Direct and simultaneous spectrofluorometric determination of naproxen and salicylate in human serum assisted by chemometric analysis

A direct method for the simultaneous determination of naproxen and salicylate in human serum is reported, based on a combination of spectrofluorometric measurements with two multivariate calibration techniques: partial least-squares (PLS-1) and the novel net analyte preprocessing (NAP). The method is rapid, selective and sensitive, and is based on the measurement of the fluorescence spectra of NH₃ alkalinized whole human sera at the excitation wavelength of 315 nm. It can be applied within the ranges of concentrations 50-200 ng ml⁻¹ for naproxen and 100-300 ng ml⁻¹ for salicylate. The employed chemometric techniques have been compared on the basis of the statistical indicators for calibration and validation. Reproducibility and interference studies in abnormal sera have also been carried out.     

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.     

聚谷氨酸发酵过程中ATR-FTIR光谱信号的分数阶基线校正

采用衰减全反射傅里叶变换红外光谱法（ATR-FTIR）,结合多元校正模型对γ-聚谷氨酸（γ-PGA）发酵过程中两种主要底物葡萄糖和谷氨酸钠的浓度进行间接测量,为优化发酵系统控制提供重要的反馈信息。光谱测量中经常出现的基线漂移会严重影响后续多元校正模型的性能,需要采用基线校正算法对光谱进行预处理。现有流行的基线校正算法多数是基于Whittaker Smoother（WS）平滑算法,这些算法均采用整数阶微分对拟合基线进行约束,表达能力有限。针对现有基线校正算法中的整数阶微分自适应性差的问题,利用更加灵活的分数阶微分对基线进行约束,提出了一种基于分数阶的基线校正算法,实现对整数阶基线校正的扩展。总共进行了5个批次的γ-PGA发酵实验,并对不同批次和全部批次的ATR-FTIR光谱数据分别进行了分数阶基线校正,模型的预测精度均得到不同程度的提升。实验结果表明,只有在批次2时,基于整数阶的基线校正效果最好;其他批次的基线校正效果最好时的阶次均为分数阶。这也表明了分数阶微分（包含整数阶微分）对基线的约束更加合理。同时发现全部批次的整体基线校正效果远远差于单一批次的效果,原因可能是各批次发酵光谱的基线是不同的,对不同的批次需要选用不同的阶次以获得最佳的基线校正。此外,γ-PGA发酵样品的ATR-FTIR光谱测量是以蒸馏水为背景,会在3 100~3 600 cm-1波数范围内出现负水峰,形成有害的干扰信号;分数阶基线校正后的光谱表明,分数阶基线校正算法将负的水峰当作基线,在一定程度上进行了消除。综上分析,分数阶基线校正算法不仅扩展了传统整数阶基线校正算法的应用范围,也为消除ATR光谱中负的水峰提供了新的解决思路。     

Chemometric quality control of chromatographic purity

It is common practice in chromatographic purity analysis of pharmaceutical manufacturing processes to assess the quality of peak integration combined by visual investigation of the chromatogram. This traditional method of visual chromatographic comparison is simple, but is very subjective, laborious and seldom very quantitative. For high-purity drugs it would be particularly difficult to detect the occurrence of an unknown impurity co-eluting with the target compound, which is present in excess compared to any impurity. We hypothesize that this can be achieved through Multivariate Statistical Process Control (MSPC) based on principal component analysis (PCA) modeling. In order to obtain the lowest detection limit, different chromatographic data preprocessing methods such as time alignment, baseline correction and scaling are applied. Historical high performance liquid chromatography (HPLC) chromatograms from a biopharmaceutical in-process analysis are used to build a normal operation condition (NOC) PCA model. Chromatograms added simulated 0.1% impurities with varied resolutions are exposed to the NOC model and monitored with MSPC charts. This study demonstrates that MSPC based on PCA applied on chromatographic purity analysis is a powerful tool for monitoring subtle changes in the chromatographic pattern, providing clear diagnostics of subtly deviating chromatograms. The procedure described in this study can be implemented and operated as the HPLC analysis runs according to the process analytical technology (PAT) concept aiming for real-time release.     

icoshift: An effective tool for the alignment of chromatographic data

The Interval Correlation Optimised Shifting algorithm (icoshift) has recently been introduced for the alignment of nuclear magnetic resonance spectra. The method is based on an insertion/deletion model to shift intervals of spectra/chromatograms and relies on an efficient Fast Fourier Transform based computation core that allows the alignment of large data sets in a few seconds on a standard personal computer. The potential of this programme for the alignment of chromatographic data is outlined with focus on the model used for the correction function. The efficacy of the algorithm is demonstrated on a chromatographic data set with 45 chromatograms of 64,000 data points. Computation time is significantly reduced compared to the Correlation Optimised Warping (COW) algorithm, which is widely used for the alignment of chromatographic signals. Moreover, icoshift proved to perform better than COW in terms of quality of the alignment (viz. of simplicity and peak factor), but without the need for computationally expensive optimisations of the warping meta-parameters required by COW. Principal component analysis (PCA) is used to show how a significant reduction on data complexity was achieved, improving the ability to highlight chemical differences amongst the samples.     

