Discrimination of vermilion (sindoor) using attenuated total reflectance fourier transform infrared spectroscopy in combination with PCA and PCA-LDA

Multi-target detection of waste composition in complex environments based on an improved YOLOX-S model

The identification of waste composition based on target-detection is crucial in promoting sustainable solid waste management. However, discrimination of different solid waste categories in the presence of incomplete and insufficient feature information remains a challenge in multi-target detection. This paper proposes an improved You Only Look Once (YOLOX-S) model that enables the effective recognition of different waste components in complex environments, which enhances feature-information extraction ability regarding different dimensions by introducing a convolutional block attention module, an adaptive spatial feature fusion module, and an improved efficient intersection-over-union loss function. The improved model was trained on a self-constructed image dataset with multiple waste components and targets in various complex scenarios, including interference from similar color backgrounds, similar waste localization, and mutual waste occlusion. The experimental results showed that the improved model achieved a mean average precision (mAP) of 85.02 %, an increase of 5.32 % over the original YOLO model's mAP, and that it reduced incidents related to inaccurate positioning and false and missed detection. Moreover, the improved model outperformed classical detection models including support vector machine, RestNet-18, and RestNet-50 on a public dataset, achieving a mAP of 94.85 %. The improved model is expected to be applied to intelligent monitoring for waste components in scenarios including indiscriminate waste disposal and illegal dumping, providing decision support for emergency management.

Analysis and discrimination of adhesive species using ATR-FTIR combined with Raman, and HS-GC-IMS together with multivariate statistical analysis

Identifying the species and origin of adhesives in criminal investigations aids in narrowing inquiry scope and supporting case detection. This study introduces two advanced combined analytical techniques for distinguishing adhesive species, including attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with Raman spectroscopy, and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) together with multivariate statistical analysis. ATR-FTIR categorized seven adhesives into three groups based on the base materials, with further differentiation achieved via Raman spectra. Analysis of volatile components identified 79 volatile organic compounds (VOCs), with esters being the most concentrated. The fingerprint profile clearly illustrated the characteristic fingerprint sequence and unique marker compounds of each adhesive, effectively enabling their differentiation. Multivariate statistical analysis methods, including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), heatmap, and hierarchical cluster analysis (HCA), were utilized to visually interpret the classification of adhesives. This integrated analytical approach provides a comprehensive analysis of adhesive compositions, facilitating the diversification and precision of adhesive species identification, and broadening the scope for detecting and analyzing trace evidence in forensic science.

Evaluation of natural ageing responses on Burmese amber durability by FTIR spectroscopy with PLSR and ANN models

Amber ageing is an inevitable process, which is very important in precious organic gemstone relics protection. In order to explore the mechanism of amber ageing and estimate the durability of Burmese amber, this research investigates the changing spectral features of Burmese ageing amber via Fourier Transform Infrared Spectroscopy (FTIR) and solid ¹³C Nuclear Magnetic Resonance spectroscopy (NMR) and develops the regression models for its micro-hardness by micro-FTIR spectra. The Partial Least Squares Regression (PLSR) and Artificial Neural Networks (ANN) methods as well as Competitive Adaptive Reweighted Sampling (CARS) algorithm for wavelength variables selection have been applied to predict and assess the Vickers hardness of amber samples with different ageing degrees. As a result, the FTIR and the solid ¹³C NMR spectra reveal that the contents of CO groups (of esters) increase substantially, and which of the other oxygenic groups (CO (of acids), COC, COCC) increase modestly in amber ageing. When comparing with the results of four different models (PLSR, ANN, CARS-PLSR and CARS-ANN), the CARS-PLSR model obtained the optimal results as follows: the squared correlation coefficient of calibration(R²cal) is 0.9230 and the root mean square error of calibration (RMSEC) is 1.2977 HV; the squared correlation coefficient of prediction (R²pre) is 0.7762 and the root mean square error of prediction (RMSEP) is 2.2208 HV. The overall results sufficiently demonstrate that FTIR spectroscopy technique coupled with appropriate chemometrics methods are very promising tools to estimate and predict the hardness property of Burmese ageing amber.

Applying a deep residual network coupling with transfer learning for recyclable waste sorting

Recyclable waste sorting has become a key step for promoting the development of a circular economy with the gradual realization of carbon neutrality around the world. This study aims to develop an intelligent and efficient method for recyclable waste sorting by the method of deep learning. Thus, RWNet models, which refers to various ResNet structures (ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152) based on transfer learning, were proposed to classify different types of recyclable waste. Cyclical learning rate and data augmentation were taken to improve the performance of RWNet models. In addition, accuracy, precision, recall, F1 score, and ROC were taken to evaluate the performance of RWNet models. Results showed that the accuracy of various RWNet models is almost at 88%, and the best accuracy is 88.8% in RWNet-152. The highest precision, recall, and F1 score in terms of weighted average value appeared in RWNet-101 (89.9%), RWNet-152 (88.8%), and RWNet-152 (88.9%), respectively. The area under the ROC curve (AUC) is higher than 0.9, except for the AUC value of plastic (0.85), which indicated that most of the recyclable waste can be well sorted by RWNet models. This study demonstrates the good performance of RWNet models that can be used to automatically sort most of the recyclable waste, which paves the way for better recyclable waste management.

