基于料酒的三维同步荧光光谱模式识别研究

提出了一种应用同步荧光光谱技术无损快速鉴别料酒品牌的新方法.利用主成分分解法和小波变换法对料酒样品的同步荧光光谱信号进行了压缩处理,分别采用同步荧光光谱数据的第一主成分和小波细节系数为特征变量进行主成分分析和聚类分析,分类结果表明小波系数作为料酒的特征变量对料酒品牌分类正确率更高.利用偏最小二乘和径向基人工神经网络方法...     

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型的预测能力更强,并更具稳健性.     

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。     

基于小波-反向搜索及表面增强拉曼的食品中色素的光谱定性分析

使用金纳米粒子为增强因子的表面增强拉曼光谱技术,通过连续小波变换将拉曼光谱信号转化到小波空间(墨西哥帽小波作为小波基)。该步骤能够减轻信号中基线变化及随机噪音的影响并找到峰位置和最佳小波尺度系数。依据小波空间中的信息,对混合物光谱及标准谱光谱进行反向搜索得到反向搜索匹配系数(Reverse match quality,RMQ),作为判断混合物中目标成分是否存在的依据。该算法可对混合物中的目标物质进行准确定性,并已成功应用于多种食品中色素鉴定。食品中色素的检出率达到99%,且结果稳健,其效果明显优于传统的命中质量系数法(Hit quality index,HQI)。这证实了小波空间反向搜索方法是一种快速而准确的拉曼光谱定性算法。     

基于近红外光谱的维生素C片无损鉴别分析

基于近红外光谱技术与化学计量学方法,建立了一种国内外不同品牌维生素C片的无损鉴别方法。采集了国内外8个品牌的维生素C片共计40个样本的近红外光谱数据,比较了完整样品以及粉末样品的近红外光谱,采用连续小波变换技术消除背景干扰和基线漂移,基于标准偏差与相对标准偏差的变量筛选方法筛选出具有代表性的波数点,结合主成分分析方法对国内外不同品牌维生素C片进行鉴别分析。结果表明:原始光谱存在着明显的背景干扰和基线漂移现象,且粉末样品的重现性要优于完整样品;单纯使用原始光谱无法辨别来自不同品牌的维生素C片;连续小波变换可以有效消除背景干扰,提高模型鉴别能力;完整样品的鉴别准确率优于粉末样品,说明国内外不同品牌维生素C片主要成分基本一致,可能是辅剂和工艺上存在细微差异。通过结合近红外光谱分析技术与化学计量学方法,可实现对国产以及进口不同品牌维生素C片的鉴别分析。     

基于小波变换的航空润滑油酸值红外光谱分析

为利用红外光谱分析技术快速、准确测定在用航空润滑油酸值,将小波变换用于润滑油红外光谱预处理中,结合均值中心化法,提取光谱有效信息建模。光谱的小波变换选择符合光谱特征的db4小波为基函数,在分解尺度9下进行光谱分解,利用软阈值法滤除各层干扰噪声,重构消噪信号。该光谱预处理法与传统的Savitzky-Golay平滑导数结合均值中心化法相比,滤噪效果好,有效压缩了建模数据量。采用偏最小二乘法,选择最佳主因子数5,建立酸值模型,并对10个在用航空润滑油油样进行了分析。     

基于小波空间特征匹配及表面增强拉曼光谱技术快速检测混合物中的甲氨蝶呤和伏立康唑

为实现混合物中目标化合物的快速检测,合成了对甲氨蝶呤和伏立康唑具有普适性响应的金纳米复合(AuNPS-PDDA)基底,借助表面增强拉曼光谱技术,结合化学计量学方法,通过连续小波变换将甲氨蝶呤和伏立康唑的光谱信号转换到小波空间,依据小波空间特征匹配分析混合物的拉曼光谱,显著减轻信号中基线变化和随机噪声的影响,成功地识别出混合物中1.0×10~(-8)mol/L甲氨蝶呤和2.86×10~(-4) mol/L伏立康唑。其中,甲氨蝶呤和伏立康唑的小波特征匹配系数均大于0.96,高于传统的命中质量系数,且采用非负最小二乘法成功实现了两种物质含量比例的确定,实验的组分比例预测值与真实值之间相关性大于0.98。实验结果表明,小波空间特征匹配结合表面增强拉曼光谱技术能有效地识别且半定量混合物中的目标化合物。     

