In-line preconcentration of oxidized and reduced glutathione in capillary zone electrophoresis using transient isotachophoresis under strong counter-electroosmotic flow

Transient isotachophoresis (tITP) can improve the sensitivity of capillary electrophoresis (CE). In general, it was carried out under the condition of suppressed electroosmotic flow (EOF). However, some special conditions, such as extreme low pH background electrolyte and coating were needed to achieve the requirements of suppressed EOF. In this work, an approach of tITP under the strong counter-EOF in open system (counter-EOF-tITP) is presented for the rapid and sensitive preconcentrating the reduced glutathione (GSH) and the oxidized glutathione (GSSG) without modifying the capillary and the commercial CE instrument. The parameters of the experimental system, such as the concentration of leading electrolyte, the injected amount of terminating electrolyte and the injected pressure of sample were investigated in detail to understand the mechanism of counter-EOF-tITP. The sensitivity enhancement factors were of 320 for GSH and 280 for GSSG. In addition, the detection limit of 23.4 and 18.0 μg L⁻¹ for GSH and GSSG was achieved, respectively. The method's applicability was demonstrated by determining GSH and GSSG in tomato and human serum.     

Application of multiple headspace-solid-phase microextraction followed by gas chromatography–mass spectrometry to quantitative analysis of tomato aroma components

The objective of this paper is to investigate the potential of multiple headspace-solid-phase microextraction (MHS-SPME) for the determination of volatile compounds in complex matrix samples. A method based on MHS-SPME for the determination of around 20 volatile compounds, responsible of tomato flavour and aroma has been developed and validated, using gas chromatography with mass spectrometry (ion trap analyser) for analysis. For this purpose, the experimental β parameter, resulting from the MHS-SPME theoretical development, has been obtained from real sample analysis (in triplicate) for each identified compound, carrying out up to 5 consecutive extractions. Later, this parameter is used to perform quantitation of real samples after just a single HS-SPME extraction. Precision, expressed as repeatability, has been evaluated by analysing six replicates of a real sample, showing relative standard deviations between 4 and 20%. For accuracy study, quantitative results have been compared with those obtained by means of standard additions on replicate samples, and no statistically significant differences between the two methods were observed. Since MHS-SPME uses the estimated total area corresponding to the complete extraction of compounds (obtained from the β parameter), quantitation can be carried out by external calibration using standards in solvent and splitless injection, instead of by SPME. Linearity, tested in the range 0.05–15 μg/mL, showed satisfactory values, with coefficients of correlation between 0.995 and 0.999. Limits of detection were in the range of 0.25–5 ng/g. MHS-SPME has been proved to be an adequate technique to avoid matrix effects in complex samples quantitation. Its applicability to the determination of volatile tomato components, together with its limitations, is discussed in this article.     

微波消解-原子荧光光谱法测定番茄不同部位中微量汞

在汞污染的土壤上种植不同品种番茄 ,采用微波溶样技术和原子荧光光谱法 ,测定番茄不同部位的汞含量 ,初步研究了汞在番茄植株各个部位的分布情况 ,同时确定了微波消解该类样品的最佳条件 ,优化了原子荧光光谱仪的工作参数。该方法具有快速、简便、准确等特点 ,汞含量在0.01～20μg/L范围内线性良好 ,相关系数r=0.9998。研究结果还表明 ,汞含量在番茄各部位中的分布规律是根 叶>茎 果实 ,只有1/10的供试番茄品种在汞胁迫下果实中汞含量超标 ,为评价番茄作为菜蔬食品在生产中受汞污染的风险以及发展新的重金属污染土壤的修复技术提供依据     

波长比和近红外光谱的番茄品质检测方法

番茄的可溶性固形物(SSC)、pH值和坚实度(Firmness)是决定番茄的口感及收获后品质的主要因素。提出一种基于波长比和近红外光谱的番茄SSC,pH和坚实度检测方法。首先用Vis/SWNIR便携式光谱仪 (波长：400～1 100 nm)和中波近红外便携式光谱仪 (波长：900～1 683 nm)在相互作用模式下,对6个不同成熟度的600个番茄样本进行光谱采集,对所采集的光谱用波长比法和波长比+自动缩放法进行预处理后,分别建立番茄SSC,pH和坚实度的预测模型,比较单一自动缩放、单一波长比、波长比+自动缩放及不做预处理四种方法的预测结果。结果显示,波长比法结合自动缩放预处理可有效提高可见/短波近红外光谱对SSC,pH和坚实度的预测精度(r_p=0.779,0.796和0.917);波长比法+自动缩放法可提高中波近红外光谱对SSC的预测效果(r_p=0.818)。这说明波长比法在优化和处理番茄光谱信息方面具有一定的潜力。     

基于可见/近红外光谱技术的番茄叶片灰霉病检测研究

利用可见/近红外光谱技术对感染灰霉病的番茄叶片感染程度进行了检测。提出了主成分分析结合BP神经网络的数据处理方法。采用主成分分析进行数据的降维,减少了计算量,提高了建模精度。通过主成分分析中的载荷值,定性地分析了不同波段对病害程度检测的重要性。将得到的最主要的几个主成分输入BP神经网络进行建模,预测结果显示,当主成分数为8,隐含层结点数为11的时候,病害程度的检测模型对未知样本预测的相关系数达到0.930,SEP为0.068 7,模型具有良好的检测效果。说明基于光谱技术和化学计量学方法的灰霉病检测模型具有很好的检测能力,为光谱技术应用于病害检测提供了新的方法。     

