An Effective Baseline Correction Algorithm Using Broad Gaussian Vectors for Chemical Agent Detection with Known Raman Signature Spectra

Raman spectroscopy, which analyzes a Raman scattering spectrum of a target, has emerged as a key technology for non-contact chemical agent (CA) detection. Many CA detection algorithms based on Raman spectroscopy have been studied. However, the baseline, which is caused by fluorescence generated when measuring the Raman scattering spectrum, degrades the performance of CA detection algorithms. Therefore, we propose a baseline correction algorithm that removes the baseline, while minimizing the distortion of the Raman scattering spectrum. Assuming that the baseline is a linear combination of broad Gaussian vectors, we model the measured spectrum as a linear combination of broad Gaussian vectors, bases of background materials and the reference spectra of target CAs. Then, we estimate the baseline and Raman scattering spectrum together using the least squares method. Design parameters of the broad Gaussian vectors are discussed. The proposed algorithm requires reference spectra of target CAs and the background basis matrix. Such prior information can be provided when applying the CA detection algorithm. Via the experiment with real CA spectra measured by the Raman spectrometer, we show that the proposed baseline correction algorithm is more effective for removing the baseline and improving the detection performance, than conventional baseline correction algorithms.

smBEVO: A computer vision approach to rapid baseline correction of single-molecule time series

Single-molecule time series inform on the dynamics of molecular mechanisms that are occluded in ensemble-averaged measures. Amplitude-based methods and hidden Markov models (HMMs) frequently used for interpreting these time series require removal of low frequency drift that can be difficult to completely avoid in real world experiments. Current approaches for drift correction primarily involve either tedious manual assignment of the baseline or unsupervised frameworks such as infinite HMMs coupled with baseline nodes that are computationally expensive and unreliable. Here, we develop an image-based method for baseline correction using techniques from computer vision such as lane detection and active contours. The approach is remarkably accurate and efficient, allowing for rapid analysis of single-molecule time series contaminated with nearly any type of slow baseline drift.

Baseline Correction of Acceleration Data Based on a Hybrid EMD–DNN Method

Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances of ground tilt, low-frequency noise caused by instruments, hysteresis of the transducer, etc., it would generate a baseline drift phenomenon in acceleration integration, failing to obtain an actual displacement response. The improved traditional baseline correction methods still have some problems, such as high baseline correction error, poor adaptability, and narrow application scope. This paper proposes a deep neural network model based on empirical mode decomposition (EMD–DNN) to solve baseline correction by removing the drifting trend. The feature of multiple time sequences that EMD obtains is extracted via DNN, achieving the real displacement time history of prediction. In order to verify the effectiveness of the proposed method, two natural waves (EL centro wave, Taft wave) and one Artificial wave are selected to test in a shaking table test. Comparing the traditional methods such as the least squares method, EMD, and DNN method, EMD–DNN has the best baseline correction effect in terms of the evaluation indexes: Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and degree of fit (R-Square).

Surface Enhanced Raman Scattering (SERS) Spectroscopy Combined With Chemical Imaging Analysis For The Early Detection of Apple Valsa Canker

Background: Apple Valsa Canker (AVC) with early incubation characteristics is a severe apple tree disease. Therefore, early detection of the infected trees is necessary to prevent the rapid development of the disease. Surface enhanced Raman Scattering (SERS) spectroscopy is a promising technique that simplifies detection procedures and reduces detection time. Meanwhile, SERS enhance signals at low laser powers and suppress biological fluorescence. In this study, the early detection of the AVC disease was carried out by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and then chemical distribution imaging was successfully applied to the analysis of disease dynamics.Results: Firstly, the microstructure, UV-Vis spectrum, and Raman spectrum of SERS metallic nano-substrates were proved to investigate the enhancement effects of the synthesized AgNPs. Secondly, the multiple spectral baseline correction (MSBC), the asymmetric least squares (AsLS), and the adaptive iterative reweighted penalized least squares (air-PLS) were adopted to eliminate the disturbances of the baseline offset. The correlation analysis method was employed to identify the best baseline correction algorithm, which was the air-PLS algorithm herein. Meanwhile, principal component analysis (PCA) was used to perform clustering analysis based on the healthy, early disease, and late disease sample datasets, demonstrating obvious clustering effects. After that, optimal spectral variables were selected to build machine learning models to detect AVC disease, incorporating the BP-ANN, ELM, RForest, and LS-SVM algorithms. The accuracy of these models was above 90%, showing excellent discriminant performance. Finally, SERS chemical imaging provided the spatiotemporal dynamic characteristics of changes in the cellulose and lignin of the phloem disease-health junction under AVC stress. The results suggested that cellulose and lignin in the cell walls of infected tissues reduced significantly.Conclusions: SERS spectroscopy combining with chemical imaging analysis for early detection of the AVC disease was considered feasible and promising. This study provided a practical method for the rapid diagnosis of apple orchard diseases.

Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors

American foulbrood is a dangerous disease of bee broods found worldwide, caused by the Paenibacillus larvae larvae L. bacterium. In an experiment, the possibility of detecting colonies of this bacterium on MYPGP substrates (which contains yeast extract, Mueller-Hinton broth, glucose, K2HPO4, sodium pyruvate, and agar) was tested using a prototype of a multi-sensor recorder of the MCA-8 sensor signal with a matrix of six semiconductors: TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from Figaro. Two twin prototypes of the MCA-8 measurement device, M1 and M2, were used in the study. Each prototype was attached to two laboratory test chambers: a wooden one and a polystyrene one. For the experiment, the strain used was P. l. larvae ATCC 9545, ERIC I. On MYPGP medium, often used for laboratory diagnosis of American foulbrood, this bacterium produces small, transparent, smooth, and shiny colonies. Gas samples from over culture media of one- and two-day-old foulbrood P. l. larvae (with no colonies visible to the naked eye) and from over culture media older than 2 days (with visible bacterial colonies) were examined. In addition, the air from empty chambers was tested. The measurement time was 20 min, including a 10-min testing exposure phase and a 10-min sensor regeneration phase. The results were analyzed in two variants: without baseline correction and with baseline correction. We tested 14 classifiers and found that a prototype of a multi-sensor recorder of the MCA-8 sensor signal was capable of detecting colonies of P. l. larvae on MYPGP substrate with a 97% efficiency and could distinguish between MYPGP substrates with 1–2 days of culture, and substrates with older cultures. The efficacy of copies of the prototypes M1 and M2 was shown to differ slightly. The weighted method with Canberra metrics (Canberra.811) and kNN with Canberra and Manhattan metrics (Canberra. 1nn and manhattan.1nn) proved to be the most effective classifiers.

Spectral Matching of Three-Component Seismic Ground Accelerations for Nuclear Power Plants

The aim of the paper is to introduce a new procedure of three-component spectral matching of seismic ground acceleration records. The procedure is straightforward, yet it is general. In principle, the procedure involves varying of both the Fourier amplitude and the phase spectra so that the modified records’ spectra agree with a target. The matching can be performed against either a target Fourier or response spectra. In the former the solution is exact, while in the latter it becomes approximate. A target spectrum representative of three directions should be provided. In the example several three-component records were matched against two target spectra. Good convergence was achieved in velocity and displacement records so that no baseline correction was necessary. The couplings among the components were preserved.