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. In this study, a hybrid Transformer network, TMNet, was developed to identify SERS spectra by integrating the Transformer encoder and the multi-layer perceptron. The Transformer encoder can obtain precise feature representations of sequential spectra with the aid of self-attention, and the multi-layer perceptron efficiently transforms the representations to the final identification results. TMNet performed excellently, with identification accuracies of 99.07% for the spectra of hair containing drugs and 97.12% for those of urine containing drugs. For the spectra with additive white Gaussian, baseline background, and mixed noises, TMNet still exhibited the best performance among all the methods. Overall, the proposed method can accurately identify SERS spectra with outstanding noise resistance and excellent generalization and holds great potential for the analysis of other spectroscopy data.

SERS with Flexible ß-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 ß-cyclodextrin-modified gold nanoparticles (ß-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (ß-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 µg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the ß-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field.

Detection of 1-OHPyr in human urine using SERS with injection under wet liquid-liquid self-assembled films of ß-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. Gold nanoparticles with ß-cyclodextrin (ß-CD@AuNPs) were synthesized, and homogeneous and ordered ß-CD@AuNP films were prepared through a liquid-liquid interface self-assembly process. The separated 1-OHPyr was injected under wet assembled films for SERS detection. Concentration as low as 0.05 µg mL-1 of 1-OHPyr in urine could still be detected, and the relative standard deviation was 5.5 %, and this was ascribed to the adsorption of ß-CD and the high-probability contact between 1-OHPyr molecules and the nanogap of assembled films under the action of capillary force. Meanwhile, a convolutional neural network (CNN), a classical DL network architecture, was adopted to build the prediction model, and the model was further simplified by genetic algorithm (GA). CNN combined with a GA obtained optimized results with determination coefficient and a root mean square error of prediction sets of 0.9639 and 0.6327, respectively, outperforming other models. Overall, the proposed method achieves fast and accurate detection of 1-OHPyr in urine, improves the assessment human exposure to PAHs and is expected to have applications in the analysis of other OH-PAHs in complex environments.

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. With multi-source features, an input-modified convolution neural network (IMCNN) was constructed to predict TVB-N. The seven E-nose sensors, spectra of effective wavelengths (EWs), and five important image features were combined with IMCNN to achieve the best result, with the root mean square error, correlation coefficient, and ratio of performance deviation of the prediction set of 3.039 mg/100 g, 0.920, and 3.59, respectively. Hence, the proposed method furnishes an approach to accurately analyze mutton freshness and provide a technical basis for investigation of other meat qualities.

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. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception-attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception-attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

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. Results showed that the spectral characteristic peaks for DON residues of 99.5-0.5 mg/L were discernible with the relative standard deviation of 4.2%, with the limit of detection of 0.11 mg/L. Meanwhile, the fully convolutional network for the spectra of matrix input form was developed and obtained the optimal quantitative performance, with a root-mean-square error of prediction of 4.41 mg/L and coefficient of determination of prediction of 0.9827. Thus, the proposed method provides a simple, sensitive, and intelligent detection for DON in FHB-infected wheat kernels.

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. This review investigates the advanced application of Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) in the detection of plant diseases. The determination of bacterial diseases and stress-induced diseases, fungal diseases, viral diseases, pests in beans, and mycotoxins related to plant diseases using RS and SERS are discussed in detail. Then, biomarkers for RS and SERS detection are analyzed with regard to plant disease diagnosis. Finally, the advantages and challenges are further illustrated. Additionally, potential alternatives are proposed for the challenges. The review is expected to provide a reference and guidance for the use of RS and SERS in plant disease diagnostics.

A Quantum Dot Fluorescence Sensor System Design for Hg²âº Trace Detection.

The detection of Hg²âº 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 Hg²âº analysis based on the fluorescent quenching of Quantum dots, which contains two major modules, i. e. the fluorescent sensing module and the signal processing module. The fluorescence sensing module is composed of a laser source, a light collimated system and a photo-detector, which enables the realization of the fluorescence excitation as well as its detection. The signal processing module realized the further amplification of the detected signal and hereafter the filtering of noises. Furthermore, the Hg²âº concentration will displayed on the QT interface using a Linux embedded system. The sensor system is low cost and small, which makes it available for rapid field test or portable applications. Experimental results show that the sensor has a good linear relationship for the Hg²âº concentration range from 15.0 x 10⁻9 to 1.8 x 10⁻6 mol · L⁻¹. The regression equation is V0/V = 1.309 13 + 3.37c, where c is Hg²âº concentration, and V0 is the voltage value for the blank case. In our work, the linearity is determined as 0. 989 26. The experiments exhibit that Ca²âº, Mn²âº and Pb²âº ions have small influence on the Hg²âº detection, and the interfere of other common ions can be neglected, which indicates a good selectivity of the sensor. Finally, it shows that our sensor has a rapid response time of 35 s and a good repeatability, thus it is potential for field test of trace Hg²âº.

[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. Finally, the models were established by SLR It was found that the model developed with MSC preprocessed spectroscopy of characteristic range performed best (RMSEP: 0.33) by comparing the predictive accuracy of the different models. The result could meet with the needs in the quantitative detection of ethyl paraoxon.

[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. Experimental results show that: the sensor detection results have a good linear relationship when the hexavalent chromium concentration is 10-500 microg x L(-1), the linear equation is Y = 1.542 47 x X-2.353 47, and the linearity is 0.998 62, the detection limit reaches 10 microg x L(-1), the sensor response time is about 90 seconds, 5 capsule samples were selected to do the contrast detection, and the results show that the sensor quantitative detection data is reliable, which meets trace hexavalent low cost, fast and field detection demands.

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. Focal position and depth of microlenses with different geometries have been determined to be ranged from several hundreds of nanometers to several microns. FDTD calculations are consistent with experimental results.

[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. A self-developed optical acquisition, conversion and processing system was used to complete rapid detection of trace Cu2+, miniaturizing and reducing the cost of the testing instruments. The experimental results show the proposed sensor responds linearly in a concentration range of 30-1 000 nmol L(-1), with the linear equation y = 0.109 77x + 11.872 32, the linearity 0.994 82, the standard deviation 3.994 24, the detection limit 30 nmol x L(-1), and the response time of the sensor 40 seconds. The experiment determines the interference of other ions with the Cu2+ test results. It shows that this sensor meets the demand of field detection for trace Cu2+ site testing.