Enhanced detection of acrylamide using a versatile solid-state upconversion sensor through spectral and visual analysis.

Acrylamide (AM) generally forms in high-temperature processes and has been classified as a potential carcinogen. In this study, we put forward a maneuverable solid-state luminescence sensor using polydimethylsiloxane (PDMS) as the matrix coupled with upconversion nanoparticles as the indicator. The core-shell upconversion nanoparticles emitting cyan light were uniformly encapsulated in PDMS. Then it was further modified with complementary DNA of AM aptamer. The nanocrystalline fluorescein isothiocyanate isomer (FITC), coupled with AM aptamer, was attached to the surface of PDMS. FITC effectively quenched the upconversion luminescence through fluorescence resonance energy transfer (FRET). The introduction of AM resulted in preferentially bound to aptamer caused the separation of the quencher and the donor, and led to luminescence recovery. The developed sensor was applied for both spectral and visual monitoring, demonstrating a detection limit (LOD) of 1.00 nM and 1.07 nM, respectively. Importantly, in the actual foodstuffs detection, there is no obvious difference between the results of this study and the standard method, which indicates the developed method has good accuracy. Therefore, this solid-state sensor has the potential for on-site detection using a smartphone device and an Android application.

A Compact Constraint Incremental Method for Random Weight Networks and Its Application.

Incremental random weight networks (IRWNs) face the issues of weak generalization and complicated network structure. There is an important reason: the learning parameters of IRWNs are determined in a random fashion without guidance, which may increase many redundant hidden nodes, and thereby leading to inferior performance. To resolve this issue, a novel IRWN with compact constraint that guides the assignment of random learning parameters (CCIRWN) is developed in this brief. Using the iteration method of Greville, a compact constraint that simultaneously assures the quality of generated hidden nodes and the convergence of the CCIRWN is built to perform learning parameter configuration. Meanwhile, the output weights of the CCIRWN are assessed analytically. Two types of learning methods for constructing the CCIRWN are proposed. Finally, the performance evaluation of the proposed CCIRWN is undertaken on the 1-D nonlinear function approximation, several real-world datasets, and data-driven estimation based on the industrial data. Numerical and industrial examples indicate that the proposed CCIRWN with compact structure can achieve favorable generalization ability.

Label-free detection of trace level zearalenone in corn oil by surface-enhanced Raman spectroscopy (SERS) coupled with deep learning models.

Surface-enhanced Raman spectroscopy (SERS) and deep learning models were adopted for detecting zearalenone (ZEN) in corn oil. First, gold nanorods were synthesized as a SERS substrate. Second, the collected SERS spectra were augmented to improve the generalization ability of regression models. Third, five regression models, including partial least squares regression (PLSR), random forest regression (RFR), Gaussian progress regression (GPR), one-dimensional convolutional neural networks (1D CNN), and two-dimensional convolutional neural networks (2D CNN), were developed. The results showed that 1D CNN and 2D CNN models possessed the best prediction performance, i.e., determination of prediction set (RP2) = 0.9863 and 0.9872, root mean squared error of prediction set (RMSEP) = 0.2267 and 0.2341, ratio of performance to deviation (RPD) = 6.548 and 6.827, limit of detection (LOD) = 6.81 × 10-4 and 7.24 × 10-4 µg/mL. Therefore, the proposed method offers an ultrasensitive and effective strategy for detecting ZEN in corn oil.

Oblique and Asymmetric Klein Tunneling across Smooth NP Junctions or NPN Junctions in 8-Pmmn Borophene.

The tunneling of electrons and holes in quantum structures plays a crucial role in studying the transport properties of materials and the related devices. 8-Pmmn borophene is a new two-dimensional Dirac material that hosts tilted Dirac cone and chiral, anisotropic massless Dirac fermions. We adopt the transfer matrix method to investigate the Klein tunneling of massless fermions across the smooth NP junctions and NPN junctions of 8-Pmmn borophene. Like the sharp NP junctions of 8-Pmmn borophene, the tilted Dirac cones induce the oblique Klein tunneling. The angle of perfect transmission to the normal incidence is 20.4∘, a constant determined by the Hamiltonian of 8-Pmmn borophene. For the NPN junction, there are branches of the Klein tunneling in the phase diagram. We find that the asymmetric Klein tunneling is induced by the chirality and anisotropy of the carriers. Furthermore, we show the oscillation of electrical resistance related to the Klein tunneling in the NPN junctions. One may analyze the pattern of electrical resistance and verify the existence of asymmetric Klein tunneling experimentally.

