近红外光谱法快速测定鲜木薯中淀粉含量

提出了用于现场快速测定鲜木薯中淀粉含量的近红外光谱法。取不同品种的鲜木薯样品90个,按国家标准方法(化学分析法)测得其淀粉含量。用近红外光谱法对此90个样品所制作的柱状样块的横截面进行光谱扫描,得其光谱图谱,并转换得到其一阶导数图。用CAUNIFT软件QPLS分析模块建立数学模型。结果表明:验证样品中淀粉的化学法测定值与近红外模型的预测值之间呈线性关系。用5份未知样品对所建模型进行外部验证,预测值与化学法测定值之间的绝对误差在2%以内,相对误差均小于5%。     

近红外光谱法测定卷烟纸中钠、钾、镁、钙和柠檬酸根的含量

采用近红外光谱法测定卷烟纸中钠、钾、镁、钙和柠檬酸根的含量。用近红外光谱法对163个具有代表性卷烟纸样品进行测定,利用偏最小二乘法建立了卷烟纸中钠、钾、镁、钙和柠檬酸根的数学定量模型。结果表明:当卷烟纸重叠张数为15张及以上时,近红外漫反射扫描光谱无明显差异;各模型相关系数分别为0.949 6,0.982 5,0.958 1,0.930 0,0.987 9;模型交互验证均方根误差分别为0.245,0.415,0.050 5,3.08,0.533;模型外部验证平均相对偏差分别为6.63%,4.87%,6.03%,2.31%,4.58%。t-检验结果表明:5种组分显著性水平均大于0.05,预测值与测定值不存在显著性差异。     

近红外光谱法用于木薯燃料酒精品质分析

提出了用近红外光谱法测定木薯燃料酒精中乙醇和水分含量,以确定木薯燃料酒精的品质。结合修正偏最小二乘法建立了样品与其近红外光谱之间的定标模型。结果表明:乙醇定标模型交叉验证均方差和相关系数分别是0.110 2,0.960 5;水分定标模型分别是0.014 3,0.975 9。近红外光谱法预测值与化学分析法的测定值一致,该模型具有很高的预测准确性,可应用于木薯酒精品质分析的快速检测。     

近红外光谱法快速测定肼-70纯度

利用重量法精确配制不同肼质量分数的肼-70样品集,采用偏最小二乘法建立了肼质量分数的近红外光谱模型,以快速测定肼-70的纯度。样品恒温时间为5 min,光谱最佳预处理方式:均值中心化,一阶导数,21点平滑,模型最佳主因子数为4。近红外光谱法与碘酸钾直接滴定法测定结果相比相对偏差绝对值小于0.13%,经t检验,两种方法测定结果无显著性差异。近红外光谱法快速、准确,可用于肼-70产品的质量控制。     

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。     

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。     

近红外光谱法测定汽油中的烯烃含量

近红外光谱法测定汽油中的烯烃含量;汽油;烯烃;近红外光谱;偏最小二乘回归     

共识径向基神经网络应用于近红外光谱法测定三七中总黄酮

将共识策略结合径向基神经网络用于近红外光谱法测定三七中总黄酮的含量中。首先采用离散小波变换对近红外光谱进行预处理,去除噪声并压缩数据。继而采用共识径向基神经网络建立校正模型。结果表明:共识策略可以使模型更稳定、更准确。     

近红外光谱法测定成品汽油中的芳烃和烯烃含量

介绍了近红外光谱测定90#汽油及93#汽油中芳烃和烯烃含量。选择1100～1300nm的近红外光谱域,在荧光指示剂吸附法的基础上,采用偏最小二乘法建立了适合测定90#汽油及93#汽油中芳烃和烯烃含量的分析模型,通过大量试验对所建分析模型的可靠性进行了验证。近红外光谱法的测定结果与荧光指示剂吸附法的测定结果具有很好的一致性。与荧光指示剂吸附法相比,近红外光谱法可以提高分析效率,降低分析成本,具有较高的分析精密度。     

近红外光谱快速分析青贮饲料pH值和发酵产物

采用近红外光谱技术,结合偏最小二乘回归法,研究了142个不同种类的秸秆青贮饲料样品的pH值和发酵产物(乳酸、乙酸、丙酸、丁酸和氨态氮),建立了干燥粉碎和新鲜样品的近红外漫反射光谱定量分析模型以及浸提液样品的近红外透射光谱定量分析模型。研究发现,pH值的近红外漫反射光谱和透射光谱的分析效果均较好,校正模型决定系数R2和验证集样品预测值与化学值的相关关系决定系数r2都大于0.80,并且干燥粉碎、新鲜和浸提液样品的RPD值分别为3.44、2.50和2.27;3种状态样品的乳酸、乙酸、丁酸和氨态氮的定量分析模型精度需进一步提高;R2在0.64~0.85之间;RPD值在1.38~1.93之间;丙酸含量的测定结果较差。方差分析显示,3种状态样品的测定结果之间均无显著性差异(P>0.05)。     

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.     

