Potential of bacterial infection diagnosis using infrared spectroscopy of WBC and machine learning algorithms

2019 ◽  
Author(s):  
Adam H. Agbaria ◽  
Ahmad Salman ◽  
Guy Beck ◽  
Itshak Lapidot ◽  
Daniel H. Rich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Bacterial Infection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Infection Diagnosis

Soybean seed vigor discrimination by using infrared spectroscopy and machine learning algorithms

2020 ◽  
Vol 12 (35) ◽  
pp. 4303-4309
Author(s):  
Gustavo Larios ◽  
Gustavo Nicolodelli ◽  
Matheus Ribeiro ◽  
Thalita Canassa ◽  
Andre R. Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Learning Algorithms ◽  
Soybean Seed ◽  
Seed Vigor ◽  
Machine Learning Algorithms ◽  
Chemometric Methods ◽  
Novel Approach

A novel approach to distinguish soybean seed vigor based on Fourier transform infrared spectroscopy (FTIR) associated with chemometric methods is presented.

scholarly journals Classifying Thermal Degradation of Polylactic Acid by Using Machine Learning Algorithms Trained on Fourier Transform Infrared Spectroscopy Data

2020 ◽  
Vol 10 (21) ◽  
pp. 7470
Author(s):  
Sung-Uk Zhang
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
3D Printing ◽  
Thermal Degradation ◽  
Fourier Transform Infrared Spectroscopy ◽  
Polylactic Acid ◽  
High Temperatures ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

Polylactic acid (PLA) is the most common polymeric material in the 3D printing industry but degrades under harsh environmental conditions such as under exposure to sunlight, high-temperatures, water, soil, and bacteria. An understanding of degradation phenomena of PLA materials is critical to manufacturing robust products by using 3D printing technologies. The objective of this study is to evaluate four machine learning algorithms to classify the degree of thermal degradation of heat-treated PLA materials based on Fourier transform infrared spectroscopy (FTIR) data. In this study, 3D printed PLA specimens were subjected to high-temperatures for extended periods of time to simulate thermal degradation and subsequently examined by using two types of FTIR spectrometers: desktop and portable spectrometers. Classifiers created by multi-class logistic regression and multi-class neural networks were appropriate prediction models for these datasets.

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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