In Situ Water Quantification in Natural Deep Eutectic Solvents Using Portable Raman Spectroscopy

Raman spectroscopy is a label-free, non-destructive, non-invasive analytical tool that provides insight into the molecular composition of samples with minimum or no sample preparation. The increased availability of commercial portable Raman devices presents a potentially easy and convenient analytical solution for day-to-day analysis in laboratories and production lines. However, their performance for highly specific and sensitive analysis applications has not been extensively evaluated. This study performs a direct comparison of such a commercially available, portable Raman system, with a research grade Raman microscope system for the analysis of water content of Natural Deep Eutectic Solvents (NADES). NADES are renewable, biodegradable and easily tunable “green” solvents, outcompeting existing organic solvents for applications in extraction from biomass, biocatalysis, and nanoparticle synthesis. Water content in NADES is, however, a critical parameter, affecting their properties, optimal use and extraction efficiency. In the present study, portable Raman spectroscopy coupled with Partial Least Squares Regression (PLSR) is investigated for rapid determination of water content in NADES samples in situ, i.e., directly in glassware. Three NADES systems, namely Betaine Glycerol (BG), Choline Chloride Glycerol (CCG) and Glucose Glycerol (GG), containing a range of water concentrations between 0% (w/w) and 28.5% (w/w), were studied. The results are directly compared with previously published studies of the same systems, using a research grade Raman microscope. PLSR results demonstrate the reliability of the analysis, surrendering R² values above 0.99. Root Mean Square Errors Prediction (RMSEP) of 0.6805%, 0.9859% and 1.2907% w/w were found for respectively unknown CCG, BG and GG samples using the portable device compared to 0.4715%, 0.3437% and 0.7409% w/w previously obtained by analysis in quartz cuvettes with a Raman confocal microscope. Despite the relatively higher values of RMSEP observed, the comparison of the percentage of relative errors in the predicted concentration highlights that, overall, the portable device delivers accuracy below 5%. Ultimately, it has been demonstrated that portable Raman spectroscopy enables accurate quantification of water in NADES directly through glass vials without the requirement for sample withdrawal. Such compact instruments provide solvent and consumable free analysis for rapid analysis directly in laboratories and for non-expert users. Portable Raman is a promising approach for high throughput monitoring of water content in NADES that can support the development of new analytical protocols in the field of green chemistry in research and development laboratories but also in the industry as a routine quality control tool.     

应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

以普通玉米籽粒为试验材料,在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上,应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型．试验结果表明,基于11个特征波长所建立的校正模型,其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0.9279和0.9390,与全光谱数据所建立的预测模型相比,在预测精度上均有所改善,表明应用遗传算法和PLS进行光谱特征选择,能获得更简单和更好的模型,为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径．     

多种分子光谱快速分析黄芩中的黄芩苷含量

为考察根据不同类型光谱信息进行黄芩质量快速分析的适应性,采用高效液相色谱(HPLC)法测定了73批黄芩样品中的黄芩苷含量并作为y值,以各样品的近红外、紫外-可见光谱及包含紫外、可见及近红外的多源复合光谱信息作为x值;根据各类光谱信息分别采用偏最小二乘回归(PLSR)与K最近邻样本保形映射(KNN-KSR)方法进行样品中黄芩苷的预测,根据验证集样本真实值与预测值的均方根偏差(RMSEP)、平均相对误差(MRE)与相关系数(R)评价预测精度。结果表明,采用KNN-KSR方法根据各类光谱信息预测黄芩苷时,各项指标均优于PLSR方法的结果;其中基于近红外光谱对黄芩苷的分析结果最好,紫外-可见光谱次之,基于多源复合光谱信息对黄芩苷的预测误差最大,但其MRE仍在6%以下,可满足工业分析的精度要求。由于多源复合光谱仪具有体积小、重量轻、成本低及便携等优点,通过优化仪器波长范围及建模方法,有望改进该仪器的分析精度,使之适应更多药材现场采购的快速检测及后续产品的质量分析与监控需求。     

The use of FT-NIR for API content assay in organic solvent media: a single calibration for multiple downstream processing streams

The use of near infrared spectroscopy (NIRS) in downstream solvent based processing steps of an active pharmaceutical ingredient (API) is reported. A single quantitative method was developed for API content assessment in the organic phase of a liquid–liquid extraction process and in multiple process streams of subsequent concentration and depuration steps. A new methodology based in spectra combinations and variable selection by genetic algorithm was used with an effective improvement in calibration model prediction ability. Root mean standard error of prediction (RMSEP) of 0.05 in the range of 0.20–3.00% (w/w) was achieved. With this method, it is possible to balance the calibration data set with spectra of desired concentrations, whenever acquisition of new spectra is no longer possible or improvements in model's accuracy for a specific selected range are necessary. The inclusion of artificial spectra prior to genetic algorithms use improved RMSEP by 10%. This method gave a relative RMSEP improvement of 46% compared with a standard PLS of full spectral length.     

