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In this study, visible/near-infrared (Vis/NIR) hyperspectral imaging was used for the nondestructive detection of storage time of strawberries. Storage time was calculated immediately after freshly picking. Support vector machine (SVM) with multiplicative scatter correction can differentiate strawberries of different storage time with an accuracy of 100%. Then, the model developed by partial least square regression with full-range spectra was used to predict the storage time of strawberries with a determination coefficient of prediction (Rp2) of 0.9999 and root-mean-square error of prediction (RMSEP) of 0.0721, and deviation was small at different periods. With the spectra of 10 important wavelengths obtained by uninformative variable elimination, the SVM model obtained relatively acceptable results with Rp2 of 0.9943 and RMSEP of 1.3213. The prediction experiments for the separately picked strawberry samples also got the similar results. Finally, distribution maps of storage time generated based on the pixel-wise spectra and established model clearly show the quality transformation of the strawberries.

Detection of illegal drug users is crucial in controlling drug-related crimes, reducing drug prevalence, and protecting human lives to ensure social stability. In this study, surface-enhanced Raman spectroscopy (SERS) and deep learning networks were employed to determine methamphetamine, ketamine, and morphine in human hair. Drugs were obtained from hair through alkaline hydrolysis and liquid-liquid extraction, and gold nanorods were employed to prepare the self-assembled film as the SERS substrate by inverted evaporation. The film showed good uniformity and excellent sensitivity, with a relative standard deviation of 15.6% and a detection limit of at least 10

Dynamic surface-enhanced Raman spectroscopy (D-SERS) based on the state change of the substrate not only significantly enhances but also provides a highly reproducible Raman signal. Hence, we develop a fast and accurate method for the detection of fenthion on fruit and vegetable peel using D-SERS and random forests (RF) with variable selection. With uniform Ag nanoparticles, the dynamic spectra of fenthion solution at different concentrations were obtained using D-SERS, and fenthion solution greater than or equal to 0.05mg/L can be detected. Then, the quantitative analysis models of fenthion were developed by RF with variable selection for spectra of different range. The model of best performance is developed by RF and spectra of characteristic range with higher RF importance (top 40%), and the root mean square error of cross-validation is 0.0101mg/L. Moreover, the fenthion residue of tomato, pear, and cabbage peel were extracted by a swab dipped in ethanol and analyzed using the above method to further validate the practical effect. Compared to gas chromatography, the maximal relative deviation is below 12.5%, and the predicted recovery is between 87.5% and 112.5%. Accordingly, D-SERS and RF with variable selection can realize the fast, simple, ultrasensitive, and accurate analysis of fenthion residue on fruit and vegetable peel.

Detection of residual farm chemicals in agricultural crops is a hot topic in the field of food safety. In this study, ediphenphos residue in rice was detected using surface-enhanced Raman spectroscopy (SERS) on a portable Raman spectrometer. A simple pretreatment method for rice samples was developed, and uniform gold nanorods were used for SERS measurement. Characteristic signals can still be detected when ediphenphos concentration in rice extraction solution was higher than or equal to 0.1 mg/L. Quantitative analysis of ediphenphos was conducted by regression models developed using partial least-squares regression, random forest and kernel principal component analysis, and root-mean-square error of cross validation, coefficient of determination and relative predicted deviation of optimal model were 0.022 mg/L, 0.9967 and 297.45, which indicated the proposed method can predict ediphenphos concentration with high precision. To validate the feasibility of practical application further, rice samples spiked with 10, 5, 1, 0.5, and 0.1 μg/g ediphenphos residue were analyzed using the above method. The predicted recovery was in the range of 93.4% to 102%, and the predicted error was small for residue of each concentration. These results demonstrated that the presented method could be used for accurate and quantitative detection of ediphenphos residue in rice. PRACTICAL APPLICATION This study developed a surface-enhanced Raman spectroscopy (SERS) method for detection of ediphenphos in rice coupled with simple extraction protocol and gold nanorods on a portable Raman spectrometer. SERS is a rapid and accurate method which can be applied in agricultural grain safety inspection.