Non-destructive method for determination of hydroxyl value of soybean polyol by LS-SVM using HATR/FT-IR

This paper presents the use of least-squares support vector machine (LS-SVM) for quantitative determination of hydroxyl value (OHV) of hydroxylated soybean oils by horizontal attenuated total reflection Fourier transform infrared (HATR/FT-IR) spectroscopy. A least-squares support vector machine (LS-SVM) calibration model for the prediction of hydroxyl value (OHV) was developed using the range 1805.1-649.9 cm(-1). Validation of the method was carried out by comparing the OHV of a series of hydroxylated soybean oil predicted by the LS-SVM model to the values obtained by the AOCS standard method. A correlation coefficient equal to 0.989 and RMSEP = 4.96 mg of KOH/g was obtained. This study demonstrates a better prediction ability of the LS-SVM technique to determine OHV in hydroxylated soybean oil samples by HATR/FT-IR spectra.     

近红外光谱混合模型定量分析不同物理状态样品的研究

近红外光谱(NIRS)以漫反射模式对非均质样本进行测量时,由于其光谱散射和吸收系数差异较大,建立的校正模型准确性和稳健性较低,因此,本研究提出了一种基于均质样本和模型转移方法建立混合模型的策略,解决非均质样本近红外光谱检测的问题.以烟叶样本为研究对象,分别建立了基于Shenk专利算法(Shenk′s)、分段直接标准化(PDS)和基于典型相关分析的模型转移算法(CTCCA)的烟粉+烟丝、烟粉+烟片混合模型,用于烟丝和烟片样本中烟碱含量的预测.结果表明,混合模型对烟丝和烟片样本的预测均方误差(RMSEP)较直接建模分别降低了1.39%和2.73%,预测结果有一定的改善,稳健性提高,3种方法中CTCCA表现最优.因此,采用近红外光谱均质模型和模型转移方法建立的混合模型对非均质样本的测定具有可行性,有利于在线近红外光谱分析技术的发展,可为近红外光谱模型的共享提供参考.     

废旧涤/棉混纺织物近红外定量分析模型的建立及预测

将中红外光谱筛选出的598个纯涤、纯棉及涤/棉混纺样本采用GB/T 2910.11-2009法测定其涤、棉准确含量,其中校正集样本252个,验证集样本346个。使用便携式近红外光谱仪获取样本的原始近红外光谱(NIRS)。校正集样本依据回归系数的分布趋势和范围选取最佳建模谱区,并采用差分一阶导、S-G平滑和均值中心化相结合的方法对原始光谱进行预处理,利用偏最小二乘法(PLS)建立涤/棉混纺织物中涤含量的近红外(NIR)定量分析模型。同时分析了样本颜色对NIRS的影响,探讨了斜线光谱样本、奇异样本和不同组织结构织物对模型预测效果的影响。结果表明:利用PLS法建立的涤/棉混纺织物定量分析模型最优组合包含1个光谱区间和9个主成分因子,校正集相关系数(RC)为0.998,标准偏差(SEC)为0.908。为验证所建模型的有效性和实用性,对346个未参与建模的涤棉样本进行了预测,并将预测结果与国标法测定值进行方差分析,两种方法结果无显著差异,预测正确率达97%以上。模型的建立为废旧涤/棉混纺织物快速、无损分拣提供了基础数据库。     

木薯品质分析的近红外光谱模型建立及其应用研究

应用近红外光谱法(NIRS)建立木薯中淀粉、水分定量分析的近红外光谱数学模型,探讨了修正偏最小二乘法(MPLS)、偏最小二乘法(PLS)以及主成分回归法(PCR)等优化处理对定标模型的影响,确定了修正偏最小二乘法(MPLS)是建立模型最适合的数学方法。并对模型预测结果的准确性进行了评价。结果表明:验证集样品的化学值和近红外预测值拟合存在较好的线性关系,相关性显著。淀粉模型预测标准偏差(Sep)为0.850,系统偏差(Bias)为-0.095,相关系数(r)为0.971。水分模型预测标准偏差(Sep)为0.075,系统偏差(Bias)为0.007,相关系数(r)为0.980。淀粉、水分定量分析的NIRS数学模型具有较高的预测准确性,可应用于木薯批量收购中的品质等分析。     

Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra

Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on its physical presentation.     

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39–14.14 mg cm⁻². The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R²), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20 s without sample destruction.     

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.     

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．     

Measurement of lactate in whole human blood with near-infrared transmission spectroscopy

We have evaluated the potential of near-infrared spectroscopy (NIRS) as a technique for rapid analysis of lactate in whole blood. To test the NIRS technique, a comparison was made with a standard clinical method using whole blood samples taken from five exercising human subjects at three different stage of exercise. To expand lactate concentration within the physiological range, standard additions method was used to generate 45 unique data points. Spectra were collected over the 2050-2400 nm spectral range with a 1 mm optical path length quartz cell. Reference lactate concentrations in the samples were determined by enzymatic measurements. Estimates and calibration of the lactate concentration with NIRS was made using partial least squares (PLS) regression analysis and leave-N-out cross validation on second derivative spectra. Separate calibrations were determined from each of the subject samples and cumulative PRESS was used to determine the number of PLS factors in the final model. The results from the PLS model presented are generated from the five individual calibration coefficient vectors and provided a correlation coefficient of 0.978 and a standard error of cross validation of 0.65 mmol l⁻¹ between the enzymatic assay and the NIRS technique. To study the parameters that impact the spectra baseline and the correlation between the calculated model and the data, referenced measurements of lactate against baseline spectrum were made for each individual. A correlation coefficient of 0.992 and a standard error of cross validation of 0.21 mmol l⁻¹ were found. The results suggest that NIRS may provide a valuable tool to assess physiological status for both research and clinical needs.