Use of headspace GC/MS combined with chemometric analysis to identify the geographic origins of black tea.

Some black teas demand high market prices. Black tea samples (306) collected from 10 geographic origins, including China (Guxi, Likou, Jinzipai, Guichi, Dongzhi, Changning, Wuyishan, Shaowu), India (Darjeeling), and Sri Lanka (Kandy), were analyzed using headspace volatilization followed by GC/MS (HS-GC/MS). Forty-eight volatile compounds were identified. The aroma compounds were mainly identified as alcohols, aldehydes, ketones, and esters. Analysis of either full-spectrum data or 22 tea compounds shared among the samples with k-Nearest Neighbor (k-NN) and Random Forest (RF) models discriminated all origins at 100% using KNN and 95% with RF using either data set. The discrimination rates using 2 key aroma compounds (linalool and geraniol) by k-NN were 100% for nine origins, with the rate for Guxi area at 89%, because 3 samples were classified to Jinzipai. The findings support the use of HS-GC/MS combined with chemometrics as a tool to identify the origin of black tea.

Quantification of deltamethrin residues in wheat by Ag@ZnO NFs-based surface-enhanced Raman spectroscopy coupling chemometric models.

Deltamethrin, one of the most toxic pyrethroids, is commonly used to inhibit pests in wheat. However, the trace levels of deltamethrin in wheat is alarming to human health. In this study, surface-enhanced Raman spectroscopy (SERS)-active silver nanoparticles-plated-zinc oxide nanoflowers (Ag@ZnO NFs) nano-sensor were employed for rapid and sensitive quantification of deltamethrin in wheat. To sufficiently utilize the chemical-related information in SERS spectra, various spectral pretreatment and chemometric models were studied. The mean centering (MC) coupling successive projection algorithm-partial least squares regression (SPA-PLS) provided optimal predictive performance (correlation coefficient of prediction (Rp) = 0.9736 and residual predictive deviation (RPD) = 4.75). The proposed method achieved the limit of detection (LOD) = 0.16 µg·kg-1, the recovery of predicted results was in the range of 96.33-109.17% and the relative standard deviation (RSD) was < 5%. The overall results suggested that SERS based Ag@ZnO NFs combined with MC-SPA-PLS could be an easy and efficient method to quantify deltamethrin residue levels in wheat.

Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering.

In this study, a novel analytical approach is proposed for the identification of pesticide residues in tea by combining surface-enhanced Raman scattering (SERS) with a deep learning method one-dimensional convolutional neural network (1D CNN). First, a handheld Raman spectrometer was used for rapid on-site collection of SERS spectra. Second, the collected SERS spectra were augmented by a data augmentation strategy. Third, based on the augmented SERS spectra, the 1D CNN models were established on the cloud server, and then the trained 1D CNN models were used for subsequent pesticide residue identification analysis. In addition, to investigate the identification performance of the 1D CNN method, four conventional identification methods, including partial least square-discriminant analysis (PLS-DA), k-nearest neighbour (k-NN), support vector machine (SVM) and random forest (RF), were also developed on the basis of the augmented SERS spectra and applied for pesticide residue identification analysis. The comparative studies show that the 1D CNN method possesses better identification accuracy, stability and sensitivity than the other four conventional identification methods. In conclusion, the proposed novel analytical approach that exploits the advantages of SERS and a deep learning method (1D CNN) is a promising method for rapid on-site identification of pesticide residues in tea.

Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR.

This work was attempted to evaluate the feasibility of a constructed on-line NIR platform coupled with efficient algorithms for rapid and robust quantification of quality parameter in cherry tomato. Specifically, a system was developed based on shortwave NIR spectroscopy for on-line quality inspection of cherry tomatoes. The spectra were recorded in diffuse reflectance mode from 900 to 1700 nm, and the conveyor belt speed was fixed to five samples per second. Three novel methods, namely variable combination population analysis (VCPA), uninformative variable elimination (UVE) and competitive adaptive reweighed sampling algorithm (CARS) were coupled with partial least square (PLS) for selecting optimal dataset, and modeling. The obtained results showed that under the optimal tuning parameters (N = 100, k = 500, ω = 14, σ = 10%), a total of 512 original variables, only 9 variables (1.75%) were extracted by VCPA. Subsequently, VCPA-PLS yielded outstanding performance in predicting soluble solid content in cherry tomatoes, with a higher correlation coefficient (RP = 0.9053), and lower root mean square errors (RMSEP = 0.382) in prediction set. This methodology demonstrated the versatile potential of the proposed installation coupled with VCPA methods for on-line detection of total soluble solids in cherry tomatoes.

Interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) for quantitative analysis of surface-enhanced Raman scattering (SERS) spectra.

Quantitative analysis of surface-enhanced Raman scattering (SERS) spectra has been a critical step in trace level analysis. In this study, a novel variable selection method called interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) was proposed for simultaneously predicting the volume ratios of various pesticides by quantitative analysis of the SERS spectra of the compounds. Four strategies, including interval selection, model population analysis (MPA), weighted bootstrap sampling (WBS) and soft shrinkage were combined in the current designed ICIOA-SIMPLS approach. Firstly, the SERS spectra were split into a series of equal-width spectral intervals. Secondly, WBS, as a random sampling method was applied based on the initial weights of spectral intervals to generate random combinations of spectral intervals, namely sub-datasets. On this basis, multivariate calibration sub-models were developed by applying SIMPLS followed by MPA to statistically analyze the outputs of sub-models and update the weights of spectral intervals. Finally, using an iterative optimization procedure the optimal spectral interval combination with the lowest root mean squares error of cross-validation (RMSECV) was searched in a soft shrinkage manner. For the sake of investigating the performance of ICIOA-SIMPLS, four methods including SIMPLS, VCPA-SIMPLS, VISSA-SIMPLS and ICIOA-SIMPLS were tested on two groups of SERS spectra for comparison. The findings revealed that the best prediction performance was obtained with ICIOA-SIMPLS. Hence, this proposed method offers a robust and effective variable selection strategy for quantitative analysis of spectroscopic datasets.

Mesoporous silica supported orderly-spaced gold nanoparticles SERS-based sensor for pesticides detection in food.

In this study, a novel sensor fabricated with compactly arranged gold nanoparticles (AuNPs) templated from mesoporous silica film (MSF) via air-water interface has been confirmed as a promising surface-enhanced Raman scattering (SERS) substrate for detecting trace levels of 2,4-dichlorophenoxyacetic acid (2,4-D), pymetrozine and thiamethoxam. The densely arranged AuNPs@MSF had an average AuNPs size of 5.15 nm with small nanogaps (<2nm) between AuNPs, and exhibited a high SERS performance. SERS spectra of pesticides were collected after their adsorption on the AuNPs@MSF. The results showed that the concentration of 2,4-D, pymetrozine and thiamethoxam gave a good linear relationship with SERS intensity. Moreover, the designed SERS-based sensor (AuNPs@MSF) was stable for 3 months with ca. 3% relative standard deviation (RSD) and was applied successfully for the analysis of 2,4-D extraction from both environmental and food samples. The proposed SERS-based sensor was further validated by HPLC and showed satisfactory result (p > 0.05).

Amplification of Raman spectra by gold nanorods combined with chemometrics for rapid classification of four Pseudomonas.

