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

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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Human serum mid-infrared spectroscopy combined with machine learning algorithms for rapid detection of gliomas

Photodiagnosis and Photodynamic Therapy ◽

10.1016/j.pdpdt.2021.102308 ◽

2021 ◽

pp. 102308

Author(s):

Fangfang Chen ◽

Chunzhi Meng ◽

Hanwen Qu ◽

Chen Cheng ◽

Chen Chen ◽

...

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Human Serum ◽

Rapid Detection ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

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Vibrational spectroscopy in practice: Detection of mineral oil in sunflower oil with near- and mid-infrared spectroscopy

NIR news ◽

10.1177/0960336018763196 ◽

2018 ◽

Vol 29 (3) ◽

pp. 6-11 ◽

Cited By ~ 1

Author(s):

Michael K-H Pfister ◽

Bettina Horn ◽

Janet Riedl ◽

Susanne Esslinger ◽

Carsten Fauhl-Hassek

Keyword(s):

Fourier Transform ◽

Infrared Spectroscopy ◽

Sunflower Oil ◽

Near Infrared ◽

Limit Of Detection ◽

Model Performance ◽

Mineral Oil ◽

Food Ingredient ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

Fourier transform infrared spectroscopy becomes increasingly important for detecting adulterations in food due to a minimal sample preparation and a fast nondestructive measurement. Sunflower oil is a popular food ingredient, which might be contaminated or even adulterated by compounds with health concerns such as mineral oil. In this context a feasibility study was performed to compare the suitability of near- and mid-infrared spectroscopy for detecting mineral oil in sunflower oil. For this purpose, sunflower oils spiked with mineral oil in the concentration range of 0.001–1.0% w/w were analyzed by Fourier transform near- and mid-infrared spectroscopy, respectively, and spectra data were preprocessed prior to partial least squares regression. Hereby, the data preparation was optimized for each technique to account for model performance influences. The model performance was fairly similar for both approaches with a slightly better precision and thus limit of detection (near infrared 0.12% w/w, mid infrared 0.16% w/w) for the near-infrared-based model compared to the mid-infrared model. Consequently, both techniques are considered suitable for the determination of mineral oil in sunflower oil in the context of food authentication.

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Can machine learning augment clinician adjudication of events in cardiovascular trials? A case study of major adverse cardiovascular events (MACE) across CVRM trials

European Heart Journal ◽

10.1093/eurheartj/ehab724.3061 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

H Lea ◽

E Hutchinson ◽

A Meeson ◽

S Nampally ◽

G Dennis ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Ischemic Stroke ◽

Cardiovascular Events ◽

Learning Algorithms ◽

Model Performance ◽

Machine Learning Algorithms ◽

Classification Models ◽

Using Data

Abstract Background and introduction Accurate identification of clinical outcome events is critical to obtaining reliable results in cardiovascular outcomes trials (CVOTs). Current processes for event adjudication are expensive and hampered by delays. As part of a larger project to more reliably identify outcomes, we evaluated the use of machine learning to automate event adjudication using data from the SOCRATES trial (NCT01994720), a large randomized trial comparing ticagrelor and aspirin in reducing risk of major cardiovascular events after acute ischemic stroke or transient ischemic attack (TIA). Purpose We studied whether machine learning algorithms could replicate the outcome of the expert adjudication process for clinical events of ischemic stroke and TIA. Could classification models be trained on historical CVOT data and demonstrate performance comparable to human adjudicators? Methods Using data from the SOCRATES trial, multiple machine learning algorithms were tested using grid search and cross validation. Models tested included Support Vector Machines, Random Forest and XGBoost. Performance was assessed on a validation subset of the adjudication data not used for training or testing in model development. Metrics used to evaluate model performance were Receiver Operating Characteristic (ROC), Matthews Correlation Coefficient, Precision and Recall. The contribution of features, attributes of data used by the algorithm as it is trained to classify an event, that contributed to a classification were examined using both Mutual Information and Recursive Feature Elimination. Results Classification models were trained on historical CVOT data using adjudicator consensus decision as the ground truth. Best performance was observed on models trained to classify ischemic stroke (ROC 0.95) and TIA (ROC 0.97). Top ranked features that contributed to classification of Ischemic Stroke or TIA corresponded to site investigator decision or variables used to define the event in the trial charter, such as duration of symptoms. Model performance was comparable across the different machine learning algorithms tested with XGBoost demonstrating the best ROC on the validation set for correctly classifying both stroke and TIA. Conclusions Our results indicate that machine learning may augment or even replace clinician adjudication in clinical trials, with potential to gain efficiencies, speed up clinical development, and retain reliability. Our current models demonstrate good performance at binary classification of ischemic stroke and TIA within a single CVOT with high consistency and accuracy between automated and clinician adjudication. Further work will focus on harmonizing features between multiple historical clinical trials and training models to classify several different endpoint events across trials. Our aim is to utilize these clinical trial datasets to optimize the delivery of CVOTs in further cardiovascular drug development. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): AstraZenca Plc

