Analysis of Sildenafil in Liquor and Health Wine Using Surface Enhanced Raman Spectroscopy.

The illegal adulteration of sildenafil in herbal food supplements and alcoholic drinks immensely threatens human health due to its harmful side-effects. Therefore, it is important to accurately detect and identify the presence of sildenafil in alcoholic drinks. In this study, Opto Trace Raman 202 (OTR 202) was used as surface enhanced Raman spectroscopy (SERS) active colloids to detect sildenafil. The results demonstrated that the Raman enhancement factor (EF) of OTR 202 colloids reached 1.84 × 107 and the limits of detection (LODs) of sildenafil in health wine and liquor were found to be as low as 0.1 mg/L. Moreover, the SERS peaks of 645, 814, 1235, 1401, 1530 and 1584 cm-1 could be qualitatively determined as sildenafil characteristic peaks and the relationship between Raman peak intensity and sildenafil concentration in health wine and liquor were different. There was a good linear correlation between Raman peak intensity, and sildenafil concentration in health wine ranged 0.1-1 mg/L (0.9687< R2 < 0.9891) and 1-10 mg/L (0.9701 < R2 < 0.9840), and in liquor ranged 0.1-1 mg/L (0.9662 < R2 < 0.9944) and 1-20 mg/L (0.9625 < R2 < 0.9922). The relative standard deviations (RSD) were less than 5.90% (sildenafil in health wine) and 9.16% (sildenafil in liquor). The recovery ranged 88.92-104.42% (sildenafil in health wine) and 90.09-104.55% (sildenafil in liquor). In general, the sildenafil in health wine and liquor could be rapidly and quantitatively determined using SERS technique, which offered a simple and accurate alternative for the determination of sildenafil in alcoholic drinks.

Gold Nanoparticles for Qualitative Detection of Deltamethrin and Carbofuran Residues in Soil by Surface Enhanced Raman Scattering (SERS).

The residues of deltamethrin (DM) and carbofuran (CBF) in soil is becoming an intractable problem causing soil hardening and environmental pollution. This paper reports a very simple method via improved reduction of chloroauric acid by the trisodium citrate method for fabricating gold nanoparticles (AuNPs), which were used as a surface enhanced Raman scattering (SERS) active colloids with the advantages of ultrasensitivity, reproducibility and chemical stability. The results demonstrated that the limits of detection (LODs) of the DM and CBF were found to be as low as 0.01 mg/L. The SERS intensity showed a good linear relationship with DM (R² = 0.9908) and CBF (R² = 0.9801) concentration from 0.01 to 10 mg/L. In a practical application, DM and CBF residues in soil were easily detected by SERS with the flexible AuNPs colloids, and the LODs of DM and CBF were found to be as low as 0.056 mg/kg and 0.053 mg/kg, respectively. Moreover, DM in soil could be qualitatively detected by the characteristic peaks at 560 and 1000 cm-1, and CBF in soil could be qualitatively detected by the characteristic peaks at 1000 and 1299 cm-1. The determination coefficient (R²p) for DM and CBF reached 0.9176 and 0.8517 in partial least squares (PLS) model. Overall, it is believed that the prepared AuNPs can provide technical support for the accurate detection of pesticide residues in soil by SERS technique.

Gold Nanoparticles with Different Particle Sizes for the Quantitative Determination of Chlorpyrifos Residues in Soil by SERS.

