ATR-FTIR combined with machine learning for the fast non-targeted screening of new psychoactive substances

Du, Yu; Hua, Zhendong; Liu, Cuimei; Lv, Rulin; Wang, Jia; Su, Mengxiang

doi:10.1016/j.forsciint.2023.111761

Cited by 9 publications

(2 citation statements)

References 41 publications

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“…That is, the true value of the data was a negative example, but it was incorrectly predicted to be a positive example. False Negative (FN): A counter-example of being incorrectly predicted, in which the true value of the data was a positive example but incorrectly predicted to be a negative example [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China

Yang,

Luo,

Lin

et al. 2024

PLoS ONE

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Purpose To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions. Methods We collected 13,245 written judgments from PKULAW.COM, a public database. 963 cases were included after the initial screening. 21 medical and ten patient factors were selected as characteristic variables by summarising previous literature and cases. Random Forest, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were used to establish prediction models of causality for the two data sets, respectively. Finally, the optimal model is obtained by hyperparameter tuning of the six models. Results We built three real data set models and three virtual data set models by three algorithms, and their confusion matrices differed. XGBoost performed best in the real data set, with a model accuracy of 66%. In the virtual data set, the performance of XGBoost and LightGBM was basically the same, and the model accuracy rate was 80%. The overall accuracy of external verification was 72.7%. Conclusions The optimal model of this study is expected to predict the causality accurately.

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Section: Methodsmentioning

confidence: 99%

A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China

Yang,

Luo,

Lin

et al. 2024

PLoS ONE

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“…Pereira et al devised a new supervised classification method that incorporated Partial Least Squares Discriminant Analysis (PLS-DA) and ATR-FTIR to pinpoint New Psychoactive Substances (NPS) in blotter papers, as well as a presumptive method for identifying drugs in seized ecstasy tablets [20,21]. Another recent study used six machine learning models-including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Extra Trees (ETs), voting, and Artificial Neural Networks (ANNs)-to catego-rize eight different categories of designer drugs including synthetic cannabinoids, synthetic cathinones, phenethylamines, fentanyl analogs, and other substances based on the IR spectral data acquired from various FTIR spectrometers [22]. Another study introduced a portable near-infrared spectrometer for the tentative identification of psychotropic drugs through library searching and mathematical pretreatment methods [23].…”

Section: Related Workmentioning

confidence: 99%

Harnessing Deep Convolutional Neural Networks Detecting Synthetic Cannabinoids: A Hybrid Learning Strategy for Handling Class Imbalances in Limited Datasets

Burlacu,

Praisler

et al. 2023

Inventions

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The aim of this research was to develop and deploy efficient deep convolutional neural network (DCNN) frameworks for detecting and discriminating between various categories of designer drugs. These are of particular relevance in forensic contexts, aiding efforts to prevent and counter drug use and trafficking and supporting associated legal investigations. Our multinomial classification architectures, based on Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectra, are primarily tailored to accurately identify synthetic cannabinoids. Within the scope of our dataset, they also adeptly detect other forensically significant drugs and misused prescription medications. The artificial intelligence (AI) models we developed use two platforms: our custom-designed, pre-trained Convolutional Autoencoder (CAE) and a structure derived from the Vision Transformer Trained on ImageNet Competition Data (ViT-B/32) model. In order to compare and refine our models, various loss functions (cross-entropy and focal loss) and optimization algorithms (Adaptive Moment Estimation, Stochastic Gradient Descent, Sign Stochastic Gradient Descent, and Root Mean Square Propagation) were tested and evaluated at differing learning rates. This study shows that innovative transfer learning methods, which integrate both unsupervised and supervised techniques with spectroscopic data pre-processing (ATR correction, normalization, smoothing) and present significant benefits. Their effectiveness in training AI systems on limited, imbalanced datasets is particularly notable. The strategic deployment of CAEs, complemented by data augmentation and synthetic sample generation using the Synthetic Minority Oversampling Technique (SMOTE) and class weights, effectively address the challenges posed by such datasets. The robustness and adaptability of our DCNN models are discussed, emphasizing their reliability and portability for real-world applications. Beyond their primary forensic utility, these systems demonstrate versatility, making them suitable for broader computer vision tasks, notably image classification and object detection.

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Spectroscopic techniques, from (onsite) screening and identification to structure elucidation of new psychoactive substances

Deconinck

2024

Comprehensive Analytical Chemistry

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ATR-FTIR combined with machine learning for the fast non-targeted screening of new psychoactive substances

Cited by 9 publications

References 41 publications

A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China

A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China

Harnessing Deep Convolutional Neural Networks Detecting Synthetic Cannabinoids: A Hybrid Learning Strategy for Handling Class Imbalances in Limited Datasets

Spectroscopic techniques, from (onsite) screening and identification to structure elucidation of new psychoactive substances

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