卷烟滤棒中三醋酸甘油酯含量的现场快速检测研究

为了提高卷烟滤棒中三醋酸甘油酯含量的检测效率,该文通过使用手持式近红外光谱仪,结合粒子群优化－极限学习机（PSO－ELM）回归算法建立了三醋酸甘油酯含量的定量预测模型,并与偏最小二乘回归（PLSR）和极限学习机回归（ELMR）进行了比较。实验结果表明：相比于PLSR和ELMR模型,所建立的PSO－ELM预测模型的决定系数R2为0.921 2,远高于PLSR预测模型的0.860 4和ELMR预测模型的0.877 2;同时,使用PSO－ELM模型的预测均方根误差（RMSEP）为0.392 12,小于PLSR预测模型的0.497 72和ELMR预测模型的0.470 18。以上实验结果表明,所建立的近红外光谱定量模型能够应用于卷烟滤棒中三醋酸甘油酯含量的快速准确测量,为实现滤棒中三醋酸甘油酯含量的现场快速检测提供了良好的技术参考。     

基于主成分回归分析和偏最小二乘回归法的樱桃糖度检测的研究

选用烟台大樱桃为研究对象,采用便携式光谱仪对樱桃糖度进行检测,利用极差标准归一化方法和小波滤波,对其可见-近红外光谱数据进行预处理,分别运用主成分回归分析(PCR)法和偏最小二乘回归(PLSR)法建立了樱桃糖度定量分析模型,并对两种模型进行了比较。实验结果表明:在600~1 100nm波段范围内对樱桃糖度进行检测是可行的,并且PLSR模型的性能优于PCR模型。     

基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用

提出一种基于粒子群算法的最小二乘支持向量机(PSO-LS-SVM)方法,用于建立红花提取过程关键质控指标的定量分析模型.近红外光谱数据经波段选择、预处理和主成分分析(降维)后,利用粒子群优化(PSO)算法对最小二乘支持向量机算法中的参数进行优化,然后使用最优参数建立固含量和羟基红花黄色素A(HSYA)浓度的定量校正模型.将校正结果与偏最小二乘法回归(PLSR)和BP神经网络(BP-ANN)比较,并将所建的3个模型用于红花提取过程未知样本的预测.结果表明,BP-ANN校正结果优于PSO-LS-SVM和PLSR,但是对验证集和未知样品集的预测能力较差,而PSO-LS-SVM和PLSR模型的校正、验证结果相近,相关系数均大于0.987,RMSEC和RMSEP值相近且小于0.074,RPD值均大于6.26,RSEP均小于5.70％.对于未知样品集,pSO-LS-SVM模型的RPD值大于8.06,RMSEP和RSEP值分别小于0.07％和5.84％,较BP-ANN和PLSR模型更低.本研究所建立的PSO-LS-SVM模型表现出较好的模型稳定性和预测精度,具有一定的实践意义和应用价值,可推广用于红花提取过程的近红外光谱定量分析.     

樱桃含糖量的无损检测实验研究

利用便携式可见-近红外光谱仪,研究了600~1100 nm波段内无损检测樱桃含糖量的可行性。以烟台大樱桃为研究对象,采集了每个樱桃的含糖量。利用小波去噪法对光谱数据进行预处理,并用主成分回归分析法(PCR)建立了樱桃含糖量定量分析模型。实验结果为:多尺度小波去噪法滤除了原始光谱中的噪声,同时保留了原始光谱的主要信息;所建立的主成分回归定量分析模型的校正样本集的相关系数(R)为0.9394,校正均方根误差(RMSEC)为0.1384;预测样本集的相关系数(R)为0.9071,预测均方根误差(RMSEP)为0.1495。同时与偏最小二乘回归法(PLSR)所建模型得出的预测结果相差很小。研究表明:应用便携式光谱技术在600~1100 nm范围内无损检测樱桃含糖量具有可行性,为樱桃内部品质的野外在线动态检测提供了理论依据。     

