Grade classification of human glioma using a convolutional neural network based on mid‐infrared spectroscopy mapping

2021 ◽  
Author(s):  
Wenyu Peng ◽  
Shuo Chen ◽  
Dongsheng Kong ◽  
Xiaojie Zhou ◽  
Xiaoyun Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Human Glioma ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
Grade Classification

Quantification and classification of cotton biodiesel content in diesel blends, using mid-infrared spectroscopy and chemometric methods

Fuel ◽  
2019 ◽  
Vol 237 ◽  
pp. 373-379 ◽  
Author(s):  
Ademar Domingos Viagem Máquina ◽  
Baltazar Vasco Sitoe ◽  
José Eduardo Buiatte ◽  
Douglas Queiroz Santos ◽  
Waldomiro Borges Neto
Keyword(s):  
Infrared Spectroscopy ◽  
Chemometric Methods ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

scholarly journals Characterisation and Classification of Italian Virgin Olive Oils by Near- and Mid-Infrared Spectroscopy

2008 ◽  
Vol 16 (3) ◽  
pp. 335-342 ◽  
Author(s):  
Nicolett Sinelli ◽  
Ernestina Casiraghi ◽  
Debora Tura ◽  
Gerard Downey
Keyword(s):  
Infrared Spectroscopy ◽  
Mid Infrared ◽  
Virgin Olive Oils ◽  
Mid Infrared Spectroscopy ◽  
Olive Oils

scholarly journals Fast Classification of Different Oils and Routes Used in Biodiesel Production Using Mid Infrared Spectroscopy and PLS2-DA

2015 ◽  
Author(s):  
Sarmento J. Mazivila ◽  
Hery Mitsutake ◽  
Felipe B. de Santana ◽  
Lucas C. Gontijo ◽  
Douglas Q. Santos ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Biodiesel Production ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
Fast Classification

scholarly journals HHT diagnosis by Mid-infrared spectroscopy and artificial neural network analysis

2013 ◽  
Vol 8 (1) ◽  
pp. 94 ◽  
Author(s):  
Andreas Lux ◽  
Ralf Müller ◽  
Mark Tulk ◽  
Carla Olivieri ◽  
Roberto Zarrabeita ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectroscopy ◽  
Network Analysis ◽  
Neural Network Analysis ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Screening method for rapid classification of psychoactive substances in illicit tablets using mid infrared spectroscopy and PLS-DA

2018 ◽  
Vol 288 ◽  
pp. 227-235 ◽  
Author(s):  
Leandro S.A. Pereira ◽  
Fernanda L.C. Lisboa ◽  
José Coelho Neto ◽  
Frederico N. Valladão ◽  
Marcelo M. Sena
Keyword(s):  
Infrared Spectroscopy ◽  
Screening Method ◽  
Psychoactive Substances ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

scholarly journals Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 4987
Author(s):  
Hongyan Zhu ◽  
Jun-Li Xu
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Learning Algorithms ◽  
Back Propagation ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700–4350 cm−1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.

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