Variations in the chemical composition within pine (Pinus sylvestris) trunks determined by diffuse reflectance infrared spectroscopy and chemometrics

The feasibility of using DRIFT (diffuse reflectance infrared Fourier transform) spectroscopy combined with a multivariate analysis method (a PLS (projection to latent structures), regression) for predicting the distribution of the main organic constituents (cellulose, glucomannan, xylan, lignin, and extractives) within the Scots pine (Pinus sylvestris) stemwood was examined. PLS calibrations were carried out to establish a mathematical correlation between the data sets of conventional (“wet-chemistry-based”) wood analysis and the DRIFT spectra of the corresponding wood samples. Based on this approach, different surface maps on variations in the content of the main organic constituents within the stemwood matrix were shown.     

MATLAB语言在光谱定量分析中的应用

利用MATLAB语言实验紫外-可见吸收光谱法和近红外漫反射光谱法的定量分析数据的处理，着重阐述了偏最小二乘法的多元校正过程。该方法简便、实用，简化并优化了计算过程，效率高，数值稳定性好。     

正交递归选择法及其应用

本文提出一种新的变量筛选法－正交递归选择法，该法可以得到预报能力较强的模型，即ＰＲＥＳＳ（预报残差平方和）值较低的模型。用该法处理构效关系问题，并与逐步回归正向选择法及ＰＬＳ回归法进行了比较，得到满意的结果。     

Genetic Algorithm Based Potential Selection in Simultaneous Voltammetric Determination of Isoniazid and Hydrazine by Using Partial Least Squares (PLS) and Artificial Neural Networks (ANNs)

The voltammetric behavior of isoniazid and hydrazine at an overoxidized polypyrrole modified glassy carbon electrode has been investigated. The obtained cyclic voltammograms showed that their oxidation peaks were overlapped and it is difficult to determine them individually from a mixture without separation. To overcome this limitation, a procedure was proposed for resolution of overlapped voltammetric signals from mixtures of isoniazid and hydrazine. In this procedure, genetic algorithm was used for the selection of potentials for partial least squares. A feed forward artificial neural network with back propagation error algorithm was used to process the nonlinear relationship between currents and concentrations of hydrazine and isoniazid. The proposed method was suitable for determination of isoniazid in pharmaceutical tablets and detection of hydrazine impurities in the same samples.     

Successive projections algorithm improving the multivariate simultaneous direct spectrophotometric determination of five phenolic compounds in sea water

This paper proposes an analytical method to determine directly and simultaneously five phenolic compounds (4-nitrophenol, 2-nitrophenol, phenol, 2,4,6-trichlorophenol and 4-chlorophenol) in sea water (Ria de Bahía Blanca, Argentine). The advantages of this method is that only requires spectrophotometric measurements (separation steps and derivatization reagents are avoided) and chemometric modelling (PLS and MLR–SPA).The statistical comparison between PLS — a well established multivariate method — and MLR–SPA — a recently presented chemometric modelling — demonstrated better analytical performance for the later one. This fact is indicative of the potentiality of MLR–SPA for solving complex analytical problems.     

Rapid screening for ethyl carbamate in stone-fruit spirits using FTIR spectroscopy and chemometrics

Ethyl carbamate (EC, urethane, C₂H₅OCONH₂) is a known genotoxic carcinogen of widespread occurrence in fermented food and beverages with the highest concentrations being found in stone-fruit spirits. Time-consuming procedures requiring extraction and gas chromatographic–mass spectrometric determination are regarded as reference procedures for the analysis of EC in alcoholic beverages. In this study, the rapid method of Fourier transform infrared (FTIR) spectroscopy in combination with partial least-squares (PLS) regression using selected wavelength bands is applied for the first time to the screening analysis of EC in stone fruit spirits (analysis time only 2 min). Apart from the actual content of EC in the sample, additional information was available from the FTIR spectra. This included data concerning the EC precursor hydrocyanic acid (HCN) and the maximum EC concentration which could be formed during storage. The PLS procedure was validated using an independent set of samples (Q² = 0.71–0.76, SEP = 0.42–0.67). The method was found to lack the accuracy required for a quantitative determination; it could only be used semi-quantitatively in the context of a screening analysis. If a rejection level of 0.8 mg L^–1 is applied as cut-off, overall correct classification rates of 85–91% for the calibration set and 77–85% for the validation set were achieved. False negative results can be avoided by lowering the cut-off to 0.6 mg L^–1. Through use of FTIR screening, 60–70% of all samples can be classified as negative and removed, leaving only conspicuous analysis results exceeding cut-off to be confirmed by complex and labour-intensive reference analyses.     

烃类混合气体的神经网络模型检测

八十年代末科学家模仿生物鼻研制一种传感器阵列与计算机模式识别的气体检测系统．传感器阵列相当于生物鼻的嗅觉细胞，计算机模式识别系统相当于嗅泡和大脑「‘］．传感器阵列对气体的响应是一个多维空间的响应模式，这种响应模式经过一定的数学处理后可以实现气体的种类识别或浓度检测［’－‘j．传感器的响应和混合气体浓度之间呈非线性关系，这一特性给定量检测多组分气体混合物造成很大的限制．应用人工神经元网络技术（ANN）可以克服这一缺陷，并使检测气体的选择性大大提高．本工作运用ANN中的反向传播（BP）算法识别由16个不同…     

A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif

The 3D-QSAR CoMSIA technique was applied to a set of 458 peptides binding to the five most widespread HLA-A2-like alleles: A^*0201, A^*0202, A^*0203, A^*0206 and A^*6802. Models comprising the main physicochemical properties (steric bulk, electron density, hydrophobicity and hydrogen-bond formation abilities) were obtained with acceptable predictivity (q ² ranged from 0.385 to 0.683). The use of coefficient contour maps allowed an A2-supermotif to be identified based on common favoured and disfavoured areas. The CoMSIA definition for the best HLA-A2 binder is as follows: hydrophobic aromatic amino acid at position 1; hydrophobic bulky side chains at positions 2, 6 and 9; non-hydrogen-bond-forming amino acids at position 3; small aliphatic hydrogen-bond donors at position 4; aliphatic amino acids at position 5; small aliphatic side chains at position 7; and small aliphatic hydrophilic and hydrogen-bond forming amino acids at position 8.