激光拉曼光谱解谱和处理的方法研究

研究一种拉曼光谱解谱和处理的方法。以化学计量学为基础,信号处理技术为工具,配合计算机算法的数据处理方法。具体为基线校正:对拉曼光谱原始信号进行基于自适应迭代重加权惩罚最小二乘法的基线校正;平滑:对进行完基线校正的拉曼光谱信号进行基于惩罚最小二乘法的平滑;峰检测:对进行完基线校正和平滑的信号进行基于连续小波变换的峰检测。这种基于惩罚最小二乘法的光谱平滑具有快速,可以连续控制平滑度并且可以进行交叉验证得到最客观的平滑值。改善了基于非对称最小二乘法的传统基线校正方法的两个缺陷。同时,基于连续小波变换的峰检测算法可以自动地并且同时考虑峰形和峰高对峰进行检测,最大限度地降低了峰检测假阳性的概率。     

基于高光谱技术的玉石鉴定及模型研究

为了给玉石鉴定提供依据以及得到优化预测模型,分别对天然玉石和假玉石的可见光高光谱图像进行分析。针对高光谱图像数据的非线性、小样本以及空间光谱维数大等问题,本研究首先对原始光谱数据进行主成分分析(PCA),使高维光谱数据降维,通过对比分析其平均光谱图和方差贡献率图,发现天然玉石与假玉石的谱线之间存在很大的差距,证明了高光谱成像技术在玉石鉴定领域的可行性。然后分别采用费希尔(Fisher)判别法、反向传输(BP)神经网络以及支持向量机(SVM)判别法建立的三种数学模型对玉石进行分类模式判别,结果显示,用Fisher判别法能直接得到预测的类别归属,用BP神经网络以及SVM判别法得到的类别鉴定准确率分别为96.37%,82.5%。研究结果表明,高光谱技术结合BP人工神经网络预测建模方法可以作为快速和非破坏性预测玉石真假的有效手段。     

小波变换方法的比较──红外光谱数据压缩

介绍了小波变换和多分辨分析的基本理论以及常用小波变换压缩数据的3种方法:(1)只保留模糊信号;(2)全部保留模糊信号及锐化信号中的较大值;(3)保留模糊信号及锐化信号中的较大值.将紧支集小波和正交三次B-样条小波压缩4-苯乙炔基-邻苯二甲酸酐的红外光谱数据进行了对比,计算表明正交三次B-样条小波变换方法效果较好,而在全部保留模糊信号及只保留锐化信号中数值较大的系数时,压缩比大而重建光谱数据与原始光谱数据间的均方差较小.     

Complexing capacity profiles of naturally occurring ligands in Tempranillo wines for Cu and Zn: an electroanalytical approach for cupric casse

Complexing capacity of naturally occurring ligands in Vitis vinifera (Tempranillo variety) wines has been studied with respect to two target metals (Cu and Zn) by differential pulse anodic stripping voltammetry (DPASV). Eight commercial wines of two certified brands of origin (CBO) and a young wine along its vinification process were monitored. Conditional stability constants and total complexing ligand(s) concentration(s) have been calculated for both metals. Discussion of the particular electrochemical responses for Cu and Zn for all samples is presented. A follow-up of the Cu stripping response allowed differentiating a commercial wine from one under processing related to the cupric casse phenomenon. Interaction of Cu with two molecular forms of cyanidin has been theoretically modeled at natural wine pH.     

Tentative identification of volatile flavor compounds in commercial Budu, a Malaysian fish sauce, using GC-MS

Budu is a famous Malaysian fish sauce, usually used as seasoning and condiment in cooking. Budu is produced by mixing fish and salt at certain ratio followed by fermentation for six months in closed tanks. In this study, four commercial brands of Budu were analyzed for their chemical properties (pH, salt content and volatile compounds). The pH of Budu samples ranged from 4.50-4.92, while the salt (NaCl) content ranged between 11.80% and 22.50% (w/v). For tentative identification of volatile flavor compounds in Budu, two GC columns have been used, DB-WAX and HP-5MS. A total of 44 volatile compounds have been detected and 16 were common for both columns. 3-Methyl-1-butanol, 2-methylbutanal, 3-methylbutanal, dimethyl disulfide, 3-(methylthio)-propanal, 3-methylbutanoic acid and benzaldehye have been identified as the aroma-active compounds in Budu due to their lower threshold values.     

Use of copper and gold electrodes as sensitive elements for fabrication of an electronic tongue: Discrimination of wines and whiskies

A low-cost method is proposed to classify wine and whisky samples using a disposable voltammetric electronic tongue that was fabricated using gold and copper substrates and a pattern recognition technique (Principal Component Analysis). The proposed device was successfully used to discriminate between expensive and cheap whisky samples and to detect adulteration processes using only a copper electrode. For wines, the electronic tongue was composed of copper and gold working electrodes and was able to classify three different brands of wine and to make distinctions regarding the wine type, i.e., dry red, soft red, dry white and soft white brands.     

