近红外光谱测定小麦粉常规营养成分的模型优化

目的：应用近红外光谱（NIR）结合偏最小二乘法（PLS）建立小麦粉常规营养成分蛋白质、水分和脂肪的含量预测模型,并选择最佳模型。方法：收集117份小麦粉样品的近红外光谱,化学法测定蛋白质、水分和脂肪的含量,利用主成分分析（PCA）随机分组,81份样品用于构建模型、36份样品用作验证模型的预测能力。探讨波长范围和光谱预处理方法对所建模型预测能力的影响。结果：3个营养成分预测能力最好的模型分别是：对于蛋白质,预处理采用矢量归一化（SNV）,波长选取7 505.9~5 446.2 cm-1和4 605.4~4 242.8 cm-1,预测模型的RPD值是7.02;对于水分,无预处理,波长选择全谱12 800~3 960 cm-1,模型的RPD值是6.83;对于脂肪,无预处理,波长在9 000~4 000 cm-1,模型的RPD值是5.06。结论：近红外光谱法可以实现对小麦粉常规营养成分的快速预测,通过选择波长范围和光谱预处理方法可以显著提高模型的预测能力。     

近红外漫反射光谱法快速测定药用植物淫羊藿总黄酮含量

利用一种基于近红外漫反射光谱技术的淫羊藿总黄酮快速定量分析方法, 测定了淫羊藿属(Epimedium)7个种中总黄酮的含量。研究结果表明, 近红外光谱经二阶导数处理、主成分分析聚类及加权多元离散校正处理后, 采用改进最小二乘法回归得到的定标模型的预测效果最佳; 定标样品集预测方程的定标决定系数、交互验证标准差及交互验证相关系数分别为0.985 5、0.023 0和0.044 3; 检验样品集方程的定标决定系数、检验标准差及偏差分别为0.965 1、0.018和0.006, 达到了快速测定淫羊藿总黄酮含量的要求。该方法为淫羊藿总黄酮的快速定量分析提供了新的依据。     

基于近红外漫反射光谱快速测定淫羊藿蛋白质含量

采用近红外漫反射光谱技术对淫羊藿(Epimedium)的蛋白质含量进行快速且无损检测。近红外漫反射光谱经二阶导数处理、标准多元离散校正及主成分分析聚类处理后, 采用改进最小二乘法回归得到的定标模型预测效果最佳, 定标决定系数、交互验证标准差及交互验证相关系数分别为0.923、0.554和0.717。近红外光谱分析法的测定结果与用凯氏定氮法所得结果无显著差异, 两种方法测定值的相关性较高(R²=0.933 9)。重复性实验表明, 近红外光谱分析法的相对标准偏差为0.937%。该研究首次采用近红外光谱分析法测定了8种淫羊藿的蛋白质含量。该方法简便、精确, 在淫羊藿资源开发利用和药材质量控制方面具有参考意义。     

南丰蜜桔可溶性固形物近红外检测的多元线性回归变量筛选

文章采用反向区间偏最小二乘法结合连续投影算法,筛选南丰蜜桔近红外检测的多元线性回归变量。对南丰蜜桔近红外光谱进行多元散射校正后,利用反向间隔偏最小二乘法,从500～1750 nm中初选出7个光谱区间,用于多元线性回归变量筛选。利用通过遗传算法和连续投影算法筛选出的变量建立了多元线性回归模型。经比较发现,利用反向区间偏最小二乘法结合连续投影算法筛选出的变量建立的多元线性回归模型,预测结果最优,模型预测相关系数为0.937,模型预测均方根误差为0.613 oBrix。结果表明,反向区间偏最小二乘法结合连续投影算法,可以有效地筛选近红外光谱的多元线性回归变量,提高南丰蜜桔可溶性固形物模型的预测精度。     

