近红外光谱与化学计量学方法用于镉污染稻米的定性鉴别

采用近红外光谱漫反射模式结合化学计量学方法对稻米镉含量是否超标进行可行性鉴别分析.本研究收集了120个样本,测定其镉含量值(合格49个,不合格71个).对光谱数据预处理方法优化,确定了平滑,一阶导数以及自归一化后的数据作为输入变量.采用竞争性自适应重加权算法筛选了45个关键变量,并对上述变量的光谱吸收带进行归属.比较了主成分分析-判别分析法、偏最小二乘识别分析、线性判别分析、K-最近邻法与簇类独立软模式法5种模式识别方法.确定采用偏最小二乘识别分析建模效果最好,模型训练集与预测集鉴别准确率分别达到98.8％与91.7％.结果表明,近红外光谱作为初筛方法可用于鉴别稻米中镉含量是否超标.     

近红外光谱结合化学计量学快速测定羊栖菜抗氧化活性

基于近红外光谱技术建立了快速、高效的羊栖菜抗氧化活性评价方法。采用紫外-可见分光光度法测定了6批共150个羊栖菜样本的抗氧化活性,包括1,1-二苯基-2-三硝基苯肼（DPPH）自由基清除能力、2,2’-联氮-双-3-乙基苯并噻唑啉-6-磺酸（ABTS）自由基清除能力和铁离子还原能力（FRAP法）。采用NIRS和偏最小二乘法（PLS）建立了3个抗氧化活性指标的定量校正模型,并采用不同光谱预处理方法和竞争性自适应重加权采样（CARS）方法优化模型性能。将校正集相关系数（RC）、预测集相关系数（RP）、校正集均方根误差（RMSEC）和预测集均方根误差（RMSEP）作为校正模型的评价指标。结果表明,3个定量校正模型的预测精度均较理想,RP和RMSEP分别为0.968和2.42%、0.967和0.73%、0.979和3.60μmol/L。基于NIRS和CARSPLS所构建的方法可以成功用于羊栖菜的抗氧化活性测定,具备分析快速、操作简便、经济环保的优点,对保障羊栖菜品质、提升羊栖菜品质控制水平有一定的指导意义。     

用于近红外光谱分析的化学计量学方法研究与应用进展

分析模型的建立是近红外光谱分析的核心技术之一,本文综述了近些年在近红外光谱分析方法中出现的一些新算法和模型建立策略,如基于核函数的非线性校正方法、集成(或共识)的建模策略、多维分辨和校正方法、基于局部样本的建模策略以及二维相关光谱等,并给出了一些方法的具体算法。     

近红外光谱分析中的化学计量学算法研究新进展

近红外光谱是一种绿色、快捷的分析技术,在科学研究、工业生产以及日常检测中得到广泛应用。化学计量学算法的应用在近红外光谱技术的发展过程中发挥了重要作用。化学计量学方法通过寻找测量变量之间的相关性,构建数学模型,量化样本间的差异性,并发现事物变化的内在规律,实现较合理准确的未知预测。这也是"大数据"战略的重要环节和主旨所在。该文针对近红外光谱吸收信号较弱、谱峰重叠严重,以及光谱测量过程中易受背景、噪声、无信息变量和外界环境因素干扰等,导致借助化学计量学方法建立的光谱与研究目标的定性定量分析模型变差问题,总结了近年来在近红外光谱领域所提出的一些化学计量学新方法,包括光谱预处理、变量选择、多元校正和模型转移,从不同角度阐述了这些方法在消除近红外光谱模型的干扰因素,提高模型的可靠性、预测准确性和适用性等方面的作用。     

核磁共振技术结合化学计量学方法用于蜂蜜的掺假鉴别

采用核磁共振技术(NMR)结合化学计量学分析手段研究了真蜂蜜和掺假蜂蜜的指纹图谱变化情况。采用无监督的主成分分析(PCA)和有监督的偏最小二乘判别分析(PLS-DA)、正交偏最小二乘判别分析(OPLS-DA)等多元统计分析方法从核磁信号中提取各组的分类信息。结果表明:建立的OPLS-DA模型能够区分真假蜂蜜,所建模型对蜂蜜真假判别的解释能力为90.5%,对未知样本的预测能力为75.5%、识别率为89.7%。置换测试验证表明,化学计量学模型具有很好的稳定性和预测性,可信赖性强,且模型稳健。通过OPLS-DA模型的载荷图和相关系数分析找到了对区分掺假蜂蜜有显著作用的标志物。结合相关系数分析,建立了辨别真假蜂蜜的多元线性回归方程。该方法可简单、快速地用于未知蜂蜜的掺假鉴别,为规范蜂蜜市场提供有利的依据。     

