Characteristic Wavelength Research Articles

Characteristic Wavelength Selection and Surrogate Monitoring for UV–Vis Absorption Spectroscopy-Based Water Quality Sensing

Ultraviolet-visible (UV–Vis) absorption spectroscopy for in situ water quality sensing has garnered increasing attention. However, the selection of the characteristic wavelengths for water quality indicators has been underexplored in existing studies, resulting in surrogate monitoring models with low accuracy and high complexity. This research used field data from the Maozhou River in Shenzhen. The accuracy of the surrogate model based on the wavelength selection method is 134.8%, 52.5%, and 13.5% improvement in accuracy compared to the single wavelength method, the PCA method, and the full spectrum method, respectively. We investigate seven characteristic wavelength optimisation algorithms and five machine learning models for surrogate monitoring of five water quality indicators: TOC, BOD5, COD, TN, and NO3-N. The results indicate that the competitive adaptive reweighted sampling (CARS) method for wavelength selection, combined with ridge regression as a surrogate monitoring model, achieved the best performance in this study. The determination coefficient (R2) of the five water quality indicators were 0.80, 0.64, 0.82, 0.97, and 0.96, respectively. The study shows that for watersheds with relatively stable water chemical components, there is no need to use overly complex nonlinear models, and the regression model with characteristic wavelength selection can achieve good prediction results. This study provides detailed technical information on river water quality spectral surrogate monitoring, offering an important practice reference.

Non-Destructive Detection of pH Value During Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging Technology

The pH value of maize silage can accurately reflect its quality. In this study, a colorimetric sensor array (CSA) combined with hyperspectral imaging (HSI) was used to predict the pH value of maize silage during secondary fermentation. Seventeen color-sensitive dyes were used to construct the CSA, which was subsequently applied to capture the volatile odor profiles of maize silage samples. Hyperspectral images of the color-sensitive dyes on the CSA were acquired using the HSI technique. Different algorithms were used to preprocess the raw spectral data of each dye, and a partial least squares regression (PLSR) model was built for each dye separately. Subsequently, the adaptive bacterial foraging optimization (ABFO) algorithm was employed to identify three color-sensitive dyes that demonstrated heightened sensitivity to pH variations in maize silage. This study further compared the capabilities of individual dyes, as well as their combinations, in predicting the pH value of maize silage. Additionally, a novel feature wavelength extraction method based on the ABFO algorithm was proposed, which was then compared with two traditional feature extraction algorithms. These methods were combined with PLSR and backpropagation neural network (BPNN) algorithms to construct a quantitative prediction model for the pH value of maize silage. The results show that the quantitative prediction model constructed based on three dyes was more accurate than that constructed based on an individual dye. Among them, the ABFO-BPNN model constructed on the basis of combined dyes had the best prediction performance, with prediction correlation coefficient (RP2), root mean square error of the prediction set (RMSEP), and ratio of performance deviation (RPD) values of 0.9348, 0.3976, and 3.9695, respectively. The aim of this study was to develop a reliable evaluation model to achieve fast and accurate predictions of silage pH.

Swelling and Evaporation Determine Surface Morphology of Grafted Hydrogel Thin Films.

We experimentally study the formation of surface patterns in grafted hydrogel films of nanometer-to-micrometer thickness during imbibition-driven swelling followed by evaporation-driven shrinking. Creases are known to form at the hydrogel surface during swelling; the wavelength of the creasing pattern is proportional to the initial thickness of the hydrogel film with a logarithmic correction that depends on microscopic properties of the hydrogel. We find that, although the characteristic wavelength of the pattern is determined during swelling, the surface morphology can be significantly influenced by evaporation-induced shrinking. We observe that the elastocapillary length based on swollen mechanical properties gives a threshold thickness for a surface pattern formation and consequently an important change in morphology.

