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Multivariate Data Analysis for Sensors and Sensor Arrays
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Multivariate Data Analysis for Sensors and Sensor Arrays

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: closed (30 August 2019) | Viewed by 68022

Special Issue Editors

Dr. Cristina Ariño

E-Mail Website
Guest Editor
Departament d’Enginyeria Química i Química Analítica, Facultat de Química, Universitat de Barcelona, Martí i Franquès 1-11, E-08028 Barcelona, Spain
Interests: electrochemical sensors; biosensors; multi-sensor arrays; voltammetric techniques; electrochemical detection; chemometrics
Special Issues, Collections and Topics in MDPI journals
Dr. Núria Serrano

E-Mail Website
Guest Editor
Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain
Interests: electrochemical sensors; screen-printed devices; chemometrics; persistent and emerging pollutants; electronic tongues; liquid chromatography; food authentication
Special Issues, Collections and Topics in MDPI journals
Dr. Xavier Cetó

E-Mail Website
Guest Editor
Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Barcelona, Spain
Interests: electrochemical (bio)sensors, screen-printed devices, electronic tongues, chemometrics, heavy metal ions, food authentication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing demand of further and complimentary information about a sample when (bio)sensors and (bio)sensor arrays are considered for analytical purposes has led to the use of multivariate data analysis strategies to overcome limitations found with classical approaches. In this direction, chemometrics allow the analysis of large amounts of data, and the possibility to extract meaningful data from complex readings, paving the way for new measurement approaches. These multisensory systems are based on chemosensors, with different selectivities towards analytes, providing complimentary data about a sample, which, after appropriate data analysis, allows the classification of samples and/or the quantification of selected analytes.

The great advances in the development of (bio)sensor arrays and in the analysis of data obtained, justifies the publication of a Special Issue devoted to the application of multivariate data analysis for (bio)sensors and (bio)sensor arrays. Works describing novel applications and strategies for multicomponent analysis using single sensors or sensor arrays, including electronic noses and tongues, can be submitted. Furthermore, manuscripts addressing the use of multivariate data analysis methods are also welcome, even not based on sensors or sensors arrays, but as long as those methodologies are suitable for those. Both research papers and review articles will be considered. We look forward to and welcome your participation in this Special Issue.

Dr. Cristina Ariño
Dr. Núria Serrano
Dr. Xavier Cetó
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Chemometrics
  • Multivariate data analysis
  • Pattern recognition
  • Principal component analysis (PCA)
  • Partial least squares regression (PLS)
  • Artificial neural networks (ANNs)
  • (Bio)sensors and Sensors arrays
  • Smart sensors
  • Lab-on-a-chip
  • Microfluidics
  • Flow injection analysis (FIA)
  • Sequential injection analysis (SIA)
  • Electronic tongues
  • Electronic noses
  • Taste sensor
  • Sensory analysis
  • Hybrid systems
  • Agri-food analysis
  • Environmental analysis
  • Biomedical applications
  • Automatic analysis and quality control

Published Papers (15 papers)

