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.

Detecting antimicrobial resistance in Escherichia coli using benchtop attenuated total reflectance-Fourier transform infrared spectroscopy and machine learning

The Analyst ◽  
2021 ◽  
Vol 146 (20) ◽  
pp. 6211-6219
Author(s):  
Hewa G. S. Wijesinghe ◽  
Dominic J. Hare ◽  
Ahmed Mohamed ◽  
Alok K. Shah ◽  
Patrick N. A. Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Antimicrobial Resistance ◽  
Fourier Transform Infrared Spectroscopy ◽  
Attenuated Total Reflectance ◽  
Total Reflectance ◽  
E Coli ◽  
Machine Learning Model

ATR–FTIR with a machine learning model predicts ESBL genotype of unknown E. coli strains with 86.5% AUC.

Viagra® and Cialis® blister packaging fingerprinting using Fourier transform infrared spectroscopy (FTIR) allied with chemometric methods

2014 ◽  
Vol 6 (8) ◽  
pp. 2722 ◽  
Author(s):  
Thieres M. C. Pereira ◽  
Josué A. Q. Júnior ◽  
Rafael S. Ortiz ◽  
Werickson F. C. Rocha ◽  
Denise C. Endringer ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Fourier Transform Infrared ◽  
Chemometric Methods

scholarly journals Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Duy Ngoc Nguyen ◽  
Tuoi Thi Phan ◽  
Phuc Do
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Test Results ◽  
High Confidence ◽  
Knowledge Processing ◽  
Learning Methods ◽  
Novel Approach

AbstractSentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert’s knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method’s dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining.

New Computational Tool Based on Machine-learning Algorithms for the Identification of Rhinovirus Infection-Related Genes

2020 ◽  
Vol 22 (10) ◽  
pp. 665-674 ◽  
Author(s):  
Yan Xu ◽  
Yu-Hang Zhang ◽  
JiaRui Li ◽  
Xiao Y. Pan ◽  
Tao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Pathogenic Mechanism ◽  
Computational Tool ◽  
Blood Samples ◽  
Common Cause ◽  
Rhinovirus Infection ◽  
Novel Approach ◽  
Experimental Approaches

Background: Human rhinovirus has different identified serotypes and is the most common cause of cold in humans. To date, many genes have been discovered to be related to rhinovirus infection. However, the pathogenic mechanism of rhinovirus is difficult to elucidate through experimental approaches due to the high cost and consuming time. Method and Results: In this study, we presented a novel approach that relies on machine-learning algorithms and identified two genes OTOF and SOCS1. The expression levels of these genes in the blood samples can be used to accurately distinguish virus-infected and non-infected individuals. Conclusion: Our findings suggest the crucial roles of these two genes in rhinovirus infection and the robustness of the computational tool in dissecting pathogenic mechanisms.

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