Spectral stability improvement through various wide fields of view collecting optics, averaging temperature and ionization conditions, reducing the impact of plasma morphology fluctuations on acquisition.

The safety of composite insulators in high-voltage transmission lines is directly related to the stable operation of the power system, which is a fundamental condition for the normal functioning of people’s lives and industrial production. Composite insulators are exposed to outdoor conditions for extended periods of time, and with the increase in service life, they are subjected to aging due to external environmental factors and electrical stresses. This aging leads to a decline in their electrical insulation, mechanical properties, and other performance, which, in severe cases, may result in power system failures. Therefore, accurate assessment and detection of the aging status of composite insulators are particularly important. Traditional detection methods such as visual inspection, hardness testing, and hydrophobicity testing have limitations, including single functionality and susceptibility to environmental interference, which cannot comprehensively and accurately reflect the aging condition of the insulators. In recent years, spectroscopy-based detection technologies have been increasingly applied for the rapid detection of composite insulators due to their advantages, such as high sensitivity, non-contact measurement, and multi-dimensional information extraction. Common spectroscopic detection methods include Ultraviolet Discharge (UV Discharge), Fourier Transform Infrared (FTIR) Spectroscopy, Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Laser-Induced Breakdown Spectroscopy (LIBS), and Terahertz (THz) Spectroscopy. These methods offer non-contact, remote, and rapid capabilities, enabling detailed analysis of the insulator’s surface microstructure, chemical composition, and aging characteristics. This paper introduces = spectroscopy-based methods for detecting the aging status of composite insulators, analyzing the advantages and limitations of these methods, and discussing the challenges of their industrial application. Furthermore, the paper reviews the research progress and practical applications of spectroscopic techniques in the evaluation of insulator aging status, systematically summarizing important achievements in the field and providing an outlook for future developments.

Water quality should be tested to ensure it is acceptable for the healthy growth of plants and animals, and water hardness is one of the important testing indexes. Herein, a novel approach was proposed to achieve high accuracy and rapid quantitative analyses of water hardness by combining one-point calibration laser-induced breakdown spectroscopy (OPC–LIBS) and aerosolization. First, the water samples are aerosolized via the aerosol generation device and the LIBS spectra of aerosols are obtained. Then, a modified OPC–LIBS model is used to determine the elemental contents of the aerosols via LIBS spectra, in which the plasma temperature is calculated using the Multi-Element Saha–Boltzmann (ME–SB) plot. One suitable standard liquid sample (the concentrations of Ca, Mg, and Sr were 50 mg/L, 50 mg/L, and 500 mg/L, respectively) was selected to evaluate the quantitative performance of the modified OPC–LIBS. Then, the Ca and Mg concentrations in the three real water samples (from the Yangtze River, reservoir, and underground) were detected and quantified by the proposed method, and the quantitative results of three LIBS calibration methods were compared with that of inductively coupled plasma optical emission spectroscopy (ICP–OES). The average relative error of Ca and Mg found in the OPC–LIBS results was lower by 22.23% than the internal standard method and 14.50% lower than the external standard method. The method combining modified OPC–LIBS and aerosolization can achieve high-precision rapid quantification of water hardness detection, which provides a new path for rapid detection of water hardness and is expected to make online detection a reality in the water quality testing field.

The high-performance identification of insulating materials is crucial for reducing resource waste, minimizing pollution, and promoting resource recycling. To achieve this, a novel method based on laser-induced breakdown spectroscopy (LIBS), named the generalized spectrum method (GSM-LIBS), was proposed in this study. Compared to traditional dimensionality reduction methods such as PCA, GSM-LIBS outperforms by integrating multiple spectral features, preserving both global and local information that may be lost in PCA-based methods. GSM-LIBS not only effectively reduces the spectral dimensions but also extracts more key features, such as peak intensity, integral intensity, intensity ratio, radiation background, and spectral shape. These features help retain important information from the spectrum, providing more precise details such as plasma state, element concentration, and spectral characteristics, thereby significantly improving analysis performance. To verify the effectiveness of GSM-LIBS, this method was applied to the classification study of seven types of insulating materials and compared with principal component analysis (PCA-LIBS). To ensure the general applicability of this study, two traditional machine learning models, k-nearest neighbor (KNN) and support vector machine (SVM), and one deep learning model, neural network (NN), were used. For the machine learning models, the accuracy of the KNN and SVM classification models on the prediction set improved from 0.935 and 0.965 to 0.979 and 0.996, respectively. For the deep learning model, the performance of the NN classification model was also significantly improved, with accuracy increasing from 0.984 to 0.994. These experimental results strongly demonstrate the feasibility and effectiveness of GSM-LIBS in effectively reducing the spectral dimensions while retaining key information.

High-voltage wiring harnesses in new energy vehicles (NEVs) are susceptible to thermal aging in high-temperature environments, whereas conventional assessment methods are difficult to deploy rapidly. This study combines laser-induced breakdown spectroscopy (LIBS) with random forest (RF) classification to assess thermal aging levels in cross-linked polyethylene (XLPE) insulation. Thirteen laboratory-aged XLPE wiring harness samples were prepared, and 100 single-shot spectra were acquired at fresh positions for each aging level. The specific methodological contribution is the use of class-wise median absolute deviation (MAD) at each wavelength as a variable-selection criterion before RF training. Three models were compared: RF, principal component analysis (PCA) combined with RF (PCA–RF), and MAD combined with RF (MAD–RF). RF achieved 100% internal hold-out accuracy for the non-aged versus 60-day comparison and 84.23% across all 13 aging levels. PCA–RF increased the multiclass accuracy to 87.31%, whereas MAD–RF reached 95.00% under the original exploratory hold-out workflow. These results indicate that wavelength-wise robust dispersion can provide a compact, discriminative representation of the present LIBS dataset.

Correction for ‘High-performance identification of insulating materials by using generalized spectrum in laser-induced breakdown spectroscopy’ by Junfei Nie et al. , Anal. Methods , 2025, 17 , 6982–6988, https://doi.org/10.1039/D5AY01151E.