Non-Destructive Analysis of Chlorpheniramine Maleate Tablets and Granules by Chemometrics-Assisted Attenuated Total Reflectance Infrared Spectroscopy

Non-destructive analysis of chlorpheniramine maleate (CPM), pharmaceutical tablets, and granules was conducted by chemometrics-assisted attenuated total reflectance infrared spectroscopy (ATR-IR). For tablets, an optimum PLSR model with eight latent factors was obtained from area-normalized and standard normal variate (SNV) pretreated ATR-IR spectral data with correlation coefficients (R²) of calibration and cross-validation of 0.9716 and 0.9602, respectively. The model capability for the 42 test set samples was proven with R² between the reference and model prediction values of 0.9632, and a root-mean-square error of prediction (RMSEP) of 1.7786. The successive PLSR model for granules was constructed from SNV and first derivative pretreated ATR-IR spectral data with two latent factors and correlation coefficients (R²) of calibration and cross-validation of 0.9577 and 0.9450, respectively.     

基于双脉冲LIBS结合偏最小二乘回归的稀土元素定量分析

激光诱导击穿光谱(LIBS)是一种以激光为激发源的等离子体发射光谱分析技术,已有将其用于稀土元素的定量分析研究,但由于稀土矿基体差异大、元素含量低,定量分析灵敏度和准确度仍有待提高。通过使用单激光分束构造双脉冲LIBS系统,并结合偏最小二乘回归(PLSR)算法实现对稀土矿石样品中的稀土元素La、Dy、Yb和Y的定量分析。结果表明,双脉冲LIBS结合PLSR可建立更加稳定的定标模型,与常规基本定标法相比,La、Dy、Yb和Y元素的相对均方根预测误差(RMSEP)从0.0061 %、0.0037%、0.0045%、0.0280 %降低至0.0044%、0.0016%、0.0029%、0.0134%,平均相对预测误差(AREP)从10.88%、15.27%、6.42%、17.20%降低至6.67%、3.62%、4.10%、7.98%。因此,双脉冲LIBS结合PLSR方法可以有效地提高LIBS对稀土矿石中稀土元素的定量分析能力。     

近红外光谱法快速鉴定油菜杂交种的纯度

建立近红外光谱技术测定油菜杂交种纯度的方法。考察了样品杯类型、光谱预处理方法和波长范围对近红外模型预测性能的影响。结果发现,由不同样品杯采集近红外光谱所建立的校正模型,其预测性能存在较大的差异,旋转杯明显优于安瓿瓶;采用消除常数偏移量对光谱进行预处理能有效地提取光谱信息,选择5 000~8 000 cm–1波数范围作为建模谱区,其包含的有效信息率最高。在最佳条件下建立油菜杂交种纯度的校正模型,其决定系数(R2)为0.980 0,交互验证均方根误差(RMSECV)为0.008 59。利用该模型对预测集进行测定,预期均方根误差(RMSEP)为0.007 59,表明该模型具有很好的预测性能,近红外光谱法用于杂交种纯度的鉴定是可行的。     

基于马氏距离和稀疏矩阵技术的LIBS煤质灰分分析

为了提高激光诱导击穿光谱(LIBS)定量测量煤质的精度问题,先对原始数据进行预处理,包括异常值剔除、基线校正,谱线筛选,再将LIBS与偏最小二乘回归法(PLSR)结合建立定量模型以应用于煤质灰分的分析。结果表明,经过预处理后训练样品的拟合度(R²)从0.9740提高到0.9841,均方根误差(RMSE)从0.9613降低到了0.7527,预测均方根误差(RMSEP)从2.2731降到2.0017,同时平均绝对误差(MAE)和平均相对误差分别从1.9747、0.1094降低到1.5572、0.0757。研究表明,基于马氏距离(MD)的异常数据剔除算法结合基于稀疏矩阵技术的基线估计与降噪算法(BEADS),能够在一定程度上能够改善数据的稳定性和光谱信噪比,有利于提高数据建模的预测精度。     