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Research

18 pages, 416 KiB  
Article
An Online Method to Detect Urban Computing Outliers via Higher-Order Singular Value Decomposition
by Thiago Souza, Andre L. L. Aquino and Danielo G. Gomes
Sensors 2019, 19(20), 4464; https://doi.org/10.3390/s19204464 - 15 Oct 2019
Cited by 1 | Viewed by 1979
Abstract
Here we propose an online method to explore the multiway nature of urban spaces data for outlier detection based on higher-order singular value tensor decomposition. Our proposal has two sequential steps: (i) the offline modeling step, where we model the outliers detection problem [...] Read more.
Here we propose an online method to explore the multiway nature of urban spaces data for outlier detection based on higher-order singular value tensor decomposition. Our proposal has two sequential steps: (i) the offline modeling step, where we model the outliers detection problem as a system; and (ii) the online modeling step, where the projection distance of each data vector is decomposed by a multidimensional method as new data arrives and an outlier statistical index is calculated. We used real data gathered and streamed by urban sensors from three cities in Finland, chosen during a continuous time interval: Helsinki, Tuusula, and Lohja. The results showed greater efficiency for the online method of detection of outliers when compared to the offline approach, in terms of accuracy between a range of 8.5% to 10% gain. We observed that online detection of outliers from real-time monitoring through the sliding window becomes a more adequate approach once it achieves better accuracy. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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11 pages, 1715 KiB  
Article
High-Throughput Chemometric Quality Assessment of Extra Virgin Olive Oils Using a Microtiter Plate Reader
by Huihui He and Weiying Lu
Sensors 2019, 19(19), 4169; https://doi.org/10.3390/s19194169 - 26 Sep 2019
Cited by 4 | Viewed by 2578
Abstract
A commercially available microtiter plate reader was applied as a high-throughput counterpart of ultraviolet-visible (UV–Vis) spectrophotometer to identify the producing location of extra virgin olive oils (EVOOs). Multiplicative scatter correction and the first derivative was used to denoise the UV–Vis spectra and eliminate [...] Read more.
A commercially available microtiter plate reader was applied as a high-throughput counterpart of ultraviolet-visible (UV–Vis) spectrophotometer to identify the producing location of extra virgin olive oils (EVOOs). Multiplicative scatter correction and the first derivative was used to denoise the UV–Vis spectra and eliminate the effects of background drift. The spectra were analyzed using chemometrics methods including the principal component analysis (PCA) and the partial least squares-discriminant analysis (PLS-DA). The PLS-DA model on full spectra using 5 latent variables showed a classification accuracy of 97.92% by cross-validation. The overall results demonstrated that the use of a UV–Vis spectrophotometer based on the microtiter plate reader combined with chemometrics can be applied to the quality assessment of EVOOs. It is demonstrated that the microtiter plate reader can be a high-throughput tool in the quality assessment of food ingredients. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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26 pages, 5495 KiB  
Article
Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources
by Jacob Thorson, Ashley Collier-Oxandale and Michael Hannigan
Sensors 2019, 19(17), 3723; https://doi.org/10.3390/s19173723 - 28 Aug 2019
Cited by 25 | Viewed by 5053
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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13 pages, 2772 KiB  
Article
Real-Time Classification of Multivariate Olfaction Data Using Spiking Neural Networks
by Anup Vanarse, Adam Osseiran and Alexander Rassau
Sensors 2019, 19(8), 1841; https://doi.org/10.3390/s19081841 - 18 Apr 2019
Cited by 12 | Viewed by 4321
Abstract
Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay [...] Read more.
Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an SNN-based solution for the classification of rank-order spiking patterns to provide continuous recognition results in real-time. The SNN classifier is deployed on a neuromorphic hardware system that enables massively parallel and low-power processing on incoming rank-order patterns. Offline learning is used to store the reference rank-order patterns, and an inbuilt nearest neighbor classification logic is applied by the neurons to provide recognition results. The proposed system was evaluated using two different datasets including rank-order spiking data from previously established olfactory systems. The continuous classification that was achieved required a maximum of 12.82% of the total pattern frame to provide 96.5% accuracy in identifying corresponding target gases. Recognition results were obtained at a nominal processing latency of 16ms for each incoming spike. In addition to the clear advantages in terms of real-time operation and robustness to inconsistent rank-orders, the SNN classifier can also detect anomalies in rank-order patterns arising due to drift in sensing arrays. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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17 pages, 3446 KiB  
Article
Dynamic Field Monitoring Based on Multitask Learning in Sensor Networks
by Di Wang and Xi Zhang
Sensors 2019, 19(7), 1533; https://doi.org/10.3390/s19071533 - 29 Mar 2019
Cited by 1 | Viewed by 2669
Abstract
Field monitoring serves as an important supervision tool in a variety of engineering domains. An efficient monitoring would trigger an alarm timely once it detects an out-of-control event by learning the state change from the collected sensor data. However, in practice, multiple sensor [...] Read more.
