涤棉用染料和涂料的红外反射性能研究

本文研究了涤、棉用染料及涂料的用量，其他物质，分子结构等因素对染料红外反射光谱性能的影响，我们合成的和筛选的一些商品染料具有较理想的红外反射率，即具有和绿色植物相同的红外反射特征。     

食品烤箱内辐照度不均匀性的数值计算

本文采用蒙特卡洛（Ｍｏｎｔｅ—Ｃａｒｌｏ）辐射计算方法、计算了食品烤箱内一组管状加热元件对食品托盘的辐照度不均匀性。对绘制的计算曲线与理论曲线进行了比较、二者吻合较好。     

基于双光源的织物红外发射率与光热吸收率的测量

为探究织物的光热吸收和红外发射性能，依据新的测量原理，采用不同色温的双光源对织物的光热吸收率和红外发射率进行综合测量研究。试验装置采用高反射率KT板并单向绝热，消除了织物透射的影响并解决了织物真实温度的测量问题。对20块不同成分、颜色、组织结构的织物试样以及黑漆铜板的表面真实温度和辐射温度分别进行了测量，并测定了参考黑体和织物表面的对流散热系数，由此计算出织物的全波段平均红外发射率和平均光热吸收率。结果表明，在2种可见光占比不同的热光源下，织物的红外发射率分别在0.65～0.85和0.60～0.95之间，存在一定的差异，主要与织物的材质和颜色有关；而织物的光热吸收率分别在35%～60%和30%～90%之间，显示出与光源色温较强的依存性，其主要是因为织物颜色对可见光波段有较强的反应；由于碘钨灯光谱与红外测温感应光谱存在一定的重合，织物的反射有一定的干扰，使用作为国标光源的氙灯没有光谱重叠的干扰，其测量结果更准确。     

红外光谱多次内反射法及其在纺织科学上的应用

红外光谱多次内反射技术已在纺织科学上得到了广泛的应用，本文介绍了该技术的装置、原理和应用情况。差示、热解、偏振等多次内反射技术在国外已得到了广泛的应用。该技术是纺织科学研究与生产不可缺少的分析测试手段。     

利用Excel计算红外水分仪校准过程中的斜率和截距

介绍了红外水分仪的校准方法以及利用Excel的图表制作功能计算红外水分仪校准过程中的斜率与截距。该方法简单精确,可以准确快速地计算出斜率与截距,快速完成对红外水分仪的全校准。     

纸张干燥技术及其能效分析(上)

纸机的干燥部是造纸厂热能消耗的最主要部分,也是节能减排工作的重点,不同的干燥方式,能源的利用效率各不相同,能源成本也存在较大差异。本文介绍了蒸汽烘缸干燥及气罩系统以及常用于涂布纸的红外干燥、热风干燥的基本原理、能源利用效率及使用成本等,并定量计算进行了说明。从辐射的原理入手,详细解释电红外与燃气红外的异同,澄清人们对这两种红外干燥器能源利用效率的误解,文中的计算结果也为干燥系统的选用或优化提供了数据支持。     

Rutcure HZG 9000新款红外固化设备

在FESPA 2005展会上，Rutland．de公司推出新一代红外瞬间固化设备Rutcure HZG 9000，这款设备的供热系统第一次采用铝合金涂层，可反射特定范围的UV光波，产生效果最佳的红外辐射能量。     

红外光线对纺织材料透射性能研究

本文研究了多种不同原材料，不同结构参数及不同平方 米重织物的红外透射性能，推导了用透射率计算不同织物的红外反射率的公式，分析了影响红外在织物中 反射性能的因素。     

红外光线对纺织材料透射性能的研究

本文研究了多种不同原材料，不同结构参数及不同平方米重织物的红外透射性能，推导了用透射率计算了不同织物的红外反射率的公式，分配了影响红外光线在织物中透射和反射性能的因素。     

红外辐射用于塑料片材成型加热原理和设计

红外辐射在热成型包装中用于塑料片材成型加热具有重要应用价值。本文在阐述红外辐射加热原理的基础上，就对材加热元件及加热装置中有关工艺和结构参考作了必要的探讨性设计，并就加热电功率问题提出了若干简单估算方法。     

Identification of pork in beef meatballs using Fourier transform infrared spectrophotometry and real-time polymerase chain reaction

