Optimized adaptive Savitzky-Golay filtering algorithm based on deep learning network for absorption spectroscopy

Inverse fitting direct absorption spectroscopy Technology: Simplified implementation and enhanced performance

The conventional direct absorption spectroscopy (DAS) technique has been plagued by the difficulty of obtaining accurate baseline, which is caused by photoelectric drift and the absence of non-absorbing regions in the transmitted light intensity signal. An inverse fitting direct absorption spectroscopy (IF-DAS) technique has been proposed to address this difficulty. The technique leverages the intrinsic nonlinear intensity response of tunable lasers to achieve baseline-free concentration measurements. It offers the advantages of being straightforward to implement, baseline-free, calibration-free, and resistant to photoelectric signal drift. Its efficacy was validated using an example under ambient temperature and atmospheric pressure conditions. The performance of the IF-DAS technique was compared with that of the conventional DAS technique through standard experimental tests. The results demonstrate that the IF-DAS technique is less susceptible to fluctuations in light intensity, exhibits superior linearity and accuracy, with an R2 value of 0.99986 and an overall error of less than 2%. This technique shows potential for application in harsh scenarios such as reactive flow fields and long-term engineering applications.

Integrated LSPR Biosensing Signal Processing Strategy and Visualization Implementation

The LSPR biosensor chip is a groundbreaking tool popular in laboratory settings for identifying disease markers. However, its use in clinical environments is not as widespread. One notable gap is the lack of a universal signal processing tool for LSPR biosensing. To escalate its precision, there is an emerging need for software that not only optimizes signal processing but also incorporates self-verification functionalities within LSPR biochemical sensors. Enter the visual LSPR sensor software-an innovative platform that processes real-time transmission or reflection spectra. This advanced software adeptly captures the nuanced structural changes at the nanostructure interface prompted by environmental fluctuations. It diligently records and computes a suite of parameters, including the resonance wavelength shift, full width at half maximum, sensitivity, and quality factor. These features empower users to tailor processing algorithms for each data capture session. Transcending traditional instruments, this method accommodates a multitude of parameters and ensures robust result validation while tactfully navigating nanostructure morphology complexities. Forsaking third-party tool dependencies, the software tackles challenges of precision and cost-effectiveness head-on, heralding a significant leap forward in nanophotonics, especially for high-throughput LSPR biosensing applications. This user-centric innovation marks substantial progress in biochemical detection. It is designed to serve both researchers and practitioners in the field of nanophotonic sensing technology, simplifying complexity while enhancing reliability and efficiency.

Multiframe-based non-local means denoising for Raman spectra

A method for denoising Raman spectra is presented in this paper. The approach is based on the principle that the original signal can be restored by averaging pixels based on structure similarity. Similarity searching and averaging are not limited to the neighbouring pixels but extended throughout the entire signal range across different frames. This approach is distinguished from the conventional single-frame neighbour pixel-based filtering. The effectiveness and robustness of the proposed method are demonstrated through denoising simulated and experimental Raman data sets with fixed denoising parameters. Several denoised results and statistical indicators are presented for the simulated data. Recovery of the experimental Raman spectrum from our newly developed cost-effective waveguide-enhanced Raman spectroscopy system is also presented and compared to the spectrum from a conventional expensive Raman microscope for the same analyte.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).

Qualitative and quantitative analysis of four benzimidazole residues in food by surface-enhanced Raman spectroscopy combined with chemometrics

In this study, surface-enhanced Raman spectroscopy (SERS) combined with chemometric methods were developed for qualitative and quantitative analysis of four benzimidazole (BMZs) residues in corn. Sulfhydryl functionalized Fe₃O₄@SiO₂@Ag-SH magnetic SERS substrates were prepared to obtain the SERS spectra of four BMZs for chemometric analysis. The partial least squares regression discrimination analysis (PLS-DA) model performed best, with a recall rate upwards 99.17%, and could successfully distinguish four BMZs. Under the support vector machine regression (SVR) model, the detection limits of carbendazim, benomyl, thiophanate-methyl and thiabendazole were 0.055 mg/L, 0.056 mg/L, 0.067 mg/L and 0.093 mg/L, respectively; the average recovery was in the range of 85.6%-107.5%. Furthermore, the method verified by HPLC, and the results showed that there was no significant difference between two methods (p > 0.05). Therefore, the strategy based on SERS coupling chemometrics can be served as a promising tool for rapid determination of BMZs residues in food.

Method of adaptive wide dynamic range gas concentration detection based on optimized direct absorption spectroscopy

For wide dynamic range gas concentration detection based on tunable diode laser absorption spectroscopy (TDLAS), direct absorption spectroscopy (DAS) and wavelength modulation spectroscopy (WMS) are usually used in combination. However, in some application scenarios such as high-speed flow field detection, natural gas leakage, or industrial production, the requirements of wide-range, fast response and calibration-free must be met. Taking applicability and cost of TDALS-based sensor into consideration, a method of optimized direct absorption spectroscopy (ODAS) based on signal correlation and spectral reconstruction is developed in this paper. This method can achieve adaptive selection of the optimal benchmark spectrum for spectral reconstruction. Moreover, methane (CH₄) is taken as an example to carry out the experimental verification. Experimental results proved that the method satisfies wide dynamic range detection of more than 4 orders of magnitude. It is worth noting that when measuring large absorbance with concentration of 75 × 10⁴ppm with DAS and ODAS method, respectively, the maximum value of residual is reduced from 3.43 to 0.07. Furthermore, whether measuring gas of small or large absorbance with different concentrations, which vary from 100 ppm to 75 × 10⁴ppm, the correlation coefficient between standard concentrations and inverted concentrations is 0.997, showing the linear consistency of the method in wide dynamic range. In addition, the absolute error is 1.81 × 10⁴ppm when measuring large absorbance of 75 × 10⁴ppm. It greatly improves the accuracy and reliability with the new method. In summary, the ODAS method can not only fulfill the measurement of gas concentration in wide range, but also further expand the application prospects of TDLAS.

