Identification of surface-enhanced Raman spectroscopy using hybrid transformer network.

Surface-enhanced Raman Spectroscopy (SERS) is extensively implemented in drug detection due to its sensitivity and non-destructive nature. Deep learning methods, which are represented by convolutional neural network (CNN), have been widely applied in identifying the spectra from SERS for powerful learning ability. However, the local receptive field of CNN limits the feature extraction of sequential spectra for suppressing the analysis results.

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network.

The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling β-cyclodextrin-modified gold nanoparticles (β-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (β-CD@AuNP/PTFE).

Detection of 1-OHPyr in human urine using SERS with injection under wet liquid-liquid self-assembled films of β-CD-coated gold nanoparticles and deep learning.

1-Hydroxypyrene (1-OHPyr), a typical hydroxylated polycyclic aromatic hydrocarbon (OH-PAH), has been commonly regarded as a urinary biomarker for assessing human exposure and health risks of PAHs. Herein, a fast and sensitive method was developed for the determination of 1-OHPyr in urine using surface-enhanced Raman spectroscopy (SERS) combined with deep learning (DL). After emulsification, urinary 1-OHPyr was separated using simple liquid-liquid extraction.

Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network.

Electronic nose (E-nose) and hyperspectral image (HSI) were combined to evaluate mutton total volatile basic nitrogen (TVB-N), which is a comprehensive index of freshness. The response values of 10 E-nose sensors were collected, and seven responsive sensors were screened via histogram statistics. Reflectance spectra and image features were extracted from HSI images, and the effective variables were selected through random frog and Pearson correlation analyses.

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels.

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed.

Surface-enhanced Raman spectroscopywith gold nanorods modified by sodium citrate and liquid-liquid interface self-extraction for detection of deoxynivalenol in Fusarium head blight-infected wheat kernels coupled with a fully convolution network.

Surface-enhanced Raman spectroscopy (SERS) and deep learning network were adopted to develop a detection method for deoxynivalenol (DON) residues in Fusarium head blight (FHB)-infected wheat kernels. First, the liquid-liquid interface self-extraction was conducted for the rapid separation of DON in samples. Then, the gold nanorods modified with sodium citrate (Cit-AuNRs) were prepared as substrate for a gigantic enhancement of SERS signal.

Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review.

Plant diseases result in 20-40% of agricultural loss every year worldwide. Timely detection of plant diseases can effectively prevent the development and spread of diseases and ensure the agricultural yield. High-throughput and rapid methods are in great demand.

A Quantum Dot Fluorescence Sensor System Design for Hg²⁺ Trace Detection.

The detection of Hg²⁺ ions usually requires large laboratory equipment, which encounters difficulties for rapid field test in most applications. In this paper, we design a reflective sensor for trace Hg²⁺ analysis based on the fluorescent quenching of Quantum dots, which contains two major modules, i. e.

[Quantitative detection of ethyl paraoxon based on SERS and PCA-SLR].

In the present paper, the surface-enhanced Raman spectroscopy (SERS) was used to build the model for the quantitative detection of ethyl paraoxon by the principal component analysis and segmented linear regression (PCA-SLR). Firstly, SERS in 820-1630 cm(-1) of ethyl paraoxon solution were measured and the spectra in 820-1630 cm(-1)(complete range) and 845-875 cm(-1) (characteristic range) of ethyl paraoxon solution were preprocessed by standard normal transformation (SNV), multiplicative scatter correction (MSC), the absolute values of first derivative and the second derivative respectively. Additionally, the number of dimensions of the spectra was reduced by PCA.

[Design and development of trace Cr(VI) sensor].

Hexavalent chromium detection in medicine capsules is generally analyzed in the laboratory, it is difficult to meet the demand for field detection, and to address this problem, a sensor which can be used for on-site detection of trace amounts of hexavalent chromium was designed. It mainly includes chemically sensitive materials, optical sensing module and signal processing module, the chemical sensitive materials is to achieve the conversion of the hexavalent chromium concentration signal, the optical sensing module is to complete a stable output of the laser light source, and the signal processing module is to complete a photoelectric conversion of the weak fluorescence signal, signal amplification, and data processing and displaying. With using the indigenously developed photoelectric acquisition, conversion and signal processing system to complete the rapid detection of trace amounts of hexavalent chromium, so the miniaturization of testing instruments and on-site detection were achieved.

[Design and development of the trace Cu2+ optical chemistry sensor].

The present paper proposes an optical chemistry sensor used for real-time detection of trace Cu2+, including the optical perception module and the signal processing module. The optical perception module gives the output of the laser light and excitation of fluorescence. The signal processing module completes optoelectronic conversion and amplification of the weak fluorescence signal as well as data processing and display.

Rapid fabrication of micro-nanometric tapered fiber lens and characterization by a novel scanning optical microscope with submicron resolution.

In numerous applications of optical scanning microscopy, a reference tapered fiber lens with high symmetry at sub-wavelength scale remains a challenge. Here, we demonstrate the ability to manufacture it with a wide range of geometry control, either for the length from several hundred nanometers to several hundred microns, or for the curvature radius from several tens of nanometers to several microns on the endface of a single mode fiber. On this basis, a scanning optical microscope has been developed, which allows for fast characterization of various sub-wavelength tapered fiber lenses.