[Background]Nitrosamines(NAms),emerging as disinfection by-products in drinking water,are highly carcinogenic.Given the significant NAms contamination reported in various regions of China,evaluating the contamination levels and health risks of NAms in terminal direct drinking water is of great urgency. [Objective]To investigate the concentration levels of NAms in piped direct drinking water at pri-mary and secondary schools in Shanghai,and to assess the potential health risks posed to different age groups through this exposure pathway. [Methods]A total of 198 water samples were collected from 66 primary and secondary schools across five districts in Shanghai from May to June 2023.The mass concentrations of eight major NAms were quantified using solid-phase extraction coupled with gas chromatography-mass spectrometry(GC-MS).A carcinogenic health risk model was employed to evaluate the lifetime cancer risks associated with NAms exposure via direct drinking water for various age groups. [Results]Among the 198 samples,NAms were detected in 196 samples,with concentrations ranging from below the limit of detection(LOD)to 106.06 ng·L-1.The average concentration of total NAms was 21.30 ng·L-1,with N-ni-trosodimethylamine(NDMA)exhibiting the highest detection rate at 98.5%.Significant differences in NAms concentrations were observed among water treatment systems utilizing ultrafiltration(UF),nanofiltration(NF),and reverse osmosis(RO)as core processes(P<0.05).The lifetime cancer risks for different age groups ranged from 1.38×10-6 to 1.11×10-5,with NDMA contributing the most to the overall risk(82.1%).The carcinogenic risk for adults(1.08×10-5)was higher than that for children and adolescents(1.38×10-6 to 2.61×10-6).Among children and adolescents under 18 years of age,the risk decreased as age increased. [Conclusion]Trace levels of NAms,primarily NDMA,are detected in the piped direct drinking water at primary and secondary schools in Shanghai,with concentrations vary significantly depending on the water treatment process.The carcinogenic risks of NAms exposure via direct drinking water for all age groups are below the acceptable level(1×10-4)recommended by the U.S.Environmental Protection Agency(EPA).

Cyanuric acid is a widely used fine chemical intermediate that acts as a free chlorine buffer in swimming pool water, wherein it is often used as a stabilizer to maintain the germicidal efficacy of chlorinated disinfectants. However, it has also been associated with health risks. Herein, we introduced the sources and functions of cyanuric acid in swimming pool water, focusing on potential health risks associated with excessive concentration of the component and the current control standards worldwide. Also, the prevention and control measures were summarized in terms of physical chemistry, biodegradation, and ultraviolet radiation to provide a basis for the development of public health policies for swimming pool management.

Excess use of ofloxacin (OFL) and norfloxacin (NOR) in duck breeding is a significant food safety issue due to its residues. Therefore, a surface-enhanced Raman spectroscopy (SERS) method was developed to detect these analytes in duck meat to protect consumer health. The SERS conditions for OFL and NOR including the adsorption time, the volume ratio of gold nanoparticles to NaCl solution, and the volume of enhancement solution, were optimized by single factor experiments, and their values were 6 min, 3:1, and 20 mu L, respectively. Furthermore, a total of 396 samples were used to establish a principal component analysis-support vector machine (PCA-SVM) model. The performance was evaluated using three pretreatment methods: adaptive iterative-penalty least squares (air-PLS) and standard normal variate (SNV), air-PLS and first derivative coupled with SNV, and air-PLS and second derivative coupled with SNV. Air-PLS and second derivative coupled with SNV was selected to be the optimal pretreatment. The sensitivity and specificity of PCA-SVM for the classification of the meat samples were 85 to 100% and 95 to 100% with an accuracy of 93%. The results show that SERS was an effective and rapid approach for the identification of OFL and NOR in duck meat.

Clenbuterol has anabolic effects as a doping agent and is banned by the World Anti-Doping Agency for use by athletes. If an athlete consumes pork with clenbuterol residues, it may cause a positive urine test result, which will seriously affect the athletic career. In this work, an electrochemical sensor for the detection of clenbuterol was proposed. Two reduced graphene oxide/Fe 3 O 4 (rGO/Fe 3 O 4 ) nanocomposites were prepared by solvent thermal and hydrothermal methods. Different rGO/Fe 3 O 4 were characterized and compared with FT-IR, XRD, SEM, and Zeta potential meters. The results show that the rGO/Fe 3 O 4 prepared by different methods vary in surface functional groups, Fe 3 O 4 crystal structures and particle sizes, surface morphology and surface charge. The electrode was modified with rGO/Fe 3 O 4 and the detection performance of the sensor for clenbuterol was investigated. Under optimal conditions, the electrochemical sensor could linearly detect clenbuterol from 1 μM - 128 μM with a detection limit of 120 nM. In addition, this electrochemical sensor has been successfully used for the detection of clenbuterol in swine urine.

