetric sensor array.

Identification of Nanoparticles with a Colorimetric Sensor Array

A simple colorimetric sensor array technique was developed for the detection of various different nanoparticles (NPs) in aqueous solutions. The sensor array consists of five different cross-reactive chemoresponsive dyes, whose visible absorbances change in response to their interactions with NPs. Although no single dye is specific for any one NP, the pattern of color changes for all dyes provides a unique molecular fingerprint for each type of NP studied. Based on the responses of various dyes, a semiquantitative determination of concentration of each type of NP could also be accomplished with excellent sensitivity (<100 ng/mL). A variety of chemically distinct NPs were unambiguously identified using a standard chemometric approaches, including gold nanospheres (2 through 40 nm diameter), gold nanorods (2.4 and 3.5 aspect ratios), and multifunctional carbon nanospheres without errors in 112 trials. This colorimetric approach may pave the way for a fast, reliable, and inexpensive method to detect nanopollution and to characterize the physiochemical properties of NPs.

Survey on Tea Discriminator

A number of beverages viz. hard drink, soft drink, packet juice, tea are being used around the globe having different chemical compositions. Organoleptic Systems are being used for a variety of detection tasks from checking quality of food products to medicinal diagnosis by detecting chemical compositions. The entire organoleptic system, from sample delivery stage to classification stage, is usually optimized to a particular problem domain in order to provide suitable sensing performance. The optimization of sample preparation, signal processing, feature extraction, classifier are as important as choice of sensors within the array in enhancing the performance of the organoleptic system because presence of irrelevant features increases the dimensionality of the search space, which can do reverse effect on the accuracy of the Pattern Recognition (PARC) techniques. Tea in present world is the most popular beverage having enormous global marketing. Various researchers have made a number of efforts to correlate tea quality with its chemical composition which led to many humanoid errors and may vary from person to person. This problem can be solved by using an instrument called “Electronic Tongue (i-tongue)”. It is developed to reduce human sensory test panels, get accurate measurement of taste, prepare optimized development time and cost. This system analyses liquid including an array of non-specific chemical sensors with partial specificity for different component in liquid samples and appropriate pattern recognition capable of recognizing the qualitative and quantitative composition of sample and complex solutions. A number of attempts have been performed using sensor array and electrochemical techniques such as Cyclic Voltammetry, Potentiometry and Conductivity to classify different types of tea.

A Multistage Architecture for Statistical Inference with Stochastic Signal Acquisition

We describe a statistical inference approach for designing signal acquisition interfaces and inference systems with stochastic devices. A signal is observed by an array of binary comparison sensors, such as highly scaled comparators in an analog-to-digital converter, that exhibit random offsets in their reference levels due to process variations or other uncertainties. These offsets can limit the performance of conventional measurement devices. In our approach, we build redundancy into the sensor array and use statistical estimation techniques to account for uncertainty in the observations and produce a more reliable estimate of the acquired signal. We develop an observational model and find a Cramer-Rao lower bound on the achievable square error performance of such a system. We then propose a two-stage inference architecture that uses a coarse estimate to select a subset of the sensor outputs for further processing, reducing the overall complexity of the system while achieving near-optimal performance. The performance of the architecture is demonstrated using a simulated prototype for parameter estimation and symbol detection applications. The results suggest the feasibility of using unreliable components to build reliable signal acquisition and inference systems.

Detection of coffee flavour ageing by solid-phase microextraction/surface acoustic wave sensor array technique (SPME/SAW)

The use of polymer coated surface acoustic wave (SAW) sensor arrays is a very promising technique for highly sensitive and selective detection of volatile organic compounds (VOCs). We present new developments to achieve a low cost sensor setup with a sampling method enabling the highly reproducible detection of volatiles even in the ppb range. Since the VOCs of coffee are well known by gas chromatography (GC) research studies, the new sensor array was tested for an easy assessable objective: coffee ageing during storage. As reference method these changes were traced with a standard GC/FID set-up, accompanied by sensory panellists. The evaluation of GC data showed a non-linear characteristic for single compound concentrations as well as for total peak area values, disabling prediction of the coffee age. In contrast, the new SAW sensor array demonstrates a linear dependency, i.e. being capable to show a dependency between volatile concentration and storage time.