Development of a Novel Spherical Light-Based Positioning Sensor in Solar Tracking

Tracking of the sun, which increases the efficiency of solar energy production systems, has shown considerable development in recent years. This development has been achieved by custom-positioned light sensors, image cameras, sensorless chronological systems and intelligent controller supported systems or by synergetic use of these systems. This study contributes to this research area with a novel spherical-based sensor which measures spherical light source emittance and localizes the light source. This sensor was built by using miniature light sensors placed on a spherical shaped three-dimensional printed body with data acquisition electronic circuitry. Besides the developed sensor data acquisition embedded software, preprocessing and filtering processes were conducted on these measured data. In the study, the outputs of Moving Average, Savitzky-Golay, and Median filters were used for the localization of the light source. The center of gravity for each filter used was determined as a point, and the location of the light source was determined. The spherical sensor system obtained by this study is applicable for various solar tracking methods. The approach of the study also shows that this measurement system is applicable for obtaining the position of local light sources such as the ones placed on mobile or cooperative robots.

Feature Domain Transform Filter for the Removal of Inherent Noise Bound to the Absorption Signal

We propose to replace the traditional time-frequency domain filtering with feature domain filtering to realize an innovation of filtering algorithm. A feature domain transform filter (FDTF) is composed of the feature domain transform layer based on principal component analysis (PCA) algorithm, the feature domain information extractor based on deep learning and the time domain transform layer. It is established to filter out the noise with the same frequency and phase as the signal and is verified on methane gas. Although FDTF is established based on the simulated data set, the filtering effects of the simulation test set and the experimental data set show that the proposed FDTF outperforms other widely used time-frequency filtering algorithms. The FDTF-assisted methane sensor has good linearity at different concentrations of methane gas. With the FDTF enhancement, the optimized methane sensor performs excellent precision and stability in real-time measurements and achieves the minimum detectable column density of 2.50 ppm·m. This is undoubtedly a successful attempt to move the signal to a new domain for parsing and separation.

Quantitative Monitoring of Leaf Area Index in Rice Based on Hyperspectral Feature Bands and Ridge Regression Algorithm

Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R2) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R2 of 0.878 and RMSE of 0.773 for the validation set 1, and R2 of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field.

Exploration of compressive sensing in the classification of frozen fish based on two-dimensional correlation spectrum

In order to classify imported frozen fish, effectively a spectral data compression method was presented based on two-dimensional correlation spectroscopy. In the experiment, the near-infrared spectral data of Oncorhynchus keta, Oncorhynchus nerka and Oncorhynchus gorbuscha of Salmonidae were collected. And two-dimensional correlation spectroscopy among the three fish samples was constructed. The study found that the auto-correlation peaks intensities at 650 nm, 1724 nm and 1908 nm were almost zero, which were taken as the separation point of the spectra. Therefore, each spectral data is divided into 4 segments and the integral of each segment is obtained. The original spectra of 201 points in each group were compressed into 4 points. Then, the compressed spectral data were input into the support vector machine to establish the discriminant model of three kinds of frozen fish. At the same time, the Competitive Adaptive Reweighted Sampling and the Successive Projections Algorithm were used to screen the original spectra. The classification results were compared with the result of the spectral data compression method of two-dimensional correlation spectroscopy. The result shows: the compression rate of the proposed method is 98.01%; the accuracy rate of support vector machine training set is 100%; the accuracy rate of validation set is up to 100%. The results shows that the proposed spectral data compression method based on two-dimensional correlation spectral technology has high compression rate and accurate classification.

Application of Excimer Lamp in Quantitative Detection of SF6 Decomposition Component SO2

Accurate quantitative detection for trace gas has long been the center of failure diagnosis for gas-insulated equipment. An absorption spectroscopy-based detection system was developed for trace SF6 decomposition SO2 detection in this paper. In order to reduce interference from other decomposition, ultraviolet spectrum of SO2 was selected for detection. Firstly, an excimer lamp was developed in this paper as the excitation of the absorption spectroscopy compared with regular light sources with electrodes, such as electrodeless lamps that are more suitable for long-term monitoring. Then, based on the developed excimer lamp, a detection system for trace SO2 was established. Next, a proper absorption peak was selected by calculating spectral derivative for further analysis. Experimental results indicated that good linearity existed between the absorbance and concentration of SO2 at the chosen absorption peak. Moreover, the detection limit of the proposed detection system could reach the level of 10-7. The results of this paper could serve as a guide for the application of excimer lamp in online monitoring for SF6-insulated equipment.