织物折皱回复角与其力学性能指标间的关系

为探究较精确、易行的测试和表征织物折皱回复角的方法,首先采用KES-F织物风格仪测试了织物的弯曲、剪切、拉伸和压缩性能,并在KES系统原有力学性能指标基础上引入2个新指标:残余弯曲曲率和残余剪切变形,进而通过数据分析建立了采用织物力学性能指标表示其经、纬向折皱回复角的回归方程;同时分析了织物折皱回复角与各力学性能指标间的相关性.结果表明:回归方程的计算值与折皱回复角的实测值差异不超过0.1°:拉伸功回复率、拉伸比功和弯曲刚度对织物折皱回复性的影响最大,可以通过提高织物的抗拉伸、弯曲和剪切性能及拉伸功回复率来改善织物的抗皱性.     

多孔橡胶材料声传递损失性能分析：模型描述

研究带有无孔薄覆盖层的多孔橡胶板构件的声传递损失特性,考察了从低频、中频到高频范围内,不同结构参数和材料特性对多孔橡胶材料隔声性能的影响.这一部分建立了多孔橡胶板构件的有限元（FE）与统计能量（SEA）混合分析模型.在仿真模型中,多孔橡胶板边界条件为固支连接,在多孔橡胶板模型的一侧有散射声场作为激励,在另外一侧有半无限声场,用以计算和预测其声传递损失.数值结果与文献进行比较,结果显示该模型具有较好的可靠性.     

应用近红外光谱对老陈醋进行定性定量的检测研究

利用近红外光谱结合DA和PLS算法在不同光谱预处理方法下,对山西老陈醋醋龄进行定性判别分析,并建立陈醋可溶性固形物(SSC)及pH值的定量模型.结果表明:原始光谱、5点平滑以及SNV校正建立的DA模型性能良好,校正集判别正确率为100％,预测集判别正确率为88.89％;原始光谱建立的可溶性固形物的PLS定量模型最优,校正集和预测集的相关系数r分别为0.99988和0.99960,RMSEC,RMSEP和RMSECV分别为0.0421,0.0911和0.0777;5点平滑建立的pH值的PLS定量模型最优,校正集和预测集的相关系数r分别为0.99733和0.97411,RMSEC,RMSEP和RMSECV分别为0.0151,0.0386和0.0468.     

电子鼻应用于猪肉丸子香味预测

为应用电子鼻技术快速、客观地评价猪肉丸子风味,实验设计了4?种肥肉、瘦肉配比（100%瘦、90%瘦、80%瘦和70%瘦）的猪肉丸子,使用电子鼻和顶空固相微萃取和气相色谱-质谱（gas chromatography-mass spectrometry,GC-MS）联用对猪肉丸子的各种挥发性物质进行检测。同时对其进行香味指标感官评定。采用线性判别和神经网络方法对不同猪肉丸子的电子鼻数据进行识别分类;并利用偏最小二乘回归方法对电子鼻传感器和挥发性物质的相关性进行分析。结果表明,4?类丸子在香味评价上差异极显著（P＜0.01）,肥肉比例高的丸子获得了较高的评分。线性判别分析和神经网络方法的分类效果显示,电子鼻对4?类猪肉丸子具有良好的分类能力。GC-MS共检测出了67?种风味化合物,其中主要是醛类、醇类、酮类等物质,挥发性风味物质的差异是造成各类丸子感官评分差异的根本原因。偏最小二乘回归模型显示电子鼻传感器数据与主要挥发性化合物相对含量具有良好的相关性。使用逐步回归建立电子鼻与评价指标数据之间的分值预测回归模型（R2＞0.9,P＜0.01）,表明丸子香味可以使用电子鼻进行预测。     

低定量卷烟纸性能及其对卷烟主流烟气的影响研究

本研究旨在探讨低定量卷烟纸的性能及其对卷烟主流烟气的影响。主要通过单因子方差分析探讨卷烟纸定量对卷烟纸不透明度是否存在相关性,并通过回归分析确定卷烟纸不透明度与卷烟纸定量的关系。结果表明:卷烟纸定量对卷烟纸不透明度具有显著影响,且两者之间存在回归关系:卷烟纸不透明度=48.2+0.9037×卷烟纸定量;且当木浆卷烟纸定量≤26 g·m^-2,采用碳酸钙作为填料时,卷烟纸不透明度较难满足GB/T 12655-2007的要求;此外,通过研究低定量卷烟纸对国内3种牌号卷烟常规主流烟气的影响,得出在23 g·m^-2到26g·m^-2低定量范围内,卷烟纸对卷烟主流烟气焦油、CO含量的影响不具有显著性。     