A study on development of Fourier-transform infrared spectrophotometric method combined with principle component analysis as well as real-time polymerase chain reaction for determination of pork–beef mixture in meatballs has been performed. A lipid component extracted from pork and beef in meatballs is analyzed using Fourier-transform infrared spectroscopy, while DNA extracted from meatball was analyzed using real-time polymerase chain reaction. The correlation between actual and predicted concentration of lard using Fourier-transform infrared spectroscopy was performed by aid of partial least squares, while grouping of lard and beef fat components in meatball was carried out by Fourier-transform infrared spectra coupled with principle component analysis. The results showed that Fourier-transform infrared spectra at wavenumbers of 1000–1200 cm^?1 coupled with partial least square and principle component analysis are successfully used for quantification and classification of pork in beef meatballs. The relationship between actual value and predicted value of lard (lipid fraction obtained from meatballs containing pork) with Fourier-transform infrared spectrophotometric method revealed good correlation, with coefficient determination (R²) value of 0.997 and standard error of calibration of 0.04%. Principle component analysis is able to classify samples containing pork and beef meatballs. Fourier-transform infrared spectroscopy using normal spectra is fast technique for identification and quantification of lard extracted from pork in meatball. In addition, real-time polymerase chain reaction using Leptin Primer–AJ 865080 can be used for amplification of pork DNA specifically in meatballs containing pork.     

Discrimination of Apples Using Near Infrared Spectroscopy and Sorting Discriminant Analysis

Near infrared spectra of apples contain the most useful information of the soluble solids content and firmness of apples. A new feature extraction method, called sorting discriminant analysis, was proposed to use a sorting method based on principal component analysis and linear discriminant analysis to extract the features of near infrared spectra. The objective of this research was to make use of feature extraction methods, such as principal component analysis, linear discriminant analysis, discriminant partial least squares, and sorting discriminant analysis to extract information from near infrared spectra of the “Huaniu” apples and the “Fuji” apples. After feature extraction, the nearest neighbor classifier was used to classify the apples, and the classification results were compared to study that which feature extraction method performed best. The experimental results showed principal component analysis + linear discriminant analysis and sorting discriminant analysis could extract discriminant information from near infrared spectra of apples better than principal component analysis and discriminant partial least squares, and sorting discriminant analysis was the best one. Sorting discriminant analysis can not only compress the high-dimensional near infrared spectra to the low-dimensional data but also project near infrared spectra to a new feature space where the data can be classified easily and effectively, and sorting discriminant analysis is superior to principal component analysis + linear discriminant analysis in most cases.     

A chemometric approach for the quantification of free fatty acids in cottonseed oil by Fourier transform infrared spectroscopy

The aim of this study was to quantify free fatty acid in cottonseed oil (Gossypium) variety by a chemometric approach using Fourier transform infrared spectroscopy. Calibration standards were prepared by gravimetrical mixing of oleic acid (0.1–40 g/100 g) in neutralized cottonseed oil containing <0.1% free fatty acids. Fourier transform infrared technique coupled with partial least square and principle component regression models were used to develop calibrations in the specific absorption region of carbonyl between 1690–1727 cm^?1. On the basis of regression coefficient and evaluated free fatty acids results with comparison to titration method, partial least square was found to be more accurate than principle component regression calibration model. All the analyzed cottonseed oil varieties showed high content of free fatty acids in the range of 17.1–38.5%. The results of the present study indicated that Fourier transform infrared method in combination with partial least square or principle component regression could be used as a greener alternative to the standard titration method.     

金属加工用水基润滑剂的分离与分析

为确定某金属加工用水基润滑剂组成成分,采用先经溶剂萃取预处理,再通过柱层析进行分离和提纯,然后对收集的馏分进行红外光谱、元素分析。用其它已知原料作为组分进行复配后,经过上述的预处理和柱层析分离,对所得馏分进行红外光谱等分析,结果与已知相符。对于金属加工用水基润滑剂以及类似体系产品,采用柱层析方法分离和提纯较简便、可行,分析结果具有较高的实用价值。     

Infrared spectroscopy technique for the nondestructive measurement of fat content in milk powder