Analyzing and Processing EEG-based Multichannel Signals Acquired during Sleeping

Abstract Processing of signals acquired from sensor systems needs accurate algorithms to extract information of interest concerning the problem under study. In this work Empirical Mode Decomposition method is used on EEG signals obtained during polysomnography examination, when electromyographic (EMG) signals are acquired too. EMD method decomposes a signal into components named Intrinsic Mode Functions (IMF) which can exhibit important time-frequency information related to signals under observation. Since EEG signals are obtained from multiple electrodes, the problem is addressed to processing of signals acquired from multiple channels according to sensor array techniques. The objective of this work is to define an automatic method to detect transient event, changes of sleep stage in EEG signals which can allow an evaluation of the state of the patient. In this first step we analyze and process EEG signals through EMD method to remove EMG contributes. The obtained results are encouraging in the definition of a future multivariate approach to quantify brain activity by evaluating correlation of the IMFs calculated for each channel.

高温超電導テープ線材の局所&lt;i&gt;J&lt;/i&gt;&lt;sub&gt;c &lt;/sub&gt;分布評価技術

The high spatial homogeneity of critical current density, Jc, in HTS tape is one of the most important requirements to realize practical superconducting tapes. However, it is hardly possible to detect local Jc distribution using conventional characterization techniques because the length scale of such Jc variation is several orders smaller than that of the conventional techniques. We have succeeded in developing novel techniques such as low-temperature laser scanning microscopy and reel-toreel Hall probe scanning microscopy for spatially resolved measurements of local Jc in HTS tapes of multiple lengths and operating conditions. In this paper, I summarize these techniques together with other useful methods including magneto-optical imaging and the Hall sensor array technique.

A new ac technique for fast and continuous characterization of coated conductor tapes

A new ac technique for high-speed characterization of long coated conductor tapes is proposed and demonstrated. The technique utilizes an excitation coil and a small pick-up coil and continuously measures the ac susceptibility of a traveling superconducting tape. The pick-up coil signal is phase-sensitively detected by a digital lock-in amplifier and the ratio between the real and imaginary components is converted into the critical current density of the superconducting tape relying on a simple critical state assumption. An advantage of the technique is that precise information about the size and the location of coils is not necessary because the ratio is not sensitive to them. The technique is also useful for the characterization of striated tapes, for which the conventional Hall sensor array technique does not give useful information.

Mixed self-assembled lipopolymers with spacer lipids enhancing sensitivity of lipid-derivative QCMs for odor sensors

Abstract An odor sensing system using a quartz crystal microbalance (QCM) sensor array and pattern recognition technique has been for years a main research topic in our group. For the general field of artificial olfaction using acoustic-wave based sensors such as QCMs it is vital to search for novel sensing materials. Here we present recent results of our ongoing study on application of pegylated lipids as coatings for QCM odor-sensors. The method presented herein is based on self-assembling of lipids and lipid-derivatives on the QCM surfaces. The disulphide-terminated lipids and lipopolymers are co-chemisorbed onto gold electrodes of QCM sensors by simple immersion in ethanolic solutions. This creates porous supports onto which additional layers of lipopolymers are physisorbed. The method allows for fabrication of lipopolymeric QCM odor-sensors with enhanced sensitivity to odorants, capable of very good discrimination among odorant samples—according to the functional group of an odorant.

Contact-Less Characterization of Long Coated Conductor Tapes Using a Hall Sensor Array and a Pick-Up Coil

A contact-less characterization technique is indispensable for the development and the application of long coated conductors. The Hall sensor array technique is a very sufficient one for high-speed measurements of the critical current density distribution along the length of coated conductor tapes. Careful analysis showed that the critical current induced in this technique corresponded to a rather low voltage criterion in comparison with the usual value, 1 muV/cm . Therefore, it is necessary to estimate the n-value in addition to the J <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> value to fully characterize tapes. We have already developed a contact-less technique for the measurement of n-value of thin films. We apply this technique to coated conductors.