便携式近红外光谱仪对废旧涤/棉混纺织物中涤含量的快速检测

利用近红外光谱技术对252个涤/棉混纺织物进行研究,建立了不同光谱特征的涤/棉混纺织物的偏最小二乘(PLS)定量分析模型。将近红外光谱异常样本与光谱正常样本分别建模,显著提高了定量分析模型的预测精度、拓宽了模型的适用范围。以涤、棉主要吸收峰区间为基本建模波段,进行双向扩展,筛选出最佳建模波段,以相关系数(R)、预测集标准差(SEP)和验证集准确率优化建模条件,并与未分别建模的PLS模型相比较。用346个未参与建模的废旧涤/棉混纺织物对模型进行外部验证,外部验证准确率为92%,识别时间8s。     

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。     

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.     

小波变换结合连续投影算法优化茶叶中咖啡碱的近红外分析模型

本文着重探讨小波变换及其它光谱预处理方法对连续投影算法(SPA)波长筛选优化及建模效果的影响。以158个不同茶叶样本作为研究对象,将各种预处理方法单独或组合后与SPA结合使用,并通过偏最小二乘法(PLS)建立咖啡碱定量模型。其中一阶微分(WT-1~(st)D)-SPA组合建立的模型最佳,预测相关系数达到0.9481,均方根误差达到0.3053,验证集相对分析误差达到3.1959,建模变量由1038减小为10,其挑选出的波长数10和通过交叉验证确定的最佳PLS成分数7也比较接近,并包含在茶叶咖啡碱主要吸收谱带范围内。结果表明,小波变换结合WT-1~(st)D方法在消除光谱部分散射误差和高频噪声的同时,能有效提高茶叶光谱的分辨率,有助于SPA算法筛选出更少、代表性和独立性更优的特征波长组合,并极大地改善了模型的精度,为茶叶中咖啡碱的近红外分析建模提供了一种快速、简便的方法。     

基于局部最小二乘支持向量机的光谱定量分析

提出了一种基于局部最小二乘支持向量机(LSSVM)的回归方法,以克服待测参数和光谱数据间的非线性。本方法首先通过欧式距离选取局部训练样本子集,然后利用该子集建立LSSVM校正模型。由于每个测试样本建模时要选取不同的训练样本,因此提出相对距离的概念用来改进高斯核函数,使LSSVM的参数对于不同的训练样本具有自调整功能。针对一批汽油样本的实验结果表明,本方法的预测精度优于常见的局部线性建模方法和全局建模方法。     

基于OSC–WT–PLS近红外光谱法测定烟草中芸香苷

采用正交信号校正(OSC)结合小波变换(WT)对烟草光谱进行光谱预处理,将预处理后的烟草光谱结合偏最小二乘法(PLS)建立了烟草光谱对芸香苷的预测模型。利用OSC滤除光谱中与芸香苷含量无关的光谱信息,确定OSC提取的最佳主成分数为7,再选择WT中的最佳小波基函数bior1.1对OSC预处理后的光谱进行压缩及进一步滤噪,然后进行PLS建模,OSC–WT–PLS所建模型决定系数r~2=0.874,校正标准偏差RMSEC=0.85,预测均方根误差RMSEP=0.743,交互验证系数Q_(ext)~2=0.887。结果表明,用OSC–WT–PLS可滤除光谱信息中与待测样品含量无关的信息、减少光谱数据量,降低建立模型的复杂度、提高建模速度及模型的预测能力、准确度。     

基于多模型共识的偏最小二乘法用于近红外光谱定量分析

建立了多模型共识偏最小二乘(cPLS)建模方法, 并应用于烟草样品近红外(NIR)光谱与常规成分氯含量之间的建模研究, 探讨了建模参数对预测结果的影响. 结果表明, cPLS方法与传统的偏最小二乘算法(PLS)相比, 所建模型更稳定可靠, 预测结果也可得到了明显改善.     

樱桃含糖量的无损检测实验研究

利用便携式可见-近红外光谱仪,研究了600~1100 nm波段内无损检测樱桃含糖量的可行性。以烟台大樱桃为研究对象,采集了每个樱桃的含糖量。利用小波去噪法对光谱数据进行预处理,并用主成分回归分析法(PCR)建立了樱桃含糖量定量分析模型。实验结果为:多尺度小波去噪法滤除了原始光谱中的噪声,同时保留了原始光谱的主要信息;所建立的主成分回归定量分析模型的校正样本集的相关系数(R)为0.9394,校正均方根误差(RMSEC)为0.1384;预测样本集的相关系数(R)为0.9071,预测均方根误差(RMSEP)为0.1495。同时与偏最小二乘回归法(PLSR)所建模型得出的预测结果相差很小。研究表明:应用便携式光谱技术在600~1100 nm范围内无损检测樱桃含糖量具有可行性,为樱桃内部品质的野外在线动态检测提供了理论依据。     

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。     

苹果产地差异对可溶性固形物近红外光谱检测模型影响的研究

为更好地利用近红外光谱预测苹果可溶性固形物含量,减少产地差异对近红外光谱检测模型的影响,以4种不同产地的富士苹果为研究对象,采用基于x-y共生距离的样本划分方法分别对不同产地的苹果选取代表性样本作为校正集,利用偏最小二乘算法,建立和比较单一产地和混合产地下的苹果可溶性固形物近红外光谱检测模型,并结合竞争性自适应重加权算法(CARS)和连续投影算法(SPA)对苹果可溶性固形物的建模变量进行筛选。相比单一产地和其它混合产地模型,混合所有4种苹果产地的校正集样本建立的模型取得了最好的预测结果,另外,结合CARS-SPA筛选的16个特征波长,模型得到了进一步简化,其预测相关系数和预测均方根误差分别为0.978和0.441°Brix。结果表明,利用多个产地的苹果样本建立的混合模型,结合有效特征波长,可提高对苹果可溶性固形物含量的预测精度,减小产地差异对可溶性固形物近红外光谱检测的影响。     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing.