The aim of this study was to investigate the potential of the infrared spectroscopy technique for nondestructive measurement of fat content in milk powder. Fat is an important component of milk powder. It is very important to be able to detect the fat content in milk powder using a rapid and nondestructive method. Near and mid infrared spectroscopy techniques were used to achieve this purpose. Least-squares support vector machine (LS-SVM) was applied to developing the fat-content prediction model based on the infrared spectral transmission values. The results based on LS-SVM were better than those of back-propagation artificial neural networks. The determination coefficient for prediction of the results predicted by the LS-SVM model was 0.9796 and the root mean square error was 0.836708. It was concluded that infrared spectroscopy technique could quantify the fat content in milk powder rapidly and nondestructively. The process is simple and easy to operate. Moreover, the prediction results were compared between near infrared and mid infrared spectral data. The results showed that the performances of model with both mid infrared and near infrared spectral data were a little worse than that of the whole infrared spectral data. The results could be beneficial for designing a simple and nondestructive spectral sensor for the quantification of fat content in milk powder.     

PROTEIN ANALYSIS IN THE DAIRY LABORATORY

The paper describes methods of analyses in direct determination of fat, protein and lactose. The Infrared Milk Analyser (IRMA) its fundamental component a double-beam infrared spectrophotometer which compares the absorption of a milk sample at specific wavelengths is described in detail. Calibration of the IRMA is discussed and the comparison of infrared and chemical analyses shown.     

大别山区石英质玉宝石矿物学特征研究

大别山区石英质玉属新近发现的一种石英质玉(简称"大别山玉"),具色彩绚丽、质地细腻温润等特点。利用偏光显微镜、扫描电子显微镜、X射线粉末衍射、化学分析、红外光谱分析等方法,重点对"大别山玉"的矿物组成、化学成分、物理光学性质、微结构、红外吸收光谱等特征进行研究。结果表明,"大别山玉"具较典型的微粒-细粒结构,主要矿物为石英,含少量的绢云母、绿泥石、萤石、黄铁矿及其它粘土矿物等次要矿物,实属石英质玉;其化学成分相对较纯,主成分为SiO2,含少量的Al2O3,CaO,MgO,Fe2O3,FeO,K2O等;其红外反射光谱以Si—O非对称伸缩振动致特征的1177,1104 cm-1谱带、Si—O—Si对称伸缩振动致800,781cm-1分裂谱带以及由Si—O弯曲振动致492 cm-1较强谱带和542 cm-1弱谱带为特征。同时,还对"大别山玉"的结晶度及水的赋存状态一并给予了探讨。     

红外光谱技术在中国酿酒行业中的应用研究进展

红外光谱法最主要的特点是快速分析、无损检测以及重现性好,因而在许多生产制造行业中得到了极为广泛的应用,近些年来,红外光谱法被引入到我国酿酒行业中,为酒体的品评以及对组分的定性与定量分析提供了一种快速、准确的分析手段.本文综述了红外光谱技术在国内酿酒行业当中的应用现状,其中涵盖了中红外、近红外两大技术分支分别在不同酒类的理化指标分析以及品质鉴定工作中的应用与研究情况,并对其应用前景做出了展望.     

红外光谱和核磁共振技术在纺丝油剂剖析中的应用

为了解国外油剂的组成,提高国内油剂生产企业的竞争力,对一种性能优良的进口纺丝油剂进行了剖析。采用硅胶柱色谱法对要剖析的纺丝油剂进行组分分离,并用重量法得出了各组分的质量百分含量,然后针对分离出来的各个未知组分,用红外光谱法结合标准红外谱图推断未知组分的结构,再用核磁共振光谱法对未知组分的化学结构进行验证与判定,得知所剖析油剂的主要组分为石蜡油、棕榈酸酯聚氧乙基醚-3、聚丙二醇和十四酸酯聚氧乙基醚-6。     

红外光谱技术在食品检测中的应用

红外光谱在食品检测研究中的应用较为广泛, 具有样品前处理简单、操作方便、易于推广, 且对气体、液体和固体样品均可准确检测等优点, 表现出良好的发展前景。本文概述红外光谱技术的检测基本原理和化学计量学在红外光谱技术中的应用, 在简要对比常用检测分析方法的优缺点基础上, 对红外光谱技术在食品鉴伪、品质研究、有害物检测和成分分析等领域的研究进展进行了综述, 详细介绍该技术与化学计量学结合评价食品品质的应用实例, 同时提出目前研究存在的局限性, 并陈述改进建议, 且对该技术在食品检测研究的未来发展方向作出展望, 以期为该技术的深入研究提供参考。