The rapid detection for danofloxacin mesylate (DFM) and ofloxacin (OFL) residues in chicken were achieved through synchronous fluorescence technology coupled with chemometric methods. First of all, the synchronous fluorescence spectra of DFM standard solution, OFL standard solution, chicken extract without antibiotics and chicken extract containing DFM and OFL were analyzed, and the wavelength difference (Delta lambda) of DFM and OFL were respectively determined as 130 and 200 nm, and the fluorescence excitation peaks of DFM and OFL were respectively determined as 288 and 325 nm for the detection of DFM and OFL in chicken, respectively. Subsequently, the effects of the concentrations of sodium hydroxide solution and the type of surfactant on the fluorescence intensities were investigated through the single factor test. The best detection conditions of DFM and OFL residues in chicken were as follows: the concentration of sodium hydroxide solution of 0. 1 mol . L-1, and the concentration of SDS solution of 0. 1 mol . L-1. Finally, the prediction models of DFM and OFL residues in chicken were established using linear regression, partial least squares regression (PLSR), and multiple linear regression (MLR) respectively. The experimental results showed that the comprehensive evaluation of DFM residues' prediction model of based on the PLSR algorithm was best among these algorithms. The coefficient of determination for the prediction set (R-P(2)) and the root mean square error for the prediction set (RMSEP) were 0. 978 3 and 1. 934 2 mg . kg(-1). The ratio of prediction to deviation (RPD) was 5. 876 5. The comprehensive evaluation of the prediction model of OFL residues based on the MLR algorithm was best among these algorithms. The R-P(2), RMSEP, and RPD were 0. 895 0, 3. 859 8 mg . kg(-1) and 2. 509 1, respectively. The adopted method was simple and fast, and could to realize the rapid detection of DFM and OFL residues in chicken.

This paper investigated on 478 duck meat samples for the identification of 2 kinds of antibiotics, that is, doxycycline hydrochloride and tylosin, that were classified based on surface-enhanced Raman spectroscopy (SERS) combined with multivariate techniques. The optimal detection parameters, including the effects of the adsorption time, and 2 enhancement substrates (i.e., gold nanoparticles as well as gold nanoparticles and NaCl) on Raman intensities, were analyzed using single factor analysis method. The results showed that the optimal adsorption time between gold nanoparticles and analytes was 2 min, and the colloidal gold nanoparticles without NaCl as the active substrate were more conducive to enhance the Raman spectra signal. The SERS data were pretreated by using the method of adaptive iterative penalty least square method (air-PLS) and second derivative, and from which the feature vectors were extracted with the help of principal component analysis. The first four principal components scores were selected as the input values of support vector machines model. The overall classification accuracy of the test set was 100%. The experimental results showed that the combination of SERS and multivariate analysis could identify the residues of doxycycline hydrochloride and tylosin in duck meat quickly and sensitively.

Surface-enhanced Raman spectroscopy (SERS) of chicken was collected by DXR (TM) micro-Raman spectrometer with gold colloid as an active substrate and NaC1 solution as the active agent. Rapid identification of sulfadimidine (SM-2) and sulfadiazine (SPD) residues in chicken was achieved. Raman peaks at 937 and 1 188 cm i were used to determine whether there are SM-2 and SPD in chicken or not. The Single-factor experiment method was used to optimize the experimental conditions, and the optimum experimental conditions were obtained: the addition amount of Gold glue was 500 L, the addition amount of NaC1 solutionwas 100 L and the adsorption time was 5 minutes. The original Raman spectra were pre-processed by adaptive iterative penalty least squares (air-PLS), normalization and second derivative. Then the eigenvectors were extracted by principal component analysis (PCA). Finally, the first four PCA scores were used as input values of the support vector machine (SVM) classification model, and the SVM classification model based on C-SVC type was established. The optimal penalty parameter c was 0. 01, and the kernel parameter g was 0. 1. The overall classification accuracy of the model was 93. 23%, the sensitivity of chicken containing SM-2-1SPD was 100%, and the specificity of chicken containing SPD was 99. 02%. The results showed that this method had good identification effects. It could be used to detect and identify SM-2 and SPD antibiotic residues in chicken quickly.

