Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

Quality Evaluation of Ephedrae herba by near Infrared Spectroscopy

ABSTRACT

A novel method was developed for the quality control of Ephedrae herba by near-infrared (NIR) spectroscopy. First, qualitative models established by discriminant analysis and support vector machine were used for the preliminary screening of unqualified samples of E. herba. Then quantitative models of ephedrine and the total alkali (ephedrine and pseudoephedrine) were established by partial least squares regression and particle swarm optimization based least square support vector machine. The contents of test samples were predicted by the established NIR quantitative models. As a result, the accuracies of unqualified identification were 98.9% by discriminant analysis and 100% by support vector machine. The performance of the particle swarm optimization based least square support vector machine models were better than the partial least squares regression models. The correlation coefficients were both more than 0.98 and relative standard errors of calibrations were less than 9% in the calibration sets of particle swarm optimization based least square support vector machine models. As for the test sets, the correlation coefficients were both more than 0.93 and the relative standard errors of prediction were less than 13%, indicating satisfactory predicted results. All of these results demonstrated that NIR spectroscopy may be a powerful tool for the quality control of E. herba.     

Characterization of Paris polyphylla var. yunnanensis by Infrared and Ultraviolet Spectroscopies with Chemometric Data Fusion

Paris polyphylla var. yunnanensis has been used for its anti-tumor, anthelmintic, and hemostatic properties. In this investigation, Fourier transform infrared and ultraviolet spectroscopy combined with chemometrics were used for qualitative analysis of P. polyphylla var. yunnanensis from different geographical origins in Yunnan Province. A total of 82 samples for each region were divided into 57 in the calibration set and 25 in the validation set by Kennard–Stone algorithm. Support vector machine and partial least square discrimination on the basis of Fourier transform infrared, ultraviolet, and low- and mid-level data fusion were investigated. Different pretreatments were compared for the appropriate model. The results indicated that the combination of Savitzky–Golay (11 points), second derivative, and standard normal variation has the best performance for support vector machine and partial least square discrimination with the lowest root mean square error of estimation and root mean square error of cross validation and the highest cross validation accuracy rate. The accuracies of calibration and validation for mid-level data fusion in the model of support vector machine were 84.21 and 96% for the partial least square discrimination values of 96.49 and 84%, which was better performance than a single technique or low-level data fusion for the classification. Moreover, the chemical information of sample collected from Kunming and Xishuangbanna was distinguishable from the others. These results provide a rapid and robust strategy for quality control of P. polyphylla var. yunnanensis for further analysis.     

Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine

A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis–NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x–y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard–Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.     

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

径向基函数网络在近红外人体无创伤血糖浓度检测基础研究中的应用

在近红外无创伤血糖浓度检测的基础研究中，对于多组分的混合物的分析，常因光谱与样品浓度之间呈现非线性响应，使得基于线性模型的校正方法失效。本文讨论了非线性校正方法径向基函数神经网络( RBFN )的有效性，并与线性校正方法中的主成分分析和偏最小二乘法作了对比研究。验证实验所用样品为①葡萄糖水溶液②包含牛血红蛋白和白蛋白的葡萄糖水溶液，结果表明：在①实验中PLS模型和RBFN预测标准偏差分别为8．2、8．9；在②实验中分别为15．6、8．8。可见在样品组分增多时，RBFN算法较线性PLS方法建立的模型预测能力强。     

Diagnostics of calibration methods: model adequacy of UV‐based determinations

Models such as ordinary least squares, independent component analysis, principle component analysis, partial least squares, and artificial neural networks can be found in the calibration literature. Linear or nonlinear methods can be used to explain the structure of the same phenomenon. Each type of model has its own advantages with respect to the other. These methods are usually grouped taxonomically, but different models can sometimes be applied to the same data set. Taxonomically, ordinary least square and artificial neural network use completely different analytical procedures but are occasionally applied to the same data set. The aim of the study of methodological superiority is to compare the residuals of models because the model with the minimum error is preferred in real analyses. Calibration models, in general, are based on deterministic and stochastic parts; in other words, the data are equal to the model + the error. Explaining a model solely using statistics such as the coefficient of determination or its related significance values is sometimes inadequate. The errors of a model, also called its residuals, must have minimum variance compared to its alternatives. Additionally, the residuals must be unpredictable, uncorrelated, and symmetric. Under these conditions, the model can be considered adequate. In this study, calibration methods were applied to the raw materials, hydrochlorothiazide and amiloride hydrochloride, of a drug, as well as a sample of the drug tablet. The applied chemical procedure was fast, simple, and reproducible. The various linear and nonlinear calibration methods mentioned above were applied, and the adequacy of the calibration methods was compared according to their residuals.     

Non-linear calibration models for near infrared spectroscopy

Different calibration techniques are available for spectroscopic applications that show nonlinear behavior. This comprehensive comparative study presents a comparison of different nonlinear calibration techniques: kernel PLS (KPLS), support vector machines (SVM), least-squares SVM (LS-SVM), relevance vector machines (RVM), Gaussian process regression (GPR), artificial neural network (ANN), and Bayesian ANN (BANN). In this comparison, partial least squares (PLS) regression is used as a linear benchmark, while the relationship of the methods is considered in terms of traditional calibration by ridge regression (RR). The performance of the different methods is demonstrated by their practical applications using three real-life near infrared (NIR) data sets. Different aspects of the various approaches including computational time, model interpretability, potential over-fitting using the non-linear models on linear problems, robustness to small or medium sample sets, and robustness to pre-processing, are discussed. The results suggest that GPR and BANN are powerful and promising methods for handling linear as well as nonlinear systems, even when the data sets are moderately small. The LS-SVM is also attractive due to its good predictive performance for both linear and nonlinear calibrations.     