柚苷酶产生菌Penicillium sp.1523分批发酵动力学研究

分析柚苷酶产生菌青霉(Penicillium sp.)1523在5 L自控发酵罐中的分批发酵动力学。探讨了青霉1523在5 L罐分批发酵过程中菌体生长、柚苷酶合成及基质消耗的变化规律,结果表明:菌体生长呈典型S型曲线,酶的合成与菌体生长趋势呈现部分相关性,属于部分偶联型。基于这些过程曲线的变化规律,结合Logistic方程和Luedeking-Piret方程,建立了柚苷酶产生菌青霉1523分批发酵过程的动力学模型,并通过数学推导和非线性拟合获得了模型中各参数的最佳取值,最终确定了能够较好表征青霉1523分批发酵过程中菌体生长、产物柚苷酶合成以及基质消耗的动力学方程。     

Blends of olive oil and sunflower oil: Characterisation and olive oil quantification using fatty acid composition and chemometric tools

A method capable of recognising the percentage of olive oil in a blend is required to verify whether its labelling complies with the statements set out by the Commission Regulation (EC) No. 1019/2002. In this study an analytical methodology was developed in order to define blends of olive oil and sunflower oil, which contain 50% of olive oil, compared to blends with 40% and 60% of it, respectively. Methyl esters of fatty acids were analysed by GC–FID and processed through chemometric tools (PCA, TFA, SIMCA and PLS). A strong differentiation of blends according to the amount of olive oil contained and a quantification model with a standard error of prediction of 1.51% were obtained. As this issue represents a significant analytical challenge, variability associated with the fatty acid composition of olive oil was first studied.     

A general method to validate breeding value prediction software

The validity of national genetic evaluations depends on the quality of input data, on the model of analysis, and on the correctness of genetic evaluation software. A general strategy was developed to validate national breeding value prediction software: performances from a real data file were replaced with simulated ones, created from simulated fixed and random effects and residuals in such a way that BLUP estimates from the evaluation software must be equal to the simulated effects. This approach was implemented for a multiple-trait model and a random regression test-day model. An example was presented on test-day observations analyzed with a random regression animal model including a lactation curve described as a sum of fixed polynomial regression and fixed spline regression on days in milk, and with genetic and permanent environmental effects modeled by using Legendre polynomials of order 2. Residuals had heterogeneous variances, and phantom parent groups were included. This method can be easily extended to other linear models. The comparison of genetic evaluation results with simulated true effects is used to demonstrate the great efficiency and usefulness of the proposed method.     

Genetic association of clinical mastitis with test-day somatic cell score and milk yield during first lactation of Finnish Ayrshire cows

In this study the genetic association during lactation of 2 clinical mastitis (CM) traits: CM1 (7 d before to 30 d after calving) and CM2 (31 to 300 d after calving) with test-day somatic cell score (SCS) and milk yield (MY) was assessed using multitrait random regression sire models. The data analyzed were from 27,557 first-lactation Finnish Ayrshire cows. Random regressions on second- and third-order Legendre polynomials were used to model the daily genetic and permanent environmental variances of test-day SCS and MY, respectively, while only the intercept term was fitted for CM. Results showed that genetic correlations between CM and the test-day traits varied during lactation. Genetic correlations between CM1 and CM2 and test-day SCS during lactation varied from 0.41 to 0.77 and from 0.34 to 0.71, respectively. Genetic correlations of test-day MY with CM1 and CM2 ranged from 0.13 to 0.51 and from 0.49 to 0.66, respectively. Correlations between CM1 and SCS were strongest during early lactation, whereas correlations between CM2 and SCS were strongest in late lactation. Genetic correlations lower than unity indicate that CM and SCS measure different aspects of the trait mastitis. Milk yield in early lactation was more strongly correlated with both CM1 and CM2 than milk yield in later lactation. This suggests that selection for higher lactation MY through selection on increased milk yield in early lactation will have a more deleterious effect on genetic resistance to mastitis than selection for higher yield in late lactation. The approach used in this study for the estimation of the genetic associations between test-day and CM traits could be used to combine information from traits with different data structures, such as test-day SCS and CM traits in a multitrait random regression model for the genetic evaluation of udder health.     

蟹酱发酵期间理化指标与感官特性的相关性分析

目的:探究蟹酱自然发酵过程中理化指标与感官特性的变化及两者间关系。方法:以渤海低值梭子蟹为原料发酵制备蟹酱,分析其不同发酵阶段的感官、理化指标变化,并对二者进行相关性分析及逐步回归分析。结果:相关性分析表明:蟹味、发酵味、腥味、鲜味、氨气味、咸味、甜味、苦涩味、质地、色泽均与水分、p H、总酸、粗蛋白、氨基酸态氮、还原糖及挥发性盐基态氮中一个或多个指标间存在显著(p<0.05)或极显著(p<0.01)线性相关。其中水分、p H、总酸、粗蛋白、氨基酸态氮和挥发性盐基态氮可用于构建蟹酱感官特性回归预测模型,蟹味、发酵味、腥味、氨气味、鲜味、咸味、甜味、苦涩味、质地和色泽的预测模型方差解释率分别为69.1%、88.4%、90.5%、68.7%、67.3%、88.2%、65.7%、63.3%、94.1%、90.1%。结论:蟹酱的理化指标与感官特性间具有良好的相关性,可以实现理化指标对感官特性的预测分析。