波长选择SCARS和偏最小二乘法建立香菇中多糖含量预测的近红外光谱模型

为实现香菇多糖含量的快速测定,利用近红外光谱漫反射技术采集了60个香菇粉末样本在12000~3800 cm-1范围内的光谱数据,利用紫外可见光谱法测定了香菇粉末样品的多糖含量。采用多种化学计量学方法,剔除掉四个异常样本后,考察了不同的光谱预处理方法以及波长选择对模型的影响,用留一交互检验法建立了偏最小二乘(PLS)模型,并用所建立的校正模型对独立预测集样本进行了预测。结果表明,当采用二阶导数及变量稳定性的竞争自适应加权抽样法(SCARS)选择的波长对光谱进行处理时,所建立的模型预测效果最佳,在隐变量数为10时,模型相关系数为0.9906,校正均方根误差(RMSEC)为0.0523 g/100 g,预测相关系数Rp=0.9781,预测均方根误差(RMSEP)=0.0577 g/100 g,该模型具有较好的预测能力,可用于香菇多糖含量的近红外光谱快速检测。     

奇异数据筛选法在玉米籽粒蛋白质近红外光谱检测中的应用

相对于传统生化测定方法,基于近红外光谱(Near infrared spectroscopy,NIRS)玉米籽粒蛋白质含量检测是一种快速、非破坏、且适用于多组分同时检测的新方法。但在建模过程中,由于奇异数据(异常值)的存在会影响近红外光谱模型的预测精度和稳定性,我们采用奇异数据筛选法剔除了玉米籽粒近红外光谱中的奇异数据并建立了玉米籽粒蛋白质含量的偏最小二乘支持向量机(Least squares support vector machine,LS-SVM)模型。本文分别采用杠杆值法(Leverage)、半数重采样法(Resampling by Half-Mean,RHM)和蒙特卡洛采样法(Monte-Carlo Sampling,MCS)剔除了玉米籽粒蛋白质光谱数据中的奇异数据并对模型结果进行比较。在剔除奇异数据的基础上,采用偏最小二乘回归法(Partial least squares regression,PLSR)提取主成分,并基于小生境蚁群算法(Niche ant colony algorithm,NACA)优化偏最小二乘支持向量机(LS-SVM)模型参数(γ和σ2),建立基于LS-SVM的玉米籽粒蛋白质定量分析模型。结果表明,采用3种奇异数据筛选法剔除奇异数据后所建LS-SVM模型的预测结果都优于采用原光谱数据所建模型,相比较而言,蒙特卡洛采样法为基于近红外光谱检测玉米籽粒蛋白质的最佳奇异数据筛选法。     

一种快速定量分析药用植物淫羊藿生物碱的方法

建立了基于近红外漫反射光谱的药用植物淫羊藿(Epimedium)生物碱的定量分析方法,测定了9种淫羊藿生物碱的含量。当聚类分析采用主成分分析、数学处理采用2,4,4,1模式、光谱散射校正采用标准正则变换+散射处理及回归分析采用改进最小二乘法时,淫羊藿生物碱近红外定标模型的定量效果最好;定标方程的定标决定系数、交互验证相关系数、定标标准差和交互验证标准差分别为0.899 7、0.845 7、0.168 6和0.085 3。近红外定标模型预测值与常规化学分析值差异很小。应用近红外漫反射光谱法测得9种淫羊藿生物碱的含量介于6.537–20.586 mg·g–1(干重)之间,淫羊藿生物碱的种间差异较大。近红外漫反射光谱法具有快速、简便和无损(不损耗受检材料)的优点,可用于淫羊藿生物碱的研究与开发。     

FT-NIRS技术应用于稻米直链淀粉含量分析研究

运用近红外光谱快速分析技术,使用偏最小二乘法建立了近红外光谱和水稻糙米直链淀粉含量的数学模型,并进行糙米直链淀粉含量预测.结果表明糙米近红外光谱与其直链淀粉含量具有良好的相关性,决定系数r2=0.8429,最大绝对误差4.82%,平均误差2.30%.该方法在不破坏样品的前提下快速分析水稻直链淀粉含量,可用于稻种资源的快速鉴定,对于水稻优质育种及其相关研究具有重要意义.     