Because antibiotics are regularly used for chicken, food safety is of utmost importance, and health experts pay attention to the effect antibiotics could have on human health. This study examines how surface-enhanced Raman spectroscopy (SERS) was used to identify two antibiotic residues in chicken, doxycycline hydrochloride (DCH) and tylosin (TYL). A single-factor experiment method was adopted to optimize the SERS detection conditions. Results show that the SERS intensities of the chicken samples containing DCH and TYL had greater effectiveness in the peaks of 672 and 771 cm-1 under gold nanoparticles (Au NPs) as the enhancement substrate at 10 min of the optimal adsorption time. The original SERS spectra were pretreated using the method of adaptive iterative penalty least square (air-PLS) and the second derivative, where the feature vectors were extracted by principal component analysis (PCA). The first four principal component scoring was selected as the input values of linear discriminant analysis (LDA) with an overall classification accuracy of 100% for the test set. The experimental results show that SERS technology can identify DCH and TYL in chicken.

为探究同时检测水中的硫酸新霉素(NEO)和磺胺二甲基嘧啶(SM2)的新方法,根据NEO和SM2在2-巯基乙醇的存在下可与邻苯二甲醛生成具有荧光特性的衍生物,建立时间分辨同步荧光法同时检测水中NEO和SM2的含量.通过研究不同组分的时间分辨同步荧光光谱,确定NEO与邻苯二甲醛衍生物、SM2与邻苯二甲醛衍生物的同步激发特征峰分别为335和291 nm波长处,最佳采集时间分别为1和80 min,最佳同步波长差分别为120和150 nm;采用单因素试验考察邻苯二甲醛溶液、2-巯基乙醇溶液和BR缓冲液的加入量对荧光强度的影响,确定最优的加入量:邻苯二甲醛溶液1.0 mL、2-巯基乙醇溶液0.25 mL、BR缓冲液0.025 mL;据此建立NEO浓度与荧光强度的线性关系,在0.5～14.0 mg·L-1范围内,得到其线性方程为Y=14.73X+6.14;建立SM2浓度与荧光强度的线性关系,在0.25～9.0mg·L-1范围内,得到其线性方程为Y=13.86X+21.49.NEO和SM2的检出限分别为0.5和0.25mg·L-1,训练集决定系数(RC2)分别为0.997 5和0.966 9,水中NEO和SM2含量的真实值与预测值之间的预测集决定系数(RP2)分别为0.998 2和0.988 9,预测集均方根误差(RMSEP)分别为0.380 3和0.257 5 mg·L-1,回收率分别处于101.8％～ 114.0％和92.3％～ 115.8％之间,相对标准偏差(RSD)分别为4.O％～8.4％和3.6％～6.6％.本方法线性关系良好,可实现水中NEO和SM2的同时测定.

以鸡肉为试验对象,运用表面增强拉曼光谱(surface-enhanced Raman scattering,SERS)技术对鸡肉中丙酸睾酮(testosterone propionate,PTS)残留的检测条件(金胶加入量、含PTS的鸡肉提取液加入量、硫酸镁溶液加入量以及反应时间)进行优化分析.结果表明,以纳米金胶作为拉曼增强基底,对鸡肉中PTS进行拉曼光谱分析,在400 cm-1～1800 cm-1拉曼光谱范围内,839 cm-1和1092 cm-1被确定为其特征峰,并在其特征峰处采取单因素试验法得出最优检测条件:金胶加入量、含PTS的鸡肉提取液加入量、硫酸镁溶液加入量和反应时间分别为500、3、30μL和1 min.检测条件优化结果为今后快速检测出鸡肉中PTS残留提供数据基础,也进一步发展SERS技术在食品中药物残留的检测方向.

In order to meet the growing demand for food safety, the rapid classification of sulfadimidine and sulfapyridine in duck meat based on surface-enhanced Raman spectroscopy (SERS) was investigated using gold nanoparticles (Au NPs) and NaCl solution as the active substrate and active agent, respectively. The results showed that the Raman characteristic peaks at 557 and 573 cm(-1) were used to determine whether sulfadimidine and sulfapyridine remained in duck meat. A single factor experimental method was used to optimize the conditions. Three pretreatment methods were employed to optimize the raw measurements. According to the classification accuracies, adaptive iterative reweighted penalty least squares and second derivative were selected to be the optimal pretreatment method. Next, principal component analysis (PCA) was performed to extract the characteristic variables. The first four score values of PCA were selected to be the input values of support vector machine (SVM) classification model, which classified duck meat samples into four categories. Nu-support vector classification and radial basis function were selected to be the employed SVM classification model and the Kernel type, respectively. The gamma and nu values were 0.25 and 0.255, respectively. The classification accuracy for the test set was 90.44%. The sensitivities and specificities of test set were calculated, and the highest sensitivity and specificity values were 96.97% and 100%, respectively. The experimental results showed that this classification model provides favorable classification results. Therefore, the adopted method may be used for the rapid classification of sulfadimidine and sulfapyridine residues in duck meat.