Chlorpyrifos (CPF) is widely used in the prevention and control of crop pests and diseases in agriculture. However, the irrational utilization of pesticides not only causes environmental pollution but also threatens human health. Compared with the conventional techniques for the determination of pesticides in soil, surface-enhanced Raman spectroscopy (SERS) has shown great potential in ultrasensitive and chemical analysis. Therefore, this paper reported a simple method for synthesizing gold nanoparticles (AuNPs) with different sizes used as a SERS substrate for the determination of CPF residues in soil for the first time. The results showed that there was a good linear correlation between the SERS characteristic peak intensity of CPF and particle size of the AuNPs with an R2 of 0.9973. Moreover, the prepared AuNPs performed great ultrasensitivity, reproducibility and chemical stability, and the limit of detection (LOD) of the CPF was found to be as low as 10 µg/L. Furthermore, the concentrations ranging from 0.01 to 10 mg/L were easily observed by SERS with the prepared AuNPs and the SERS intensity showed a good linear relationship with an R2 of 0.985. The determination coefficient (Rp2) reached 0.977 for CPF prediction using the partial least squares regression (PLSR) model and the LOD of CPF residues in soil was found to be as low as 0.025 mg/kg. The relative standard deviation (RSD) was less than 3.69% and the recovery ranged from 97.5 to 103.3%. In summary, this simple method for AuNPs fabrication with ultrasensitivity and reproducibility confirms that the SERS is highly promising for the determination of soil pesticide residues.

Application of Near-infrared Spectroscopy and Multiple Spectral Algorithms to Explore the Effect of Soil Particle Sizes on Soil Nitrogen Detection.

Soil nitrogen is the key parameter supporting plant growth and development; it is also the material basis of plant growth. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near-infrared (NIR) spectroscopy is widely used for rapid detection of soil nutrients. In this study, the variation law of soil NIR reflectivity spectra with soil particle sizes was studied. Moreover, in order to precisely study the effect of particle size on soil nitrogen detection by NIR, four different spectra preprocessing methods and five different chemometric modeling methods were used to analyze the soil NIR spectra. The results showed that the smaller the soil particle sizes, the stronger the soil NIR reflectivity spectra. Besides, when the soil particle sizes ranged 0.18-0.28 mm, the soil nitrogen prediction accuracy was the best based on the partial least squares (PLS) model with the highest Rp2 of 0.983, the residual predictive deviation (RPD) of 6.706. The detection accuracy was not ideal when the soil particle sizes were too big (1-2 mm) or too small (0-0.18 mm). In addition, the relationship between the mixing spectra of six different soil particle sizes and the soil nitrogen detection accuracy was studied. It was indicated that the larger the gap between soil particle sizes, the worse the accuracy of soil nitrogen detection. In conclusion, soil nitrogen detection precision was affected by soil particle sizes to a large extent. It is of great significance to optimize the pre-treatments of soil samples to realize rapid and accurate detection by NIR spectroscopy.

Research on the Effects of Drying Temperature on Nitrogen Detection of Different Soil Types by Near Infrared Sensors.

Soil is a complicated system whose components and mechanisms are complex and difficult to be fully excavated and comprehended. Nitrogen is the key parameter supporting plant growth and development, and is the material basis of plant growth as well. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near infrared sensors are widely used for rapid detection of nutrients in soil. However, soil texture, soil moisture content and drying temperature all affect soil nitrogen detection using near infrared sensors. In order to investigate the effects of drying temperature on the nitrogen detection in black soil, loess and calcium soil, three kinds of soils were detected by near infrared sensors after 25 °C placement (ambient temperature), 50 °C drying (medium temperature), 80 °C drying (medium-high temperature) and 95 °C drying (high temperature). The successive projections algorithm based on multiple linear regression (SPA-MLR), partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were used to model and analyze the spectral information of different soil types. The predictive abilities were assessed using the prediction correlation coefficients (RP), the root mean squared error of prediction (RMSEP), and the residual predictive deviation (RPD). The results showed that the loess (RP = 0.9721, RMSEP = 0.067 g/kg, RPD = 4.34) and calcium soil (RP = 0.9588, RMSEP = 0.094 g/kg, RPD = 3.89) obtained the best prediction accuracy after 95 °C drying. The detection results of black soil (RP = 0.9486, RMSEP = 0.22 g/kg, RPD = 2.82) after 80 °C drying were the optimum. In conclusion, drying temperature does have an obvious influence on the detection of soil nitrogen by near infrared sensors, and the suitable drying temperature for different soil types was of great significance in enhancing the detection accuracy.

Spectral Analysis and Sensitive Waveband Determination Based on Nitrogen Detection of Different Soil Types Using Near Infrared Sensors.

