Forensic classification of ballpoint pen inks using high performance liquid chromatography and infrared spectroscopy with principal components analysis and linear discriminant analysis

Several varieties of blue ballpoint pen inks were analyzed by high performance liquid chromatography (HPLC) and infrared spectroscopy (IR). The chromatographic data extracted at four wavelengths (254, 279, 370 and 400 nm) was analyzed individually and at a combination of these wavelengths by the soft independent modeling of class analogies (SIMCA) technique using principal components analysis (PCA) to estimate the separation between the pen samples. Linear discriminant analysis (LDA) measured the probability with which an observation could be assigned to a pen class. The best resolution was obtained by HPLC using data from all four wavelengths together, differentiating 96.4% pen pairs successfully using PCA and 97.9% pen samples by LDA. PCA separated 60.7% of the pen pairs and LDA provided a correct classification of 62.5% of the pens analyzed by IR. The results of this study indicate that HPLC coupled with chemometrics provided a better discrimination of ballpoint pen inks compared to IR. The need to develop a suitable IR method for analysing blue ballpoint pen inks has been emphasized and it is hoped that the development of such a method would indeed provide a valuable tool for the non-destructive analysis of blue ballpoint pen ink samples for forensic purposes.     

冠心病微量元素谱的计算机模式识别

用计算机多元分析研究冠心病微量元素谱，识别冠心病患者与健康者；非线性映射法判别率男性８６．６％，女性９６．２％；ＳＩＭＣＡ差别法正确回判率男性８５．０％，女性８８．３％。     

SIMCA法判别分析木材生物腐朽的研究

木材是一种生物质材料,容易受到各种微生物的危害,生物腐朽可以迅速导致木材结构的破坏,因此,对木材生物腐朽的快速、准确地检测或鉴定具有重要意义。近几年来,近红外光谱和SIMCA方法正被用于识别或检测食品、药品和农产品等研究中,因此,本研究尝试利用近红外光谱结合SIMCA方法来检测木材的生物腐朽。研究结果表明,应用近红外光谱和SIMCA方法能有效地判别木材的生物腐朽类型,通过培训集样本建立的基于PCA分析的SIMCA判别模型对未腐朽、白腐和褐腐三种类型样本进行回判,判别准确率分别为100%, 82.5%和100%;而对未知腐朽类型的样本(包括未腐朽、白腐和褐腐样本),判别准确率分别为100%, 85%和100%;SIMCA方法对未腐朽和褐腐类型的判别准确率均达到100%,但对白腐样本都有错判,造成这种错判的主要原因可能是由于样本包括的信息不够丰富以及腐朽初期白腐和褐腐试样的性质差异太小等。     

桉树杂交种与其亲本的近红外光谱判别

研究桉树控制授粉后目标性状的基因作用方式是探索其基因重组规律的重要内容。常规的数量统计分析精度往往不高,而DNA分析的专业要求高,且费时费力。该研究利用近红外光谱(NIRs)研究不同基因型桉树杂交种、亲本及杂交种与亲本间近红外光谱信息的关系,探索NIRs用于桉树杂交种与其亲本判别的可行性和准确性。以控制授粉的桉树亲本及其杂交F1代材料为对象,每种基因型从各自田间试验分别选取10个单株,采集树冠中上部新鲜健康叶片。用手持式近红外仪Phazir Rx(1624)采集桉树杂交种与其亲本叶片的NIRs信息。每单株选10片完全生理成熟的健康叶片,避开叶脉扫描其正面光谱5次,以50条NIRs信息的均值代表单个叶片的NIRs信息,最终每个基因型获得10条NIRs信息。对原始NIRs采用二阶多项式S.G一阶导数预处理。预处理后的NIRs用于多元统计分析,首先对桉树杂交亲本和子代样本进行主成分分析(PCA),直观展示不同基因型的分类情况。然后运用簇类独立软模式(SIMCA)和偏最小二乘判别分析(PLS-DA)两种有监督的判别模式验证NIRs用于桉树杂交种与其亲本树种的分类判别效果。PCA结果显示,不同的亲本间、杂交种间及杂交种与亲本间样本的主因子得分可以清晰地将各基因型分开。SIMCA模式判别分析中,桉树杂交种样本到亲本PCA模型的样本距离显示,待判别样本能够形成单独的聚类,且能直观反映两者的遗传相似。PLS-DA判别结果显示,桉树杂交亲本的PLS模型能通过预测其杂交子代的响应变量将其与亲本准确分开。结果表明,桉树叶片的NIRs信息可以准确地反映桉树杂交子代遗传信息的传递规律,NIRs判别模型可以准确地将各种基因型予以区分。因此,NIRs信息不仅可用于桉树杂交种和纯种的定性判别,还可以分析桉树基因重组过程中加性遗传效应的大小,从而为桉树遗传基础分析及其育种改良研究提供理论支撑。     

枸杞产地的红外指纹图谱与聚类分析法研究

本文首次采用傅里叶变换红外 (FTIR)光谱法并结合SMICA聚类分析法对不同产地 :宁夏玉西、宁夏中宁及内蒙古托克托旗的枸杞进行了聚类分析。结果表明 ,聚类分析技术对来自不同产地的枸杞可进行鉴别 ,该法快速、准确 ,为客观评价中药材的来源提供了一种新的方法。     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

Multivariate quality control solved by one‐class partial least squares regression: identification of adulterated peanut oils by mid‐infrared spectroscopy