Normal Form Formulae of Turing–Turing Bifurcation for Partial Functional Differential Equations With Nonlinear Diffusion

ABSTRACTFor most systems, the appearance of Turing bifurcation means that it is possible to excite Turing–Turing bifurcation, thus inducing superimposed spatial patterns, multistable spatial patterns co‐existing, and others.It is undoubtedly of interest to qualitatively analyze the structures and stability of these spatially heterogeneous solutions generated by Turing–Turing bifurcation. Therefore, in this paper, with the aid of the center manifold theory and the normal form method, for general partial functional differential equations with nonlinear diffusion, the third‐order normal form is firstly derived. It is locally topologically equivalent to the primitive partial functional differential equations at the Turing–Turing bifurcation point. And then the explicit formulae for the coefficients in the normal form associated with three different spatial modes are presented. As an application, a predator–prey model with predator–taxis is considered. It is theoretically revealed that the system admits the coexistence of a pair of stable steady states with a single characteristic wavelength and the coexistence of four stable steady states with different single characteristic wavelengths. Further, the parameter regions in which these phenomena will arise are quantitatively given. It is illustrated that the self‐diffusion of prey and predator–taxis complement each other to promote the formation for the spatially homogeneous distributions of populations.

MAGNESIUM –PICRATE COMPLEXES AS COUPLING AGENTS TO ENHANCE UV-VISIBLE SPECTROPHOTOMETRIC DRUG ANALYSIS

This research investigates the production of magnesium-picrate complexes and their use as coupling agents to improve UV-Visible spectrophotometric detection and quantification of vital medicinal medicines. The analyzed pharmaceuticals comprise Diclofenac Sodium, Ciprofloxacin HCl, Levoceitrizine 2HCl, Levofloxacin Hemihydrate, Nimesulide, Piroxicam BCD, and Terbinafine HCl. The synthesized magnesium-picrate complexes, upon heating with medication solutions, exhibited substantial increases in absorbance at their specific characteristic wavelengths. The increases were documented as 70.82%, 47.95%, 64.11%, 64.05%, 39.24%, 81.16%, and 87.10%, respectively, reflecting increased molar absorptivity. The 90% yield of magnesium-picrate complexes and their capacity to improve spectrum detection highlight their promise in optimizing UV-Visible spectrophotometric techniques for pharmaceutical quality assurance.

Hydrodynamic modes in nano-channels

This paper investigates the local hydrodynamics of a dense fluid confined in nanoscale slit-pores with different heights. Using non-equilibrium molecular dynamics simulations of the fluid system, we induce a steady-state sinusoidal velocity profile across the channel having a characteristic wavelength, thus, probing the fluid response to a specific Fourier mode. As expected, for sufficiently large channel heights and wavelengths there is an excellent agreement between the hydrodynamic predictions and simulation data. As the wavelength decreases to around 5 molecular diameters, the classical hydrodynamics fails to predict the steady-state velocity profile; we attribute this to the non-local nature of the fluid response and the presence of density gradients in the wall–fluid interfacial region. Using generalized hydrodynamics and the Fourier spectrum of the density profile, we derive the strain rate amplitude and shear pressure corrections due to these two effects. The local relaxation from the steady-state to the zero flow situation is tracked for different channel heights and wavelengths. The relaxation is in general visco-elastic in the wall–fluid region, and we argue that this phenomenon is the mechanism behind the “enhanced viscosity” used in the literature. We also report a surprising dynamics for the fluid located between the wall–fluid region and bulk region, which cannot be explained by classical hydrodynamics; here, an initial exponential relaxation abruptly transitions into a linear relaxation. The work highlights the many different physical mechanisms present in nano-confined fluids, and that the fluid response is in general position and wavelength dependent.

Detection of Viable but Nonculturable E. coli Induced by Low-Level Antimicrobials Using AI-Enabled Hyperspectral Microscopy.

Rapid detection of bacterial pathogens is essential for food safety and public health, yet bacteria can evade detection by entering a viable but nonculturable (VBNC) state under sublethal stress, such as antimicrobial residues. These bacteria remain active but undetectable by standard culture-based methods without extensive enrichment, necessitating advanced detection methods. This study developed an AI-enabled hyperspectral microscope imaging (HMI) framework for rapid VBNC detection under low-level antimicrobials. The objectives were to (i) induce the VBNC state in Escherichia coli K-12 by exposure to selected antimicrobial stressors, (ii) obtain HMI data capturing physiological changes in VBNC cells, and (iii) automate the classification of normal and VBNC cells using deep learning image classification. The VBNC state was induced by low-level oxidative (0.01% hydrogen peroxide) and acidic (0.001% peracetic acid) stressors for 3days, confirmed by live-dead staining and plate counting. HMI provided spatial and spectral data, extracted into pseudo-RGB images using three characteristic spectral wavelengths. An EfficientNetV2-based convolutional neural network architecture was trained on these pseudo-RGB images, achieving 97.1% accuracy of VBNC classification (n=200), outperforming the model trained on RGB images at 83.3%. The results highlight the potential for rapid, automated VBNC detection using AI-enabled hyperspectral microscopy, contributing to timely intervention to prevent foodborne illnesses and outbreaks.

