Identification and classification of recyclable waste using laser-induced breakdown spectroscopy technology

This study investigated the rapid identification of ceramics via laser-induced breakdown spectroscopy (LIBS) to realize the identification of ancient ceramics from different regions. Ceramics from different regions may have large differences in their elemental composition. Thus, using LIBS technology for ceramic identification is feasible. The spectral intensities of 11 common elements, namely, Si, Al, Fe, Ca, Mg, Ti, Mn, Na, K, Sr, and Ba, in ceramics were selected as classification indices. Principal component analysis (PCA) and kernel principal component analysis (KPCA) combined with the back propagation (BP) neural network were used to identify ceramics. Furthermore, the effects of the PCA and KPCA data processing methods were compared. Finally, this work aimed to select a suitable method for obtaining spectral data on ceramics identified by LIBS through experiments. Results revealed that LIBS technology could aid the routine, rapid, and on-site analysis of archeological objects to rapidly identify or screen various types of objects.

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.

Laser induced breakdown spectroscopy online monitoring of laser cleaning quality on carbon fiber reinforced plastic

LIBS spectroscopy and random forest: A new approach to explore brightness analysis and the source discrimination of white porcelains excavated from Xi'an, Shaanxi province (Tang dynasty, 618-907 CE).

In this scientific work, using the method of laser-induced breakdown spectroscopy (LIBS), the spectra of beef samples and impurities in meat products, namely, banana, pineapple, kiwi, bergamot, poria coconut, Chinese angelica, chicken blood vine, were measured by using developed experimental devices. The purpose of the research was to evaluate the qualitative characteristics of additives to meat semi-finished products for the potential formation of the desired properties of the products due to the analysis of the received spectrograms of trace elements of the samples when applying the LIBS method, quantitative analysis for processing the received information. The determined values of the electron temperature of the plasma, the electron density of the used raw material samples, and the assessment of the local heat balance were used as evaluation criteria. When processing the obtained data, the characteristics of the laser-induced plasma surface of the presented samples were analyzed; the electron temperature and electron density were determined, and a quantitative analysis of trace elements was carried out. LIBS technology allows rapid real-time monitoring and qualitative analysis of trace elements online and over long distances. During the research, it turned out that quantitative analysis requires further study and optimisation of experimental conditions, such as pre-treatment of samples. These conditions optimise defocusing, double laser pulse, and sample temperature, which increases the signal/noise ratio of all spectral lines. The combination of fluorescence spectroscopy and Raman technology enables higher detection sensitivity and better molecule control, creating a quantitative analysis method model that can reduce matrix effects and overcome the self-absorption effect. Among the difficulties of using LIBS technology, several elements can be noted online simultaneously, compared to Raman. The combination of spectroscopy and fluorescence spectroscopy can obtain more comprehensive information about the composition of materials, which can become a potential platform for monitoring trace elements in food products.

An electronic nose (E-nose) was developed to distinguish different stored eggs by using principal component analysis (PCA),linear discriminant analysis (LDA),BP neural network (BPNN) and the combination of a genetic algorithm and BP neural network (GANN). Results showed that the E-nose can distinguish eggs of different storage time under cool and room-temperature storage by LDA and PCA. LDA preceded PCA. Better prediction values were obtained by GANN than by BPNN. Relationships were established between the E-nose signal and egg quality indices by quadratic polynomial step regression (QPSR). The prediction models indicated good prediction performance with correlation coefficient between predicted and measured values greater than 0.90.

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model’s decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice.

The assets of laser-induced breakdown spectroscopy (LIBS) for the future of heritage science

Monitoring storage time and quality attribute of egg based on electronic nose

The objective of this study was to investigate the potential of an electronic nose (E-nose) technique for detecting eggshell crack. An E-nose was developed with eight sensors for distinguishing intact eggs and cracked eggs. Pattern recognition was conducted using principal component analysis (PCA), linear discriminant analysis (LDA), back-propagation neural network (BPNN), and the combination of genetic algorithm and BP neural network (GANN). The results proved that the E-nose coupled with LDA and PCA can distinguish between intact egg and cracked egg after one week or two weeks of storage, and the LDA method had better classification results. Furthermore, the E-nose using BPNN and GANN can distinguish between intact egg and cracked egg, and a greater distinguishing effect can be obtained with increased storage time. A better predicted rate was obtained by GANN than by BPNN.

Al-In-Sn-O (AITO) thin film refers to a novel wide-bandgap transparent conductive material, which is formed by doping the aluminum element into In-Sn-O material. It is of promising application in deep ultraviolet optoelectronic devices. Al/Al+In+Sn and Sn/Al+In+Sn are capable of impacting the optical and electrical properties of AITO thin film. Three groups of AITO thin film samples with different sputtering powers, sputtering pressures, and sputtering times were prepared with magnetron sputtering. The concentration ratio of Al/Al+In+Sn and Sn/Al+In+Sn in AITO samples was quantitatively analyzed with laser-induced breakdown spectroscopy (LIBS) technology. A single calibration curve was drawn based on the sputtering parameters of each group, and the comprehensive calibration curves of two concentration ratios under any sputtering parameters were plotted. The accuracy of the comprehensive calibration curve was determined with samples prepared under random sputtering parameters, and the energy dispersive x-ray spectroscopy analysis results were compared with the LIBS technical analysis results. The relative error was less than 5%, so the LIBS technical analysis was demonstrated to be accurate. By building the comprehensive calibration curve, a novel method to conduct rapid online analysis of AITO thin films and timely determination of photoelectrical properties is presented, and the new application of LIBS technology is developed in thin film semiconductor materials.

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

We have successfully demonstrated that although there are significant analytical challenges involved in the qualitative discrimination analysis of sub-mm sized (microfragment) glass samples, the task can be solved with very good accuracy and reliability with the multivariate chemometric evaluation of laser-induced breakdown spectroscopy (LIBS) data or in combination with pre-screening based on refractive index (RI) data. In total, 127 glass samples of four types (fused silica, flint, borosilicate and soda–lime) were involved in the tests. Four multivariate chemometric data evaluation methods (linear discrimination analysis, quadratic discrimination analysis, classification tree and random forest) for LIBS data were evaluated with and without data compression (principal component analysis). Classification tree and random forest methods were found to give the most consistent and most accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda–lime glasses. The developed methods can be used in forensic analysis.

A rapid method for measuring the rock brittleness index: Rapid characterization of rock brittleness based on LIBS technology