The Partial least squares class model (PLSCM) was recently proposed for multivariate quality control based on a partial least squares (PLS) regression procedure. This paper presents a case study of quality control of peanut oils based on mid‐infrared (MIR) spectroscopy and class models, focusing mainly on the following aspects: (i) to explain the meanings of PLSCM components and make comparisons between PLSCM and soft independent modeling of class analogy (SIMCA); (ii) to correct the estimation of the original PLSCM confidence interval by considering a nonzero intercept term for center estimation; (iii) to investigate the potential of MIR spectroscopy combined with class models for identifying peanut oils with low doping concentrations of other edible oils. It is demonstrated that PLSCM is actually different from the ordinary PLS procedure, but it estimates the class center and class dispersion in the framework of a latent variable projection model. While SIMCA projects the original variables onto a few dimensions explaining most of the data variances, PLSCM components consider simultaneously the explained variances and the compactness of samples belonging to the same class. The analysis results indicate PLSCM is an intuitive and easy‐to‐use tool to tackle one‐class problems and has comparable performance with SIMCA. The advantages of PLSCM might be attributed to the great success and well‐established foundations of PLS. For PLSCM, the optimization of model complexity and estimation of decision region can be performed as in multivariate calibration routines. Copyright © 2011 John Wiley & Sons, Ltd.     

Use of chemometrics and mid infrared spectroscopy for the selection of extraction alternatives to reference analytical methods for total fat isolation

Chemometric techniques have been used to compare two methods for fat extraction, namely focused microwave-assisted Soxhlet extraction (FMASE) and dynamic ultrasound-assisted extraction (DUAE), with the conventional Folch method, frequently used as reference. The data generated by a mid infrared spectrometer, after appropriate treatment, provide a simple and effective way for the detection of potential alterations of the fat obtained with the assistance of auxiliary energies, in this case, microwaves and ultrasounds. The results thus obtained are as compared with those from the Folch method, a mild extraction method with a view to finding faster alternatives for routine analysis. Moreover, classification of the samples between cookies and snacks based on extraction kinetics studies was possible, thus demonstrating the importance of these studies for the development of analytical methods. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were used for both purposes, namely detection of alterations and classification of the samples as a function of their extraction kinetics, while K Nearest Neighbours (KNN) and Soft Independent Modelling of Class Analogy (SIMCA), based on PCA, models were generated in order both to predict the extraction kinetics of unknown samples, thus adjusting the extraction time as a function of the matrix, and find out explanation to the different extraction kinetics.     

木材种类的近红外光谱和模式识别

木材的种类识别是木材加工和贸易的一个重要环节,传统的木材种类识别方法主要有显微检测法和木材纹理识别法,其操作繁琐,耗时长,成本高,不能满足当前需求。本研究利用木材的近红外光谱(NIRS)结合模式识别方法,以期实现木材种类的快速准确识别。采用近红外光谱结合主成分分析法(PCA)、偏最小二乘判别分析法(PLSDA)和簇类独立软模式法(SIMCA)三种模式识别对58种木材进行种类鉴别研究;5点平滑、标准正态变量变换(SNV)、多元散射校正(MSC)、Savitzky-Golay一阶导数(SG 1st-Der)和小波导数(WD)五种光谱预处理方法用于木材光谱的预处理;校正集和测试集样品的正确识别率(CRR)用于模型的评价。采用PCA方法,通过样品的前三个主成分空间分布图分辨木材种类的聚类情况。在建立PLSDA模型,原始光谱的正确识别率最高,分别为88.2%和88.2%;5点平滑处理的光谱校正集和测试集的CRR分别为88.1%和88.2%;SNV处理的光谱校正集和测试集的CRR分别为84.4%和84.5%;MSC处理的光谱校正集和测试集的CRR分别为83.1%和84.2%;SG 1st-Der处理的光谱校正集和测试集的CRR分别为81.8%和82.7%;WD(小波基为“Haar”,分解尺度为80)处理的光谱校正集和测试集的CRR分别为87.3%和87.2%。可知,在PLSDA模型中,木材光谱未经预处理种类识别效果最后好。在建立SIMCA模型过程中,原始光谱的校正集和测试集的CRR分别为99.7%和99.4%;5点平滑处理的光谱校正集和测试集的CRR分别为100%和100%;SNV处理的光谱校正集和测试集的CRR分别为99.5%和99.1%;MSC处理的光谱校正集和测试集的CRR分别为99.0%和98.4%;SG 1st-Der的光谱校正集和测试集的CRR分别为81.8%和82.7%;WD处理的光谱校正集和测试集的CRR分别为100%和100%。可知,在SIMCA模型中,木材光谱经平滑和小波导数处理后的识别效果最好,且光谱的校正集和测试集CRR都为100%。采用三种模式结合五种不同的预处理方法对木材近红外光谱进行定性建模识别时,由于木材样本属性复杂,主成分分布图相互交织,PCA无法识别出58种木材;原始光谱的PLSDA模型可以得到较好的判别模型,但校正集和测试集的CRR只有88.2%和88.2%;木材光谱经过5点平滑或WD预处理后的SIMCA模型可达到最好的识别效果,校正集和测试集的CRR均为100%,且WD-SIMCA模型因子数比5点平滑SIMCA模型小,模型更为简化,故WD-SIMCA为58种木材种类识别的最优模型。研究表明光谱预处理方法可以有效的提高木材种类识别精度,有监督模式识别方法SIMCA可以用来建立有效的木材识别模型,近红外光谱结合模式识别可以为木材种类的识别提供一种快速简便的分析方法。