An intelligent portable biosensor for fast and accurate nitrate determination using cyclic voltammetry

Massah, Jafar; Vakilian, Keyvan Asefpour

doi:10.1016/j.biosystemseng.2018.09.007

Cited by 81 publications

(32 citation statements)

References 43 publications

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“…The developed sensor exhibited a linear relationship was obtained in the region of the low concentration (0 − 4.0 × 10 − 6 M). Several other works have been reported on enzymatic (using niR) nitrate biosensors in the recent years on account of their excellent selectivity such as an intelligent portable biosensor usable for up to 10 days [129], a portable nitrate biosensing device using bi-modal approach combining EC and spectroscopy-based measurement [130], and amperometric nitrate biosensor based on CNT/PPy/niR biofilm exhibiting LOD, sensitivity and linear range of 0.17 mM, 300 nA/mM and 0.44mM to 1.45 mM, respectively [131].…”

Section: ) N Detectionmentioning

confidence: 99%

Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring

2021

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Development and deployment of sensing technologies is one of the main steps in achieving sustainability in crop production through precision agriculture. Key sensing methodologies developed for monitoring soil moisture and nutrients with recent advances in the sensing devices reported in literature using those techniques are overviewed in this article. The soil moisture determination has been divided into four main sections describing soil moisture measurement metrics and laboratory-based testing, followed by in-situ, remote and proximal sensing techniques. The application, advantages and limitations for each of the mentioned technologies are discussed. The nutrient monitoring methods are reviewed beginning with laboratory-based methods, ion-selective membrane based sensors, bio-sensors, spectroscopy-based methods, and capillary electrophoresis-based systems for inorganic ion detection. Attention has been given to the core principle of detection while reporting recent sensors developed using the mentioned concepts. The latest works reported on the different sensing methodologies point towards the trend of developing low-cost, easy to use, field-deployable or portable sensing systems aimed towards improving technology adoption in crop production leading to efficient site-specific soil and crop management which in turn will bring us closer to reaching sustainability in the practice of agriculture.

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Section: ) N Detectionmentioning

confidence: 99%

Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring

2021

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“…The model used in this study had 16 input variables, which made it impossible to provide a statistical model. Therefore, some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly [61]. Instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model.…”

Section: Step 3: Developing the Proposed Ann Models As A Decision Supmentioning

confidence: 99%

Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)

2020

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Due to the urban population’s growth and increasing demand for the renewal of old houses, the successful completion of Residential Building Projects (RBPs) has great socioeconomic importance. This study aims to propose a framework to predict the success of RBPs in the construction phase. Therefore, a 3-step method was applied: (1) Identifying and ranking Critical Success Factors (CSFs) involving in RBPs using the Delphi method, (2) Identifying and selecting success criteria and defining the Project Success Index (PSI), and (3) Developing an ANN model to predict the success of RBPs according to the status of CSFs during the construction phase. The model was trained and tested using the data extracted from 121 RBPs in Tehran. The main findings of this study were a prioritized list of most influential success criteria and an efficient ANN model as a Decision Support System (DSS) in RBPs to monitor the projects in advance and take necessary corrective actions. Compared with previous studies on the success assessment of projects, this study is more focused on providing an applicable method for predicting the success of RBPs. Doi: 10.28991/cej-2020-03091612 Full Text: PDF

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“…These properties are primarily due to bioreceptors, which are selected for their inherent specificities such as enzymes [ 3 ], antibodies [ 4 ], and aptamers [ 5 ]. However, the very aspect that makes biosensors so specific and sensitive can also limit the sensor stability due to the degradation of the bioreceptor [ 6 ]. Additionally, as the bioreceptor is specific to an individual analyte, the particular sensor’s scope is limited to the specific analyte to which the bioreceptor can bind.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Schackart

Yoon

2021

Sensors

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Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

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An intelligent portable biosensor for fast and accurate nitrate determination using cyclic voltammetry

Cited by 81 publications

References 43 publications

Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring

Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring

Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

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