By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrared (NIR) spectrum is proposed, and it is used in the determination of the chlorogenic acid in plant samples as a preprocessing tool for partial least square (PLS) modeling. It is shown that the benefit of the proposed method lies not only in its performance to improve the quality of PLS model and the prediction precision, but also in its simplicity and practicability. It may become a convenient and efficient tool for preprocessing NIR spectral data sets in multivariate calibration.     

近红外光谱相似性评估结合局部回归方法无损检测苹果糖度

基于Bayesian相似性评估方法结合偏最小二乘局部回归,对苹果近红外数据库进行数据挖掘。通过相似性计算方法搜索出与预测样品相近的近红外光谱,形成校正子集后采用局部回归方法获得待测样品的相关信息。该方法所建立局部模型的平均检验标准偏差(SEV)约为0.57,分析30个预测样品的预测标准偏差(SEP)约为0.61;基于马氏距离的传统方法建立的偏最小二乘局部模型的平均SEV为0.59,分析30个待测样品的预测SEP为0.64;而采用整个数据库建立的全局偏最小二乘模型的SEV约为0.65,分析30个预测样品SEP约为0.70。基于Bayesian相似性评估的局部回归方法在苹果糖度的近红外无损定量分析中获得较好的应用结果,在实际应用中该方法比全局回归方法具有更强的适用性,为近红外光谱分析提供了新的分析工具。     

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

基于独立分量和神经网络的近红外多组分分析方法

采用小波变换对光谱数据进行压缩,用独立分量分析(ICA)方法提取近红外光谱数据矩阵的独立成分和相应的混合矩阵,再用BP神经网络对混合矩阵和实测浓度矩阵进行建模,提出了基于独立分量分析-神经网络回归(ICA-NNR)的近红外分析建模方法。进一步研究了独立分量数和网络中间隐层的神经元数对模型性能的影响,经优化后的ICA-NNR模型在相关系数与均方根误差两个指标上均优于直接用光谱矩阵作为输入所建立的模型。本方法用于玉米中水分、淀粉、蛋白质3种主要成分含量的同时测定,检验样品集的化学检测值与近红外预测值的相关系数分别达到:淀粉r=0.971,蛋白质r=0.976,水分r=0.975。     

Multivariate calibration of spectral data using dual-domain regression analysis

To date, few efforts have been made to take simultaneous advantage of the local nature of spectral data in both the time and frequency domains in a single regression model. We describe here the use of a novel chemometrics algorithm using the wavelet transform. We call the algorithm dual-domain regression, as the regression step defines a weighted model in the time-domain based on the contributions of parallel, frequency-domain models made from wavelet coefficients reflecting different scales. In principle, any regression method can be used, and implementation of the algorithm using partial least squares regression and principal component regression are reported here. The performance of the models produced from the algorithm is generally superior to that of regular partial least squares (PLS) or principal component regression (PCR) models applied to data restricted to a single domain. Dual-domain PLS and PCR algorithms are applied to near infrared (NIR) spectral datasets of Cargill corn samples and sets of spectra collected on batch chemical reactions run in different reactors to illustrate the improved robustness of the modeling.     

A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables

An algorithm is proposed for extracting relevant information from near-infrared (NIR) spectra for multivariate calibration of routine components in complex plant samples. The algorithm is a combination of wavelet transform (WT) data compression and a procedure for uninformative variable elimination (UVE). After compression of the NIR spectra by WT, the UVE approach is used to eliminate the irrelevant wavelet coefficients. Finally, a calibration model is built from the retained wavelet coefficients to enable prediction. Because irrelevant information can be removed from the spectra used for multivariate calibration, the model based on the extracted relevant features is better than those obtained with full-spectrum data. Both prediction precision and calculation speed are improved.     

主成分分析-支持向量回归建模方法及应用研究

将主成分分析(PCA)用于近红外光谱的特征提取,并与支持向量回归(SVR)相结合,实现了主成分分析-支持向量回归(PCA-SVR)用于近红外光谱定量分析的建模方法。与单纯的SVR方法相比,不仅提高了运算速度,而且提高了模型的预测准确度。将PCA-SVR方法用于烟草样品中总糖和总挥发碱含量的测定,所得结果的预测均方根误差分别为1.323和0.0477;回收率分别为91.8%~112.6%和88.9%~120.2%。     

Continuous wavelet transform applied to removing the fluctuating background in near-infrared spectra

A novel method based on continuous wavelet transform (CWT) was proposed as a preprocessing tool for the near-infrared (NIR) spectra. Due to the property of the vanishing moments of the wavelet, the fluctuating background of the NIR spectra can be successfully removed through convolution of the spectra with an appropriate wavelet function. The vanishing moments of a wavelet and the scale parameter are two key factors that govern the result of the background elimination. The result of its application to both the simulated spectra and the NIR spectra of tobacco samples demonstrates that CWT is a competitive tool for removing fluctuating background in spectra.