Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

Multicommutation-NIR determination of Hexythiazox in pesticide formulations

A multicommutated flow-system was designed in order to increase analytical throughput and for controlling thermal effects on the NIR spectra for determination of Hexythiazox in pesticide formulations. An on-line standard addition was carried out showing the versatility and repeatability of multicommutation for the on-line mixing and dilution of solutions. Results obtained for commercial samples were statistically comparable with those obtained by an HPLC-reference method. Multicommutation-NIR allows the analysis of 52 samples per hour, in front of the 30 samples per hour analyzed by the NIR-batch procedure and the 7 samples per hour analyzed by HPLC-reference method.     

Improving the robustness of a partial least squares (PLS) model based on pure component selectivity analysis and range optimization: case study for the analysis of an etching solution containing hydrogen peroxide

Pure component selectivity analysis (PCSA) was successfully utilized to enhance the robustness of a partial least squares (PLS) model by examining the selectivity of a given component to other components. The samples used in this study were composed of NH₄OH, H₂O₂ and H₂O, a popular etchant solution in the electronic industry. Corresponding near-infrared (NIR) spectra (9000-7500 cm⁻¹) were used to build PLS models. The selective determination of H₂O₂ without influences from NH₄OH and H₂O was a key issue since its molecular structure is similar to that of H₂O and NH₄OH also has a hydroxyl functional group. The best spectral ranges for the determination of NH₄OH and H₂O₂ were found with the use of moving window PLS (MW-PLS) and corresponding selectivity was examined by pure component selectivity analysis. The PLS calibration for NH₄OH was free from interferences from the other components due to the presence of its unique NH absorption bands. Since the spectral variation from H₂O₂ was broadly overlapping and much less distinct than that from NH₄OH, the selectivity and prediction performance for the H₂O₂ calibration were sensitively varied depending on the spectral ranges and number of factors used. PCSA, based on the comparison between regression vectors from PLS and the net analyte signal (NAS), was an effective method to prevent over-fitting of the H₂O₂ calibration. A robust H₂O₂ calibration model with minimal interferences from other components was developed. PCSA should be included as a standard method in PLS calibrations where prediction error only is the usual measure of performance.     

A simple and direct isolation of whey components from raw milk by gel filtration chromatography and structural characterization by Fourier transform Raman spectroscopy

A simple and economical method to isolate whey protein from fresh raw milk is developed by serial defatting, casein eliminating, lactose removing, and separating by gel filtration chromatography. Four major whey components, including immunoglobulin (Ig), bovine serum albumin (BSA), β-lactoglobulin (β-Lg) and -lactalbumin (-Lac), and a non-protein of low molecular mass (1.7 kDa) but strong absorbance at 280 nm, are detected simultaneously. The small non-protein molecule is rich in aromatic amino acids and thiol groups as supported by the structural characterization with near infrared Fourier transform Raman spectroscopy (FT-Raman). FT-Raman results show that the secondary structure of Ig is dominated by anti-parallel β-pleated sheet; BSA is mainly in -helix; both β-form and unordered structure are important in β-Lg; while -Lac is mostly in -helix coupling with random coil. Differences in the Raman profile for each whey component reflect their intrinsic compositional differences and distinct spatial arrangement. The S–S linkages diverging around 510–540 cm⁻¹ indicate that the conformation of disulfide bonds in each whey components is different, which may be responsible for their diversified behaviors in solubility, rheological and functional properties.     

油页岩含油率近红外光谱原位分析方法研究

为实现油页岩含油率的原位检测,采用便携式近红外光谱分析技术,针对吉林扶余油页岩基地2号钻井的66个岩芯样品开展了原位检测的分析建模方法研究。采用自制便携式近红外光谱仪器获得反射率、吸光度、K-M函数三种数据形式光谱数据,结合主成分-马氏距离(PCA-MD)剔除异常样品、无信息变量消除法(UVE)波长筛选及二者组合的四种建模数据优化方法,采用相同的数据预处理方法进行偏最小二乘(PLS)和反向传播神经网络(BPANN)两种方法的建模分析研究,确定最佳分析模型及方法。结果表明(1)不论是否采用四种不同的数据优化方法,两种建模方法所用建模数据库适合采用反射率或K-M函数的光谱数据形式;(2)两种建模方法,采用四种不同的数据优化方法,对相同数据库建模精度的影响不同：采用PLS建模方法、以PCA-MD和UVE+ PCA-MD两种方法进行数据优化、可以提高K-M函数光谱数据形式数据库的建模分析精度,采用BPANN建模方法、以UVE、PCA-MD 与UVE组合的 三种方法进行数据优化、对三种数据形式数据库的建模精度均有所提高;(3)除以反射率光谱数据并进行PCA-MD数据优化外,采用BPANN方法的建模精度好于PLS法。其中采用反射率光谱数据形式、只进行UVE数据优化外的BPANN建模精度最高,预测相关系数为0.92、标准偏差为0.69%。     