Impact of maturity and variety on the hyperspectral detection model for determining soluble solid content in fresh apricots

The hyperspectral technique is a non-destructive method for quantifying the organic matter content, and has emerged as a prevalent approach for measuring soluble solid content (SSC) in fruits. However, existing hyperspectral models often face challenges in accurately predicting new samples of different maturities and varieties, thus limiting their wide application. The aim of this study was to investigate the effects of maturity and variety on the hyperspectral detection model for SSC of fresh apricot, and to establish a robust global model. Standard normal variable transformation method was used to reduce the influence of uneven light distribution on spherical fruit. There are four data sets: 6-1-pre-ripe stage, 6-1-ripening stage, Wanghong-ripening stage, and Jinmei-ripening stage. Through the Mahalanobis distance-concentration gradient (MD-CG) method, representative samples (5, 10, 15) were selected from the fresh apricot data set of other maturity and varieties as representative and typical sample points, and included in the training set for model update. This greatly improves the stability of the model. By integrating representative samples from the other three datasets into the 6-1-ripening (6-1-R) training set, combined with feature wavelength selection and radial basis function neural network updates, the global model effectively predicted SSC (RPD > 1.4) for the remaining three categories of fresh apricot. The global model is mainly affected by the variation of fresh apricot varieties, and the influence of maturity on the model accuracy is relatively small. The results show that using MD-CG recalibration model as a rapid detection strategy can determine the content of SSC in fresh apricots of different maturity or varieties, thus providing a solid foundation for its industrial application.

Hyperspectral Remote Sensing Estimation of Rice Canopy LAI and LCC by UAV Coupled RTM and Machine Learning

Leaf chlorophyll content (LCC) and leaf area index (LAI) are crucial for rice growth and development, serving as key parameters for assessing nutritional status, growth, water management, and yield prediction. This study introduces a novel canopy radiative transfer model (RTM) by coupling the radiation transfer model for rice leaves (RPIOSL) and unified BRDF model (UBM) models, comparing its simulated canopy hyperspectra with those from the PROSAIL model. Characteristic wavelengths were extracted using Sobol sensitivity analysis and competitive adaptive reweighted sampling methods. Using these wavelengths, rice phenotype estimation models were constructed with back propagation neural network (BPNN), extreme learning machine (ELM), and broad learning system (BLS) methods. The results indicate that the RPIOSL-UBM model’s hyperspectra closely match measured data in the 500–650 nm and 750–1000 nm ranges, reducing the root mean square error (RMSE) by 0.0359 compared to the PROSAIL model. The ELM-based models using the RPIOSL-UBM dataset proved most effective for estimating the LAI and LCC, with RMSE values of 0.6357 and 6.0101 μg · cm−2, respectively. These values show significant improvements over the PROSAIL dataset models, with RMSE reductions of 0.1076 and 6.3297 μg · cm−2, respectively. The findings demonstrate that the proposed model can effectively estimate rice phenotypic parameters from UAV-measured hyperspectral data, offering a new approach to assess rice nutritional status and enhance cultivation efficiency and yield. This study underscores the potential of advanced modeling techniques in precision agriculture.

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.

Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using near-infrared (NIR) reflectance spectra in the 940-1660 nm range. Various preprocessing techniques, including Savitzky-Golay (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (Rp) of 0.93, and a residual predictive deviation (RPD) of 3. The associated methods and theoretical foundation provide a scientific basis for the quality control and processing of maize.