NIRS结合TQ软件快速测定山茱萸中莫诺苷的含量

应用近红外光谱法对山茱萸中莫诺苷的含量进行快速测定。利用高效液相色谱法测定山茱萸药材中莫诺苷的含量,采用偏最小二乘法建立其含量与近红外光谱之间的模型,并对未知样品进行含量测定。所建莫诺苷的定量模型的相关系数(R2)为0.9857,校正均方差(RMSEC)、内部交叉验证均方差(RMSECV)和预测均方差(RMSEP)分别为0.105、0.2738和0.234。结果表明该方法所测结果准确,且不需复杂的前处理、绿色、无损,适用大批量山茱萸中莫诺苷的快速测定。     

NIRS结合TQ软件快速测定山茱萸中莫诺苷的含量北大核心CSCD

应用近红外光谱法对山茱萸中莫诺苷的含量进行快速测定。利用高效液相色谱法测定山茱萸药材中莫诺苷的含量,采用偏最小二乘法建立其含量与近红外光谱之间的模型,并对未知样品进行含量测定。所建莫诺苷的定量模型的相关系数(R2)为0.9857,校正均方差(RMSEC)、内部交叉验证均方差(RMSECV)和预测均方差(RMSEP)分别为0.105、0.2738和0.234。结果表明该方法所测结果准确,且不需复杂的前处理、绿色、无损,适用大批量山茱萸中莫诺苷的快速测定。     

Monitoring complex media fermentations with near-infrared spectroscopy: comparison of different variable selection methods

Near-infrared spectroscopy (NIRS) is known to be a suitable technique for rapid fermentation monitoring. Industrial fermentation media are complex, both chemically (ill-defined composition) and physically (multiphase sample matrix), which poses an additional challenge to the development of robust NIRS calibration models. We investigated the use of NIRS for at-line monitoring of the concentration of clavulanic acid during an industrial fermentation. An industrial strain of Streptomyces clavuligerus was cultivated at 200-L scale for the production of clavulanic acid. Partial least squares (PLS) regression was used to develop calibration models between spectral and analytical data. In this work, two different variable selection methods, genetic algorithms (GA) and PLS-bootstrap, were studied and compared with models built using all the spectral variables. Calibration models for clavulanic acid concentration performed well both on internal and external validation. The two variable selection methods improved the predictive ability of the models up to 20%, relative to the calibration model built using the whole spectra.     

近红外光谱技术预测猪肉的pH、嫩度和蒸煮损失

以冷却猪肉为研究对象,评价近红外光谱（NIR）技术用于肉类物理特性预测的可行性以及不同的光谱处理方法和建模方法对预测准确性的影响。试样取自排酸24h的同一批猪胴体的小里脊肉,采集4000—10000cm-1的光谱。经外部验证的偏最小二乘（PLS）模型在预测pH时表现出良好的相关性（Rc^2=0．88,Rp^2=0．80,SEC=0．08,SEP=0．084）,嫩度与蒸煮损失模型的相关性分别是Rc^2=0．50和0．57,R;=0．34和0．50。在各种光谱预处理方法中,平滑处理结合多元散射校正（MSC）或标准正态变量变换（SNV）的效果最好。     

Quantification of polymeric mannose in wine extracts by FT-IR spectroscopy and OSC-PLS1 regression

FT-IR spectroscopy has being a widespread technique in the agro-industry for the quick assess of food components, including the wine. Using the region of wavenumbers 1200–800 cm⁻¹ of the FT-IR spectra wine polysaccharides, Partial Least Squares Regression (PLS1) independent calibration models were built for mannose quantification in complex matrices from white and in red wine extracts. With PLS1 it was not possible to build a calibration model that included both white and red wine extracts. However, a predictive ability of the model for quantification of mannose from mannoproteins based on this FT-IR spectral region was achieved by the application of orthogonal signal correction (OSC)-PLS1.     

Evaluation of Three Approaches for Real-Time Monitoring of Roller Compaction with Near-Infrared Spectroscopy

Three different approaches have been evaluated for monitoring ribbon density through real-time near-infrared spectroscopy measurements. The roll compactor was operated to produce microcrystalline cellulose (MCC) ribbons of varying densities. The first approach used the slope of the spectra which showed a variation through the ribbon that could be attributed to density. A second qualitative approach was also developed with a principal component analysis (PCA) model with spectra taken in-line during the production of ribbons in an ideal roll pressure range. The PCA (i.e., real-time) density scans show that the model was able to qualitatively capture the density responses resulting from variation in process parameters. The third approach involved multivariate partial least squares (PLS) calibration models developed at wavelength regions of 1,120–1,310 and 1,305–2,205 nm. Also, various PLS models were developed using three reference methods: caliper, pycnometer, and in-line laser. The third approach shows a quantitative difference between the model-predicted and the measured densities. Models developed at high-wavelength region showed highest accuracy compared with models at low-wavelength region. All the PLS models showed a high accuracy along the spectra collected throughout the production of the ribbons. The three methods showed applicability to process control monitoring by describing the changes in density during in-line sampling.     

基于NIRS法快速检测荞麦蛋白质与淀粉含量的研究

为建立近红外光谱技术测定荞麦蛋白质与淀粉含量的方法,本研究以217份荞麦样品为试验材料,采用最小二乘回归预测和交叉验证构建近红外预测模型。分析表明:前处理采用多元散射校正法(MSC),维数(Rank)分别为5和5,光谱区间6803.9~6094.2/cm所建立的荞麦蛋白质与淀粉含量模型的预测效果较好,其决定系数(R~2)分别为0.9481和0.9167,交叉验证均方根(RMSECV)分别为0.68和2.08,相对分析误差(RPD)分别为4.39和3.46,均大于3.0,外部验证相关系数均大于0.96。本试验所建立的蛋白质与淀粉含量近红外预测模型具有较高的准确度和稳健性,可用于荞麦品质的快速测定。     

基于相关分析和偏最小二乘回归的黄绵土土壤全氮和碱解氮含量的高光谱预测

以采取植被恢复措施的陕西省吴起县为研究区,实地采集24个土壤剖面不同层次的黄绵土土样100个,在进行土壤样本全氮（TN）和碱解氮（AHN）含量及实验室反射光谱数据测量和分析的基础上,用相关分析（CA）结合偏最小二乘回归（PLS）方法建立黄绵土土壤TN和AHN含量的校正模型,并用独立样本对校正模型进行验证.结果表明： 利用6种光谱变换方式建立的校正模型中,微分光谱建立的校正模型是预测研究区土壤TN含量的最佳模型,校正和验证R²分别为0.929和0.935,均方根误差（RMSE）分别为0.045和0.047 g·kg^-1,相对预测偏差（RPD）为3.12;而归一化变换建立的校正模型是预测土壤AHN含量的最佳模型,校正和验证R²分别为0.873和0.773,RMSE分别为9.946和16.204 mg·kg^-1,RPD为1.538.所建立的全氮预测模型可以对0～40 cm土层的TN进行有效预测,而碱解氮的预测模型对同一深度只能进行粗略预测.本研究为采取植被恢复措施的退化生态系统区黄绵土土壤全氮的快速预测提供了一种较好的方法,但是对于碱解氮的准确、快速预测,需要进一步研究.     

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

This study assessed the utility of near-infrared (NIR) spectroscopy for the real-time monitoring of content uniformity and critical quality attributes (tensile strength, Young’s modulus, and relative density) of ribbed roller compacted flakes made by axially corrugated or ribbed rolls. A custom-built setup was used to capture off-line NIR spectra from the flakes containing micronized chlorpheniramine maleate, microcrystalline cellulose, lactose, and magnesium stearate. The partial least square regression method was employed to build calibration models from these off-line NIR spectra using experimental design and validated using test set validation. During calibration model development, various factors, such as spectral acquisition mode, probe positioning, spectral preprocessing method, and beam size, were investigated to improve the prediction ability of the models. The statistical results obtained for calibration models and their validation revealed that dynamic spectral acquisition and proper probe positioning were very crucial to minimize the incorporation of variability in NIR spectra resulting from the flake’s undulation. Calibration and validation statistics also suggested the importance of selecting appropriate spectral preprocessing method and beam size. In this study, best calibration models resulted from standard normal variate followed by first derivative preprocessed dynamic spectra captured using beam size ~1.2 mm. Best calibration models constructed from off-line NIR spectra were used in real-time analysis of flake attributes. Finally, adequacy of best calibration models was established from real-time prediction results. Overall, with the proposed setup, it was possible to monitor the roller compaction process in real time for various properties associated with the ribbed flakes in a rapid, efficient, and nondestructive manner.     

