应用近红外光谱和偏最小二乘回归法预测玉米中淀粉含量

以普通玉米籽粒为试验材料,应用偏最小二乘回归法建立了基于近红外光谱数据的测定玉米籽粒中淀粉含量的校正模型。校正模型的校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别#30．31％、0．42％和0．29％,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0．9255和0．9310,表明所建立的校正模型具有较高的预测精度和较好的推广性,为玉米籽粒中淀粉含量的快速、无损测定提供了新的途径：     

CCD近红外光谱快速测定柴油中的芳烃含量

研究了采用电感耦合器件（ＣＣＤ）近工外光谱仪在短波近红外区域（７００－１１００ｎｍ）、利用偏最小二乘回归（ＰＬＳ）测定柴油中芳烃含量的方法。考虑样品颜色对短波近红外光谱的影响,对波长范围的选择和基线处理方式进行了研究。考察了样品进入光路的时间对测定结果的影响。将ＣＣＤＮＩＲ对未知样品的预测结果与傅里叶变换近红外光谱（ＦＴ－ＮＩＲ）在长波近红外范围（１０００－２０００ｎｍ）的预测结果及液相色谱的测定     

中药材三七中皂苷类成分的近红外光谱快速无损分析新方法

提出了用近红外漫反射光谱快速无损测定三七中皂苷类成分的新方法采用 HPLC分析了中药材三七固皂昔R_1,人参皂苷Hg_1,Rb_1和Rd的含量,用吸附树脂 比色法测定了三七总皂苷（PNS）的含量,共获得R_1,Bg_1,Rb_1,Rd,PNS的含 量范围分别为1,58-5．08,21,68-46.13,11.46-40.41粉．在3500-1100cm~(-1) 扫描样品,以交叉验证误差均方根（RMsECV）为指标,通过筛选,近红外波段和光 谱预处理方法．采用偏最小二乘算法建立了近红外光谱与5个组分PHLC分析值之间 的校正模型,预测了8个未知样本．R_1,Rg_1,Rb_1,Rd及PNS校正模型的RMSECV 分别为0．40,1.47,1.94,0RMSEP分别为0．53,3．15,2．14,0．70,9．03． 该方法快速无损,结果可靠,为中药材复杂体系中化学组分的测定提供了新的绿色 分析手段．     

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．     

多模型共识偏最小二乘法用于近红外光谱定量分析

将多模型共识偏最小二乘法用于近红外光谱定量分析。利用随机抽取的训练子集建立一系列偏最小二乘模型,选取其中性能较好的部分模型作为成员模型,用这些成员模型来预测未知样品。将该方法用于一组生物样本的近红外光谱与样品中人血清白蛋白、γ-球蛋白以及葡萄糖含量之间的建模研究,并与单模型偏最小二乘法了进行比较。结果 PLS对独立测试集中三种组分进行50次重复预测的平均RMSEP分别为0.1066,0.0853和0.1338,RMSEP的标准偏差分别为0.0174,0.0144和0.0416;而本方法重复预测的平均RMSEP分别为0.0715,0.0750和0.0781,RMSEP的标准偏差分别为0.0033,0.2729×10-4和0.0025。     

PDS用于不同温度下的近红外光谱模型传递研究

采用合适的计算方法可降低测定环境对近红外光谱校正模型稳健性的影响。该文以喷气燃料为研究对象,考察了分段直接校正算法对所建模型预测结果的影响,通过选择转移样品数及窗口宽度,建立了最佳的校正模型和光谱转移参数。结果表明,在20℃下建立近红外光谱校正模型,直接预测30℃下喷气燃料的密度,预测集样品均方根误差（RMSEP）为0.2031,而30℃近红外光谱采用分段直接校正算法模型转移后,预测集样品均方根误差（RMSEP）降低为0.1354,预测结果得到明显改善,有效地解决了样品温度对近红外光谱分析结果的影响。     

近红外光谱测定溶液中甲醇的含量

测量了含微量甲醇(体积分数为0.04%～0.24%)的系列乙醇水溶液的近红外光谱,利用近红外光谱分析建立了预测甲醇含量的定量分析模型。比较了用外部检验法(Test Set-Validation)和交叉检验法(Cross-Validaton)建立的数学模型,研究了使用外部检验法时,校正集和检验集样品数的改变对模型预测结果的影响。结果发现,当校正集样品数为15检验集样品数为6(总样品数为21)时,使用外部检验法建立的数学模型预测结果较好,其校正集的均方根误差和检验集的预测均方根误差(分别为RMSEE和RMSEP)均较小(分别为0.0115和0.0105),而且很接近。结果表明,近红外光谱方法简单,准确而且实用。     