Genetic algorithm based artificial neural network and partial least squares regression methods to predict of breakdown voltage for transformer oils samples in power industry using ATR-FTIR spectroscopy

The current study proposes a novel analytical method for calculating the breakdown voltage (BV) of transformer oil samples considered as a significant method to assess the safe operation of power industry. Transformer oil samples can be analyzed using the Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with multivariate calibration methods. The partial least squares regression (PLSR) back propagation-artificial neural network (BP-ANN) methods and a genetic algorithm (GA) for variable selection are used to predict and assess breakdown voltage in transformer oil samples from various Iranian transformer oils. As a result, the root mean square error (RMSE) and correlation coefficient for the training and test sets of oil samples are also calculated. In the GA-PLS-R method, the squared correlation coefficient (R²pred) and root mean square prediction error (RMSEP) are 0.9437 and 2.6835, respectively. GA-BP-ANN, on the other hand, had a lower RMSEP value (0.2874) and a higher R²pred function (0.9891). Considering the complexity of transformer oil samples, the performance of GA-BP-ANN has resulted in an efficient approach for predicting breakdown voltage; consequently, it can be effectively used as a new method for quantitative breakdown voltage analysis of samples to evaluate the health of transformer oil. .

Near-infrared spectroscopy combined with chemometrics to classify cosmetic foundations from a crime scene

Cosmetic smears are a form of trace evidence that can link the crime scene, suspects, and victims. Foundation and lipstick are the most common sources of cosmetics that can easily smear, with most current research focused on the evidential analysis of lipsticks. This research aims to create a database of cosmetic foundations on different materials and to access the robustness of using Near-infrared with chemometrics as a non-destructive technique to identify unknown samples collected from a crime scene. Small amounts of six shades of three brands of foundations were smeared on clothing materials, which were then analysed with a combination of Near-infrared with chemometric analysis. Principle component analysis (PCA) was used to reduce data dimensionality and explore potential patterns in sample separation and Linear Discriminant Analysis (LDA) was utilised to assign unknown samples to one of the established classes. The selected techniques proved to be promising for database construction and as a preliminary method of analysis, with 93% of the spectra being correctly classified. Notably, darker foundation shades were less likely to be correctly classified (90% classified correctly) compared to lighter ones (96.7% classified correctly). This could not be improved with Standard Normal Variate (SNV) data pre-treatment or selecting specific NIR regions. This finding is of particular importance; according to the Crime Survey for England and Wales (year ending March 2020) police recorded sexual offences demonstrated that those in Mixed and Black or Black British ethnic groups were significantly more likely to be a victim of sexual assault compared to White, Asian or Other ethnic groups. It is, therefore, crucial to add a wide range of foundation shades, particularly of darker tones, to the future database.

Discrimination of electrical tapes using ATR-FTIR spectroscopy and chemometrics

Electrical tapes are recovered during criminal investigations as physical evidence in cases of rape, kidnapping, and explosion incidents. The analysis of such evidence can provide an evidentiary link between the suspect, the victim, object, or the crime scene. In the present study, 25 brands of electrical tapes have been analyzed using ATR-FTIR (attenuated total reflectance Fourier transform infrared) spectroscopy. Samples (1 cm² ) were analyzed in the mid IR (Infrared) region from 4000-600 cm^-1 , and the functional groups of various components have been profiled. Chemometric methods-PCA (principal component analysis) and PCA-LDA (linear discriminant analysis) have been employed to interpret the data and classify the samples into its respective classes. Preliminary assessment of sample clustering due to similar chemical composition was visualized using PCA. PCA-LDA applied for classification purpose yielded classification accuracy (calibration) of 92.98% for the adhesive side and 88% for the backing side. The validation results showed classification accuracy of 89.47% for the adhesive side and 84% for the backing side. Blind validation study was carried out using 5 samples, and classification accuracy of 100% and 80% was obtained for the adhesive and the backing side, respectively. In the current study, a preliminary substrate study was carried out, and the results showed that the backing samples could be more accurately matched to their correct source of origin than the adhesive side.

Rapid and non-destructive analysis of eye-cosmetics using ATR-FTIR spectroscopy and chemometrics

Cosmetic evidence recovered during crime investigations, particularly in cases of physical and sexual assault against women can be utilised as associative evidence in the court of law. This evidence can provide a link between the suspect, the victim, and the crime scene and assist in solving criminal cases. A mismatched profile of exhibit's source of origin can also be utilised to definitely exclude the suspect exhibits. In the present study, ATR-FTIR (attenuated total reflectance-fourier transform infrared) spectroscopy has been employed for the analysis of eye-cosmetics (eyeliner and eyeshadow) samples. Chemometric tool- PCA (principal component analysis) has been used for the recognition of patterns in the data. PCA-LDA (linear discriminant analysis) utilized for classification purpose showed calibration accuracy of 100% and 98% for eyeliner and eyeshadow respectively while validation result showed 97% and 97% respectively. Preliminary substrate study has been performed in the current study. Result suggests that substrates such as cotton cloth and tissue paper hinder the analysis of eyeliner while the stain of eyeshadow on substrates such as cotton cloth, tissue paper, glass, and plastic could be correctly matched with its parent source.

A rapid and nondestructive approach for forensic identification of cigarette inner liner papers using shift-excitation Raman difference spectroscopy and chemometrics

In forensic science, cigarettes are considered as crucial physical evidence because it helps to establish the connection between the criminal and the crime scene. In the present study, SERDS has been used for the examination of 25 different brands or series of cigarette inner liner paper. The discrimination power is calculated by using three methods, i.e., visual discrimination of the spectra, hierarchical cluster analysis (HCA) and principal component analysis (PCA). They are 100.00%, 92.42% and 100.00%, respectively. Cigarette inner liner paper samples were divided into four categories based on HCA and assignment of Raman special peaks: (1) talcum powder, (2) zinc oxide, (3) talcum powder and zinc oxide and (4) zinc oxide and barium sulfate. The PCA-FDA model was constructed for identifying the unknown samples, it delivered 100.00% calibration accuracy and validation accuracy. The results suggest that SERDS combined with the chemometric methods is a rapid, nondestructive and accurate method for the differentiation of cigarette inner liner papers.