鸡精调味料中谷氨酸钠含量测量不确定度的评定

分析鸡精调味料中谷氨酸钠含量测量不确定度的影响因素,建立数学模型,对各不确定度分量进行了分析和计算。当鸡精调味料中谷氨酸钠的含量为49．15％时,谷氨酸钠含量测定结果的扩展不确定度为0．34％(k=2)。     

Determination of total polyphenol index in wines employing a voltammetric electronic tongue

This work reports the application of a voltammetric electronic tongue system (ET) made from an array of modified graphite-epoxy composites plus a gold microelectrode in the qualitative and quantitative analysis of polyphenols found in wine. Wine samples were analyzed using cyclic voltammetry without any sample pretreatment. The obtained responses were preprocessed employing discrete wavelet transform (DWT) in order to compress and extract significant features from the voltammetric signals, and the obtained approximation coefficients fed a multivariate calibration method (artificial neural network-ANN-or partial least squares-PLS-) which accomplished the quantification of total polyphenol content. External test subset samples results were compared with the ones obtained with the Folin–Ciocalteu (FC) method and UV absorbance polyphenol index (I₂₈₀) as reference values, with highly significant correlation coefficients of 0.979 and 0.963 in the range from 50 to 2400 mg L⁻¹ gallic acid equivalents, respectively. In a separate experiment, qualitative discrimination of different polyphenols found in wine was also assessed by principal component analysis (PCA).     

Alternative common bases and signal compression for wavelets application in chemometrics

Representation or compression of data sets in the wavelet space is usually performed to retain the maximum variance of the original or pretreated data, like in the compression by means of principal components. In order to represent together a number of objects in the wavelet space, a common basis is required, and this common basis is usually obtained by means of the variance spectrum or of the variance wavelet tree. In this study, the use of alternative common bases is suggested, both for classification and regression problems. In the case of classification or class-modeling, the suggested common bases are based on the spectrum of the Fisher weights (a measure of the between-class to within-class variance ratio) or on the spectrum of the SIMCA discriminant weights. In the case of regression, the suggested common bases are obtained by the correlation spectrum (the correlation coefficients of the predictor variables with a response variable) or by the PLS (Partial Least Squares regression) importance of the predictors (the product between the absolute value of the regression coefficient of the predictor in the PLS model and its standard deviation). Other alternative strategies apply the Gram–Schmidt supervised orthogonalization to the wavelet coefficients. The results indicate that, both in classification and regression, the information retained after compression in the wavelets space can be more efficient than that retained with a common basis obtained by variance.     

Determination of Total Sugar in Tobacco by Near-Infrared Spectroscopy and Wavelet Transformation-Based Calibration

Based on wavelet transformation (WT) and mutual information (MI), a simple and effective procedure is proposed for multivariate calibration of near-infrared spectroscopy. In such a procedure, the original spectra of the training set are first transformed into a set of wavelet representations by wavelet prism transform. Then, the MI value between each wavelet coefficient variable and the dependent variable is calculated, resulting in a MI spectrum; by retaining a subset set of coefficients with higher MI, an update training set consisting of wavelet coefficients is obtained and reconstructed/converted back to the original domain. Based on this, a partial least square (PLS) model can be constructed and optimized. The optimal wavelet and decomposition level are determined by experiment. A NIR quantitative problem involving the determination of total sugar in tobacco is used to demonstrate the overall performance of the proposed procedure, named RPLS, meaning PLS in reconstructed original domain coupled with MI-induced variable selection in wavelet domain (RPLS). Three kinds of procedures, that is, conventional full-spectrum PLS in original domain (FPLS), PLS in original domain coupled with MI-induced variable selection (OPLS), and direct PLS in MI-based wavelet coefficients (WPLS), are used as reference. The result confirms that it can build more accurate and robust calibration models without increasing the complexity.     

Comparison of chemometric methods for the screening of comprehensive two-dimensional liquid chromatographic analysis of wine

Two chemometric methods are compared for the rapid screening of comprehensive two-dimensional liquid chromatographic (LC × LC) analysis of wine. The similarity index and Fisher ratio methods were both found to be able to distinguish geographical variability and to determine potentially significant peaks for further quantitative and qualitative study. An experimental data set consisting of five different wine samples and multiple simulated data sets were analyzed in the investigation of the screening methods. Several statistical analyses are employed in the understanding and verification of the results from the similarity index and Fisher ratio methods. The sum rank difference (SRD) method was used to compare the rankings of the two different methods as applied to the different data sets and to determine the amount of variability associated with the ranking of the peak differences. The major advantage the similarity index method offers is that it is an unsupervised method; no a priori knowledge of the samples (i.e., class identification) is required, while the Fisher ratio method is supervised. Both methods are rapid and require little user intervention other than the determination of a threshold for inclusion/exclusion of compounds from further analysis.