近红外光谱法快速测定再造烟叶成品小片中主要化学成分

提出了近红外光谱法快速测定再造烟叶成品小片中烟碱、总糖、还原糖、总氮、钾、氯等6种主要化学成分的方法。直接采集再造烟叶成品小片,结合偏最小二乘回归算法建立了近红外光谱的分析模型。结果表明:再造烟叶成品小片的近红外光谱能真实、有效地表征待测样品的内在化学物质组成与含量信息;除总氮外,其余5种成分的再造烟叶成品小片近红外光谱分析模型的相关系数均大于0.90;烟碱、总糖、还原糖、总氮、钾、氯等6种成分的预测误差分别为0.024 3,0.399 1,0.270 3,0.059 9,0.050 3,0.031 1。小片光谱分析模型效果与粉末光谱模型较接近,可以替代粉末模型用于再造烟叶成品小片化学成分含量的测定。     

用人工神经网络-近红外光谱法测定冬虫夏草中的甘露醇

提出了用近红外漫反射光谱技术快速分析发酵冬虫夏草菌粉中甘露醇含量的新方法。采用比色法测定样品中的甘露醇，其含量范围为8．082％-14．548％。在7501．7-6097．8cm^-1与5453．7-4246．5cm^-1波段，分别采用PCR、PLSR和BP神经网络方法建立了样品近红外光谱的一阶微分光谱与其甘露醇含量之间的相关模型。BP神经网络模型的内部交叉验证误差均方根为0．475，预测误差均方根为0．608，均优于PCR和PLSR的处理结果。这表明，BP神经网络法对非线性检测对象具有较好的建模效果，可用于中药近红外光谱分析的非线性校正。     

近红外光谱法快速检测何首乌炮制过程中多糖含量的研究

该研究基于近红外光谱技术建立了何首乌在蒸制过程中多糖含量变化的快速定量模型。采用蒽酮-浓硫酸法测定了不同蒸制时间何首乌的多糖含量并结合采集的近红外光谱数据建立模型,以校正集相关系数(R²_c)、预测集相关系数(R²_p)、交叉验证集均方根误差(RMSECV)和预测集均方根误差(RMSEP)作为评价指标,考察了不同预处理方法和变量筛选方法的效果。结果显示,随着蒸制时间的延长,何首乌多糖含量先上升后下降并趋于平稳,采用平滑+变量标准化+随机蛙跳变量筛选建立的偏最小二乘法回归模型的R²_c=0.96,RMSECV=0.74,R²_p=0.96,RMSEP=0.28;外部预测相对偏差小于3.0%。所建立的多糖含量定量模型质量较高,预测能力较强,可为探索何首乌炮制工艺标准化及质量评价提供参考。     

原料乳中蛋白质与脂肪的近红外光谱快速定量研究

本文对快速无损检测原料乳中蛋白质与脂肪含量的近红外光谱(NIRS)技术进行了研究。对采集的250组蛋白质及脂肪含量不同的原料乳近红外光谱进行马氏距离(Mahalanobis Distance)剔除异常光谱,结合主成分分析(Principal Component Analysis,PCA),筛选出最佳建模光谱区间,采用反向传播神经网络(Back Propagation Neutral Network,BPNN)建立原料乳中蛋白含量与脂肪含量的定量模型,获得了较好的预测结果,预测模型R2分别为0.9883、0.9878,预测均方根差(RMSEP)分别为1.83%、1.85%。研究结果表明,通过合理选择光谱范围及建模方法,可得到预测精度与稳定性均较高的近红外光谱定量模型,适用于原料乳中蛋白质与脂肪含量的测定。     

声光可调-近红外光谱技术分析烟草主要化学成分

建立了声光可调-近红外光谱方法(AOTF-NIR)检测烟草主要化学成分的方法。应用AOTF-NIR光谱仪测定了不同地区、不同等级烟草样品的近红外光谱,用Unscrambler(定量分析软件将光谱与对应的化学成分值相关联,建立了烟草中总糖、还原糖、总烟碱和钾的回归模型。用这些模型对未知样品进行了预测。总糖、还原糖、总烟碱和钾模型预测的平均相对标准偏差分别为2.71%、3.13%、4.04%和6.42%。     

