Multivariate image analysis of a set of FTIR microspectroscopy images of aged bovine muscle tissue combining image and design information

In this paper we present an algorithm for analysing sets of FTIR microscopic images of tissue sections. The proposed approach allows one to investigate sets of many FTIR tissue images both with respect to sample information (variation from image to image) and spatial variations of tissues (variation within the image). The algorithm is applied to FTIR microscopy images of beef loin muscles containing myofibre and connective tissue regions. The FTIR microscopy images are taken of sub-samples from five different beef loin muscles that were aged for four different lengths of time. The images were investigated regarding variation due to the ageing length and due to the homogeneity of the connective tissue regions. The presented algorithm consists of the following main elements: (1) pre-processing of the spectra to overcome large quality differences in FTIR spectra and differences due to scatter effects, (2) identification of connective tissue regions in every image, (3) labelling of every connective tissue spectrum with respect to its location in the connective tissue region, and (4) analysis of variations in the FTIR microscopic images in regard to ageing time and pixel position of the spectra in the connective tissue region. Important spectral parameters characterising collagen and proteoglycan structure were determined. Figure Effective optical path length estimated by EMSC     

Processing MALDI Mass Spectra to Improve Mass Spectral Direct Tissue Analysis

Profiling and imaging biological specimens using MALDI mass spectrometry has significant potential to contribute to our understanding and diagnosis of disease. The technique is efficient and high-throughput providing a wealth of data about the biological state of the sample from a very simple and direct experiment. However, in order for these techniques to be put to use for clinical purposes, the approaches used to process and analyze the data must improve. This study examines some of the existing tools to baseline subtract, normalize, align, and remove spectral noise for MALDI data, comparing the advantages of each. A preferred workflow is presented that can be easily implemented for data in ASCII format. The advantages of using such an approach are discussed for both molecular profiling and imaging mass spectrometry.     

TiO2 *nH2 O凝胶预处理对水热合成SrTiO3 粉的影响

以TiCl4为钛源, 首先制备TiO2 * nH2O凝胶, 然后在80℃的水热条件下制备了SrTiO3粉.利用X射线衍射(XRD)、透射电子显微镜(TEM)和红外光谱(FTIR)研究了TiO2 * nH2O凝胶水洗方式、阴离子(Cl-和NO3-)以及TiO2 * nH2O热处理对SrTiO3粉性能的影响.结果表明, 水洗和热处理都能使TiO2 * nH2O凝胶产生晶化; TiO2 * nH2O的晶化程度对产物SrTiO3颗粒的粒度和粒度分布有很大影响,以非晶质TiO2 * nH2O为钛源制备的SrTiO3颗粒粒度大且粒度分布宽.以结晶TiO2 * nH2O为钛源制备的SrTiO3颗粒粒度小且粒度分布窄,而且可以得到纳米颗粒.水热反应液相中存在Cl-或NO3-能使产物SrTiO3颗粒粒度稍有增大.综合以上结果, TiO2 * nH2O凝胶水洗对产物颗粒的影响主要是由于使凝胶产生了晶化,而由阴离子脱除产生的影响很小.因此,在不考虑阴离子对其它工程化影响(如设备腐蚀等)的前提下,可采用热处理代替水洗.     

Multivariate Calibration of Near Infrared Spectroscopy in the Presence of Light Scattering Effect: A Comparative Study

When analyzing heterogeneous samples using spectroscopy, the light scattering effect introduces non-linearity into the measurements and deteriorates the prediction accuracy of conventional linear models. This paper compares the prediction performance of two categories of chemometric methods: pre-processing techniques to remove the non-linearity and non-linear calibration techniques to directly model the non-linearity. A rigorous statistical procedure is adopted to ensure reliable comparison. The results suggest that optical path length estimation and correction (OPLEC) and Gaussian process (GP) regression are the most promising among the investigated methods. Furthermore, the combination of pre-processing and non-linear models is explored with limited success being achieved.     

鸡肉中兽药残留检测技术研究进展

鸡肉中兽药残留检测是食品安全检测关注的热点,作为目前普遍发展的样品前处理技术,超临界流体萃取、加速溶剂萃取、毛细管固相微萃取及其与色谱质谱联用检测兽药残留受到广泛关注。以鸡肉为主要检测基质对这些新技术的基本原理、特点及在兽药残留分析中的应用进行了综述,同时对兽药残留分析的研究方向进行了展望。     

基于偏最小二乘法的三种光谱定量测定LDPE/PP共混物组分含量的比较研究

分别利用中红外(mid-infrared, MIR)、近红外(near-infrared, NIR)和拉曼光谱(Raman)采集了31种不同比例的低密度聚乙烯/聚丙烯(LDPE/PP)共混物样本的光谱,利用偏最小二乘法(partial least-square, PLS)建立了光谱数据与LDPE含量的模型,研究了不同光谱范围和预处理方法对模型准确性的影响,并比较了三种光谱定量测量的准确性。结果表明,对于光谱差异小、存在噪音或基线干扰的谱图,预处理方法和光谱范围对模型的准确性均有较大的影响;经过三种预处理以及选择合适的光谱范围建立的模型决定系数(R2)分别从未处理前的0.887 6,0.849 3和0.875 7提升到0.990 6,0.997 3和0.997 2,校正均方根误差(root mean square error of calibration, RMSEC)则分别从10.15,11.75和10.67降低到2.941,1.561和1.598;三种光谱在经过预处理之后均能够较好地定量测量LDPE的含量,NIR和Raman模型准确性更高,由于两者的测量速度快,因此尤其适合于进行快速、准确的定量测量。     

A local pre-processing method for near-infrared spectra,combined with spectral segmentation and standard normal variate transformation

Pre-processing of near-infrared (NIR) spectral data has become a necessary part of chemometrics modeling and is widely used in many practical applications. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve subsequent qualitative or quantitative analysis. Herein, a localized version of standard normal variate (SNV) is proposed, in which the correction parameters are estimated from local spectral areas. The method of determining the optimal spectral segmentation is also presented. Compared with full range methods, the local method demonstrates advantages in spectral linearity correction, model interpretation and prediction accuracy. Several benchmark NIR data sets were studied in our experiments; the proposed method achieved comparable performance against proven full range methods, with the reduction of prediction errors being statistically significant in many cases.     

Second-order cover inequalities

We introduce a new class of second-order cover inequalities whose members are generally stronger than the classical knapsack cover inequalities that are commonly used to enhance the performance of branch-and-cut methods for 0–1 integer programming problems. These inequalities result by focusing attention on a single knapsack constraint in addition to an inequality that bounds the sum of all variables, or in general, that bounds a linear form containing only the coefficients 0, 1, and –1. We provide an algorithm that generates all non-dominated second-order cover inequalities, making use of theorems on dominance relationships to bypass the examination of many dominated alternatives. Furthermore, we derive conditions under which these non-dominated second-order cover inequalities would be facets of the convex hull of feasible solutions to the parent constraints, and demonstrate how they can be lifted otherwise. Numerical examples of applying the algorithm disclose its ability to generate valid inequalities that are sometimes significantly stronger than those derived from traditional knapsack covers. Our results can also be extended to incorporate multiple choice inequalities that limit sums over disjoint subsets of variables to be at most one.