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

Analytical Methods ◽

10.1039/d0ay01238f ◽

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.

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

Applied Sciences ◽

10.3390/app10217470 ◽

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.

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174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot

Journal of Animal Science ◽

10.1093/jas/skaa278.231 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 126-127

Author(s):

Lucas S Lopes ◽

Christine F Baes ◽

Dan Tulpan ◽

Luis Artur Loyola Chardulo ◽

Otavio Machado Neto ◽

...

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Final Decision ◽

Relevant Parameter ◽

Good Prediction ◽

Quality Traits ◽

C4.5 Decision Tree

Abstract The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd animals. Beef production presents high variability in its numerous carcass and beef quality traits. Machine learning algorithms and software provide an opportunity to evaluate the interactions between traits to better classify animals. Four different treatment levels of wet distiller’s grain were applied to 97 Angus-Nellore animals and used as features for the classification problem. The C4.5 decision tree, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) Artificial Neural Network algorithms were used to predict and classify the animals based on recorded traits measurements, which include initial and final weights, sheer force and meat color. The top performing classifier was the C4.5 decision tree algorithm with a classification accuracy of 96.90%, while the RF, the MLP and NB classifiers had accuracies of 55.67%, 39.17% and 29.89% respectively. We observed that the final decision tree model constructed with C4.5 selected only the dry matter intake (DMI) feature as a differentiator. When DMI was removed, no other feature or combination of features was sufficiently strong to provide good prediction accuracies for any of the classifiers. We plan to investigate in a follow-up study on a significantly larger sample size, the reasons behind DMI being a more relevant parameter than the other measurements.

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Classification of Daily Irradiance Profiles and the Behaviour of Photovoltaic Plant Elements: The Effects of Cloud Enhancement

Applied Sciences ◽

10.3390/app11115230 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5230

Author(s):

Isabel Santiago ◽

Jorge Luis Esquivel-Martin ◽

David Trillo-Montero ◽

Rafael Jesús Real-Calvo ◽

Víctor Pallarés-López

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Automatic Classification ◽

Sampling Frequency ◽

Machine Learning Algorithms ◽

Unsupervised Machine Learning ◽

Average Efficiency ◽

Clear Sky ◽

Photovoltaic Plant

In this work, the automatic classification of daily irradiance profiles registered in a photovoltaic installation located in the south of Spain was carried out for a period of nine years, with a sampling frequency of 5 min, and the subsequent analysis of the operation of the elements of the installation on each type of day was also performed. The classification was based on the total daily irradiance values and the fluctuations of this parameter throughout the day. The irradiance profiles were grouped into nine different categories using unsupervised machine learning algorithms for clustering, implemented in Python. It was found that the behaviour of the modules and the inverter of the installation was influenced by the type of day obtained, such that the latter worked with a better average efficiency on days with higher irradiance and lower fluctuations. However, the modules worked with better average efficiency on days with irradiance fluctuations than on clear sky days. This behaviour of the modules may be due to the presence, on days with passing clouds, of the phenomenon known as cloud enhancement, in which, due to reflections of radiation on the edges of the clouds, irradiance values can be higher at certain moments than those that occur on clear sky days, without passing clouds. This is due to the higher energy generated during these irradiance peaks and to the lower temperatures that the module reaches due to the shaded areas created by the clouds, resulting in a reduction in its temperature losses.

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Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020058 ◽

2021 ◽

Vol 10 (2) ◽

pp. 58

Author(s):

Muhammad Fawad Akbar Khan ◽

Khan Muhammad ◽

Shahid Bashir ◽

Shahab Ud Din ◽

Muhammad Hanif

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Learning Algorithms ◽

Remote Sensing Data ◽

Kappa Coefficient ◽

Machine Learning Algorithms ◽

Landsat 8 ◽

Sensing Data ◽

Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

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