Field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network

Identification of surface-enhanced Raman spectroscopy using hybrid transformer network

Surface-enhanced Raman Spectroscopy (SERS) is extensively implemented in drug detection due to its sensitivity and non-destructive nature. Deep learning methods, which are represented by convolutional neural network (CNN), have been widely applied in identifying the spectra from SERS for powerful learning ability. However, the local receptive field of CNN limits the feature extraction of sequential spectra for suppressing the analysis results. In this study, a hybrid Transformer network, TMNet, was developed to identify SERS spectra by integrating the Transformer encoder and the multi-layer perceptron. The Transformer encoder can obtain precise feature representations of sequential spectra with the aid of self-attention, and the multi-layer perceptron efficiently transforms the representations to the final identification results. TMNet performed excellently, with identification accuracies of 99.07% for the spectra of hair containing drugs and 97.12% for those of urine containing drugs. For the spectra with additive white Gaussian, baseline background, and mixed noises, TMNet still exhibited the best performance among all the methods. Overall, the proposed method can accurately identify SERS spectra with outstanding noise resistance and excellent generalization and holds great potential for the analysis of other spectroscopy data.

Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae

Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called "Chenpi". That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC-MS and LC-MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning-convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.

Characteristics and Statistical Analysis of Large and above Hazardous Chemical Accidents in China from 2000 to 2020

To investigate the occurrence and development pattern of large-scale hazardous chemicals emergencies, a statistical analysis of 195 large and above accidents of hazardous chemicals in China during 2000-2020 was conducted. A general description of the characteristics of larger and above accidents based on statistical data was analyzed, and then the system risk of the hazardous chemical industry was calculated and evaluated by the entropy weight method and the TOPSIS method comprehensively. Results show that: (1) The geographical distribution of large and above hazardous chemical accidents (LAHCA) varies significantly; (2) The high-temperature season has high probabilities of having large and above accidents; (3) Human factors and management factors are the main causes of LAHCA; (4) During the period from 2000 to 2020, due to the rapid development of the chemical industry, the overall risk of accidents involving hazardous chemicals were upswing accompanied by volatility, and the risk of serious accidents remains high. The development history of safety regulations in China's hazardous chemical sector and the industry's projected course for future growth were then discussed. Finally, based on the findings of the aforementioned statistics and research, specific recommendations were provided for the safety management of the hazardous chemical sector. This study expects to provide a practical and effective reference for the construction of safety management as well as accident prevention in the hazardous chemical industry.

Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN

Textile fibre is very common in daily life, and its classification and identification play an important role in textile recycling, archaeology, public security, and other industries. However, traditional identification methods are time-consuming, laborious, and often destructive to the samples. In order to quickly, accurately, and nondestructively classify and recognize textile fibres, this study established a textile fibre classification and recognition method based on hyperspectral imaging (HSI) and a one-dimensional convolutional neural network (1D-CNN) model. Hyperspectral images of 25 kinds of commercial textile fibres were collected and denoised by pixel fusion. Four traditional machine learning classification models, k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and partial least squares-discriminant analysis (PLS-DA), were used to identify the data. The results show that RF has the highest classification accuracy, reaching 91.4%. Then a back propagation neural network (BPNN) model and a one-dimensional convolutional neural network (1D-CNN) model were constructed and compared with the traditional machine learning methods. The results show that the 1D-CNN models have 97.9% and 98.6% accuracy on the training and test sets, respectively. The precision (Pr), sensitivity (Se), specificity (Sp), and F1 score (F1 score) of the models reached 98.7%, 98.6%, 99.9%, and 98.6%, respectively, which were significantly better than the four traditional machine learning models. It seems that 1D-CNN combined with the HSI technique may be a potential method in the detection and classification of textile fibres.

Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo Simulation

In spite of the wide use of Raman spectroscopy for chemical analysis in different fields, not any automated identification of Raman spectra is universally adopted. However, the interest in this field is witnessed by the large number of papers published in the last decades. The problem of Raman-spectra classification becomes particularly challenging when low irradiation is requested, either for safety reasons or to avoid target photodegradation. This often leads to spectra characterized by a low signal-to-noise ratio, where methods based on correlation usually fail. For this reason, a method based on peak identification through FMFs is presented, discussed and validated over a large set of samples. In particular, a Monte Carlo simulation has been employed to determine the best parameters of the fuzzy membership functions based on the analysis of performances of the classification procedure. The ROC curves have been analyzed, and AUC and best accuracy are employed as key parameters to evaluate the classification performances on different amounts of ammonium nitrate (from 300 to 1500 μg) and different laser exposure levels (from 3.1 to 250 mJ/cm2).

A water-soluble azobenzene-dicyano pendant polymeric chemosensor for the colorimetric detection of cyanide in 100% aqueous media and food samples

A polymeric chemosensor (P1) was developed for the colorimetric detection of cyanide in aqueous media and cyanogenic food samples.