Conventional Surface-Enhanced Raman Spectroscopy (SERS) for fast detection of drugs in urine on the portable Raman spectrometer remains challenges because of low sensitivity and unreliable Raman signal, and spectra process with manual intervention. Here, we develop a novel detection method of drugs in urine using chemometric methods and dynamic SERS (D-SERS) with mPEG-SH coated gold nanorods (GNRs). D-SERS combined with the uniform GNRs can obtain giant enhancement, and the signal is also of high reproducibility. On the basis of the above advantages, we obtained the spectra of urine, urine with methamphetamine (MAMP), urine with 3, 4-Methylenedioxy Methamphetamine (MDMA) using D-SERS. Simultaneously, some chemometric methods were introduced for the intelligent and automatic analysis of spectra. Firstly, the spectra at the critical state were selected through using K-means. Then, the spectra were proposed by random forest (RF) with feature selection and principal component analysis (PCA) to develop the recognition model. And the identification accuracy of model were 100%, 98.7% and 96.7%, respectively. To validate the effect in practical issue further, the drug abusers'urine samples with 0.4, 3, 30ppm MAMP were detected using D-SERS and identified by the classification model. The high recognition accuracy of >92.0% can meet the demand of practical application. Additionally, the parameter optimization of RF classification model was simple. Compared with the general laboratory method, the detection process of urine's spectra using D-SERS only need 2 mins and 2μL samples volume, and the identification of spectra based on chemometric methods can be finish in seconds. It is verified that the proposed approach can provide the accurate, convenient and rapid detection of drugs in urine.

Succinylcholine chloride (SCC) is a common poison that threatens human life. At present, there is a lack of research on its on-site rapid detection methods. In this work, the use of gold nanorods as an enhanced substrate based on the high affinity between the quaternary ammonium salt structure can achieve rapid SERS detection of SCC in plasma. The long alkane chain structure of cetyltrimethylammonium bromide (CTAB) and the quaternary ammonium salt structure of SCC have a high molecular affinity, so that the target molecule can show a strong and obvious characteristic signal of SERS. Combined with a simple pretreatment method, acetonitrile is used as a protein precipitation agent to effectively remove matrix interference. The constructed SERS substrate can achieve the sensitive detection of 2 × 10-8 M level of SCC in plasma samples and has high detection reproducibility. The entire pre-processing and testing process can be completed within 7 min, which can be used as an important technical basis for the preliminary identification of on-site SCC-related drug cases. The research results provide an effective solution for the establishment of SCC analysis strategies in complex matrices, and can provide new ideas for solving the problems of difficult identification of common poisons in the field and the lack of rapid detection methods on site.

With the advanced development of miniaturization and integration of instruments, Raman spectroscopy (RS) has demonstrated its great significance because of its non-invasive property and fingerprint identification ability, and extended its applications in public security, especially for hazardous chemicals. However, the fast and accurate RS analysis of hazardous chemicals in field test by non-professionals is still challenging due to the lack of an effective and timely spectral-based chemical-discriminating solution. In this study, a platform was developed for the field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network (DASN) incorporated into a cloud server. With the Raman spectra of 300 chemicals, DASN stands out with identification accuracy of 100% and outweighs other machine learning and deep learning methods. The network feature maps for the spectra of methamphetamine and ketamine focus on the main peaks of 1001 and 652 cm−1, which indicates the powerful feature extraction capability of DASN. Its receiver operating characteristic (ROC) curve completely encloses the other models, and the area under the curve is up to 1, implying excellent robustness. With the well-built platform combining RS, DASN, and cloud server, one test process including Raman measurement and identification can be performed in tens of seconds. Hence, the developed platform is simple, fast, accurate, and could be considered as a promising tool for hazardous chemical identification in public security on the scene.

Pesticide residue in paddy water is one of the main factors affecting the quality and safety of rice, however, the negative effect of this residue can be effectively prevented and reduced through early detection. This study developed a rapid detection method for fonofos, phosmet, and sulfoxaflor in paddy water through chemometric methods and surface-enhanced Raman spectroscopy (SERS). Residue from paddy water samples was directly used for SERS measurement. The obtained spectra from the SERS can detect 0.5 mg/L fonofos, 0.25 mg/L phosmet, and 1 mg/L sulfoxaflor through the appearance of major characteristic peaks. Then, we used chemometric methods to develop models for the intelligent analysis of pesticides, alongside the SERS spectra. The classification models developed by K-nearest neighbor identified all of the samples, with an accuracy of 100%. For the quantitative analysis, the partial least squares regression models obtained the best predicted performance for fonofos and sulfoxaflor, and the support vector machine model provided optimal results, with a root-mean-square error of validation of 0.207 and a coefficient of determination of validation of 0.99952, for phosmet. Experiments for actual contaminated samples also showed that the above models predicted the pesticide residue values with high accuracy. Overall, using SERS with chemometric methods provided a simple and convenient approach for the detection of pesticide residues in paddy water.