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.     

构建支持向量机-偏最小二乘法为药物构效关系建模

为研究药物构效关系积累样本数据的过程中,需为小样本建模。此时较易造成过拟合,影响模型的预测性能和稳定性。为此可用偏最小二乘(PLS)法从样本数据中成对地提取最优成分,消除自变量间的复共线性,并有效的降维,然后应用最小二乘支持向量机对成对成分进行非线性回归,并以基于误差修正的策略调整,使之更有效地表达自、因变量间的非线性关系。由此构建为EB-LSSVM-PLS算法,所建模型的预报精度高,稳定性良好。将其应用于新型黄烷酮类衍生物的QSAR建模,效果令人满意,其泛化性能优于其它方法。     

Simultaneous Determination of Eight Bioactive Components of Radix Dipsaci by Near-infrared Spectroscopy

A new method was developed using Fourier transform near-infrared spectroscopy and high-performance liquid chromatography with diode array detection for the identification and determination of eight major compounds in crude and sweated Radix Dipsaci. Partial least square regression was selected for the analysis. Multiplicative scatter correction, first derivative, and a Savitzky–Golay filter were used for the spectral pretreatment of the crude material, while standard normal variation, first derivative, and the Savitzky–Golay filter were used for the sweated samples. The correlation coefficients of the calibration models were above 0.99 and the root mean square error of calibration, the root mean square error of prediction, and root mean square error of cross-validation were under 0.63. The developed models were used to analyze unknown crude and sweated Radix Dipsaci with satisfactory results. The established methods were rapid, simple, nondestructive, and useful for quality control of Radix Dipsaci.     

偏最小二乘法结合傅里叶变换红外漫反射光谱快速测定煤质

发热量、挥发分和灰分等是衡量煤质的质量指标。通常,烟煤的发热量、挥发分和灰分是按行业标准方法[1]分别进行测定的,操作过程繁琐,速度慢,且费用高。随着化学计量学多元校正方法的发展与广泛应用,董庆年采用最小二乘法结合红外光谱成功测定了灰分[2];而应用偏最小二乘法[3](P     

啤酒主要成分的近红外光谱法测定

根据近红外光谱的振动吸收强度与有机分子官能团含量的线性关系,用偏最小二乘法,对啤酒的近红外光谱与其中的酒精度、原麦汁浓度以及总酸含量等3种主要成分进行了线性回归,并建立起相关的模型。用该模型对未知啤酒样品中的上述3种成分的含量进行预测,取得了令人非常满意的结果。可望作为啤酒厂的一种快捷而准确的检测方法予以推广。     

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．     

Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.     

基于局部最小二乘支持向量机的光谱定量分析

提出了一种基于局部最小二乘支持向量机(LSSVM)的回归方法,以克服待测参数和光谱数据间的非线性。本方法首先通过欧式距离选取局部训练样本子集,然后利用该子集建立LSSVM校正模型。由于每个测试样本建模时要选取不同的训练样本,因此提出相对距离的概念用来改进高斯核函数,使LSSVM的参数对于不同的训练样本具有自调整功能。针对一批汽油样本的实验结果表明,本方法的预测精度优于常见的局部线性建模方法和全局建模方法。     

傅立叶变换近红外光谱法快速评价涪陵榨菜品质

应用傅立叶变换近红外光谱技术,建立了评价涪陵榨菜品质的定量分析模型.测定了58份涪陵榨菜的近红外光谱数据,通过光谱预处理方法消除噪声,以偏最小二乘法(PLS)建立回归模型.最终得到评价其品质的水分、总酸(以乳酸计)和氨基酸含量近红外光谱分析模型的决定系数(R2)依次为0.957 8、0.975 4、0.950 4,交叉...     

预测毛细管区带电泳有效淌度的支持向量回归建模方法

提出预测毛细管电泳迁移行为的支持向量回归建模方法。以核苷为实际研究对象,利用正交试验获得的数据,结合二标记物技术,用支持向量回归算法建立毛细管区带电泳的柱温、电压、缓冲液浓度和pH值与3种核苷的有效淌度之间的相关模型。将其与偏最小二乘回归和人工神经网络方法相比较,结果表明所建模型的预测准确性优于后两者,适宜用于毛细管电泳迁移行为的预测。     

Random subspace regression ensemble for near-infrared spectroscopic calibration of tobacco samples.

An ensemble, a model-independent technique based on combining several models for classification/regression tasks, allows us to achieve a high accuracy that is often not achievable with single models. Such combinations have gained increasing attention in many fields. This paper proposes the use of random subspace (RS)-based regression ensemble as an alternative method for near-infrared (NIR) spectroscopic calibration of tobacco samples. Because of the considerable reduction of variables in a random subspace, multiple linear regression (MLR) is used as the base algorithm and the method is therefore also referred to as RS-MLR. The overall performance of the proposed RS-MLR method is compared to those of partial least square regression (PLSR), kernel principal component regression (KPCR) and kernel partial least square regression (KPLSR). The results reveal that the RS-MLR method not only has a simple concept but also can produce a more parsimonious and more accurate calibration model than PLSR, KPCR and KPLSR, at a lower computational cost. Besides, we also found that the RS-MLR method is very appropriate for the so-called small sample problems and that the calibration models built by RS-MLR are less sensitive to overfitting.     

Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n = 180), validation (n = 90) and prediction (n = 90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs, and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer.