Ensemble Multivariate Calibration Based on Mutual Information for Food Analysis Using Near-Infrared Spectroscopy

An ensemble multivariate calibration algorithm, termed as MISEPLS, is proposed. In MISEPLS, when constructing a member model, the variables that have mutual information (MI) with the response less than a threshold are eliminated; thus, the modeling can be performed in a subset of original variables and some problems arising from multi-collinearity can be avoided. Through experiments on three near-infrared (NIR) spectroscopic datasets from the food industry, MISEPLS proves to be superior to the single-model full-spectrum PLS and MIPLS (PLS combined with MI-induced variable selection). MISEPLS can improve the accuracy and robustness of a calibration model, without increasing its complexity.     

烟草组分的近红外光谱和支持向量机分析

测定了120个产自福建、安徽和云南烟草样品的近红外光谱. 在利用支持向量机(SVM)技术建立其定量、定性分析模型之前, 用小波变换技术对光谱变量进行了有效的压缩, 然后采用径向基核函数建立了75个烟草样品的分类模型, 同时建立了总糖、还原糖、烟碱和总氮4个组分的定量分析模型, 并利用45个烟草样品对模型进行了检验. 仿真实验表明, 建立的SVM分类模型分类准确率达到100%, 而4个组分的定量分析模型的预测决定系数(R2)、预测均方差(RMSEP)和平均相对误差(RME)3个指标值显示其模型泛化能力非常强, 预测效果良好, 可见这是一种有效的近红外光谱的建模分析方法.     

Quantitative Determination of the Components in Corn and Tobacco Samples by Using Near-Infrared Spectroscopy and Multiblock Partial Least Squares

Multiblock partial least squares (MB-PLS) are applied for determination of corn and tobacco samples by using near-infrared diffuse reflection spectroscopy. In the model, the spectra are separated into several sub-blocks along the wavenumber, and different latent variable number was used for each sub-block. Compared with ordinary PLS, the importance and the contribution of each sub-block can be balanced by super-weights and the usage of different latent variable numbers. Therefore, the prediction obtained by the MB-PLS model is superior to that of the ordinary PLS, especially for the large data sets of tobacco samples with a large number of variables.     

近红外光谱法快速测定豆粕中混入的玉米粉含量

通过对豆粕样品和混有部分玉米粉的豆粕样品的近红外光谱分析，结果表明：混合样品中淀粉的含量与样品在２０６０ｎｍ，２０９０ｎｍ和 ２１６６ｎｍ处的光谱数据具有高的相关系数，然后应用回归分析计算出线性方程，再根据玉米粉中的淀粉含是一计算混合样品中的玉米粉含量。     

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.     

土壤总氮近红外光谱分析的波段优选

利用移动窗口偏最小二乘( MWPLS)和Savitzky-Golay(SG)平滑方法优选土壤总氮的近红外(NIR)光谱分析模型.从全部97个土壤样品中随机选出35个样品作为检验集;基于偏最小二乘交叉检验预测偏差(PLSPB),将余下62个样品划分为具有相似性的建模定标集(37个样品)、建模预测集(25个样品).最优波段为1692～2138 nm,SG平滑的导数阶数(OD)、多项式次数(DP)、平滑点数(NSP)分别为0,6,69,PLS因子数为11,建模预测均方根偏差(M-RMSEP)、建模预测相关系数(M-Rp)分别为0.015％,0.931,检验预测均方根偏差(V-RM-SEP)、检验预测相关系数(V-RP)分别为0.018％,0.882.其结果可为设计专用NIR仪器提供有价值的参考.     

Qualitative Analysis of Maize Haploid Kernels Based on Calibration Transfer by Near-Infrared Spectroscopy

Haploid breeding is one of the most important modern crop selection technologies. Near-infrared spectroscopy (NIRS) has been used to identify haploids rapidly and to non-destructively accelerate the selection process. However, the change of the external environment weakens the performance of the model, as the training and the test spectra may be collected separately from different environments. Thus, a novel calibration transfer method is proposed to calibrate the model in order to reduce the impact of the environment. The near-infrared spectra of 400 maize kernels of two varieties were collected from 9000 to 4000?cm^?1. Principal component analysis was performed to construct a feature space and extract features. In the constructed feature space, the calibration transfer method was used to calibrate test sets. Finally, support vector machine was employed to establish a haploid identification model. The results show that when the spectra of the test set and the training set were collected in the same environment, the corrected acceptance of the model was above 90%. While the spectra of the test set and the training set were collected from different environments, the corrected acceptance was 77.87%. However, when the model used the calibration transfer method, the corrected acceptance increased by 12.46%. Moreover, compared with direct standardization, this calibration transfer method achieved better results without detailed sample chemical information and many standards. The results demonstrate that the calibration transfer method based on NIRS was effective for identifying maize haploid kernels in variable environments.     