A Handheld Colorimeter for Remote and Onsite Recognition of Baking Levels at High Temperature — Pork Floss as a Case Study

AbstractPork floss is a common dried meat product in Asia. The endpoint of the baking process is traditionally determined by subjective human experts and indirect temperature measurements, which can often result in unstandardized production. Current colorimeters are unavailable for onsite measurement due to limitations associated with contact measurement and environmental temperature. Instead of the abovementioned human experts and tabletop colorimeters, a handheld colorimeter was built based on the expertise of human specialists and utilizing a tabletop colorimeter and other optical steps. First, the selected samples were used to determine the upper and lower limits distinguishing light, medium, and heavy baking levels by using a tabletop colorimeter. Second, independent light sources and spectrometers were utilized to choose the characteristic and reference wavelengths at 450 and 830 nm, separately. Third, the handheld colorimeter, instead of human expert observation, was designed with functions such as distance sensing and Internet of Things capabilities. The baked index was derived from the calibration reflection and established statistical models. Here, the calibration reflection was defined by the normalized intensity at 450 nm relative to 830 nm, and statistical models were founded from the determined samples of upper and lower limits at 95–700 mm. The developed handheld colorimeter demonstrated high agreement rates of 96.84% and 93.86% in separate comparisons with tabletop colorimeters and human experts, respectively. This work indicated the accurate and stable recognition of samples within two limits and overall. Field validation confirmed the performance of remote, economic, and onsite recognition against environmental temperature and noise.

Prediction of the Quality of Anxi Tieguanyin Based on Hyperspectral Detection Technology.

Anxi Tieguanyin belongs to the oolong tea category and is one of the top ten most famous teas in China. In this study, hyperspectral imaging (HSI) technology was combined with chemometric methods to achieve the rapid determination of free amino acid and tea polyphenol contents in Tieguanyin tea. Here, the spectral data of Tieguanyin tea samples of four quality grades were obtained via visible near-infrared hyperspectroscopy in the range of 400-1000 nm, and the free amino acid and tea polyphenol contents of the samples were detected. First derivative (1D), normalization (Nor), and Savitzky-Golay (SG) smoothing were utilized to preprocess the original spectrum. The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). The contents of free amino acid and tea polyphenol in Tieguanyin tea were predicted by the back propagation (BP) neural network, partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM). The results revealed that the free amino acid content of the clear-flavoured Tieguanyin was greater than that of the strong-flavoured type, that the tea polyphenol content of the strong-flavoured Tieguanyin was greater than that of the clear-flavoured type, and that the content of the first-grade product was greater than that of the second-grade product. The 1D preprocessing improved the resolution and sensitivity of the spectra. When using CARS, the number of wavelengths for free amino acids and tea polyphenols was reduced to 50 and 70, respectively. The combination of 1D and CARS is conducive to improving the accuracy of late modelling. The 1D-CARS-RF model had the highest accuracy in predicting the free amino acid (RP2 = 0.940, RMSEP = 0.032, and RPD = 4.446) and tea polyphenol contents (RP2 = 0.938, RMSEP = 0.334, and RPD = 4.474). The use of hyperspectral imaging combined with multiple algorithms can be used to achieve the fast and non-destructive prediction of free amino acid and tea polyphenol contents in Tieguanyin tea.

Terahertz scanning near-field optical microscopy for biomedical detection: Recent advances, challenges, and future perspectives.

Terahertz (THz) radiation is widely recognized as a non-destructive, label-free, and highly- sensitive tool for biomedical detections. Nevertheless, its application in precision biomedical fields faces challenges due to poor spatial resolution caused by intrinsically long wavelength characteristics. THz scanning near-field optical microscopy (THz-SNOM), which surpasses the Rayleigh criterion, offers micrometer and nanometer-scale spatial resolution, making it possible to perform precise bioinspection with THz imaging. THz-SNOM is attracting considerable attention for its potential in advanced biomedical research and diagnosis. Currently, its family typically includes four members based on distinct principles, which are suitable for different biological applications. This review provides an overview of the principles of these THz-SNOM modalities, outlines their various applications, identifies the obstacles hindering their performance, and envisions their future development.