Trinitrotoluene (TNT) is a primary component in chemical explosives, making them a common focus in public safety detection. However, it is very difficult to achieve selective and sensitive detection of the TNT molecule in practical application. In the present study, a simple surface enhanced Raman scattering (SERS) sensing based on monoethanolamine (MEA) - modified gold nanoparticles (Au NPs) was expanded for high selectivity and sensitive detecting of TNT in an envelope, luggage, lake water, and clothing through a quickly sampling and detection process. The monoethanolamine molecule based on Meisenheimer complex lights up ultra-high Raman scattering of a nonresonant molecule on the superficial coat of gold nanoparticles. Using this detection sensor, a molecular bridge can be established to selectively detect trinitrotoluene with a detection limit of 21.47 pM. We were able to rapidly identification trinitrotoluene molecule with a powerful selective over the familiar interfering substances nitrophenol, picric acid, 2,4-dinitrophenol, and 2,4-dinitrotoluene. The outcome in this work supply an efficient solution to the test of trinitrotoluene and to establishing a SERS sensor analytical strategy. The studies have demonstrated that the MEA-Au NPs based SERS sensing can be potentially used in field detection the trace amount of chemical explosives for public security.

Detection of residual farm chemicals in agricultural crops is a hot topic in the field of food safety. In this study, ediphenphos residue in rice was detected using surface-enhanced Raman spectroscopy (SERS) on a portable Raman spectrometer. A simple pretreatment method for rice samples was developed, and uniform gold nanorods were used for SERS measurement. Characteristic signals can still be detected when ediphenphos concentration in rice extraction solution was higher than or equal to 0.1 mg/L. Quantitative analysis of ediphenphos was conducted by regression models developed using partial least-squares regression, random forest and kernel principal component analysis, and root-mean-square error of cross validation, coefficient of determination and relative predicted deviation of optimal model were 0.022 mg/L, 0.9967 and 297.45, which indicated the proposed method can predict ediphenphos concentration with high precision. To validate the feasibility of practical application further, rice samples spiked with 10, 5, 1, 0.5, and 0.1 μg/g ediphenphos residue were analyzed using the above method. The predicted recovery was in the range of 93.4% to 102%, and the predicted error was small for residue of each concentration. These results demonstrated that the presented method could be used for accurate and quantitative detection of ediphenphos residue in rice.This study developed a surface-enhanced Raman spectroscopy (SERS) method for detection of ediphenphos in rice coupled with simple extraction protocol and gold nanorods on a portable Raman spectrometer. SERS is a rapid and accurate method which can be applied in agricultural grain safety inspection.

Au nanorods coated Fe3O4 (Fe3O4@NRs) microspheres were designed as functional surface-enhanced Raman scattering substrate with a feature of magnetic property and used for detection of pesticide residues that are annually used in agriculture by near-infrared (NIR) excitation. With this strategy, the Fe3O4 microspheres were synthesized by hydrothermal method and surface functionalized with polyethylenimine, and then coated with Au nanorods densely. The Raman spectra were carried out by NIR excitation and 4-ATP was chosen as the probe molecule. The results showed a good SERS activity of the Fe3O4@NRs microspheres. Moreover, this substrate could be used for pesticide analysis by portable Raman spectrometer with NIR excitation. Especially, the microspheres could be transferred from pesticides contaminated fruits peel to specially cleaned glass slide with the aid of the external magnetic field, by which the strong fluorescence of the apple components can be avoided while performing the pesticide analysis of fruits peel. Copyright © 2015 John Wiley & Sons, Ltd.

