影响聚己内酰胺切片质量的主要因素

以原材料切片为研究侧重点,通过试验分析不同切片原材料特性对聚己内酰胺质量造成的影响。具体试验分析中主要考虑切片特性包括黏度、含水率、二氧化钛含量等,根据试验分析可知,实际聚己内酰胺生产中切片相对黏度、切片含水率（质量分数）和包装储存切片含水率（质量分数）应分别控制为2.4～2.8、0.03%～0.04%和0.04%,二氧化钛含量（质量分数）则控制在较低水平。     

提高聚己内酰胺粘度分析准确度的探讨

通过对影响聚己内酰胺(PA6)切片相对粘度分析准确度因素的讨论，提出了减小分析误差，提高分析准确度的方法。     

聚己内酰胺固相缩聚工艺

对采用固相缩聚从低黏度聚己内酰胺树脂制备高黏度聚己内酰胺树脂的工艺条件进行了研究。研究结果表明，催化剂Z能有效地提高增黏速率，控制其用量和合适的工艺条件可以制得所需黏度的聚己内酰胺树脂。     

己内酰胺质量对锦纶DTY染色性能影响的探讨

通过对原料质量、聚合反应过程的分析，提出聚酰胺聚合生产中液体和固体己内酰胺原料互换使用时出现切片黏度、后加工染色变化等问题的解决方案。将聚合釜上部TI131温度升高，减少醋酸加入量，解决了切片后加工染色性能改变的问题。     

聚己内酰胺切片中可萃取物含量的影响因素分析

本文综述了各种因素对聚己内酰胺中可萃取物含量的影响。聚己内酰胺切片中的可萃取物在前纺的熔融和纺丝过程中会变成气体蒸发出来,不仅造成熔体挤出成形时产生毛丝,妨碍成形过程的正常进行,而且恶化工作条件。影响切片中可萃取物的含量的因素有切片的粒径、萃取温度、萃取时间、浴比(萃取水流量)和浓度梯度等。对影响聚己内酰胺切片中可萃取物含量的因素进行分析,为实际生产中增强纤维的机械强度、获得更高质量的纤维材料提供可行性经验。     

聚己内酰胺切片的热学及流变性能研究

借助热分析技术和毛细管流变仪,分别对两种全消光聚己内酰胺纺丝切片的热学性能和流变性能进行了研究和分析,旨在为熔融纺丝生产工艺控制提供一定的理论依据。结果表明:两种切片在纺丝温度范围内热降解程度均可满足正常纺丝,A公司的切片较易发生结晶;剪切速率越大,熔体受温度的影响越小,稳定性越好;在相同剪切速率下,A切片的黏流活化能较B切片的高,表观黏度对温度的依赖性大。     

降低PA6切片中可萃取物含量的工艺研究

通过研究切片的质量、萃取水的温度及浴比对聚己内酰胺切片中可萃取物含量的影响。探讨了己内酰胺的聚合过程中的萃取工艺，为提高切片质量，降低能耗和物耗提供可行的经验。     

阿加菲公司的两釜聚酯工艺

阿加菲T程设计公司，建成第一套两釜工艺的连续式聚醢聚合装置，于2005年2月一次开车成功，并生产出合格的产品。新工厂建在泰国，由PTA与EG生产薄膜用聚醢切片，特性黏度为0．6dL／g。UPR反应器出料处的预聚合物特性黏度可达0．3-0．4dL/g，可由一般切粒机切成切片。此装置可以生产膜用切片，瓶级切片的基础切片1：1及其它聚醢切片，     

PA6纤维生产现状及展望

综述聚己内酰胺(PA6)纤维及其单体己内酰胺(CPL)的国内外生产、消费现状。分析了国内PA6切片和纤维的应用领域和发展前景,并指出今后应大力开发CPL新技术和高附加值PA6纤维,提高PA6纤维在服用领域的比例。     

聚己内酰胺切片生产中干燥工艺的研究

将干燥技术应用于大型的实际生产中,解决了以己内酰胺为原料生产聚己内酰胺的提高生产负荷过程中出现的黏度降低现象,并确定了干燥过程中氮气的流速和温度的最佳值。     

近红外光谱技术结合支持向量回归法测定烯草酮的含量

采用近红外光谱(NIR)技术结合支持向量回归法(SVR)建立了烯草酮乳油的定量分析方法。通过添加烯草酮原药、烯草酮助剂到二甲苯溶剂来配制不同浓度的校正集,采用SVR法建立了烯草酮的定量分析模型,模型的决定系数(R2)、校正集均方根误差(RMSEC)、检验集均方根误差(RMSEV)、预测集均方根误差(RMSEP)分别为1.0000、0.0260、0.0569和0.0550。结果表明,近红外光谱技术结合支持向量回归法可以准确地定量分析乳油中烯草酮的含量,方法简单、快捷,在农药质量检测中具有实际应用价值。     