稀土钼磷杂多蓝的合成、性质及防治植物病毒的研究

采用电解法合成了Ｋｅｇｇｉｎ结构稀土钼磷二,四电子杂多蓝,通式为ＬｎＨ２「ＰＭｏ＾Ｖ１１０Ｍｏ２＾ｖＯ４０」,ｎＨ２Ｏ和ＬｎＨ４「ＰＭｏ＾ＶＩ８Ｍｏ＾ｖ４Ｏ４０」．ｎＨ２Ｏ（Ｌｎ＝Ｌａ,Ｃｅ,Ｐｒ,Ｎｄ,Ｓｍ,Ｇｄ）。     

微波消化、ICP-AES测定蕃茄粉中 8种微量元素

以微波消化蕃茄粉,电感耦合等离子体-原子发射光谱(ICP-AES)快速测定样品中的8种微量元素,各元素的相对标准偏差均小于3%,回收率在95.9%-102.7%之间,方法简便、快速,准确度高,结果令人满意.     

基于高光谱技术的灰霉病胁迫下番茄叶片SPAD值检测方法研究

对灰霉病胁迫下番茄叶片中叶绿素含量(SPAD)的高光谱图像信息进行了研究。首先获取380～1 030 nm波段范围内健康和染病番茄叶片的高光谱图像,然后基于ENVI软件处理平台提取高光谱图像中感兴趣区域的光谱信息,经平滑(Smoothing)、标准化(Normalize)等预处理后,建立了基于Normalize预处理的偏最小二乘回归(PLSR)和主成分回归(PCR)模型。再基于PLSR获得的4个变量建立反向传播神经网络(BPNN)和最小二乘-支持向量机(LS-SVM)模型。4个模型中,LS-SVM的预测效果最好,其决定系数R2为0.901 8,预测集均方根误差RMSEP为2.599 2。结果表明,基于健康和染病番茄叶片的高光谱图像响应特性检测叶绿素含量(SPAD)是可行的。     

空间分辨光谱和可见/近红外光谱的番茄颜色等级判别

比较分析空间分辨光谱和单点可见/近红外光谱（可见/短波近红外光谱和中波近红外光谱）对番茄颜色的识别能力。根据番茄表面和内部颜色将600个样品分为6个等级（green, breaker, turning, pink, light red和red）。分别利用新型空间分辨光谱系统（550~1 650 nm）,可见/短波近红外光谱仪（400~1 100 nm）和中波近红外光谱仪（900~1 700 nm）采集番茄的空间分辨（spatially-resolved, SR）光谱和单点可见/近红外（SP Vis/NIR）光谱,建立番茄等级的偏最小二乘判别（PLSDA）模型,比较其对番茄颜色等级的预测效果。结果表明, SR光谱组合可在最佳单一SR光谱基础上进一步提高番茄颜色的识别能力,对番茄表面颜色和内部颜色的识别率可分别达到98.8%和84.6%。光源-检测器距离较近的SR光谱对番茄表面颜色的识别有帮助,而光源-检测器距离较远的SR光谱能较好的判别番茄内部颜色。SP NIR光谱在对番茄表面颜色判别中与SR光谱具有一定可比性,其分类准确率可达到95%,但SP Vis/NIR光谱在对番茄内部颜色识别中具有较低的分类准确率,分类结果远不如SR光谱,说明SR光谱比SP Vis/NIR光谱对番茄颜色的判别更具潜力。     

扫描次数对番茄叶漫反射光谱和模型精度的影响研究

近红外光谱分析技术因其诸多优点成为近年来发展最快的定量和定性分析技术之一,它是依据有机分子中的含氢基团(C—H, O—H, N—H)振动的合频与各级倍频的吸收获得信息。但是,近红外光谱易受到光谱仪的工作状态和扫描时各种参数的设置,如波长的准确性、仪器的分辨率、噪声、扫描次数、样品粒度的均匀性等的影响。为了给建立实际模型时选择最佳的测量条件提供依据,文章以番茄叶为研究对象,分析了扫描次数对番茄叶漫反射光谱和叶绿素定量模型精度的影响。结果发现扫描次数对番茄叶漫反射光谱和模型精度有较大的影响。随着扫描次数的增加,近红外光谱的均方根噪声的方差值逐渐变小。扫描次数越多,光谱的质量较高,但是仪器产生的系统误差也会增大;扫描次数越少, 光谱的质量较差,但是光谱较为光滑,系统的误差较小。在128次扫描次数下,叶绿素定量模型的决定系数较高,但是模型不稳定。当扫描次数为32时,模型的决定系数较低,但该模型比较稳定,检测时间也较短。同时,不同扫描次数下所建模型都用于预测叶绿素含量,发现没有显著差异(α＝0.05)。在采集光谱建立模型时,应考虑综合因素选择合适的扫描次数。