温控近红外光谱用于葡萄糖的高灵敏检测

温控近红外光谱在混合体系定量分析以及分子间相互作用分析中已得到应用.本工作利用互因子分析方法（MFA）探索了温控近红外光谱技术测定血清样品中低浓度葡萄糖含量的可行性.测量了不同温度下葡萄糖含量分别为1.0～15.0mmol/L和0.0～1.0 mmol/L的两组血清溶液的近红外光谱,并利用MFA进行了定量计算.结果表明,利用该方法可以实现样品中微量葡萄糖的准确定量,MFA提取的标准信号（SS）的相对强度和葡萄糖浓度之间的线性相关系数R分别为0.9923和0.9895,预测均方根误差（RMSEP）分别为0.35和0.07 mmol/L.     

基于光谱比值分析的无标样近红外模型传递方法

测量环境及光谱仪台间差异导致近红外光谱(NIRS)模型传递到从机后,常产生较大误差。该文使用标准正态变量变换(SNV)+微分处理光谱消除光谱散射和基线漂移的影响,提出通过仪器间光谱信号比值分析筛选波长的方法(Screening wavelengths based on spectrum ratio analysis,SWSRA),选出仪器间一致性较好且样本间差异大的光谱特征波长,采用筛选出的波长信号建立待测性质的偏最小二乘近红外光谱定标模型。以80个玉米样品中水分、油、蛋白质含量及72个黄芩样品中黄芩苷含量的NIRS预测对该方法进行了检验。结果表明,SWSRA主机模型预测从机样品的各成分含量的平均相对误差均小于4.3%,明显优于全波长模型直接传递的结果,且其预测均方根残差RMSEP与文献报道的其他模型传递方法的结果相当或更优。SWSRA方法具有模型参数少、稳健、简便易行等优点,可以在同类型近红外光谱仪器之间实现模型的无标样传递。     

近红外光谱法定量分析水溶液中葡萄糖、麦芽糖和β-环糊精

本文应用正交设计法配制了葡萄糖、麦芽糖和伊环糊精混合水溶液的29个样品（其中25个用作校正集样品,4个用作预测集样品）,扫描此混合水溶液样品的近红外（NIR）光谱,并用Unscrambler定量分析软件将近红外光谱与对应的化学成分值相关联,在不同波长范围下建立了水溶液中葡萄糖、麦芽糖和p环糊精的近红外原始光谱、一阶导数光谱的偏最小二乘法（PLS）定量分析数学模型,根据模型评价参数选择了最佳模型,并且用这些模型对未知样品进行了预测。结果显示,对浓度范围分别在22.73-104.17mg·mL^-1、22.73-104.17mg·mL^-1和1.85-8.41mg·mL^-1的葡萄糖、麦芽糖和仔环糊精水溶液,其NIR数学模型的相关系数分别为0.9946、0.9976和0.9036,模型的预测相对偏差（RMSEP）分别为2.52mg·mL^-1、1.95mg·mL^-1和0.87mg·mL^-1,预测标准偏差（SEP）分另4为2.58mg·mL^-1、2.01mg·mL^-1和0.90mg·mL^-1。     

Determination of alcohol content in beverages using short-wave near-infrared spectroscopy and temperature correction by transfer calibration procedures

This paper reports the utilization of short-wave near-infrared (SW-NIR) transmission spectroscopy for rapid and conclusive analysis of alcoholic content (% v/v) in beverages. This spectral region is interesting because common visible diode array spectrometers can be utilized, reducing time and costs in comparison with traditional near-infrared or mid-infrared instruments. A correction of external temperature influence is necessary, and for this purposes two calibration transfer procedures were compared: piecewise direct standardization (PDS) and orthogonal signal correction (OSC). The RMSEP found for the alcoholic content model at 20 °C was 0.13% v/v and, after application of transfer calibration procedures at other temperatures (15, 25, 30 and 35 °C) using the model built at 20 °C, errors of the same order of magnitude were obtained.     