Assessment of NIR spectroscopy for nondestructive analysis of physical and chemical attributes of sulfamethazine bolus dosage forms

The goal of this study was to assess the utility of near infrared (NIR) spectroscopy for the determination of content uniformity, tablet crushing strength (tablet hardness), and dissolution rate in sulfamethazine veterinary bolus dosage forms. A formulation containing sulfamethazine, corn starch, and magnesium stearate was employed. The formulations were wet granulated with a 10% (wt/vol) starch paste in a high shear granulator and dried at 60°C in a convection tray dryer. The tablets were compressed on a Stokes B2 rotary tablet press running at 30 rpm. Each sample was scanned in reflectance mode in the wavelengths of the NIR region. Principal component analysis (PCA) of the NIR tablet spectra and the neat raw materials indicated that the scores of the first 2 principal components were highly correlated with the chemical and physical attributes. Based on the PCA model, the significant wavelengths for sulfamethazine are 1514, (1660–1694), 2000, 2050, 2150, 2175, 2225, and 2275 nm; for corn starch are 1974, 2100, and 2325 nm; and for magnesium stearate are 2325 and 2375 nm. In addition, the loadings show large negative peaks around the water band regions (≈1420 and 1940 nm), indicating that the partial least squares (PLS) models could be affected by product water content. A simple linear regression model was able to predict content uniformity with a correlation coefficient of 0.986 at 1656 nm; the use of a PLS regression model, with 3 factors, had anr ² of 0.9496 and a sandard error of calibration of 0.0316. The PLS validation set had anr ² of 0.9662 and a standard error of 0.0354. PLS calibration models, based on tablet absorbance data, could successfully predict tablet crushing strength and dissolution in spite of varying active pharmaceutical ingredient (API) levels. Prediction plots based on these PLS models yielded correlation coefficients of 0.84 and 0.92 on independent validation sets for crushing strength and Q₁₂₀ (percentage dissolved in 120 minutes), respectively. Published: September 20, 2005 The opinions expressed in this paper are of the authors' personal views. They do not necessarily reflect the views or policies of the FDA.     

Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media.

An adaptive calibration procedure is used to build selective multivariate calibration models for the measurement of glucose, lactate, glutamine, and ammonia in undiluted serum-based cell culture media. This adaptive procedure removes metabolism-induced covariance between these analytes in a series of calibration samples collected during the cultivation of PC-3 human prostate cancer cells. Partial least-squares calibration models are generated from single-beam near-infrared (NIR) spectra collected over the 4800- to 4200-cm(-1) combination spectral range. Calibration models were generated with both the full spectral range and optimized spectral ranges. In both cases, the number of model factors was optimized and model validity was determined by comparing analyte concentrations predicted from a series of independent and unaltered samples that were obtained during a subsequent cultivation of the PC-3 cells. Similar analytical performance was achieved with fewer model factors when the optimized spectral range was used. The lowest standard errors of prediction were 0.82, 0.94, 0.55, and 0.76 mM for glucose, lactate, glutamine, and ammonia, respectively. Different spectral ranges were optimal for each analyte and the optimized spectral range coincided with the distinguishing spectral features of the analyte. The results of this study demonstrate that NIR spectroscopy can be used effectively in the off-line measurement of important nutrients (glucose and glutamine) and byproducts (lactate and ammonia) in a serum-based animal cell culture medium.     

Studies on Essential Oil trom Zanthoxylum avicennae, (Lam.) DC. and Its Antimildew Active

By means of rotatory belt fractional distillation, LC, HPLC, PGC and derivative methods, 27 compounds were separated from the essential oil which isolated from Zanthoxylum avicennae (Lam.) DC. by stream distillation. Twenty two of them were identified by the methods of IR, GC/IR,, 1H-NMR,18C-NMR, GC/MS/DS and the preparation of derivatives. Among them, 4-methyl-6-acetoxyhexanal was first found in nature. Fifteen compounds have been tested their antimildew activities. Citral, l-octanol, 4-methyl-6-acetoxyhexanal and linalool showed strong activities. The relationships between the structure of compounds of essential oil and their respective antimildew activities are also dealt with in this paper.