基于近红外光谱分析技术的栽培香菇产地快速鉴别

建立了一种基于近红外光谱分析技术的香菇产地鉴别方法。利用近红外光谱仪扫描不同主产地的香菇干样,获得样品的近红外漫反射光谱。利用偏最小二乘判别分析(PLSDA)分别建立了吉林、湖北、福建3个省份栽培香菇的产地判别模型,同时使用光谱预处理和波长筛选技术对判别模型进行优化,最后使用预测样品对模型进行验证。结果表明,使用原始光谱建立的模型能够初步实现对产地的判别,使用光谱预处理技术扣除光谱中的背景信息,同时利用波长筛选技术选择特定波长对模型进行优化后,可进一步提高预测正确率。该方法为香菇产地真实性溯源提供了一种新方法,对香菇产业发展具有重要的实际意义。     

近红外漫反射光谱聚类分析用于血竭的鉴别

建立用近红外漫反射光谱法鉴别血竭的方法，采用聚类分析方法进行分类鉴别，快速、准确地鉴别了不同产地的血竭。近红外漫反射光谱法快速、简便、无损，可用于血竭等中药的分类鉴别。     

小波变换结合多维偏最小二乘方法用于近红外光谱定量分析

将小波变换和多维偏最小二乘法相结合用于近红外光谱定量校正模型的建立。首先将原始光谱进行小波变换分解，得到系列小波细节系数，通过选取一组受外界因素少、信息强的小波系数组成三维光谱阵，然后再采用多维偏最小二乘法建立校正模型。实验结果表明，该方法所建近红外校正模捌的预测能力更强，并更具稳健性。     

银镜增强的近红外漫反射光谱法测定血清尿素

针对近红外漫反射光谱(NIRDRS)技术灵敏度低或检出限高的缺点,采用银镜作为吸附基底以改善其灵敏度.银镜的强反射能力不仅能够降低光谱的背景干扰,还能增强光谱的响应信号.研究了NIRDRS技术结合银镜基质用于快速定量分析血清尿素含量的可行性.直接采集富集了血清的银镜基质的NIRDRS光谱,结合光谱预处理和变量选择方法,采用偏最小二乘回归建立了定量校正模型并进行快速预测.结果表明,采用银镜基质结合NIRDRS技术可以准确地测定含量为2.8~26.1 mmol/L的血清尿素,预测值与参考值的相关系数(R2p)为0.9823,样品回收率为86.0%~117.0%,且预测得到的最大误差值低至1.45 mmol/L.     

Authentication of Camellia oleifera Abel Oil by Near Infrared Fourier Transform Raman Spectroscopy

Adulteration of Camellia oleifera Abel. oil with other cheaper oil has been a long‐term problem in Taiwan because the price of Camellia oleifera Abel. oil is much higher than that of other edible oils due to its distinguished physiological properties. To develop an efficient method for determining the authenticity of Camellia oleifera Abel. oil is of great importance. In previous study (Appl. Spectrosc. 2003 , 57, 413), we showed that the Raman intensity ratio of ν₁₆₅₆/ν₁₄₃₉ was capable of reflecting precisely the degree of unsaturation in edible oils. Accordingly, we further present this Raman method to determine the authenticity of Camellia oleifera Abel. oil. It showed that the intensity ratio (I_{ν1656/ν1439}) changed concomitantly with the magnitude of double bonds in the binary mixtures of Camellia oleifera Abel. oil blended with other edible oil. A linear relationship with a high correlation coefficient (R² = 0.9938) between the Raman intensity ratio of ν₁₆₅₆/ν₁₄₃₉ and the percentage of Camellia oleifera Abel. oil was obtained, which could be used to determine the authenticity of Camellia oleifera Abel. oils collected from various markets. It shows that FT‐Raman spectroscopy provides a direct, simple, rapid, and non‐invasive method to probe the authenticity of Camellia oleifera Abel. oil.     

近红外光谱技术结合主成分分析法用于子宫内膜癌的诊断

应用近红外光谱技术结合化学计量学方法研究了子宫内膜癌组织近红外光谱特征提取和早期诊断的可行性. 测定了154 例子宫内膜组织切片的近红外光谱, 选取适宜的波段和光谱预处理方法进行主成分分析, 很好地区分了癌变、增生和正常子宫内膜组织切片, 并且分辨出处于不同分化期的组织切片, 为子宫内膜癌的早期诊断提供了可靠依据. 该法快速、简便, 有望发展成为一种新型的肿瘤无创诊断方法.     