Compared with the chemical analytical technique, the soil nitrogen acquisition method based on near infrared (NIR) sensors shows significant advantages, being rapid, nondestructive, and convenient. Providing an accurate grasp of different soil types, sensitive wavebands could enhance the nitrogen estimation efficiency to a large extent. In this paper, loess, calcium soil, black soil, and red soil were used as experimental samples. The prediction models between soil nitrogen and NIR spectral reflectance were established based on three chemometric methods, that is, partial least squares (PLS), backward interval partial least squares (BIPLS), and back propagation neural network (BPNN). In addition, the sensitive wavebands of four kinds of soils were selected by competitive adaptive reweighted sampling (CARS) and BIPLS. The predictive ability was assessed by the coefficient of determination R² and the root mean square error (RMSE). As a result, loess ( 0.93 < R p 2 < 0.95 , 0.066 g / kg < RMSE p < 0.075 g / kg ) and calcium soil ( 0.95 < R p 2 < 0.96 , 0.080 g / kg < RMSE p < 0.102 g / kg ) achieved a high prediction accuracy regardless of which algorithm was used, while black soil ( 0.79 < R p 2 < 0.86 , 0.232 g / kg < RMSE p < 0.325 g / kg ) obtained a relatively lower prediction accuracy caused by the interference of high humus content and strong absorption. The prediction accuracy of red soil ( 0.86 < R p 2 < 0.87 , 0.231 g / kg < RMSE p < 0.236 g / kg ) was similar to black soil, partly due to the high content of iron-aluminum oxide. Compared with PLS and BPNN, BIPLS performed well in removing noise and enhancing the prediction effect. In addition, the determined sensitive wavebands were 1152 nm-1162 nm and 1296 nm-1309 nm (loess), 1036 nm-1055 nm and 1129 nm-1156 nm (calcium soil), 1055 nm, 1281 nm, 1414 nm-1428 nm and 1472 nm-1493 nm (black soil), 1250 nm, 1480 nm and 1680 nm (red soil). It is of great value to investigate the differences among the NIR spectral characteristics of different soil types and determine sensitive wavebands for the more efficient and portable NIR sensors in practical application.

Rapid Determination of Thiabendazole Pesticides in Rape by Surface Enhanced Raman Spectroscopy.

Thiabendazole is widely used in sclerotium blight, downy mildew and black rot prevention and treatment in rape. Accurate monitoring of thiabendazole pesticides in plants will prevent potential adverse effects to the Environment and human health. Surface Enhanced Raman Spectroscopy (SERS) is a highly sensitive fingerprint with the advantages of simple operation, convenient portability and high detection efficiency. In this paper, a rapid determination method of thiabendazole pesticides in rape was conducted combining SERS with chemometric methods. The original SERS were pretreated and the partial least squares (PLS) was applied to establish the prediction model between SERS and thiabendazole pesticides in rape. As a result, the SERS enhancing effect based on silver Nano-substrate was better than that of gold Nano-substrate, where the detection limit of thiabendazole pesticides in rape could reach 0.1 mg/L. Moreover, 782, 1007 and 1576 cm−1 could be determined as thiabendazole pesticides Raman characteristic peaks in rape. The prediction effect of thiabendazole pesticides in rape was the best ( R p 2 = 0.94, RMSEP = 3.17 mg/L) after the original spectra preprocessed with 1st-Derivative, and the linear relevance between thiabendazole pesticides concentration and Raman peak intensity at 782 cm−1 was the highest (R² = 0.91). Furthermore, five rape samples with unknown thiabendazole pesticides concentration were used to verify the accuracy and reliability of this method. It was showed that prediction relative standard deviation was 0.70–9.85%, recovery rate was 94.71–118.92% and t value was −1.489. In conclusion, the thiabendazole pesticides in rape could be rapidly and accurately detected by SERS, which was beneficial to provide a rapid, accurate and reliable scheme for the detection of pesticides residues in agriculture products.

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy.

