LIF技术与PLS-DA算法联合辨识矿井涌水水源类型的研究

快速的矿井涌水水源辨识对于矿井的水灾预警及灾后救援意义重大。常规方法使用离子浓度做为判别因子,耗时过长,因此提出一种激光诱导荧光光谱(LIF)技术与偏最小二乘判别分析(PLS-DA)算法联合快速辨识矿井涌水水源类型的方法。实验使用405 nm激光对被测水体进行激发,获取矿井5个不同含水层100组水样的荧光光谱,根据光谱曲线特征,对数据进行压缩处理,获取合适的光谱数据。每种水样使用15组共75组光谱数据作为建模集,剩余的25组水样的光谱数据作为测试集。为验证实验结果,设计了簇类的独立软模式(SIMCA)算法与PLS-DA算法构建的实验模型进行对比。实验发现矿井不同含水层水样的荧光光谱差异较大,在不进行任何预处理的情况下,以PLS模型为基础的PLS-DA算法较SIMCA算法的建模正确率高,达到了100%,其校正及验证结果与实际分类变量的相关系数均大于0.951,校正集均方根误差(RMSECV)和验证集均方根误差(RMSEP)均小于0.123,利用模型对测试集中五种水样样本的识别正确率均为100%。     

Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy

Type 2 diabetes mellitus (T2DM) and its attendant complications, such as diabetic nephropathy (DN), impose a significant societal and economic burden. The investigation of discovering potential biomarkers for T2DM and DN will facilitate the prediction and prevention of diabetes. Phospholipids (PLs) and their metabolisms are closely allied to nosogenesis and aggravation of T2DM and DN. The aim of this study is to characterize the human plasma phospholipids in T2DM and DN to identify potential biomarkers of T2DM and DN. Normal phase liquid chromatography coupled with time of flight mass spectrometry (NPLC-TOF/MS) was applied to the plasma phospholipids metabolic profiling of T2DM and DN. The plasma samples from control (n = 30), T2DM subjects (n = 30), and DN subjects (n = 52) were collected and analyzed. The significant difference in metabolic profiling was observed between healthy control group and DM group as well as between control group and DN group by the help of partial least squares discriminant analysis (PLS-DA). PLS-DA and one-way analysis of variance (ANOVA) were successfully used to screen out potential biomarkers from complex mass spectrometry data. The identification of molecular components of potential biomarkers was performed on Ion trap-MS/MS. An external standard method was applied to quantitative analysis of potential biomarkers. As a result, 18 compounds in 7 PL classes with significant regulation in patients compared with healthy controls were regarded as potential biomarkers for T2DM or DN. Among them, 3 DM-specific biomarkers, 8 DN-specific biomarkers and 7 common biomarkers to DM and DN were identified. Ultimately, 2 novel biomarkers, i.e., PI C18:0/22:6 and SM dC18:0/20:2, can be used to discriminate healthy individuals, T2DM cases and DN cases from each other group.     

基于拉曼光谱技术鉴别ABS废旧塑胶原料的方法研究

塑胶微粒原料已渗透到人类衣食住行的方方面面,并广泛应用于能源、工业、农业、交通乃至航空航天和海洋开发等各重要领域不可或缺的材料。在利益的诱惑下,废旧塑胶的走私现象屡禁不止。我国作为塑胶原料进口大国,现有检测方法耗时长,难以实现现场检测,因此,开发一种用于现场的废旧塑胶微粒判别方法,对快速通关和海关缉私有重要意义。拉曼光谱技术具有快速、无损、样品用量小、无需前处理且适应性强等优点,已在现场快速鉴别领域得到广泛应用。在研究塑胶废旧机理的基础上,将拉曼光谱技术结合化学判别方法,应用于废旧塑胶原料识别。选取两类成分相似的实际通关塑胶原料样品,包含标准品及废旧品各160份,并对样品的拉曼光谱信息进行了采集。对比分析了两种塑胶原料的原始拉曼光谱,并对样品的拉曼光谱特征峰进行了归属分析。选取1 603 cm-1作为归一化参照峰位,进一步探究废旧塑胶的成分变化,对比统计了废旧塑胶原料及标准塑胶原料的相对峰强变化,结果表明废旧塑胶原料发生了化学老化。基于主成分分析法（PCA）对原始拉曼光谱及预处理拉曼光谱进行降维处理,结果表面预处理拉曼光谱的前2主成分空间分离度较好,通过对原始拉曼光谱数据进行背景扣除及平滑预处理,可减少荧光背景及噪声对鉴别的影响,提高鉴别的准确度。将样品一半划分为校正集用于模型建立,另一半划分为预测集用于模型验证,基于偏最小二乘判别分析（PLS-DA）,建废旧塑胶原料鉴别模型,该模型对建模训练集鉴别正确率为100％,模型验证集鉴别正确率为99.06％。研究表明,基于拉曼光谱技术,结合测试数据预处理及偏最小二乘判别分析方法,可以有效地实现塑胶原料的现场、快速、准确鉴别,为开发现场检测装备及方法提供理论参考。     

Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: A case study

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.     

Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition

A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.     

FT-MIR and UV–vis data fusion strategy for origins discrimination of wild Paris Polyphylla Smith var. yunnanensis

Paris Polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz has multiple therapeutic properties and the origins may affect clinical efficacy. Tracing the geographical origin is important to the authentication and quality assessment of this species. 177 wild samples collected from central, southeast and northwest Yunnan Province, China, were analyzed by single analytical method and data fusion strategies (low- and mid-levels) using Fourier transform mid-infrared (FT-MIR) and ultraviolet-visible (UV–vis) spectroscopies combined with chemometrics (partial least squares discrimination analysis (PLS-DA) and support vector machines grid search (SVM-GS)), for categorizing samples from different geographic origins. According to the results, mid-level data fusion strategy presented a better generalization performance and accuracy rates based on latent variables selected by PLS-DA than single analytical method and low-level data fusion strategy. Accuracy rates were almost 100% when both of the PLS-DA and SVM-GS were employed for classifying samples picked from southeast and northwest districts based on mid-level dataset. For samples collected from central of Yunnan where was divided into seven categories in this paper, the accuracy rates of training set and test set of PLS-DA and SVM-GS were preferable (>87%). Based on the mid-level data set, both of the classification results of PLS-DA and SVM-GS presented satisfying accuracy for 177 samples. Additionally, as small as possible parameters showed in mid-level data set, it suggested that this method was robust and generalized. Therefore, the comprehensive method was established for the origin traceability of wild P. Polyphylla Smith var. yunnanensis, which is meaningful for the quality control of herbal medicines.