光谱对煤粉发热量检测系统的研究

在现有近红外检测技术的基础上,设计了一种基于近红外的煤粉发热量检测系统。选取100个煤粉样品建立煤粉发热量的近红外模型,利用验证集的50个样本对模型的精度和稳定性进行了验证分析,结果表明:煤粉发热量的预测值与真实值的相关系数达到0.995 8,相对偏差小于2%。系统具有良好的预测精度和稳定性,能够满足对煤粉发热量的快速检测的需求,而且该系统体积小、结构简单、操作方便,具有很好的可移植性。     

LS-SVM的梨可溶性固形物近红外光谱检测的特征波长筛选

为提高梨可溶性固形物含量(soluble solids content,SSC)的近红外光谱模型的精度和稳定性,以160个梨样品为实验对象,分别对原始光谱、多元散射校正(MSC)和标准正态变量变换(SNV)处理后的光谱,经无信息变量消除算法(UVE)挑选后,再结合遗传算法(GA)和连续投影算法(SPA),筛选梨可溶性固形物的近红外光谱特征波长。将筛选后的波长作为输入变量建立梨可溶性固形物的最小二乘支持向量机(LS-SVM)模型。结果表明经过SNV-UVE-GA-SPA从全波段3112个波长中筛选出的30个特征波长建立的梨可溶性固形物LS-SVM模型效果最好,该模型的预测集相关系数(R_p)和预测均方根误差(RMSEP)分别为0.956和0.271。该模型简单可靠,预测效果好,能满足梨的可溶性固形物含量的快速检测,为在线检测和便携式设备开发提供了理论基础。     

小麦品质近红外检测系统的设计与试验研究

为实现快速无损地检测小麦品质设计了基于光栅技术的近红外检测系统,测试了该系统的准确性、重复性和稳定性,选取MPA光谱仪为参比仪器,分别采集56份小麦样品的光谱,建立偏最小二乘回归模型并验证。该系统的四个模型的决定系数R2分别为92.38%,93.48%,93.16%,94.44%,交叉验证标准差RESECV为0.405,0.374,0.383,0.346,相对分析误差RPD为3.62,3.39,3.82,4.24;预测集验证模型的R2为96.97%,94.22%,96.62%,96.34%,预测标准差RMSEP为0.221,0.305,0.233,0.243。MPA光谱仪的建模结果R2 为95.99%,RESECV 为0.293,RDP为5;预测集验证模型的R2为98.31%,RMSEP为0.165。实验表明：小麦品质近红外检测系统所得模型具有良好的预测性,稳定性和重复性;所得光谱波长与吸光度具有重现性;其模型对平均光谱的预测效果优于单张光谱;该系统工作稳定,性能优良,可应用于小麦品质质量检测。     

软玉中结构水类型和近红外光谱解析

利用傅里叶变换近红外光谱分析技术, 对源自国内外不同产地的软玉进行了测试与分析。结果表明在7 000～7 400 cm-1波数范围内,部分产地的软玉中出现四个由羟基倍频振动致近红外吸收谱带,分别归属OH(Mg MgMg),OH(MgMgFe2+),OH(MgFe2+Fe2+),OH(Fe2+Fe2+Fe2+)的倍频振动所致, 并与软玉中透闪石矿物晶体结构中M1和M3位置的Mg2+,Fe2+占位有关。随着透闪石中Fe2+/(Fe2++ Mg2+)比值的增大,亦可导致软玉中羟基NIR谱带的进一步分裂,谱带数目和强度增加,并向低波数方向位移。文中对软玉中羟基NIR光谱的产地鉴别意义一并给予了讨论。     

NIR‐Triggered Release of Nitric Oxide with Upconversion Nanoparticles Inhibits Platelet Aggregation in Blood Samples

There is a great challenge to overcome the limitation of tissue penetration depth, while maximizing the benefit of light‐triggered biochemical cascades in a well‐defined mode simultaneously. Here, a new method of near‐infrared (NIR) light‐triggered release of nitric oxide (NO) by developing upconversion nanoparticles (UCNPs)‐based conjugate chemistry is reported. As the key nanotransducer in the design, core–shell‐structured UCNPs are encapsulated with a layer of SiO₂ and then covalently linked with a potent NO‐releasing donor (S‐nitroso‐N‐acetyl‐dl ‐penicillamine, SNAP). It is featured with highly localized breakage of chemical bonds of SNAP molecules by NIR–UV upconversion, enabling simultaneous NO release in a light dosage‐dependent manner. The biological effects of NO releasing are demonstrated by cellular imaging and inhibition of platelet aggregation from blood samples. This work provides a flexible and robust platform to generate cell‐signaling gas molecules trigged by NIR laser with deep tissue penetration.