scholarly journals Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

2020 ◽  
Vol 12 (10) ◽  
pp. 1404-1418
Author(s):  
Ruben F. Kranenburg ◽  
Joshka Verduin ◽  
Yannick Weesepoel ◽  
Martin Alewijn ◽  
Marcel Heerschop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Scene Detection ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer

scholarly journals Deep Learning and Conventional Machine Learning for Image-Based in-Situ Fault Detection During Laser Welding: A Comparative Study

2021 ◽  
Author(s):  
Christian Knaak ◽  
Moritz Kröger ◽  
Frederic Schulze ◽  
Peter Abels ◽  
Arnold Gillner
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Near Infrared ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Detection Rates ◽  
Feature Extraction And Selection ◽  
Welding Defects

An effective process monitoring strategy is a requirement for meeting the challenges posed by increasingly complex products and manufacturing processes. To address these needs, this study investigates a comprehensive scheme based on classical machine learning methods, deep learning algorithms, and feature extraction and selection techniques. In a first step, a novel deep learning architecture based on convolutional neural networks (CNN) and gated recurrent units (GRU) is introduced to predict the local weld quality based on mid-wave infrared (MWIR) and near-infrared (NIR) image data. The developed technology is used to discover critical welding defects including lack of fusion (false friends), sagging and lack of penetration, and geometric deviations of the weld seam. Additional work is conducted to investigate the significance of various geometrical, statistical, and spatio-temporal features extracted from the keyhole and weld pool regions. Furthermore, the performance of the proposed deep learning architecture is compared to that of classical supervised machine learning algorithms, such as multi-layer perceptron (MLP), logistic regression (LogReg), support vector machines (SVM), decision trees (DT), random forest (RF) and k-Nearest Neighbors (kNN). Optimal hyperparameters for each algorithm are determined by an extensive grid search. Ultimately, the three best classification models are combined into an ensemble classifier that yields the highest detection rates and achieves the most robust estimation of welding defects among all classifiers studied, which is validated on previously unknown welding trials.

scholarly journals Machine Learning Algorithms for Soil Properties Prediction with Treated Vis–NIR Spectrums from the Itatiaia National Park

2019 ◽  
Author(s):  
Yuri Andrei Gelsleichter ◽  
Lúcia Helena Cunha dos Anjos ◽  
Elias Mendes Costa ◽  
Gabriela Valente ◽  
Paula Debiasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Properties ◽  
National Park ◽  
Near Infrared ◽  
Learning Algorithms ◽  
Management Plan ◽  
Machine Learning Algorithms ◽  
Total Carbon ◽  
Least Squares Regression ◽  
Near Infrared Reflectance

Visible and near-infrared reflectance (Vis–NIR) techniques are a plausible method to soil analyses. The main objective of the study was to investigate the capacity to predicting soil properties Al, Ca, K, Mg, Na, P, pH, total carbon (TC), H and N, by using different spectral (350–2500 nm) pre-treatments and machine learning algorithms such as Artificial Neural Network (ANN), Random Forest (RF), Partial Least-squares Regression (PLSR) and Cubist (CB). The 300 soil samples were sampled in the upper part of the Itatiaia National Park (INP), located in Southeastern region of Brazil. The 10 K-fold cross validation was used with the models. The best spectral pre-treatment was the Inverse of Reflectance by a Factor of 104 (IRF4) for TC with CB, giving an averaged R² among the folds of 0.85, RMSE of 1.96; and 0.67 with 0.041 respectively for H. Into the K-folds models of TC, the highest prediction had a R² of 0.95. These results are relevant for the INP management plan, and also to similar environments. The good correlation with Vis–NIR techniques can be used for remote sense monitoring, especially in areas with very restricted access such as INP.

Handheld near infrared spectrometer and machine learning methods applied to the monitoring of multiple process stages in industrial sugar production

2021 ◽  
pp. 130919
Author(s):  
Bruno Henrique da Silva Melo ◽  
Rafaella Figueiredo Sales ◽  
Lourival da Silva Bastos Filho ◽  
Jorge Souza Povoas da Silva ◽  
Aluska Gabrielle Carolino de Almeida Sousa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Sugar Production ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Multiple Process ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer

scholarly journals First report of rapid, non-invasive, and reagent-free detection of malaria through the skin of patients with a beam of infrared light

2022 ◽  
Author(s):  
Gabriela Garcia ◽  
Tharanga Kariyawasam ◽  
Anton Lord ◽  
Cristiano Costa ◽  
Lana Chaves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Predictive Accuracy ◽  
Human Subjects ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Infrared Light ◽  
Non Invasive ◽  
Full Capacity

Abstract We describe the first application of the Near-infrared spectroscopy (NIRS) technique to detect Plasmodium falciparum and P. vivax malaria parasites through the skin of malaria positive and negative human subjects. NIRS is a rapid, non-invasive and reagent free technique which involves rapid interaction of a beam of light with a biological sample to produce diagnostic signatures in seconds. We used a handheld, miniaturized spectrometer to shine NIRS light on the ear, arm and finger of P. falciparum (n=7) and P. vivax (n=20) positive people and malaria negative individuals (n=33) in a malaria endemic setting in Brazil. Supervised machine learning algorithms for predicting the presence of malaria were applied to predict malaria infection status in independent individuals (n=12). Separate machine learning algorithms for differentiating P. falciparum from P. vivax infected subjects were developed using spectra from the arm and ear of P. falciparum and P. vivax (n=108) and the resultant model predicted infection in spectra of their fingers (n=54).NIRS non-invasively detected malaria positive and negative individuals that were excluded from the model with 100% sensitivity, 83% specificity and 92% accuracy (n=12) with spectra collected from the arm. Moreover, NIRS also correctly differentiated P. vivax from P. falciparum positive individuals with a predictive accuracy of 93% (n=54). These findings are promising but further work on a larger scale is needed to address several gaps in knowledge and establish the full capacity of NIRS as a non-invasive diagnostic tool for malaria. It is recommended that the tool is further evaluated in multiple epidemiological and demographic settings where other factors such as age, mixed infection and skin colour can be incorporated into predictive algorithms to produce more robust models for universal diagnosis of malaria.

scholarly journals Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions

Pharmaceutics ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1432
Author(s):  
Nimra Munir ◽  
Michael Nugent ◽  
Darren Whitaker ◽  
Marion McAfee
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Process Analytical Technology ◽  
Learning Algorithms ◽  
Hot Melt Extrusion ◽  
Machine Learning Algorithms ◽  
Melt Extrusion ◽  
Future Directions ◽  
And Control ◽  
Hot Melt

In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry.

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