Targeted Bottom–Up Mass Spectrometry Approach for the Relative Quantification of Post-Translational Modification of Bovine κ-Casein during Milk Fermentation

κ-casein (κ-CN) is one of the key components in bovine milk, playing a unique role in the structuration of casein micelles. It contains in its chemical structure up to sixteen amino acid residues (mainly serine and threonine) susceptible to modifications, including glycosylation and phosphorylation, which may further be formed during milk processing. In this study, changes in post-translational modification (PTM) of κ-CN during bovine milk fermentation were investigated. One-to-five-day fermented milk samples were produced. A traditional bottom–up proteomics approach was used to establish a multiple-reaction monitoring (MRM) method for relative quantification of κ-CN PTM. Endoproteinase Glu-C was found to efficiently digest the κ-CN molecule. The developed LC-MS method was validated by performing assessments of linearity, precision, repeatability, reproducibility, limit of detection (LOD), and limit of quantification (LOQ). Among the yielded peptides, four of them containing serine and threonine residues were identified and the unmodified as well as the modified variants of each of them were relatively quantified. These peptides were (1) IPTINTIASGEPTSTTE _{[140, 158]}, (2) STVATLE _{[162, 168]}, (3) DSPE _{[169, 172]}, and (4) INTVQVTSTAV _{[180, 190]}. Distribution analysis between unmodified and modified peptides revealed that over 50% of κ-CN was found in one of its modified forms in milk. The fermentation process further significantly altered the composition between unmodified/modified κ-CN, with glycoslaytion being predominant compared to phosphorylation (p < 0.01). Further method development towards α and β-CN fractions and their PTM behavior would be an asset to better understand the changes undergone by milk proteins and the micellar structure during fermentation.     

分散液液微萃取技术及其在生物样品分析中的研究进展

分散液液微萃取是一种新型微萃取技术,具有易操作、低成本、耗时短、环境友好、萃取效率高等优点。该文着眼于分散液液微萃取技术中萃取剂的性质及辅助分散方式,综述了常规分散液液微萃取、离子液体分散液液微萃取、超声辅助分散液液微萃取等多种萃取模式,并重点归纳总结了近5年分散液液微萃取技术在生物样品分析领域的应用进展。     

HPLC-ELSD与GC-MS法测定牛乳甘油三酯sn-2位脂肪酸组成

研究建立了快速\\准确测定牛乳脂甘油三酯sn-2位脂肪酸组成的方法,利用胰酶专一水解甘油三酯sn-1和sn-3位置上的脂肪酸得到sn-2单甘油酯和游离脂肪酸,再通过蒸发光散射高效液相色谱分离出sn-2位单甘油酯,然后对其进行衍生,用气相色谱质谱联用仪对sn-2脂肪酸组成进行分析,结果显示用蒸发光散射高效液相色谱法分离sn-2位单甘油酯时方法的回收率达到83.3%~85.1%,该法省去了传统测定中费时费力的薄层色谱分离步骤.用气相色谱质谱联用法对产物进行分析,精密度高,结果可靠,分析结果表明,牛乳脂肪sn-2位脂肪酸由2.57%月桂酸、7.68%豆蔻酸、34.74%棕榈酸、11.56%亚油酸、22.53%油酸和15.21%硬酯酸组成.     

乳腺癌的临床光谱检测

基于激光与生物组织的相互作用原理检测乳腺癌是目前国际研究热点。实验室自行开发研制的近红外乳腺检测仪, 采用780 nm的低频调制激光透照乳房,并在计算机控制下做二维扫描,对侧采用光电倍增管接收透射光信号,将其转化为电信号,实现了对乳腺癌疾病的检测。系统对光强信号采样后进行数据处理和成像,得到近红外光乳腺透照图像。该系统在中国医学科学院肿瘤医院进行了临床试验。共采集了50名患者100多个数据。其中恶性肿瘤患者34例,良性13例,其他3例。近红外诊断结果与患者同期的钼靶检测结果、超声检测结果和病理结果做了对照,正确率达到72.5%,介于超声(77.50%)和钼靶(71.88%)之间。说明近红外方法和所使用的样机对判断乳腺疾病有可行性,为近红外图像方法提供了新的研究思路。     

荧光衍生化-磁分散固相萃取/高效液相色谱检测池塘水与蔬菜中3种挥发性异味物质

以6-碳酰氯左氧氟沙星(LFC-Cl)为衍生试剂,磁性氧化石墨烯(MGO)为吸附剂,建立了2-甲基异莰醇(2-MIB)、土臭素(GSM)和3-甲基-1-丁醇(3-MB)的高效液相色谱荧光检测(HPLC-FLD)分析方法。在浓度为0.05 mol/L的4-二甲氨基吡啶(DMAP)乙腈溶液中,衍生温度60℃,反应时间90 min,超声波辅助条件下,可完成上述3种醇类挥发性异味物质(VOCs)的衍生化。在吸附剂用量20 mg、萃取时间20 min、解吸剂为乙腈(含1%甲酸)、解吸时间3 min条件下,能实现3种异味物质衍生物的富集和净化。在优化的HPLC条件下,15 min内可实现3种衍生物的基线分离和高灵敏检测。方法检出限(LOD)为0.020～0.95 ng/L,定量下限(LOQ)为0.10～3.3 ng/L,线性、精密度和回收率良好。与已报道的方法相比,该方法具有灵敏度高、样品前处理简单、仪器普适性好等优势,可用于池塘水、蔬菜中VOCs的快速、定量测定,为食品和环境水样监测提供了一种新方法。     