基于高光谱成像的大豆籽粒含水率检测研究

Using hyperspectral imaging technology for rapid, non-destructive detection of soybean grain moisture content provides technical support for high-quality soybean harvesting. A total of 90 samples of soybean grains from different varieties were collected, with hyperspectral images acquired in the wavelength range of 900–1700 nm. The moisture content of each soybean grain sample was determined using the direct drying method as specified in GB 5009.3-2016. The samples were divided into a calibration set and a prediction set based on a 4:1 ratio using the sample partitioning method of Joint X-Y Distance. Eight preprocessing methods were applied to the raw spectral data, including baseline correction, moving average, Savitzky-Golay filtering, normalization, standard normal variate transformation, multiple scatter correction, first derivative, and deconvolution. Feature wavelengths were then extracted using the successive projections algorithm and the competitive adaptive reweighted sampling algorithm. Finally, a partial least squares regression model for predicting the moisture content of soybean grains was developed based on these feature wavelengths. The results show that the correlation coefficient and the root mean square error of the optimal model for the prediction set were 0.92 and 0.2371, respectively. The moisture spectrum inversion model can precisely and rapidly predict the moisture content of soybean grains non-destructively, thereby determining the timing of mechanical soybean harvesting and enhancing the quality of soybean harvesting, storage, and processing.

Identification Method for Railway Rail Corrugation Utilizing CEEMDAN-PE-SPWVD.

Rail corrugation intensifies wheel-rail vibrations, often leading to damage in vehicle-track system components within affected sections. This paper proposes a novel method for identifying rail corrugation, which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), permutation entropy (PE), and Smoothed Pseudo Wigner-Ville Distribution (SPWVD). Initially, vertical acceleration data from the axle box are decomposed using CEEMDAN to extract intrinsic mode functions (IMFs) with distinct frequencies. PE is used to evaluate the randomness of each IMF component, discarding those with high permutation entropy values. Subsequently, correlation analysis is performed on the retained IMFs to identify the component most strongly correlated with the original signal. The selected component is subjected to SPWVD time-frequency analysis to identify the location and wavelength of the corrugation occurrence. Filtering is applied to the IMF based on the frequency concentration observed in the time-frequency analysis results. Then, frequency-domain integration is performed to estimate the rail's corrugation depth. Finally, the algorithm is validated and analyzed using both simulated data and measured data. Validation results show that this approach reliably identifies the wavelength and depth characteristics of rail corrugation. Additionally, the time-frequency analysis results reveal variations in the severity of corrugation damage at different locations.

Field Grading of Longan SSC via Vis-NIR and Improved BP Neural Network

Soluble solids content (SSC) measurements are crucial for managing longan production and post-harvest handling. However, most traditional SSC detection methods are destructive, cumbersome, and unsuitable for field applications. This study proposes a novel field detection model (Brix-back propagation neural network, Brix-BPNN), designed for longan SSC grading based on an improved BP neural network. Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. Among these, the model preprocessed with Savitzky–Golay smoothing and the first derivative (SG-D1) demonstrated a 7.02% improvement in accuracy compared to the original spectral model. Subsequently, the BP network structure was refined, and the competitive adaptive reweighted sampling (CARS) algorithm was employed for feature wavelength extraction. The results show that the improved Brix-BPNN model, integrated with the CARS, achieves the highest prediction performance, with a 2.84% increase in classification accuracy relative to the original BPNN model. Additionally, the number of wavelengths is reduced by 92% compared to the full spectrum, making this model both lightweight and efficient for rapid field detection. Furthermore, a portable detection device based on visible-near-infrared (Vis-NIR) spectroscopy was developed for longan SSC grading, achieving a prediction accuracy of 83.33% and enabling fast, nondestructive testing in field conditions.

An Optimized Bidirectional Long Short-Term Memory Model Based on Hyperspectral Analysis of Protein Content in Milk Powder.

Protein content is an important index in the assessment of dairy nutrition. As a crucial source of protein absorption in people's daily life, the quality of milk powder products not only has a deep impact on the development of the dairy industry, but also seriously damages the health of consumers. It is of great significance to find a faster and more accurate method for detecting milk protein content. This paper utilizes the chemical content of milk powder and hyperspectral data as independent variables. By comparing 14 kinds of preprocessing algorithms, the mean-centered (MC) method is selected to preprocess the data, and then the combined method of competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) is used to screen the feature wavelength, so as to establish the model and learn the internal dynamic change law of the feature. Furthermore, the Attention mechanism was introduced to assign different weights to bidirectional long short-term memory (BiLSTM) hidden states through mapping weighting and learning parameter matrix. To reduce the loss of information and strengthen the influence of important information, at the same time, in order to solve the difficult problem of hyperparameter selection of the model, the whale optimization algorithm (WOA) is proposed to optimize the hyperparameter selection of the model. The test results showed that with WOA-BiLSTM-Attention model algorithm, the coefficient of determination (R 2) of 0.9975 and root mean square error (RMSEP) of 0.0337 in comparison with R 2 and RMSEP values obtained from BiLSTM-Attention model algorithm, which were higher by 0.799% lower by 56.5%, respectively. This study provides algorithm support and theoretical basis for fast non-destructive testing based on deep learning algorithm to predict protein content in milk powder.

Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis.

The non-destructive detection of fruit quality is indispensable in the agricultural and food industries. This study aimed to explore the application of hyperspectral imaging (HSI) technology, combined with machine learning, for a quality assessment of pears, so as to provide an efficient technical method. Six varieties of pears were used for inspection, including 'Sucui No.1', 'Zaojinxiang', 'Huangguan', 'Akizuki', 'Yali', and 'Hongli No.1'. Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. They could enhance the predictive capability and reduce computational complexity. Furthermore, in order to construct a quality prediction model, integrating hyperspectral data from six pear varieties resulted in an RPD (Ratio of Performance to Deviation) exceeding 2.0 for all the quality parameters, indicating that increasing the fruit sample size and variety number further strengthened the robustness of the model. The Backpropagation Neural Network (BPNN) model could accurately distinguish six distinct pear varieties, achieving prediction accuracies of above 99% for both the calibration and test sets. In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.

Novel Spectrophotometric Method for Robust Detection of Trace Copper and Cobalt in High-Concentration Zinc Solution.

In the purification process of zinc hydrometallurgy, the spectra of copper and cobalt seriously overlap in the whole band and are interfered with by the spectra of zinc and nickel, which seriously affects the detection results of copper and cobalt in zinc solutions. Aiming to address the problems of low resolution, serious overlap, and narrow characteristic wavelengths, a novel spectrophotometric method for the robust detection of trace copper and cobalt is proposed. First, the Haar, Db4, Coif3, and Sym3 wavelets are used to carry out the second-order continuous wavelet transform on the spectral signals of copper and cobalt, which improves the resolution of copper and cobalt and eliminates the background interference caused by matrix zinc signals and reagents. Then, the information ratio and separation degree are defined as optimization indexes, a multi-objective optimization model is established with the wavelet decomposition scale as a variable, and the non-inferior solution of multi-objective optimization is solved by the state transition algorithm. Finally, the optimal second-derivative spectra combined with the fine zero-crossing technique are used to establish calibration curves at zero-crossing points for the simultaneous detection of copper and cobalt. The experimental results show that the detection performance of the proposed method is far superior to the partial least squares and Kalman filtering methods. The RMSEPs of copper and cobalt are 0.098 and 0.063, the correlation coefficients are 0.9953 and 0.9971, and the average relative errors of copper and cobalt are 3.77% and 2.85%, making this method suitable for the simultaneous detection of trace copper and cobalt in high-concentration zinc solutions.

Design of a Nested Hollow-Core Anti-Resonant Fiber Sensor for Simultaneous Measurement of Temperature and Strain.

A highly sensitive sensor, which can detect the temperature and strain simultaneously, is proposed using a hollow-core anti-resonant fiber with composite nested tubes. The sensing fiber contains two kinds of nested tubes, and two different sensing mechanisms, the resonance coupling effect and the intermodal interference, are realized in the same section of a hollow-core anti-resonant fiber fully filled with ethanol. Five conjoined nested anti-resonant tubes are introduced to suppress the confinement loss of the higher-order mode LP02. One hybrid conjoined nested tube, which consists of a half-circular anti-resonant tube and a half-circular resonant tube, is introduced to induce a resonant coupling between the LP02 mode in the core and the dielectric mode in the nested resonant tubes. Numerical investigations demonstrate the shifts of the feature wavelengths of the resonance coupling effect, and the intermodal interference shows different velocities with temperature and strain, while a simultaneous measurement of temperature and strain can be realized with high sensitivities (3.36 nm/°C and -0.003 nm/με to temperature and strain, respectively). Since the sensor can be fabricated by full infiltration with liquid into the large-size core and cladding tubes of hollow-core anti-resonant fibers, and special post-processing, such as selective infiltration or coating, is notneeded. The proposed sensors based on hollow-core anti-resonant fibers with functional liquid infiltration provide a more efficient and versatile platform for the temperature and strain sensing.