Highly accurate classification of biological spores by culture medium for forensic attribution using multiple chemical signature types and machine learning

Ippoliti, Paul J.; Nargi, Fran; Han, Jason M.F.; Casale, Amanda; Walsh, Matthew M.; Boettcher, Tara; Dettman, Josh

doi:10.1007/s00216-020-02660-2

Cited by 4 publications

(3 citation statements)

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“…Feature extraction is the process of using domain knowledge about the data in order to extract potentially useful features. 51 , 52 …”

Section: Methodsmentioning

confidence: 99%

“…Feature extraction is the basis for high-performance detection using machine learning methods because the detection models have to be optimized. Feature extraction is the process of using domain knowledge about the data in order to extract potentially useful features. , …”

Section: Methodsmentioning

confidence: 99%

“…Feature extraction is the process of using domain knowledge about the data in order to extract potentially useful features. 51,52 Herein, a new feature extraction method was used to achieve high-performance learning algorithms that ultimately improve predictive accuracy of the model. The ReLU, which is an element-wise nonlinear function, was used for feature extraction.…”

Section: ∑ ∑mentioning

confidence: 99%

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High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach

et al. 2020

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Traditional methods for detection of lead ions in water samples are costly and time-consuming. In this work, an accurate smartphone-based colorimetric sensor was developed utilizing a novel machine learning algorithm. In the presence of Pb 2+ ions in the solution of specifically functionalized gold nanoparticles, the color of solution turns from red to purple. Indeed, the color variation of the solution is proportional to Pb 2+ concentration. The smartphone camera captures the corresponding color change, and the image is processed by an efficient artificial intelligence protocol. The nonlinear regression approach was used for concentration estimation, in which the parameters of the proposed model are obtained using a new feature extraction algorithm. In prediction of Pb 2+ concentration, the average absolute error and root-mean-square error were 0.094 and 0.124, respectively. The influence of pH of the medium, temperature, oligonucleotide concentration, and reaction time on the performance of the proposed sensor was carefully investigated and understood to achieve the best sensor response. This novel sensor exhibited good linearity for the detection of Pb 2+ in the concentration range of 0.5–2000 ppb with a detection limit of 0.5 ppb.

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“…Feature extraction is the process of using domain knowledge about the data in order to extract potentially useful features. 51 , 52 …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: ∑ ∑mentioning

confidence: 99%

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