微机模拟处理pH络合滴定法研究——Ⅱ.终点确定方法和最佳实验条件模拟估计程序

本文利用简便的滴定过程模型,确立了微机模拟处理实验数据,方便、准确地求解络合滴定等当点的方法。设计了最佳实验条件模拟估计程序。该法测定Zn~(2+),Pb~(2+),Cu~(2+),Fe~(3+)结果的相对误差Δ<1％。pH=6-7时,测定Ca~(2+)的相对误差为-1.61％;σ<0.001mmol,(N=5)。     

基于并行PLS算法的化学计量学软件研究

现有化学计量学软件普遍采用的偏最小二乘(PLS)算法均以单线程方式计算,建模速度缓慢,给应用带来较大的不便。随着多核处理器的普及,采用多线程并行计算技术可显著提高算法执行速度。本文将多线程并行计算技术引入化学计量学软件开发,提出并实现了PLS算法的并行化计算。利用标准数据集进行了性能对比实验,结果表明在四核计算机中多线程并行计算比单线程计算有大约3.1倍的速度提升。     

Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools

Sensory evaluation is the application of knowledge and skills derived from several different scientific and technical disciplines, physiology, chemistry, mathematics and statistics, human behavior, and knowledge about product preparation practices. This research was aimed to evaluate aftertaste sensory attributes of commercial non-alcoholic beer brands (P1, P2, P3, P4, P5, P6, P7) by several chemometric tools. These attributes were bitter, sour, sweet, fruity, liquorice, artificial, body, intensity and duration. The results showed that the data are in a good consistency. Therefore, the brands were statistically classified in several categories. Linear techniques as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed over the data that revealed all types of beer are well separated except a partial overlapping between zones corresponding to P4, P6 and P7. In this research, for the confirmation of the groups observed in PCA and in order to calculate the errors in calibration and in validation, PLS-DA technique was used. Based on the quantitative data of PLS-DA, the classification accuracy values were ranked within 49-86%. Moreover, it was found that the classification accuracy of LDA was much better than PCA. It shows that this trained sensory panel can discriminate among the samples except an overlapping between two types of beer. Also, two types of artificial networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Back Propagation (BP) learning method. The highest classification success rate (correct predicted number over total number of measurements) of about 97% was obtained for RBF followed by 94% for BP. The results obtained in this study could be used as a reference for electronic nose and electronic tongue in beer quality control.     

神经网络用于热固性酚醛树脂的研究

运用人工神经网络研究了热固性酚醛树脂的合成反应空间与产物空间的映射关系，建立了可用于定量预测树脂固化产物热性能的模型，并进行了实验验证，神经网络方法对于复杂高分子体系的合成－性能定量关系的研究显示出良好的应用前景。     

有机酸指纹图谱结合化学计量学分析鉴定黄酒的方法研究

目的 建立不同黄酒的高效液相有机酸指纹图谱,并结合化学计量学分析对黄酒进行鉴别研究。方法 采用Diamonsil Plus C18(150mm×4.6mm,5μm)色谱柱,以磷酸(0.1%)-甲醇(98∶2,v/v)为流动相,流速0.55 mL/min等度洗脱,30℃,检测波长200nm及210nm 检测不同黄酒中的有机酸;采用ChemPattern先进化学计量学系统解决方案软件对有机酸图谱进行多元统计分析及相似度阈值分析,建立模式识别,最终完成对不同黄酒的鉴别。结果 4种不同类型黄酒有机酸指纹图谱共有模式包含36个峰,其中8个色谱峰进行了鉴别,并初步对确认的峰进行了含量测定。结果显示,多种分析方法的分类结果准确一致,可有效对共有模式下的黄酒进行鉴别及初步质量评价。结论 该方法简便易行、准确可靠,适用于共有模式内黄酒的鉴别评价。     

Prediction of processed cheese instrumental texture and meltability by mid-infrared spectroscopy coupled with chemometric tools

The objective of this study was to determine the potential of mid-infrared spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses (n = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 °C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and mid-infrared spectroscopy (4000–640 cm⁻¹). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values (R² = 0.64). The hardness and springiness models gave approximate quantitative results (R² = 0.77) and the cohesiveness (R² = 0.81) and Olson and Price meltability (R² = 0.88) models gave good prediction results.     