近红外光谱技术用于花生油中棕榈油含量的测定

本文采用近红外光谱技术采集样品的近红外光谱数据,光谱经一阶求导后,采用偏最小二乘法(PLS)建立花生油中棕榈油含量的定标模型,并用交互验证法对模型进行了验证。模型相关系数为0.9963,校正均方根(RMSEC)为0.937。该模型应用于实际样品的检测,结果令人满意。     

汽油族组成的近红外光谱快速测定

以荧光指示剂吸附色谱法（FIA）测定汽油族组成结果为基础，采用近红外光谱和化学计量学方法建立了快速、准确测定催化裂化馏出口汽油族组成（饱和烃、烯烃和芳烃）的分析模型；试验表明，该法分析速度快、重复性好、成本低，特别适用于生产中间控制分析。     

Tea Classification by Near Infrared Spectroscopy with Projection Discriminant Analysis and Gene Expression Programming

Traditional gene expression programming for classification is designed for binary decisions. Herein, projection discriminant analysis for direct multiclass categorization using gene expression programming is described. Gene expression programming was first employed to examine new synthetic variables that were built as nonlinear combinations of the original features. The data were projected on planes spanned by these new synthetic variables and the nearest centroid was employed to classify new samples. A new objective function was formulated to determine optimum synthetic variables. Direct multiclass categorization using a gene expression programming algorithm was used to classify six tea varieties analyzed by near infrared spectroscopy. Compared with traditional gene expression programming, principal component analysis, and linear discriminant analysis, direct multiclass categorization with gene expression programming algorithm was more efficient. Visual inspection of high dimensional data by this approach also facilitated classification and comprehension of data.     

Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

Near infrared (NIR) spectrometry was used for the rapid characterization of quality parameters in desi chickpea flour (besan). Partial least square regression, principal component regression (PCR), interval partial least squares (iPLS), and synergy interval partial least squares (siPLS) were used to determine the protein, carbohydrate, fat, and moisture concentrations of besan. Spectra were collected in reflectance mode using a lab-built predispersive filter-based instrument from 700 to 2500?nm. The quality parameters were also determined by standard methods. The root mean square error (RMSE) for the calibration and validation sets was used to evaluate the performance of the models. The correlation coefficients for moisture, fat, protein, and carbohydrates in chickpea flour exceeded 0.96 using PLS and PCR models using the full spectral range. Wavelengths from 2100 to 2345?nm had the lowest RMSE for quality parameters by iPLS. The error was further decreased by 0.41, 0.1, and 1.1% for carbohydrates, fats, and proteins by siPLS. The NIR spectral regions yielding the lowest RMSE of prediction were 1620–2345?nm for carbohydrates, 1180–1590?nm and 1860–2094?nm for fat, and 1700–2345?nm for proteins. The study shows that chickpea flour quality parameters were accurately determined using the optimized wavelengths.     

应用近红外光谱分析技术快速分析饲料质量

以饲料质量分析为例,综述了近红外光谱分析技术及仪器的发展、回归校正技术及在快速分析饲料质量中的应用,引用文献２５篇     

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)。     

A Variable Selection Method of Near Infrared Spectroscopy Based on Automatic Weighting Variable Combination Population Analysis

Near-infrared spectroscopy (NIR) is widely used in food quantitative and qualitative analysis. Variable selection technique is a critical step of the spectrum modeling with the development of chemometrics. In this study, a novel variable selection strategy, automatic weighting variable combination population analysis (AWVCPA), is proposed. Firstly, binary matrix sampling (BMS) strategy, which provides each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, the variable frequency (Fre) and partial least squares regression (Reg), two kinds of information vector (IVs), are weighted to obtain the value of the contribution of each spectral variables, and the influence of two IVs of Rre and Reg is considered to each spectral variable. Finally, it uses the exponentially decreasing function (EDF) to remove the low contribution wavelengths so as to select the characteristic variables. In the case of near infrared spectra of beer and corn, yeast and oil concentration models based on partial least squares (PLS) of prediction are established. Compared with other variable selection methods, the research shows that AWVCPA is the best variable selection strategy in the same situation. It has 72.7% improvement comparing AWVCPA-PLS to PLS and the predicted root mean square error (RMSEP) decreases from 0.5348 to 0.1457 on beer dataset. Also it has 64.7% improvement comparing AWVCPA-PLS to PLS and the RMSEP decreases from 0.0702 to 0.0248 on corn dataset.