Quantitative Analysis of Uncoated Eszopiclone Tablets by Near-Infrared Spectroscopy

This study employed near-infrared (NIR) spectroscopy to analyze content uniformity, moisture content, compression force, tablet hardness, average particle size, and particle-size distribution. The content uniformity, moisture content, compression force, tablet hardness, and average particle size models yielded high correlation coefficients (R²) of 0.99582, 0.99725, 0.99620, 0.96294, and 0.98421, respectively, whereas the particle size distribution models showed good predictive ability. Conventional criteria such as R², root-mean-square error of calibration, and the root-mean-square error of prediction were used to evaluate the accuracy and precision of the model. To ensure the accuracy and predictability of the content model for low-dose tablets, additional validation and reliability evaluations were performed using 70%, 80%, 100%, 120%, and 130% drug concentrations as well as 90% and 110% active content formulations. Near-infrared spectroscopy with multivariate modeling is a rapid, nondestructive technique for the characterization of the manufacturing process.     

近红外光谱结合无参数校正增强实现不同年份烟叶总糖含量模型更新

近红外光谱技术因快速、无损等特点,已广泛应用于烟草行业质量快速分析。然而,由于采收时间、环境差异等因素的影响,建立的近红外定量模型在新批次样本中的预测性能通常变差,因此必须对原有模型进行维护和更新。该研究采用半监督无参数校正增强（SS－PFCE）方法,通过约束优化,对主模型的回归系数进行修正。首先建立了2016年烟叶样本总糖含量的原始定量模型,其预测相关系数（Rp）为0.997 8、预测均方根误差（RMSEP）为0.310 8。采用SS－PFCE方法对模型更新后,分别预测2017年、2018年和2020年样本的总糖含量,3个测试集的Rp值比未更新模型提高了0.13%、1.32%和4.29%,RMSEP分别下降了15.26%、58.69%和36.53%。与重新建立的定量分析模型相比,更新后的模型具有更优的预测性能,同时大大降低了建模成本。研究表明,SS－PFCE方法可高效地实现不同年份烟叶样本的模型维护,在实际生产中具有重要的应用价值。     

近红外光谱主成分-所有可能回归法定量分析烤烟、小麦样品中的组分含量

利用主成分-所有可能回归法,建立了烤烟、小麦样品不同组份的近红外光谱定量分析模型。结果表明,烤烟样品的总糖、还原糖以及小麦样品的蛋白质含量的预测模型均有好的定量分析结果,且其预测结果与PLS法预测结果相当。     

Screening Inorganic Arsenic in Rice by Visible and Near-Infrared Spectroscopy

The potential of near-infrared spectroscopy (NIRS) for screening the inorganic arsenic (i-As) content in commercial rice was assessed. Forty samples of rice were freeze-dried and scanned by NIRS. The i-As contents of the samples were obtained by acid digestion-solvent extraction followed by hydride generation atomic absorption spectrometry, and were regressed against different spectral transformations by modified partial least square (MPLS) regression. The second derivative transformation equation of the raw optical data, previously standardized by applying standard normal variate (SNV) and De-trending (DT) algorithms, resulted in a coefficient of determination in the cross-validation (1-VR) of 0.65, indicative of equations useful for correct separation of the samples in low, medium and high groups. The standard deviation (SD) to standard error of cross-validation (SECV) ratio, expressed in the second derivative equation, was similar to those obtained for other trace metal calibrations reported in NIRS reflectance. Spectral information relating to starch, lipids and fiber in the rice grain, and also pigments in the caryopsis, were the main components used by MPLS for modeling the selected prediction equation. This pioneering use of NIRS to predict the i-As content in rice represents an important reduction in labor input and cost of analysis.     