Pesticide residue detection is a hot issue in the quality and safety of agricultural grains. A novel method for accurate detection of pirimiphos-methyl residues in wheat was developed using surface-enhanced Raman spectroscopy (SERS) and chemometric methods. A simple pretreatment method was conducted to extract pirimiphos-methyl residue from wheat samples, and highly effective gold nanorods were prepared for SERS measurement. Raman peaks assignment was calculated using density functional theory. The Raman signal of pirimiphos-methyl can be detected when the concentrations of residue in wheat extraction solution and contaminated wheat is as low as 0.2 mg/L and 0.25 mg/L, respectively. Quantification of pirimiphos-methyl was performed by applying regression models developed by partial least squares regression, support vector machine regression and random forest with principal component analysis using different preprocessed methods. As for the contaminated wheat samples, the relative deviation between gas chromatography-mass spectrometry value and predicted value is in the range of 0.10%&ndash, 6.63%, and predicted recovery is 94.12%&ndash, 106.63%, ranging from 23.93 mg/L to 0.25 mg/L. Results demonstrated that the proposed SERS method is an effective and efficient analytical tool for detecting pirimiphos-methyl in wheat with high accuracy and excellent sensitivity.

A new, novel, rapid method to detect and direct readout of drugs in human urine has been developed using dynamic surface-enhanced Raman spectroscopy (D-SERS) with portable Raman spectrometer on gold nanorods (GNRs) and a classification algorithm called support vector machines (SVM). The high-performance GNRs can generate gigantic enhancement and the SERS signals obtained using D-SERS on it have high reproducibility. On the basis of this feature of D-SERS, we have obtained SERS spectra of urine and urine containing methamphetamine (MAMP). SVM model was built using these data for fast identified and visual results. This general method was successfully applied to the detection of 3, 4-methylenedioxy methamphetamine (MDMA) in human urine. To verify the accuracy of the model, drug addicts' urine containing MAMP were detected and identified correctly and rapidly with accuracy more than 90%. The detection results were displayed directly without analysis of their SERS spectra manually. Compared with the conventional method in lab, the method only needs a 2 μL sample volume and takes no more than 2 min on the portable Raman spectrometer. It is anticipated that this method will enable rapid, convenient detection of drugs on site for the police.

We report the use of Polystyrene/Ag (PS/Ag) nanoparticles as dynamic surface-enhanced Raman spectroscopy (dynamic-SERS) substrates for sensitive detection of low levels of organophosphorus pesticides. The PS particles clearly observed using Raman microscopy provide the masterplate for in situ growth of Ag NPs, leading to multiple active sites for SERS measurements. Besides obtaining the fingerprints of target molecules and recording time-resolved Raman spectra, this dynamic-SERS method can be used as an ultra-sensitive analytical technique which can enhance 1-2 orders of magnitude the signals of analytes in comparison to that of the traditional methods. On the other hand, importantly, it shows much better correlations between concentration and intensity than does the conventional SERS technique so that it can build the foundation for quantitative analysis of analytes. The as-prepared individual PS/Ag nanoparticle has been demonstrated for the sensitive detection of organophosphorus paraoxon and sumithion. SERS spectra are acquired at different concentrations of each pesticide and linear calibration curves are obtained by monitoring the strongest intensity value of bands arising from stronger stretching mode as a function of analyte concentration. The limits of detection and limits of quantitation are reported for two pesticides. The limit of detection for paraoxon is 96 nM (0.026 ppm) and for sumithion is 34 nM (0.011 ppm). The limits of quantitation are 152 nM (0.042 ppm) and 57 nM (0.016 ppm) for paraoxon and sumithion, respectively. It can be seen that these two organophosphorus pesticides can be detected in the low nM range based on this dynamic-SERS analytical method. Also, in the real sample experiments of paraoxon and sumithion, the results confirm that this dynamic-SERS technique would have potential applicability for quantitative analysis with slight interference.

Ag-coated sea-urchin-like Fe3O4@C core-shell particles can be synthesized by a facile one-step solvothermal method, followed by deposition of high-density Ag nanoparticles onto the carbon surface through an in situ growth process, respectively. The as-synthesized Ag-coated Fe3O4@C particles can be used as a surface-enhanced Raman scattering (SERS) substrate holding reproducible properties under an external magnetic force. The magnetic function of the particles allows concentrating the composite particles into small spatial regions, which can be exploited to decrease the amount of material per analysis while improving its SERS detection limit. In contrast to the traditional SERS substrates, the present Fe3O4@C@Ag particles hold the advantages of enrichment of organic pollutants for improving SERS detection limit and recycled utilization.