鱼粉近红外光谱模型传递应用研究

为探讨光栅型与傅里叶变换型近红外分析仪之间模型传递的应用效果,选取国产鱼粉为近红外光谱样本,DS2500F型近红外分析仪为源仪器,MPA型近红外分析仪为目标仪器,采用分段直接校正(PDS)方法实现近红外光谱传递。分别建立水分、粗蛋白质、粗脂肪、蛋氨酸和赖氨酸等组分的预测模型,通过交互验证决定系数(R_cv²)、交互验证标准误差(RMSECV)、马氏距离(MD)、系统偏差(Bias)、预测均方根误差(RMSEP)和相对分析误差(RPD)等参数,多维度评估光谱传递后所建预测模型的效果。结果表明,DS2500F仪器的近红外光谱传递到MPA型仪器时,所建国产鱼粉的水分、粗蛋白质、粗脂肪、蛋氨酸、赖氨酸的预测模型与MPA型仪器原始预测模型各参数对比无显著差异,预测效果基本一致,说明国产鱼粉在DS2500F仪器上的近红外光谱通过传递可以替代MPA型仪器的原始光谱,间接实现了模型传递,且具有良好的适用性和共享性,可提高近红外预测模型的应用效率。     

Direct determination of lycopene content in tomatoes (Lycopersicon esculentum) by attenuated total reflectance infrared spectroscopy and multivariate analysis

Lycopene is a potent antioxidant that has been shown to play critical roles in disease prevention. Efficient assays for detection and quantification of lycopene are desirable as alternatives to time- and labor-intensive methods. Attenuated total reflectance infrared (ATR-IR) spectroscopy was used for quantification of lycopene in tomato varieties. Calibration models were developed by partial least-squares regression (PLSR) using quantitative measures of lycopene concentration from liquid chromatography as reference method. IR spectra showed a distinct marker band at 957 cm(-1) for trans Carbon-Hydrogen (CH) deformation vibration of lycopene. PLSR models predicted the lycopene content accurately and reproducibly with a correlation coefficient (sigma) of 0.96 and standard error of cross-validation <0.80 mg/100 g. ATR-IR spectroscopy allowed for rapid, simple, and accurate determination of lycopene in tomatoes with minimal sample preparation. Results suggest that the ATR-IR method is applicable for high-throughput quantitative analysis and screening for lycopene in tomatoes.     

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.     

多模型共识偏最小二乘法用于近红外光谱定量分析

将多模型共识偏最小二乘法用于近红外光谱定量分析。利用随机抽取的训练子集建立一系列偏最小二乘模型,选取其中性能较好的部分模型作为成员模型,用这些成员模型来预测未知样品。将该方法用于一组生物样本的近红外光谱与样品中人血清白蛋白、γ-球蛋白以及葡萄糖含量之间的建模研究,并与单模型偏最小二乘法了进行比较。结果 PLS对独立测试集中三种组分进行50次重复预测的平均RMSEP分别为0.1066,0.0853和0.1338,RMSEP的标准偏差分别为0.0174,0.0144和0.0416;而本方法重复预测的平均RMSEP分别为0.0715,0.0750和0.0781,RMSEP的标准偏差分别为0.0033,0.2729×10-4和0.0025。     

近红外光谱技术定量分析连作滁菊土壤中的阿魏酸含量

应用近红外光谱(NIRS)技术定量分析连作滁菊土壤样品中阿魏酸的含量.通过标准杠杆值、学生残差和马氏距离判断异常光谱,经二阶导数和Norris平滑滤噪预处理后,在6000~4000 cm-1范围,最佳因子数为7,采用偏最小二乘法(PLS)构建数学模型.结果表明,模型校正集和验证集与高效液相色谱仪(HPLC)测定的参考值之间均呈现良好相关关系,校正相关系数Rc为0.9914,交叉验证相关系数Rcv为0.9935,校正集误差均方根(RMSEC)为0.484,预测误差均方根(RMSEP)为0.539,交叉验证误差均方根(RMSECV)为0.615.研究结果表明,NIRS分析技术能够实现连作土壤中阿魏酸的快速检测,结果准确可靠.     

中药材三七提取液近红外光谱的支持向量机回归校正方法

提出近红外光谱的支持向量机回归校正建模方法.以中药材三七渗漉提取液为实际分析对象,对其近红外光谱数据进行预处理和主成分分析后,用支持向量机回归算法建立人参皂苷R_g1,R_b1和R_d以及三七总皂苷的近红外光谱校正模型.以R_g1,R_b1和R_d的HPLC测定值及三七总皂苷的比色法测定值为参照,将本文方法与偏最小二乘回归和径向基神经网络建模方法相比较,结果表明,本文所建模型的预测准确性优于后两者,可推广应用于中药提取过程的近红外光谱分析.     