Geographical origin classification of olive oils by PTR-MS

The volatile compositions of 192 olive oil samples from five different European countries were investigated by PTR-MS sample headspace analysis. The mass spectra of all samples showed many masses with high abundances, indicating the complex VOC composition of olive oil. Three different PLS-DA models were fitted to the data to classify samples into ‘country’, ‘region’ and ‘district’ of origin, respectively. Correct classification rates were assessed by cross-validation. The first fitted model produced an 86% success rate in classifying the samples into their country of origin. The second model, which was fitted to the Italian oils only, also demonstrated satisfactory results, with 74% of samples successfully classified into region of origin. The third model, classifying the Italian samples into district of origin, yielded a success rate of only 52%. This lower success rate might be due to either the small class set, or to genuine similarities between olive oil VOC compositions on this tight scale.     

Raman Spectroscopic Barcode Use for Differentiation of Vegetable Oils and Determination of Their Major Fatty Acid Composition

In this study, differentiation of vegetable oils and determination of their major fatty acid (FA) composition were performed using Raman spectral barcoding approach. Samples from seven different sources (sunflower, corn, olive, canola, mustard, soybean and palm) were analyzed using Raman spectroscopy. Second derivative of the spectral data was utilized to generate unique barcodes of oils. Chemometric analyses, namely, principal component analysis (PCA) and partial least square (PLS) methods were used for data analysis. PCA was applied for classification of the samples according to the differences in their levels arising from their barcode data. A successful differentiation based on second derivative barcodes of Raman spectra (2D‐BRS) of vegetable oils was obtained. In addition, PLS method was applied on 2D‐BRS in order to determine the major FA composition of these samples. Coefficient of determination values for palmitic, stearic, oleic, linoleic, α‐linolenic, cis‐11 eicosenoic, erucic and nervonic acids were in the range of 0.970–0.989. Limit of detection and limit of quantification values were found to be satisfactory (0.09–8.09 and 0.30–26.95 % in oil) for these fatty acids . Advantages of both chemometric analysis and spectral barcoding approach have been utilized in the present study. Taking the second derivative of the Raman spectra has minimized background variability and sensitivity to intensity fluctuations. Spectral conversion to the barcodes has further increased the quality of information obtained from Raman spectra and also made it possible to improve the visualization of the data. Converting Raman spectra of oils into barcodes enables simpler presentation of the valuable information, and still allows further analysis such as classification of vegetable oils and prediction of their major fatty acids with high accuracy.     

Rapid Determination of Free Fatty Acid in Extra Virgin Olive Oil by Raman Spectroscopy and Multivariate Analysis

We introduce a visible Raman spectroscopic method for determining the free fatty acid (FFA) content of extra virgin olive oil with the aid of multivariate analysis. Oleic acid was used to increase the FFA content in extra virgin olive oil up to 0.80% in order to extend the calibration span. For calibration purposes, titration was carried out to determine the concentration of FFA for the investigated oil samples. As calibration model for the FFA content (FFA%), a partial least squares (PLS) regression was applied. The accuracy of the Raman calibration model was estimated using the root mean square error (RMSE) of calibration and validation and the correlation coefficient (R ²) between actual and predicted values. The calibration curve of actual FFA% obtained by titration versus predicted values based on Raman spectra was established for different spectral regions. The spectral window (945–1600 cm⁻¹), which includes carotenoid bands, was found to be a useful fingerprint region being statistically significant for the prediction of the FFA%. High R ² and small RMSE values for calibration and validation could be obtained, respectively.     

Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data

The potential of mid-infrared (MIR) and near-infrared (NIR) spectroscopy for their ability to differentiate between apple juice samples on the basis of apple variety and applied heat-treatment was evaluated. The heat-treatment involved exposure of juice samples (15 ml) for 30 s in a 900 W microwave oven and the apple varieties used to produce the juice samples were Bramley, Elstar, Golden Delicious and Jonagold. The chemometric procedures applied to the MIR and NIR data were partial least squares regression (PLS1 for differentiation on the basis of heat-treatment, PLS2 for varietal differentiation) and linear discriminant analysis (LDA) applied to principal component (PC) scores. PLS1 and PLS2 gave the highest level of correct classification of the apple juice samples according to heat-treatment (77.2% for both MIR and NIR data) and variety (78.3–100% for MIR data; 82.4–100% for NIR data), respectively.