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。     

Classification of Liquor Using Near-Infrared Spectroscopy and Chemometrics

The authenticity of Chinese liquor concerns consumer health and economic issues. The traditional characterization methods are time-consuming and require experienced analysts. The use of near-infrared (NIR) spectroscopy and chemometrics to classify Chinese liquor samples was investigated using 128 liquors. The spectral region between 5340 cm^?1 and 7400 cm^?1 was found to be most informative. Principal component analysis was employed to characterize liquor and principal components were extracted as inputs of training classifiers. Several supervised pattern recognition methods including K-nearest neighbor, perceptron, and multiclass support vector machine were used as algorithms of constructing classifiers. The initial principal components and all spectral variables were used as the input of training models. In terms of the misclassification ratio, the support vector machine approach was the most accurate. The results indicated that near-infrared spectroscopy and chemometrics are an alternative to conventional methods for the characterization of liquor.     

小波多尺度分段直接校正法用于近红外光谱模型传递的研究

分段直接校正(PDS)算法是目前最常用的近红外光谱模型传递方法,但它在对整个谱区进行校正时,始终依赖大小不变的传递窗口.为了提高传递效果,本研究在PDS基础上提出了一种新的算法--小波多尺度分段直接校正法(WMPDS),用于混胺的近红外光谱模型传递,并详细讨论了模型的传递参数和传递结果.本算法首先对混胺的近红外光谱进行...     

Characterization of Automobile Plastics by Principal Component Analysis and Near-Infrared Spectroscopy

Based on near-infrared diffuse reflection spectroscopy, multivariate calibration models for discarded automobile plastic were constructed using principal component analysis and clustering analysis to rapidly characterize four widely employed materials: polypropylene, polyethylene, acrylonitrile butadiene styrene, and polymethylmethacrylate with an accuracy rate of 97%. The method was shown to rapidly discriminate waste automobile plastic.     

荧光法测定糖果中的总糖含量

合成了带有荧光基团的分子探针,光谱显示探针对糖类物质具有较强的荧光响应.通过条件优化,选取328 nm作为荧光激发波长,p H 10.0的缓冲溶液为最佳测定介质,当测试溶液中葡萄糖浓度为0.05~1 mmol/L时,显示良好的响应线性.方法对果糖中总糖含量测定的检测限为6.2μmol/L,回收率在76.4%~90.0%,RSD达到3.18%.     

A Robust Near-Infrared Calibration Model for the Determination of Chlorophyll Concentration in Tree Leaves with a Calibration Transfer Method

A method based on piecewise direct standardization was developed to directly predict leaf chlorophyll concentrations by correction of near-infrared spectra to construct a robust calibration model. Chinar, camphor, and gingko leaves collected from two growth intervals were evaluated. Spectral pretreatment methods and wavelength selection were investigated. The first derivative combined with stability competitive adaptive reweighted sampling before piecewise direct standardization provided the best performance. Under the optimized parameters, the root mean square error of prediction was significantly reduced by using piecewise direct standardization. This study demonstrates that the calibration model may be used to rapidly characterize chlorophyll concentrations across species and growth intervals.     

Feasibility of Rapid Diagnosis of Colorectal Cancer by Near-Infrared Spectroscopy and Support Vector Machine

The feasibility of diagnosing colorectal cancers based on the combination of near-infrared (NIR) spectroscopy and supervised pattern recognition methods was investigated. A total of fifty-eight colorectal tissues were collected and prepared. The spectra were first preprocessed by standard normalize variate (SNV) and first derivatives of Savitzky-Golay polynomial filter for removing unwanted background variances. The information of CH-stretching overtones and combination regions proved to be the most valuable. Four pattern recognition methods including K-nearest neighbor classifier (KNN), perceptron, Fisher discriminant analysis (FDA), and support vector machine (SVM) were used for constructing classifiers. In terms of the total accuracy, sensitivity and specificity, the SVM classifier achieved the best performance; the sensitivity and specificity were 92.8% and 86.7%, respectively. These findings suggest that NIR spectroscopy offers the possibility of constructing a simple, feasible and sensitive method for diagnosing colorectal cancer, avoiding the need of laborious visual inspection from experts.     

近红外反射光谱法分析玉米秸秆纤维素含量的研究

利用近红外反射光谱分析技术和偏最小二乘回归法（PLS）,通过比较不同光谱范围和光谱预处理方法,采用二阶导数光谱预处理,在7540．3-5361．1cm^-1和4882.9—4504．9cm^-1谱区内建立了近红外光谱测定玉米秸秆纤维素含量的校正模型。利用15个玉米秸秆样品对所建模型的实际预测效果进行了验证,预测值与化学值的相关系数（r）可达0．9953,最大相对误差仅为5．20。结果表明,近红外光谱技术可以快速、准确地测定玉米秸秆纤维素,该结果对玉米秸秆材料的快速鉴定和筛选利用具有重要的意义。