Determining Frying Oil Degradation by Near Infrared Spectroscopy Using Chemometric Techniques

A rapid method for the determination of some important physicochemical properties in frying oils has been developed. Partial least square regression (PLS) calibration models were applied to the physicochemical parameters and near infrared spectroscopy (NIR) spectral data. PLS regression was used to find the NIR region and the data pre-processing method that give the best prediction of the chemical parameters. Calibration and validation were appropriated by leave one out cross validation and test set validation techniques for predicting free fatty acids (FFA), total polar materials (cTPM; measured by chromatographic method and iTPM measured by an instrumental method), viscosity and smoke point of the frying oil samples. For PLS models using the cross validation techniques, the best correlations (r) between NIR predicted data and the standard method data for iTPM in oils were 93.79 and root mean square error of prediction (RMSEP) values were 5.53. For PLS models using the test set validation techniques, the best correlations (r) between NIR predicted data and standard method data for FFA, cTPM, viscosity and smoke point in oils were 92.58, 94.61, 81.95 and 84.07 and RMSEP values were 0.121, 3.96, 22.30 and 8.74, respectively. In conclusion, NIR technique with chemometric analysis was found very effective in predicting frying oil quality changes.     

Estimating the content of free fatty acids in high‐oleic sunflower seeds by near‐infrared spectroscopy

The content of free fatty acids (FFA) in vegetable oils represents an important quality factor in oil crops. The objective of the investigation was to develop a near‐infrared (NIR) calibration for estimating the FFA content in high‐oleic sunflower seeds. A sample set of different varieties from the harvest of 2004 as well as of 2005 from two locations in Germany was used; additionally seeds from 2003 were stored under unsuitable conditions to obtain samples utilised for calibration with an extended FFA range. A direct titration method for FFA determination was developed and adjusted to the official AOCS method. The modified method is sufficiently reliable, much faster than the AOCS method and therefore suitable for use in the calibration of NIR spectrometers. The developed NIR spectroscopy (NIRS) calibration was calculated with a modified partial least square algorithm, standard normal variate and detrend scatter correction and the 2^nd derivative of the spectra of ground sunflower seeds. The standard error of prediction of the validated calibration was 0.20, and the multiple coefficient of determination (RSQ_val) reached 0.94. The obtained results demonstrated clearly the efficiency and how cost effective the NIRS method is for the estimation of FFA content in sunflower seeds.     

傅里叶变换近红外光谱法快速测定聚氯乙烯的K值

研究了傅里叶变换近红外光谱技术快速测定聚氯乙烯K值的分析方法。采用漫反射方式和偏最小二乘法 (PLS),建立了聚氯乙烯K值定量分析数学模型,其决定系数(R2)和均方差(RMSECV)分别为97．96％和0．362。实验结果表明:近红外分析法和实验室标准分析方法测定结果基本一致。本方法重复性好,具有快速、操作简单、无污染等诸多优点。     

Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data

In this paper we have tried to build effective model for classification of motor oils by base stock and viscosity class. Three (3) sets of near infrared (NIR) spectra (1125, 1010, and 1050 spectra) were used for classification of motor oils into 3 or 4 classes according to their base stock (synthetic, semi-synthetic, and mineral), kinematic viscosity at low temperature (SAE 0W, 5W, 10W, and 15W) and kinematic viscosity at high temperature (SAE 20, 30, 40, and 50). The abilities of three (3) different classification methods: regularized discriminant analysis (RDA), soft independent modelling of class analogy (SIMCA), and multilayer perceptron (MLP) - were also compared. In all cases NIR spectroscopy was found to be quite effective for motor oil classification. MLP classification technique was found to be the most effective one.     

Comparative study of methods of determining oil content of sunflower seed

An extraction-gravimetric method (AOCS Official Method Ai 3-75) was compared with 2 instrumental techniques, near-infrared reflec-tance (NIR) spectroscopy and wide-line nuclear magnetic resonance (NMR), for the determination of the oil content of oilseed-type hybrid sunflower seed. Eight sunflower seed samples of varying oil contents, replicated 5 times, were analyzed by the 3 procedures. The overall mean oil contents and standard deviations for the 8 samples were: AOCS method, 44.5% ± 0.33%; NMR, 44.8% ± 0.27%; and NIR, 44.2% ± 0.81%. Analysis of variance of the means of the 3 methods of analysis indicated no difference (p>0.05) in oil content due to the method. However, there was a difference (p>0.001) in total oil content due to replicated analyses of the same sample with the NIR method. With the AOCS and NMR methods, no effect (p>0.05) of replicated analyses of the same sample was found. The NMR method was more precise and repro-ducible than the other 2 methods. Although the NIR mean oil contents were not significantly different from the means of the other 2 methods, the coefficient of variations for all samples were consistently higher for the NIR analyses than for the AOCS and NMR analyses.     