宽谱调谐二极管激光吸收光谱的数据预处理研究

二极管激光吸收光谱以温度扫描实现快速宽谱调谐,其原始数据不便于传统的处理及运算,适当的预处理是进行后续数据处理的基础。在分析了温度调谐数据特征的基础上,利用Savitzky-Golay滤波器去除了波长序列中温度检测噪声的干扰,选择单调的波长区间对信号幅值进行三次样条插值,以固定的波长点对应采样点,得到一组位置固定的幅值序列用于后期处理。此方法将两列检测数据合并为一列,相同的起始点和固定的波长位置不仅将多周期平均后的信噪比提升为原始数据的6倍,也使多组数据的比较及评价成为可能,一致的波长分辨率保证了线形检测的精确度,减小了吸收峰的频带宽度,有助于后续的处理、运算以及评价。     

超高效液相色谱-串联质谱法测定水产品中孔雀石绿前处理方法的改进

采用超高效液相色谱-串联质谱法(UPLC-MS/MS)测定水产品中孔雀石绿的残留量,以GB/T 19857-2005检测方法为基础,将前处理步骤进行优化.称取5 g样品,加入质量浓度为100μg/L的内标标准溶液100μL,混匀,加入20 m L乙腈、5 g酸性氧化铝,震荡、离心,取4 m L溶液氮气吹干,用流动相定容至1 m L,过0.22μm滤膜,上机测试.按照标准方法测定标准曲线相关系数r:孔雀石绿为0.999,隐色孔雀石绿为0.999.孔雀石绿的回收率分别为71.7%、95.2%和89.9%,精密度分别为4.47%、4.26%和8.57%.隐色孔雀石绿的回收率分别为93.3%、98.8%和89.7%,精密度分别为8.73%、6.87%和9.71%.试验结果满足相关标准和体系文件的要求.     

Preprocessed barley,rye, and triticale as a feedstock for an integrated fuel ethanol-feedlot plant

Rye, triticale, and barley were evaluated as starch feedstock to replace wheat for ethanol production. Preprocessing of grain by abrasion on a Satake mill reduced fiber and increased starch concentrations in feedstock for fermentations. Higher concentrations of starch in flours from preprocessed cereal grains would increase plant throughput by 8–23% since more starch is processed in the same weight of feedstock. Increased concentrations of starch for fermentation resulted in higher concentrations of ethanol in beer. Energy requirements to produce one L of ethanol from preprocessed grains were reduced, the natural gas by 3.5–11.4%, whereas power consumption was reduced by 5.2–15.6%.     

基于LSTM的浮选钼精矿品位软测量模型

战略性稀有金属钼矿品位低,组分复杂、嵌布粒度细等特点,其有价金属分离回收难。浮选作为微细粒钼矿分离回收的主要选矿方法之一,其浮选钼精矿品位一直是选厂的关键性产品指标。国内大多数选厂采取轮班制采样,人工化验得到精矿品位结果,但此方式严重滞后于浮选工艺,难以满足对生产过程进行实时监测和操作指导。LSTM是一种特殊的循环神经网络,引入门机制有效的传递或选择性遗忘长时间序列中的信息,解决RNN中的长期依赖、梯度消失和爆炸问题。本文分析整理东坡选厂中各平台源数据,结合选厂浮选工艺及机理,筛选出多个影响浮选钼精矿品位的变量作为模型输入;将输入变量进行异常值判定,缺失值填充和数据降噪等数据预处理,建立高质量浮选钼精矿品位数据库;软测量模型采用PyCharm软件编码,使用BatchNorm批量规范化处理样本数据,加入Dropout正则化防止过拟合,建立基于LSTM的浮选钼精矿品位软测量模型,通过前向传播算法更新神经网络结构参数,并于Linear模型和CNN模型的预测性能指标结果比较。结果表明：基于LSTM的浮选钼精矿品位软测量模型预测准确度高,样本数据误差波动平稳,浮动范围小,模型泛化能力强,模型平均绝对百分比误差MAPE为1.13%,均方根误差RMSE为0.7049%,决定系数R2为0.8763,实现了浮选钼精矿品位的在线预测。     

Visualized attribute analysis approach for characterization and quantification of rice taste flavor using electronic tongue

This paper deals with a novel visualized attributive analysis approach for characterization and quantification of rice taste flavor attributes (softness, stickiness, sweetness and aroma) employing a multifrequency large-amplitude pulse voltammetric electronic tongue. Data preprocessing methods including Principal Component Analysis (PCA) and Fast Fourier Transform (FFT) were provided. An attribute characterization graph was represented for visualization of the interactive response in which each attribute responded by specific electrodes and frequencies. The model was trained using signal data from electronic tongue and attribute scores from artificial evaluation. The correlation coefficients for all attributes were over 0.9, resulting in good predictive ability of attributive analysis model preprocessed by FFT. This approach extracted more effective information about linear relationship between electronic tongue and taste flavor attribute. Results indicated that this approach can accurately quantify taste flavor attributes, and can be an efficient tool for data processing in a voltammetric electronic tongue system.