Field monitoring serves as an important supervision tool in a variety of engineering domains. An efficient monitoring would trigger an alarm timely once it detects an out-of-control event by learning the state change from the collected sensor data. However, in practice, multiple sensor data may not be gathered appropriately into a database for some unexpected reasons, such as sensor aging, wireless communication failures, and data reading errors, which leads to a large number of missing data as well as inaccurate or delayed detection, and poses a great challenge for field monitoring in sensor networks. This fact motivates us to develop a multitask-learning based field monitoring method in order to achieve an efficient detection when considerable missing data exist during data acquisition. Specifically, we adopt a log likelihood ratio (LR)-based multivariate cumulative sum (MCUSUM) control chart given spatial correlation among neighboring regions within the monitored field. To deal with the missing data problem, we integrate a multitask learning model into the LR-based MCUSUM control chart in the sensor network. Both simulation and real case studies are conducted to validate our proposed approach and the results show that our approach can achieve an accurate and timely detection for an out-of-control state when a large number of missing data exist in the sensor database. Our model provides an effective field monitoring strategy for engineering applications to accurately and timely detect the products with abnormal quality during production and reduce product losses. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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17 pages, 2388 KiB  
Article
Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage
by Suming Zhang, Gengxing Zhao, Kun Lang, Baowei Su, Xiaona Chen, Xue Xi and Huabin Zhang
Sensors 2019, 19(7), 1485; https://doi.org/10.3390/s19071485 - 27 Mar 2019
Cited by 61 | Viewed by 4350
Abstract
Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. [...] Read more.
Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R2 = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R2 = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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16 pages, 3160 KiB  
Article
Organoleptic Analysis of Drinking Water Using an Electronic Tongue Based on Electrochemical Microsensors
by Manuel Gutiérrez-Capitán, Marta Brull-Fontserè and Cecilia Jiménez-Jorquera
Sensors 2019, 19(6), 1435; https://doi.org/10.3390/s19061435 - 23 Mar 2019
Cited by 13 | Viewed by 8132
Abstract
The standards that establish water’s quality criteria for human consumption include organoleptic analysis. These analyses are performed by taste panels that are not available to all water supply companies with the required frequency. In this work, we propose the use of an electronic [...] Read more.
The standards that establish water’s quality criteria for human consumption include organoleptic analysis. These analyses are performed by taste panels that are not available to all water supply companies with the required frequency. In this work, we propose the use of an electronic tongue to perform organoleptic tests in drinking water. The aim is to automate the whole process of these tests, making them more economical, simple, and accessible. The system is composed by an array of electrochemical microsensors and chemometric tools for multivariable processing to extract the useful chemical information. The array of sensors is composed of six Ion-Sensitive Field Effect Transistors (ISFET)-based sensors, one conductivity sensor, one redox potential sensor, and two amperometric electrodes, one gold microelectrode for chlorine detection, and one nanocomposite planar electrode for sensing electrochemical oxygen demand. A previous study addressed to classify water samples according to taste/smell descriptors (sweet, acidic, salty, bitter, medicinal, chlorinous, mouldy, and earthy) was performed. A second study comparing the results of two organoleptic tests (hedonic evaluation and ranking test) with the electronic tongue, using Partial Least Squares regression, was conducted. The results show that the proposed electronic tongue is capable of analyzing water samples according to their organoleptic characteristics, which can be used as an alternative method to the taste panel. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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12 pages, 1762 KiB  
Article
Non-Targeted HPLC-UV Fingerprinting as Chemical Descriptors for the Classification and Authentication of Nuts by Multivariate Chemometric Methods
by Guillem Campmajó, Gemma J. Navarro, Nerea Núñez, Lluís Puignou, Javier Saurina and Oscar Núñez
Sensors 2019, 19(6), 1388; https://doi.org/10.3390/s19061388 - 21 Mar 2019
Cited by 13 | Viewed by 4258
Abstract
Recently, the authenticity of food products has become a great social concern. Considering the complexity of the food chain and that many players are involved between production and consumption; food adulteration practices are rising as it is easy to conduct fraud without being [...] Read more.
Recently, the authenticity of food products has become a great social concern. Considering the complexity of the food chain and that many players are involved between production and consumption; food adulteration practices are rising as it is easy to conduct fraud without being detected. This is the case for nut fruit processed products, such as almond flours, that can be adulterated with cheaper nuts (hazelnuts or peanuts), giving rise to not only economic fraud but also important effects on human health. Non-targeted HPLC-UV chromatographic fingerprints were evaluated as chemical descriptors to achieve nut sample characterization and classification using multivariate chemometric methods. Nut samples were extracted by sonication and centrifugation, and defatted with hexane; extracting procedure and conditions were optimized to maximize the generation of enough discriminant features. The obtained HPLC-UV chromatographic fingerprints were then analyzed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to carry out the classification of nut samples. The proposed methodology allowed the classification of samples not only according to the type of nut but also based on the nut thermal treatment employed (natural, fried or toasted products). Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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11 pages, 3022 KiB  
Article
A Simple Procedure to Assess Limit of Detection for Multisensor Systems
by Ekaterina Oleneva, Maria Khaydukova, Julia Ashina, Irina Yaroshenko, Igor Jahatspanian, Andrey Legin and Dmitry Kirsanov
Sensors 2019, 19(6), 1359; https://doi.org/10.3390/s19061359 - 18 Mar 2019
Cited by 30 | Viewed by 5690
Abstract
Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach [...] Read more.
Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach for LOD estimation in multisensor analysis. The suggested approach is based on the assessment of evolution of mean relative error values in calibration series with growing analyte concentration. The LOD value is estimated as the concentration starting from which MRE values become stable from sample to sample. This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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15 pages, 8913 KiB  
Article
Selective Detection of Hydrogen Sulfide and Methane by a Single MOX-Sensor
by Alexey Shaposhnik, Pavel Moskalev, Elena Sizask, Stanislav Ryabtsev and Alexey Vasiliev
Sensors 2019, 19(5), 1135; https://doi.org/10.3390/s19051135 - 06 Mar 2019
Cited by 15 | Viewed by 4028
Abstract
In this paper, we describe a technique for the qualitative and quantitative analysis of such gas mixtures as “hydrogen sulfide in air” and “methane in air” using temperature modulation of a single metal oxide sensor. Using regression analysis in the principal components plane [...] Read more.
In this paper, we describe a technique for the qualitative and quantitative analysis of such gas mixtures as “hydrogen sulfide in air” and “methane in air” using temperature modulation of a single metal oxide sensor. Using regression analysis in the principal components plane (PC1, PC2), we performed a selective determination of analytes on the minimum set of their concentrations in the training set, which is essential for solving practical problems. An important feature of this work is the difference in test gas concentrations from their concentrations in the training set. For the qualitative analysis of gas mixtures in a wide range of concentrations, we have developed an improved method for processing arrays of multidimensional data. For this improvement, we form a Mahalanobis neighborhood for polynomial regression lines constructed from the projection of training samples for each analyte on the (PC1, PC2) plane. Using the temperature modulation mode for the metal oxide sensor allowed us to increase its response when determining hydrogen sulfide by two to four orders of magnitude compared with the constant temperature mode. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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18 pages, 1162 KiB  
Article
Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods
by Guo-Zhu Wang, Jing Li, Yong-Tao Hu, Yuan Li and Zhi-Yong Du
Sensors 2019, 19(4), 929; https://doi.org/10.3390/s19040929 - 22 Feb 2019
Cited by 3 | Viewed by 3068
Abstract
Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with [...] Read more.
Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate “fault–symptom” table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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15 pages, 633 KiB  
Article
LCSS-Based Algorithm for Computing Multivariate Data Set Similarity: A Case Study of Real-Time WSN Data
by Rahim Khan, Ihsan Ali, Saleh M. Altowaijri, Muhammad Zakarya, Atiq Ur Rahman, Ismail Ahmedy, Anwar Khan and Abdullah Gani
Sensors 2019, 19(1), 166; https://doi.org/10.3390/s19010166 - 04 Jan 2019
Cited by 11 | Viewed by 4058
Abstract
Multivariate data sets are common in various application areas, such as wireless sensor networks (WSNs) and DNA analysis. A robust mechanism is required to compute their similarity indexes regardless of the environment and problem domain. This study describes the usefulness of a non-metric-based [...] Read more.
Multivariate data sets are common in various application areas, such as wireless sensor networks (WSNs) and DNA analysis. A robust mechanism is required to compute their similarity indexes regardless of the environment and problem domain. This study describes the usefulness of a non-metric-based approach (i.e., longest common subsequence) in computing similarity indexes. Several non-metric-based algorithms are available in the literature, the most robust and reliable one is the dynamic programming-based technique. However, dynamic programming-based techniques are considered inefficient, particularly in the context of multivariate data sets. Furthermore, the classical approaches are not powerful enough in scenarios with multivariate data sets, sensor data or when the similarity indexes are extremely high or low. To address this issue, we propose an efficient algorithm to measure the similarity indexes of multivariate data sets using a non-metric-based methodology. The proposed algorithm performs exceptionally well on numerous multivariate data sets compared with the classical dynamic programming-based algorithms. The performance of the algorithms is evaluated on the basis of several benchmark data sets and a dynamic multivariate data set, which is obtained from a WSN deployed in the Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology. Our evaluation suggests that the proposed algorithm can be approximately 39.9% more efficient than its counterparts for various data sets in terms of computational time. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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13 pages, 5258 KiB  
Article
Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis
by Xavier Cetó, Núria Serrano, Miriam Aragó, Alejandro Gámez, Miquel Esteban, José Manuel Díaz-Cruz and Oscar Núñez
Sensors 2018, 18(12), 4479; https://doi.org/10.3390/s18124479 - 18 Dec 2018
Cited by 23 | Viewed by 4816
Abstract
The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant [...] Read more.
The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25). Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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17 pages, 1671 KiB  
Article
Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array
by Yonghui Xu, Xi Zhao, Yinsheng Chen and Wenjie Zhao
Sensors 2018, 18(10), 3264; https://doi.org/10.3390/s18103264 - 28 Sep 2018
Cited by 36 | Viewed by 3993
Abstract
As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the [...] Read more.
As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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14 pages, 3550 KiB  
Article
Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning
by Saeedeh Taghadomi-Saberi, Sílvia Mas Garcia, Amin Allah Masoumi, Morteza Sadeghi and Santiago Marco
Sensors 2018, 18(6), 1922; https://doi.org/10.3390/s18061922 - 13 Jun 2018
Cited by 17 | Viewed by 7744
Abstract
The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective [...] Read more.
The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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