Combining an Integrated Sensor Array with Machine Learning for the Simultaneous Quantification of Multiple Cations in Aqueous Mixtures

In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

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Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

Chemistry Proceedings ◽

10.3390/csac2021-10447 ◽

2021 ◽

Vol 5 (1) ◽

pp. 21

Author(s):

Edgar G. Mendez-Lopez ◽

Jersson X. Leon-Medina ◽

Diego A. Tibaduiza

Keyword(s):

Machine Learning ◽

Dimensionality Reduction ◽

Performance Measures ◽

Sensor Array ◽

Three Dimensional ◽

Electronic Tongue ◽

Sensor Arrays ◽

Classification Performance ◽

Supervised Machine Learning ◽

Electrochemical Tests

Electronic tongue type sensor arrays are made of different materials with the property of capturing signals independently by each sensor. The signals captured when conducting electrochemical tests often have high dimensionality, which increases when performing the data unfolding process. This unfolding process consists of arranging the data coming from different experiments, sensors, and sample times, thus the obtained information is arranged in a two-dimensional matrix. In this work, a description of a tool for the analysis of electronic tongue signals is developed. This tool is developed in Matlab® App Designer, to process and classify the data from different substances analyzed by an electronic tongue type sensor array. The data processing is carried out through the execution of the following stages: (1) data unfolding, (2) normalization, (3) dimensionality reduction, (4) classification through a supervised machine learning model, and finally (5) a cross-validation procedure to calculate a set of classification performance measures. Some important characteristics of this tool are the possibility to tune the parameters of the dimensionality reduction and classifier algorithms, and also plot the two and three-dimensional scatter plot of the features after reduced the dimensionality. This to see the data separability between classes and compatibility in each class. This interface is successfully tested with two electronic tongue sensor array datasets with multi-frequency large amplitude pulse voltammetry (MLAPV) signals. The developed graphical user interface allows comparing different methods in each of the mentioned stages to find the best combination of methods and thus obtain the highest values of classification performance measures.

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P2AR.3 - Virtual Gas Sensor Array by Cyclic Optical Activation: Optimization of Activation Profile by Machine Learning

10.5162/imcs2018/p2ar.3 ◽

2018 ◽

Author(s):

G. Njio ◽

T. Wagner

Keyword(s):

Machine Learning ◽

Gas Sensor ◽

Sensor Array ◽

Gas Sensor Array ◽

Activation Profile ◽

Optical Activation

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Ολοκληρωμένη συστοιχία αισθητήρων βασισμένη σε πολυμερή για την αναγνώριση πτητικών αερίων

10.12681/eadd/27787 ◽

2012 ◽

Author(s):

Πέτρος Οικονόμου

Keyword(s):

Binary Mixtures ◽

Sensor Array ◽

Gas Sensing ◽

Limit Of Detection ◽

Principal Component ◽

Polymeric Materials ◽

Integrated Sensor ◽

Electromagnetic Model ◽

The Individual ◽

Electronic Elements

In this research the sensor type of planar InterDigitated Capacitors (IDCs), also known as chemcapacitors, is demonstrated and its application in the detection of Volatile Organic Compounds (VOCs) and humidity is explored. The IDC layout configuration is studied by using a dedicated electromagnetic model and the behavior of different planar IDE structures/geometries coated with different polymeric materials of different dielectric permittivity values (εp) is determined. This study leads to the optimum design geometry of the planar IDCs that will be used for the detection of analytes. In order to identify the more suitable sensing materials for the targeted application, a methodology based on swelling measurements of the polymeric material upon exposure to analytes of interest was developed and applied in the prediction of the response of a chemcapacitor upon exposure to different VOCs. The integrated sensor array is characterized by the responses of each sensor to exposure to several pure analytes, binary mixtures of analytes and complex environments. Several parameters were examined such as sensitivity, selectivity, limit of detection, aging. Also the total response of the sensor array is analyzed by conjunction of the individual responses of each sensor and the use of suitable Principal Component Analysis, PCA, models that have been developed. Fabrication of a hybrid low-power gas sensing module is presented. This module is realized with integration on the same device of the sensor array with the appropriate electronic elements. The latter provide the power, control and read-out electronics of the output signal. The proposed hybrid micro-device is characterized in terms of the response of each sensor of the sensor array upon exposure to different pure analytes and their binary mixtures. Evaluation of the results obtained by the characterization of the hybrid gas sensing module demonstrate the ability of use such a device in analytical methods under conditions of constant or alterable concentration of VOCs/humidity or their mixtures in applications either at constant temperature or at temperature changing over time simulating that way real time applications.

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