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.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

TEMPORAL SHIFTS IN BIOLUMINESCENCE EMISSION SPECTRA FROM THE DEEP-SEA FISH, Searsia koefoedi

Abstract— The luminescence of freshly collected exudate from the post-cleithral organ of the deep-sea searsiid fish. Searsia koefoedi , was increased in intensity by the addition of hydrogen peroxide and exhibited emission maxima at 408 nm and 478 nm. Initially, the spectrum was unimodal with a long wavelength peak and a short wavelength shoulder. With time, the short wavelength peak increased in relative magnitude; the spectrum became bimodal, then the short wavelength peak predominated. The measured time-dependent changes in the spectral distribution resulted from differences in the rates of decay of the two peaks. The short wavelength peak exhibited first order exponential decay with a mean (± standard deviation) decay constant of -0.13 ± 0.02 min^-1 ( N = 4). Decay of the long wavelength peak proceeded approximately twice as fast and was best defined by a double exponential function.     

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。     

EXCITATION WAVELENGTH DEPENDENCE OF PROTOTROPIC DISSOCIATION AND TAUTOMERISM OF SALICYLAMIDE IN THE LOWEST EXCITED SINGLET STATE

Abstract—The fluorescence spectra of salicylamide in cyclohexane, ethanol and at different pH in water were studied. The short and long wavelength fluorescences observed in the organic solvents originate from emissions of a weakly or non-intramolecularly hydrogen bonded conformer and from phototautomerization of a strongly intramolecularly hydrogen bonded conformer, respectively. Evidence for at least 2 conformers in the ground state exists in the excitation wavelength dependence of the ratio of short wavelength to long wavelength emission. In water, prototropic dissociation of the phenolic group of salicylamide in the lowest excited singlet state also shows an excitation wavelength dependence, indicating that the weakly or non-intramolecularly hydrogen bonded conformer in water is predominately responsible for photodissociation.     

Non-destructive analysis of the two subspecies of African elephants, mammoth, hippopotamus, and sperm whale ivories by visible and short-wave near infrared spectroscopy and chemometrics

Visible (VIS) and short-wave near infrared (SW-NIR) spectroscopy was used for non-destructive analysis of ivories. VIS-SW-NIR (500-1000 nm) spectra were measured in situ for five kinds of ivories, that is two subspecies of African elephants, mammoth, hippopotamus, and sperm whale. Chemometrics analyses were carried out for the spectral data from 500 to 1000 nm region. The five kinds of ivories were clearly discriminated from each other on the scores plot of two principal components (PCs) obtained by principal component analysis (PCA). It was noteworthy that the ivories of the two subspecies of African elephants were discriminated by the scores of PC 1. The loadings plot for PC 1 showed that the discrimination relies on the intensity changes in bands due to collagenous proteins and water interacting with proteins. It was found that the scores plot of PC 2 is useful to distinguish between the ivories of the two subspecies of African elephants and the other ivories. We also developed a calibration model that predicted the specific gravity of five kinds of ivories from their VIS-SW-NIR spectral data using partial least squares (PLS)-1 regression. The correlation coefficient and root mean square error of cross validation (RMSECV) of this model were 0.960 and 0.037, respectively.     

Rapid Simultaneous Determination of Andrographolides in Andrographis paniculata by Near-Infrared Spectroscopy

The potential of near-infrared spectroscopy (NIRS) for the quality control of traditional Chinese medicine has been evaluated. Seven quantitative parameters, andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, and alcohol-soluble extract of Andrographis paniculata, were evaluated by NIRS. The reference values of andrographolides were determined by high-performance liquid chromatography, and the others were obtained using the standard methods of the 2015 Chinese Pharmacopoeia. The predicted values were determined by a quantitative model using NIRS based on partial least square regression. Different spectral preprocessing methods, spectral ranges, and optimum number of factors were selected to optimize the models. All models were estimated by the combination of various parameters, including the correlation coefficient of calibration for andrographolide, deoxyandrographolide, dehydroandrographolide, neoandrographolide, moisture, ash content, alcohol-soluble extract (values of 0.980, 0.984, 0.989, 0.983, 0.987, 0.988, 0.979, respectively), root mean square error of calibration (values of 0.156, 0.038, 0.050, 0.029, 0.604, 0.431, 0.135, respectively), root mean square error of prediction (values of 0.169, 0.041, 0.050, 0.033, 0.280, 0.493, 0.140, respectively), root mean square error of cross-validation (values of 0.626, 0.114, 0.158, 0.046, 1.145, 0.774, 0.508, respectively), and ratio of standard deviation to standard error of prediction (values of 4.583, 4.690, 4.796, 4.899, 4.899, 4.690, 5.099, respectively). The results show that the calibration models by NIRS are reliable and can be applied for the quantification for seven parameters from A. paniculata for quality control in traditional Chinese medicine production and processing.     

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.