扫描电子显微镜-能谱分析仪(SEM-EDS)中面扫描法在高分子材料失效分析中的应用

用扫描电子显微镜搭载的有窗X射线能谱仪（EDS能谱）对荧光样品进行分析时,由于电子束的激发作用,样品受激自发产生的荧光将严重干扰EDS能谱探头对特征X射线的分析,最终导致定量困难甚至难以定性. 新型无窗EDS能谱由于其优异的低能量端信号采集与分析能力,结合单颗粒小区域采集的方法,可以成功实现荧光样品的正常EDS能谱分析. 由于无窗EDS能谱仪没有传统薄膜窗口的吸收作用,其最低可分析元素为锂. 通过优化加速电压,并借助样品表面钝化膜的保护作用,克服了背景噪音与样品污染的干扰,成功实现对单质锂的检测.     

基于牛乳加工工艺的NIRS研究

详细地分析了均质、巴氏杀菌等不同加工工艺对牛乳近红外光谱的影响,首次发现各种加工后的牛乳与原料乳间在1 890 nm附近有明显的区别,通过这一特征波长点可以很好地判断牛乳是否经过加工处理,该结果为近红外技术在牛乳质量控制方面的研究提供了理论基础。论述了均质后吸光度急剧下降的液体乳,经杀菌工艺后吸光度反而升高以及常温原料乳随着均质压力的不断增强,其吸光度在整个光谱区域均呈下降趋势,脂肪球的物理结构变化,最终导致了吸光度的下降的原因。以商品乳中是否含有复原乳的鉴别为例,探讨了复原乳检测的机理,并利用SIMCA方法创建了复原乳定性判别模型,在光谱1 800～2 200 nm间,采用2D和Norris5.5预处理方法,判别结果表明对于商品乳中复原乳的正确辨别率可以达到98.1%。     

乳腺组织的拉曼光谱线性模型研究

本研究甄选10个代表乳腺组织拉曼活性成分的基谱,在国内首次构建一种乳腺组织拉曼谱的线性回归模型。用2 000多个正常和非正常乳腺组织拉曼谱对该模型进行统计检验,模型显著性F检验的置信度全部为1,多元决定系数的平均值为0.95,表明线性模型假设成立、拟合效果良好。10个基谱代表脂肪、细胞质、细胞间质、DNA、血液、β-胡萝卜素、胆固醇等的拉曼谱,基谱的归一化拟合系数间接反映出这些成分的相对含量。用该模型拟合正常和肿瘤乳腺组织的拉曼宏观谱,病变前后细胞质和DNA的拟合系数增大、脂肪拟合系数减小,这反映出它们相对含量的增减,与已知的病理学结果一致。该研究有助于理解乳腺肿瘤组织的生化变化,并可能为分析该变化提供了有效手段。     

Gas Chromatographic Fingerprint Analysis for the Comparison of Seized Cannabis Samples

Cannabis sativa L. is widely used as recreational illegal drugs. Illicit Cannabis profiling, comparing seized samples, is challenging due to natural Cannabis heterogeneity. The aim of this study was to use GC–FID and GC–MS herbal fingerprints for intra (within)- and inter (between)-location variability evaluation. This study focused on finding an acceptable threshold to link seized samples. Through Pearson correlation-coefficient calculations between intra-location samples, ‘linked’ thresholds were derived using 95% and 99% confidence limits. False negative (FN) and false positive (FP) error rate calculations, aiming at obtaining the lowest possible FP value, were performed for different data pre-treatments. Fingerprint-alignment parameters were optimized using Automated Correlation-Optimized Warping (ACOW) or Design of Experiments (DoE), which presented similar results. Hence, ACOW data, as reference, showed 54% and 65% FP values (95 and 99% confidence, respectively). An additional fourth root normalization pre-treatment provided the best results for both the GC–FID and GC–MS datasets. For GC–FID, which showed the best improved FP error rate, 54 and 65% FP for the reference data decreased to 24 and 32%, respectively, after fourth root transformation. Cross-validation showed FP values similar as the entire calibration set, indicating the representativeness of the thresholds. A noteworthy improvement in discrimination between seized Cannabis samples could be concluded.     

浊点萃取-荧光光度法测定苯酚

基于非离子表面活性剂TritonX-100,以浊点萃取结合荧光光度法测定水中的苯酚,考察影响浊点萃取的各种因素。最佳实验条件为：pH=3.0的磷酸氢二钠-磷酸二氢钾缓冲溶液中,5%的TritonX-100用量2.0mL、平衡温度82℃、平衡时间8min。在此条件下,苯酚被萃取到TritonX-100表面活性剂相与水相分开。