Quantitative determination of acyclovir in plasma by near infrared spectroscopy

The aim of this study was to assess the feasibility of near infrared spectroscopy (NIRS) for analysis of acyclovir in plasma. This methodology was based on the direct measurement of the transmission spectra of liquid samples and a multivariate calibration model (partial least squares, PLS) to determine the acyclovir concentration in plasma sample. The PLS calibration set was built on using the spiked samples by mixing different amounts of acyclovir. Concentration of acyclovir in the plasma samples was calculated employing a 6-factors PLS calibration using the spectral information in the range of 6102-5450 cm^− 1. The root mean square errors of prediction (RMSEP) found was 1.21 for acyclovir. The developed PLS-NIRS procedure allows the determination of 120 samples/h does not require any sample pretreatment and avoids waste generation.     

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.     

近红外光谱无损定量分析吡嗪酰胺片

近红外光谱;径向基神经网络;吡嗪酰胺;定量分析     

A Variable Selection Method of Near Infrared Spectroscopy Based on Automatic Weighting Variable Combination Population Analysis

Near-infrared spectroscopy (NIR) is widely used in food quantitative and qualitative analysis. Variable selection technique is a critical step of the spectrum modeling with the development of chemometrics. In this study, a novel variable selection strategy, automatic weighting variable combination population analysis (AWVCPA), is proposed. Firstly, binary matrix sampling (BMS) strategy, which provides each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, the variable frequency (Fre) and partial least squares regression (Reg), two kinds of information vector (IVs), are weighted to obtain the value of the contribution of each spectral variables, and the influence of two IVs of Rre and Reg is considered to each spectral variable. Finally, it uses the exponentially decreasing function (EDF) to remove the low contribution wavelengths so as to select the characteristic variables. In the case of near infrared spectra of beer and corn, yeast and oil concentration models based on partial least squares (PLS) of prediction are established. Compared with other variable selection methods, the research shows that AWVCPA is the best variable selection strategy in the same situation. It has 72.7% improvement comparing AWVCPA-PLS to PLS and the predicted root mean square error (RMSEP) decreases from 0.5348 to 0.1457 on beer dataset. Also it has 64.7% improvement comparing AWVCPA-PLS to PLS and the RMSEP decreases from 0.0702 to 0.0248 on corn dataset.     

Determination of Iridoids in Gentiana rigescens by Infrared Spectroscopy and Multivariate Analysis

Gentiana rigescens is a famous herbal medicine in China for treatment of convulsion, rheumatism, and jaundice. Here, the infrared determination of gentiopicroside, swertiamarin, sweroside, and loganic acid in G. rigescens from different areas and varieties was presented for the first time. Reference information for the iridoids were obtained by high-performance liquid chromatography. Partial least squares was used to characterize the relationship between spectra matrix and concentration vector for the determination of the analytes. For determination of gentiopicroside, the appropriate performance of partial least squares model was acquired with coefficient of determination of calibration and coefficient of determination of prediction values of 0.965 and 0.868. The root mean square error of estimation (RMSEE), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) values were 2.612, 5.292, 5.239?mg g^?1, and 2.701, respectively, based on the first derivative and multiplicative scatter correction. For determination of the total iridoids, the best results were obtained using the coefficient of determination of calibration and coefficient of determination of prediction of 0.943 and 0.834, RMSEE, RMSECV, RMSEP and RPD of 3.896, 7.536, 6.543?mg g^?1 and 2.438, respectively, based on the first derivative. Both models were reliable and robust. The results demonstrated that infrared spectroscopy provided a rapid, low-cost tool to monitor the quality of G. rigescens by the determination of the iridoids.     

茶叶浸出液中农药残留表面拉曼光谱检测与初步定量分析

选取甲基对硫磷和水胺硫磷为研究对象,改良了传统的QuEChERS前处理工艺,以自制纳米金溶胶为增强基底,利用表面增强拉曼光谱(SERS)技术,对茶叶浸出液中的农药残留进行检测。通过比对两种有机磷农药的拉曼特征峰进行定性分析。同时,选取570,1034,1107和1202 cm^-1等拉曼位移附近的特征峰光谱数据,利用微分等数学手段,结合偏最小二乘法(PLSR)建立回归方程,预测样品中农药残留含量。所得预测数值与气相色谱-质谱联用(GC-MS)法检测值对比,验证本方法的可行性与可信度。结果表明:基于SERS技术对上述两种有机磷农药的检出限可达0.05 mg/L;通过数学模型分析建立回归方程,其线性相关系数范围为0.9077~0.9824,预测均方根误差(RMSEP)范围为0.77%~2.68%;利用回归方程得到的预测值与GC-MS检测结果基本接近,相对误差范围-5.16%~9.03%,回收率为81.4%~115.1%,说明可以用SERS技术对茶叶浸出液中的有机磷农药残留进行定性和初步定量分析。