This paper demonstrates the application of surface-enhanced Raman scattering (SERS) using positive charged gold nanorods (Au NRs) as an enhancement substrate to classify Pseudomonas spp. coupled with multivariate methods. Four species of Pseudomonas as dominant spoilage bacteria of food were isolated from rotten chicken, namely, Pseudomonas gessardii (P9), Pseudomonas psychrophila (P8), Pseudomonas psychrophila (S2) and Pseudomonas fluorescens (T3). Au NRs were synthesized with positive charge by seed-mediated growth method which can be adsorbed onto the surface of the bacteria by electrostatic adsorption. SERS spectra were collected individually for four types of Pseudomonas and pretreated by mean centering (MC), then principal component analysis (PCA) and hierarchical clustering analysis (LDA) were used to achieve data dimensionality reduction and visualize the result of differentiation for the species of Pseudomonas. Particularly, the classification accuracy of LDA was reached to 100%. Following we applied hierarchical clustering analysis (HCA) to cluster each species of Pseudomonas and the results of HCA consistent with the results of 16S rRNA. This study has shown that SERS combined with LDA and HCA can be used as a reliable method to classify Pseudomonas.

Development of a novel wavelength selection method for the trace determination of chlorpyrifos on Au@Ag NPs substrate coupled surface-enhanced Raman spectroscopy.

A novel wavelength selection method, namely interval combination population analysis-minimal redundancy maximal relevance (ICPA-mRMR), was employed for the trace level detection of chlorpyrifos (CPS) coupled surface-enhanced Raman spectroscopy (SERS). Herein, a highly sensitive SERS enhancement substrate, Au@Ag nanoparticles (NPs), was synthesized possessing strong enhancement of Raman signals for CPS quantification (enhancement factor: 2.5 × 106). Compared with other established methods such as partial least squares (PLS), synergy interval partial least squares-genetic algorithm (siPLS-GA) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS), ICPA-mRMR yielded the best results with higher correlation coefficients (Rc = 0.9917, RP = 0.9895), ratios of performance to deviation (RPD = 6.8797), and lower root mean square errors (RMSEC = 0.1998, RMSEP = 0.2271). The proposed method was employed for the determination of trace level CPS in tea samples, and the recovery percentages were in the range 90%-108%. Meanwhile, this method was validated using a standard GC-MS method indicating no significant difference (P > 0.05). The proposed methodology offers a rapid, sensitive and powerful analytical platform for the detection of pesticide residues in food.

Strain Controlled Ferromagnetic-Antiferromagnetic Transformation in Mn-Doped Silicene for Information Transformation Devices.

A reliable control of magnetic states is central to the use of magnetic nanostructures. Here, by using state-of-the-art density-functional theory calculations, we find that Mn atoms decorated silicene has an anomalously fixed magnetic moment and a high Curie temperature. In addition, a tunable magnetic exchange coupling is achieved for Mn-silicene system with the application of biaxial strain, which induces a transformation from the ferromagnetic (FM) to the antiferromagnetic (AFM) state. As such, an atomic "bit" could be obtained by superimposing strain field once the FM and AFM states are referred to as "1" and "0". Such piezospin nanodevices, which convert mechanical energy into magnetic moment, would offer great potential for future information transmission, as they ultimately combine small size, high-speed operation, and low-power consumption.

Electrically controllable surface magnetism on the surface of topological insulators.

We study theoretically the RKKY interaction between magnetic impurities on the surface of three-dimensional topological insulators, mediated by the helical Dirac electrons. Exact analytical expression shows that the RKKY interaction consists of the Heisenberg-like, Ising-like, and Dzyaloshinskii-Moriya (DM)-like terms. It provides us a new way to control surface magnetism electrically. The gap opened by doped magnetic ions can lead to a short-range Bloembergen-Rowland interaction. The competition among the Heisenberg, Ising, and DM terms leads to rich spin configurations and an anomalous Hall effect on different lattices.

[Clinical analysis of 38 patients with cephalic and cervical adenoid cystic carcinoma].

OBJECTIVE: To assess the clinical features and the treatment result of cephalic and cervical adenoid cystic carcinoma (ACC). METHOD: A retrospective analysis was performed on 38 patients with cephalic and cervical ACC by their clinical manifestation, the therapy and follow-up results. RESULT: We had 38 patients of operation. All patients followed up from 3 years to 13 years. The overall 3-year survival rate of patients was 71.1%, the overall 5-year survival rate of patients was 63.2%. CONCLUSION: Cephalic and cervical ACC is not easy to exact diagnosis in early stage. It progresses slowly. It also has the tendency of recurrence and cancerous metastasis. Thus it, is necessary to do both thorough surgery.