Application of Electronic Nose for Measuring Total Volatile Basic Nitrogen and Total Viable Counts in Packaged Pork During Refrigerated Storage

The objective of this study was to predict the total viable counts (TVC) and total volatile basic nitrogen (TVB‐N) in pork using an electronic nose (E‐nose), and to assess the freshness of chilled pork during storage using different packaging methods, including pallet packaging (PP), vacuum packaging (VP), and modified atmosphere packaging (MAP, 40% O₂/40% CO₂/20% N₂). Principal component analysis (PCA) was used to analyze the E‐nose signals, and the results showed that the relationships between the freshness of chilled pork and E‐nose signals could be distinguished in the loadings plots, and the freshness of chilled pork could be distributed along 2 first principal components. Multiple linear regression (MLR) was used to correlate TVC and TVB‐N to E‐nose signals. High F and R² values were obtained in the MLR output of TVB‐N (F = 32.1, 21.6, and 24.2 for PP [R² = 0.93], VP [R² = 0.94], and MAP [R² = 0.95], respectively) and TVC (F = 34.2, 46.4, and 7.8 for PP [R² = 0.98], VP [R² = 0.89], and MAP [R² = 0.85], respectively). The results of this study suggest that it is possible to use the E‐nose technology to predict TVB‐N and TVC for assessing the freshness of chilled pork during storage.     

Measuring the relation between computer use and reading literacy in the presence of endogeneity

This work studies the relation between computer use for reading activities and academic literacy in 15-year-old students in Chile, Uruguay, Spain, and Portugal. Data used is from the PISA 2009 test. Special attention is given to potential bias problems when the computer use is an endogenous variable. Few studies in this area address this issue: existing literature has shown that different types of computer use have different implications on performance. The limitations of observational data have also been emphasized to establish cause–effect relations between computer use and academic performance. It is important, however, to consider the computer use endogeneity hypothesis (above all at home) since students decide on the frequency of computer use at home. The results found show that by controlling for endogeneity, computer use for reading is not related to reading performance neither in digital or printed format, with the exception of Chile that shows a negative relation in the case of reading from a printed format. The results considering endogeneity differ considerably from results when endogeneity is not taken into account. The work shows the relevance of experimental type studies in order to make sound statements with regard to the computer use and academic performance relation. In turn, school reading activities in a digital environment are suggested that could have an impact on reading performance.     

A New Strategy for Quantitative Proportions in Complex Systems of Blended Oils by Triacyglycerols and Chemometrics Tools

Many oils sold in China and India are a blend of various oils to improve performance, stability, and nutritional characteristics, which are required in their respective markets. Quantitative analysis of the proportions of constitutive components is fundamental to the conformity and adulteration checking of edible blended oil products. A multi linear regression model with constrained linear least squares and exhaustion calculation was applied in this study. The source of the varieties in the model is a database (614 pure oils) of triacylglycerols (TAGs) collected by GC–FID and HPLC–RID. There were 20 groups of binary and ternary blended oils consisting of two or three oils out of five kinds, namely soybean, corn, peanut, rapeseed, and sunflower, which were analyzed and processed separately. Results showed that the method was able to predict the proportions of constitutive components in the edible blended oils, given that relative errors required less than 20%, the accuracy was 98.2% for the binary system if the proportion of each oil in blended oils was more than 20%, while the accuracy was 84.7% for the ternary system if the proportion of each oil in blended oils was more than 10%. The quantitative method is based on a simple analysis to determine the TAGs composition and thus it is useful for quick segregation and quality control of blended oils in routine analysis.     

结合MLR和ARIMA模型的时空建模及预测

针对传统时间序列建模预测过程中忽略空间因素影响和时空交互的问题,提出了一种基于时空多元回归（MLR）的ARIMA预测方法,并应用于某省月均气温的时空预测研究中。通过时序分解去除时空变量明显的季节变化;运用全子集回归法确定显著影响气温的因素,继而得到去季节项数据的MLR模型,从而去除气温的时空趋势变化得到随机变化项;对各站点的随机项时间序列分别进行ARIMA建模;将随机项的预测值与前两项预测值重组,获得最终各站点的时空预测值。实验结果表明,预测值与观测值整体相关系数为0.993 4,误差绝对值均值约为0.9 ℃。     

MRL and PLS Correlations between the 1H NMR Spectra of Vacuum Residues and the Deasphalting Conditions with the DMO Yields

Abstract

The areas of signals present in 12 consecutive frequency bins of the ¹H nuclear magnetic resonance (NMR) spectra of different vacuum residues (VRs) were correlated with the yield of the deasphalting/demetallization process (DEMEX) under fixed and variable conditions (antisolvent/load ratio and rectification temperature). Prediction models for the demetallized oil (DMO) obtained under variable process conditions generated by partial least squares (PLS) regression had R² = 0.950 and the CV-q² was 0.943. Models obtained with multiple linear regression (MLR) for the same process had R² = 0.93 but with a significantly lower CV-q² (0.608) value. The prediction models for the DMO yield under a set of fixed conditions was such that R² = 0.930 for the PLS whereas for the MRL method it was 0.970. Again, the CV-q² value was higher for the PLS (0.998) than for the MRL method (0.610). The proposed correlation methods are much faster and significantly less costly than the conventional ones used for predicting the yield of the DMO process.     

无源测向定位的平滑窗最大似然配准法

为解决无源测向定位中雷达站缓慢漂移的系统误差影响定位跟踪精度的问题，提出了平滑窗最大似然配准法，来更真实地配准测向系统误差．根据平滑时问窗开始位置的不同，分别建立了前向平滑、后向平滑和前后向平滑三种配准模型．理论分析和计算机仿真表明，平滑窗最大似然配准法能有效地配准缓慢漂移的系统误差．     

采用M5’模型树和测量数据识别抽汽式机组汽耗量特性

汽轮机组特性随着机组老化而变化,传统上采用定期现场实测,需停机和采用专门的设备与系统,费用很高,因此基于现有自动化系统历史测量数据的特性曲线识别方法十分必要。一般汽轮机组汽耗量特性具有非凸和非连续等特点,常规的多元线性回归拟合不能适应。M5’模型树算法是一种多输入单输出系统的分段线性化的数据挖掘算法。提出采用M5’模型树的抽汽式机组汽耗量特性模型和其模型结构及参数识别算法,用于滚动利用最新的电厂测量历史数据获取最新的汽耗量特性。该方法简单、有效,逼近能力强,自动化程度高,在处理非凸形和非连续性的特性方程具有优势。通过多个热电厂的实时数据进行验证,具有很高的预测精度,效果优于多元线性回归拟合方程。     

5′-甲硫腺苷核苷酶抑制剂的QSAR研究

通过构建33个甲硫腺苷(MTA)类似物的化学结构模型,并使用Molconn-Z统计的电子拓扑指数、形状指数和分子连接性指数作为受试化合物分子结构参数,结合多元线性回归(MLR)方法对MTA类似物的抑制活性进行了QSAR研究。所得模型的R和R2adj分别为0.862和0.711,表明该模型具有较好的预测和反映真实情况的能力。模型表明,MTA类似物的抑制活性与其所含硫和氮原子的电子拓扑指数以及分子形状指数相关,这为预测其他类似物的活性和设计新的抑制剂提供了理论依据。