Thiabendazole (TBZ) is widely used in sclerotium blight, downy mildew as well as root rot disease prevention and treatment in plant. The indiscriminate use of TBZ causes the excess pesticide residues in soil, which leads to soil hardening and environmental pollution. Therefore, it is important to accurately monitor whether the TBZ residue in soil exceeds the standard. For this study, density functional theory (DFT) was used to theoretically analyze the molecular structure of TBZ, gold nanoparticles (AuNPs) were used to enhance the detection signal of surface-enhanced Raman spectroscopy (SERS) and the TBZ residue in red soil extracts was quantitatively determined by SERS. As a result, the theoretical Raman peaks of TBZ calculated by DFT were basically consistent with the measured results. Moreover, 784, 1008, 1270, 1328, 1406 and 1576 cm-1 could be determined as the TBZ characteristic peaks in soil and the limits of detection (LOD) could reach 0.1 mg/L. Also, there was a good linear correlation between the intensity of Raman peaks and TBZ concentration in soil (784 cm-1: y = 672.26x + 5748.4, R² = 0.9948; 1008 cm-1: y = 1155.4x + 8740.2, R² = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation (RSD) ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300⁻1700 cm-1 SERS of TBZ were analyzed by the partial least squares (PLS) and backward interval partial least squares (biPLS). Also, the prediction accuracy of TBZ in soil (Rp² = 0.9769, RMSEP = 0.556 mg/L, RPD = 5.97) was the highest when the original spectra were pretreated by standard normal variation (SNV) and then modeled by PLS. In summary, the TBZ in red soil extracts could be quantitatively determined by SERS based on AuNPs, which was beneficial to provide a new, rapid and accurate scheme for the detection of pesticide residues in soil.

Density Functional Theory Analysis of Deltamethrin and Its Determination in Strawberry by Surface Enhanced Raman Spectroscopy.

Deltamethrin is widely used in pest prevention and control such as red spiders, aphids, and grubs in strawberry. It is important to accurately monitor whether the deltamethrin residue in strawberry exceeds the standard. In this paper, density functional theory (DFT) was used to theoretically analyze the molecular structure of deltamethrin, gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were used to enhance the surface enhanced Raman spectroscopy (SERS) detection signal. As a result, the theoretical Raman peaks of deltamethrin calculated by DFT were basically similar to the measured results, and the enhancing effects based on AuNPs was better than that of AgNPs. Moreover, 554, 736, 776, 964, 1000, 1166, 1206, 1593, 1613, and 1735 cm−1 could be determined as deltamethrin characteristic peaks, among which only three Raman peaks (736, 1000, and 1166 cm−1) could be used as the deltamethrin characteristic peaks in strawberry when the detection limit reached 0.1 mg/L. In addition, the 500⁻1800 cm−1 SERS of deltamethrin were analyzed by the partial least squares (PLS) and backward interval partial least squares (BIPLS). The prediction accuracy of deltamethrin in strawberry (Rp2 = 0.93, RMSEp = 4.66 mg/L, RPD = 3.59) was the highest when the original spectra were pretreated by multiplicative scatter correction (MSC) and then modeled by BIPLS. In conclusion, the deltamethrin in strawberry could be qualitatively analyzed and quantitatively determined by SERS based on AuNPs enhancement, which provides a new detection scheme for deltamethrin residue determination in strawberry.

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor.

Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly.

Characterization of on-road nitrogen oxides and black carbon emissions from high emitters of heavy-duty diesel vehicles in China.

Heavy-duty diesel vehicles (HDDVs) significantly contribute to atmospheric nitrogen oxides (NOX) and black carbon (BC), with high emitters within the HDDV fleet impacting the total emissions. However, emission patterns and contributions of high emitters are rarely explored from a fleet-perspective. We investigated NOX and BC emission factors (EFs) from 1925 HDDVs in Shenzhen by the plume-chasing method, and found that the fleet-average EFs decreased with stricter emission standards. Unexpectedly, the average NOX EF for the China IV fleet was comparable with that for the China III fleet due to possible ineffective aftertreatment in high-emitter sectors of China IV HDDVs. Decreasing trend in average NOX EF since 2017 reflected the effective emission controls by the implementation of China V standard. Besides, semi-trailer tractors exhibited a higher incidence of NOX over-emissions, whereas BC high emitters were more pronounced in box trucks. Total NOX and BC emissions from HDDVs in Shenzhen were revisited, reaching 54.0 and 1.1 Gg·yr-1, with updated NOX EF correcting a 26.2 % underestimation in national guidelines. Notably, eliminating high emitters yields greater emission reduction benefits than merely retiring old HDDVs, with BC reduction outpacing NOX. This study provides new insights into the implementation of targeted emission reduction measures for HDDVs.