近红外光谱法快速测定化橘红中柚皮苷的含量

利用近红外光谱快速测定技术,建立化橘红药材中柚皮苷含量测定模型;用高效液相色谱法(HPLC)测定44批不同产地的化橘红中柚皮苷的含量作为参考值,使用2350～2199 nm、1750～1600 nm和1301～1150 nm波长范围的近红外光谱检测技术,利用偏最小二乘回归分析结合交叉验证法,建立快速测定化橘红中柚皮苷含量的模型;结果表明,所建立的校正模型的相关系数和内部交叉验证均方差分别为:R~2=0.978,RMSECV=0.997,预测结果良好。该法的建立证明了近红外光谱技术应用于化橘红药材中柚皮苷含量测定的可行性;近红外光谱结合偏最小二乘法(NIR-PLS)可以快速评估化橘红药材中柚皮苷的含量,可以应用于大批化橘红药材中柚皮苷的含量测定。     

In Situ Determination of Nitrate in Water Using Fourier Transform Mid-Infrared Attenuated Total Reflectance Spectroscopy Coupled with Deconvolution Algorithm

Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy has been used to determine the nitrate content in aqueous solutions. However, the conventional water deduction algorithm indicated considerable limits in the analysis of samples with low nitrate concentration. In this study, FTIR-ATR spectra of nitrate solution samples with high and low concentrations were obtained, and the spectra were then pre-processed with deconvolution curve-fitting (without water deduction) combined with partial least squares regression (PLSR) to predict the nitrate content. The results show that the typical absorption of nitrate (1200−1500 cm⁻¹) did not clearly align with the conventional algorithm of water deduction, while this absorption was obviously observed through the deconvolution algorithm. The first principal component of the spectra, which explained more than 95% variance, was linearly related to the nitrate content; the correlation coefficient (R²) of the PLSR model for the high-concentration group was 0.9578, and the ratio of the standard deviation of the prediction set to that of the calibration set (RPD) was 4.22, indicating excellent prediction performance. For the low-concentration group model, R² and RPD were 0.9865 and 3.15, respectively, which also demonstrated significantly improved prediction capability. Therefore, FTIR-ATR spectroscopy combined with deconvolution curve-fitting can be conducted to determine the nitrate content in aqueous solutions, thus facilitating rapid determination of nitrate in water bodies with varied concentrations.     

On multivariate calibration with unlabeled data

In principal component regression (PCR) and partial least‐squares regression (PLSR), the use of unlabeled data, in addition to labeled data, helps stabilize the latent subspaces in the calibration step, typically leading to a lower prediction error. For using unlabeled data in PLSR, a non‐sequential approach based on optimal filtering (OF) has been proposed in the literature. In this work, a sequential version of the OF‐based PLSR and a PCA‐based PLSR (PLSR applied to PCA‐preprocessed data) are proposed. It is shown analytically that the sequential version of the OF‐based PLSR is equivalent to that of PCA‐based PLSR, which leads to a new interpretation of OF. Simulated and experimental data sets are used to point out the usefulness and pitfalls of using unlabeled data. Unlabeled data can replace labeled data to some extent, thereby leading to an economic benefit. However, in the presence of drift, the use of unlabeled data can result in an increase in prediction error compared to that obtained with a model based on labeled data alone. Copyright © 2011 John Wiley & Sons, Ltd.     

Development and validation of methods for the determination of miokamycin in various pharmaceutical preparations by use of near infrared reflectance spectroscopy

New methods for the determination of the nominal content of miokamycin in three commercial pharmaceutical preparations available in many different forms are proposed. Solid samples, grinding of which is the sole pretreatment required, are analysed by near infrared (NIR) spectroscopy, using a fibre-optic probe. The active principle is quantified by partial least-squares regression (PLSR). The three proposed methods were validated with a view to their use as control methods; the selectivity of the method, and the repeatability, intermediate precision, accuracy, linearity and robustness of each PLSR calibration model used were determined. The relative standard error of prediction (RSEP) was < 1.5% and the validation results testify to the suitability of the proposed methods.     

Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine

It is important to monitor quality of tobacco during the production of cigarette. Therefore, in order to scientifically control the tobacco raw material and guarantee the cigarette quality, fast and accurate determination routine chemical of constituents of tobacco, including the total sugar, reducing sugar, Nicotine, the total nitrogen and so on, is needed. In this study, 50 samples of tobacco from different cultivation areas were surveyed by near-infrared (NIR) spectroscopy, and the spectral differences provided enough quantitative analysis information for the tobacco. Partial least squares regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The quantitative analysis models of 50 tobacco samples were studied comparatively in this experiment using PLSR, ANN, radial basis function (RBF) SVM regression, and the parameters of the models were also discussed. The spectrum variables of 50 samples had been compressed through the wavelet transformation technology before the models were established. The best experimental results were obtained using the (RBF) SVM regression with gamma=1.5, 1.3, 0.9, and 0.1, separately corresponds to total sugar, reducing sugar, Nicotine, and total nitrogen, respectively. Finally, compared with the back propagation (BP-ANN) and PLSR approach, SVM algorithm showed its excellent generalization for quantitative analysis results, while the number of samples for establishing the model is smaller. The overall results show that NIR spectroscopy combined with SVM can be efficiently utilized for rapid and accurate analysis of routine chemical compositions in tobacco. Simultaneously, the research can serve as the technical support and the foundation of quantitative analysis of other NIR applications.     

Chemometric methods in energy dispersive X-ray fluorescence

Chemometric methods like principal component regression (PCR) are an excellent tool for the determination of matrix parameters from scattered radiation. PCR is used for the determination of carbon, hydrogen and oxygen from water and oil-based samples. This information is used in combination with fundamental parameters to determine zink in liquid samples. The method allows an accurate prediction of element concentrations in strong varying matrices.     

Combining synchronous fluorescence spectroscopy with multivariate methods for the analysis of petrol-kerosene mixtures

Synchronous fluorescence spectroscopy (SFS) is a rapid, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. The present study demonstrates the use of SFS and multivariate methods for the analysis of petroleum products which is a complex mixture of multiple fluorophores. Two multivariate techniques principal component regression (PCR) and partial least square regression (PLSR) have been successfully applied for the classification of petrol-kerosene mixtures. Calibration models were constructed using 35 samples and their validation was carried out with varying composition of petrol and kerosene in the calibration range. The results showed that the method could be used for the estimation of kerosene in kerosene-mixed petrol. The model was found to be sensitive, detecting even 1% contamination of kerosene in petrol.     

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.     

Non-invasive determination of ethanol, propylene glycol and water in a multi-component pharmaceutical oral liquid by direct measurement through amber plastic bottles using Fourier transform near-infrared spectroscopy

Fourier transform near-infrared (FT-NIR) spectroscopy was used to quantify rapidly the ethanol (34-49% v/v), propylene glycol (20-35% v/v) and water (11-20% m/m) contents within a multi-component pharmaceutical oral liquid by measurement directly through the amber plastic bottle packaging. Spectra were collected in the range 7302-12,000 cm-1 and calibration models set-up using partial least-squares regression (PLSR) and multiple linear regression. Reference values for the three components were measured using capillary gas chromatography (ethanol and propylene glycol) and Karl Fischer (water) assay procedures. The calibration and test sets consisted of production as well as laboratory batches that were made to extend the concentration ranges beyond the natural production variation. The PLSR models developed gave standard errors of prediction (SEP) of 1.1% v/v for ethanol, 0.9% v/v for propylene glycol and 0.3% m/m for water. For each component the calibration model was validated in terms of: linearity, repeatability, intermediate precision and robustness. All the methods produced statistically favourable outcomes. Ten production batches independent of the calibration and test sets were also challenged against the PLSR models, giving SEP values of 1.3% v/v (ethanol), 1.0% v/v (propylene glycol) and 0.2% m/m (water). NIR transmission spectroscopy allowed all three liquid constituents to be non-invasively measured in under 1 min.     

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。