润滑油基础油粘度性能的近红外光谱研究

本文采用傅立叶（FT）近红外光谱仪,在近红外长波光谱范围内,测定了不同润滑油基础油的近红外光谱,分别建立润滑油基础油40℃粘度、100℃粘度和粘度指数3项指标的偏最小二乘和BP神经网络近红外光谱分析的校正模型。研究数据表明,近红外光谱能够获得与润滑油基础油粘度相关的光谱信息。人工神经网络方法作为一种处理非线性问题的数据分析手段,能较好的定量研究近红外光谱信息与润滑油基础油之间的关系。     

Use of NIR spectroscopy for estimation of FA composition of soy flour

The feasibility of NIR spectroscopy for the determination of FA composition in soy flour was examined. NIR spectra were obtained for a small amount of soybean powder (about 8 mg) in a modified single-grain cup using an NIR instrument by scanning the wavelengths from 1100 to 2500 nm at 2-nm intervals. The relationship between the NIR spectral patterns of soybean powder and the FA compositions was examined: As the linoleic acid ratio increased, the NIR absorbance at 1708 nm, where the linoleic acid moiety has an absorption band, became stronger downward in the second-derivative NIR spectra. The correlation coefficients between the standardized NIR readings at 1708 nm and the linoleic acid ratio or the oleic acid ratio in the FA composition of soy flour were −0.853 and 0.877, respectively. A rough estimation of the linoleic acid moiety or oleic acid moiety in soy flour could be successfully carried out with even a very small amount of soy flour according to the NIR spectral pattern due to the wavelength assignments of moieties.     

Near Infrared Spectroscopy for On-line Monitoring of Alkali- Free Cloth/Phenolic Resin Prepreg During Manufacture

A NIR method was developed for the on-line monitoring of alkali-free cloth/phenolic resin prepreg during its manufacturing process. First, the sizing content of the alkali-free cloth was analyzed, and then the resin, soluble resin and volatiles content of the prepreg was analyzed simultaneously using the FT-NIR spectrometer. Partial least square (PLS) regression was used to develop the calibration models, which for the sizing content was preprocessed by 1stDER +MSC, for the volatile content by 1stDER +VN, for the soluble resin content by 1stDER +MSC and for the resin content by the VN spectral data preprocessing method. RMSEP of the prediction model for the sizing content was 0.732 %, for the resin content it was 0.605, for the soluble resin content it was 0.101 and for volatiles content it was 0.127. The results of the paired t-test revealed that there was no significant difference between the NIR method and the standard method. The NIR spectroscopy method could be used to predict the resin, soluble resin and the volatiles content of the prepreg simultaneously, as well as sizing content of alkali-free cloth. The processing parameters of the prepreg during manufacture could be adjusted quickly with the help of the NIR analysis results. The results indicated that the NIR spectroscopy method was sufficiently accurate and effective for the on-line monitoring of alkali-free cloth/phenolic resin prepreg.     

Comparative particle size determination of phenacetin bulk powder by using Kubelka-Munk theory and principal component regression analysis based on near-infrared spectroscopy

Purpose: The purpose of this study is to investigate the effect of particle size on Kubelka-Munk theoretical light diffusion of NIR spectra. And a chemoinfometrical method for quantitative determination of particle size of phenacetin crystalline powder based on Fourier-transformed near-infrared (FT-NIR) spectroscopy was established. Method: The sample powders of phenacetin were obtained by sieving using 37-590 μm screens. NIR spectra were taken with a total 60 NIR data points by using a FT-NIR spectrometer. Results: The NIR absorption patterns were almost identical for the samples. However, the base line of the spectrum was observed to increase with decreasing sample powder particle size. The scatter coefficient constants (S) were calculated from NIR spectra based on Kubelka and Munk light scattering theory. The S values determined were found to be constant within the wavenumbers studied; i.e., 4000-10 000 cm⁻¹. However, this light scatter coefficient, S, for phenacetin particle was found to be inversely proportional to particle size. Subsequently, the principal component regression (PCR) analysis based on a three-principal component model was applied in the analyses of NIR spectra of phenacetin powder samples with various particle sizes. By applying this method, the particle size for phenacetin can be estimated form the light scatter coefficient, S. The resulting plot showed a linear relationship between the predicted and actual particle size with a slope of 0.9167, an intercept of 20.61, and a correlation coefficient of 0.9147. The loading vector of PC1 showed a plateau at 4500-10 000 cm⁻¹, and the score of PC1 increased with increase of particle size. Conclusion: Quantitative particle size evaluation of phenacetin crystalline powder using the PCR method based on NIR spectra was useful for practical purpose in pharmaceutical industry, and their background was clarified by using Kubelka-Munk scattering theory.