Species profiles, in-situ photochemistry and health risk of volatile organic compounds in the gasoline service station in China.

Accompanying with increases in vehicle population and gasoline consumption, gasoline evaporation accounted for an enlarged portion of total volatile organic compound (VOC) emissions in China, raising increasing environmental concerns especially in megacities. In this study, an intensive sampling campaign was performed in a gasoline service station, to reveal emission characteristics, environmental and health impacts of VOCs. It was strikingly found that 24 % of air samples exceeded the national standard of 4 mg/m3 for non-methane hydrocarbons (NMHCs) on the boundary of the station, with the equipment of Stage I and II controls. VOC groups and species profiles showed that alkanes dominated total VOCs. As typical markers of evaporative loss of gasoline, C4-5 species (i-pentane, n-pentane and n-butane) as well as methyl tert-butyl ether (MTBE) accounted for 49.6 % of VOCs. Species profile and diagnostic ratios indicated the prominent contribution of gasoline evaporative losses from refueling or breathing processes, as well as the interference of vehicle exhaust in the ambient air at the site. Intensive O3 production was reproduced by the photochemical box model, demonstrating that O3 formation was co-limited by both VOCs (especially trans-2-butene) and NOx. Inhalation health risk assessment proved that exposure to hazardous VOCs caused non-cancer risk (HQ = 3.08) and definitely posed cancer risks at a probability of 1.3 × 10-4 to workers. Remarkable health risks were mainly imposed by halocarbons, aromatics and alkenes, in which 1,2-dichloropropane caused the highest non-cancer risk (HQ = 1.3) and acted as the primary carcinogen (ICR = 5.1 × 10-5). This study elucidated the high unqualified rate in gasoline service stations after the implementation of latest standards in China, where new regulations targeted halocarbons and updates in existing vapor recovery systems were suggested for VOC mitigation.

Rapid detection of cadmium and its distribution in Miscanthus sacchariflorus based on visible and near-infrared hyperspectral imaging.

Monitoring the effectiveness of Miscanthus sacchariflorus to meet the basic requirements for environmental remediation projects is an important step in determining its use as a productive bioenergy crop for phytoremediation. Conventional chemical methods for the determination of cadmium (Cd) contents involve time-consuming, monotonous and destructive procedures and are not suitable for high-throughput screening. In the present study, visible and near-infrared hyperspectral imaging technology combined with chemometric methods was used to assess the Cd concentrations in M. sacchariflorus. The total Cd concentrations in different plant tissues were measured using an inductively coupled plasma-mass spectrometer. Partial least-squares regression and least-squares support vector machine were implemented to estimate Cd contents from spectral reflectance. Successive projections algorithm and competitive adaptive reweighted sampling (CARS) methodology were used for selecting optimal wavelength. The CARS-partial least-squares regression model resulted in the most accurate predictions of Cd contents in M. sacchariflorus leaves, with a determination coefficient (R2) of 0.87 and a root mean square error (RMSE) value of 97.78 for the calibration set, and an R2 value of 0.91 and a RMSE value of 75.95 for the prediction set. The CARS-least-squares support vector machine model resulted in the most satisfactory predictions of Cd contents in roots, with R2 values of 0.95 (RMSE, 0.92 × 103) for the calibration set and 0.90 (RMSE, 1.64 × 103) for the prediction set. Finally, the Cd concentrations in different plant tissues were visualized on the prediction maps by predicted spectral features on each hyperspectral image pixel. Thus, visible and near-infrared imaging combined with chemometric methods produces a promising technique to evaluate M. sacchariflorus' Cd phytoremediation capability in high-throughput metal-contaminated field applications.