高光谱技术——生态学领域研究的新方法

高光谱技术是一种新的地物探测技术,该技术以其敏锐的地物光谱特征探测能力为精准识别地物属性提供了强有力的手段,在生态系统过程与属性研究中具有广阔的应用前景。该文以可见光-近红外光谱分析技术为例概述了高光谱技术的原理、特点与优势,以及高光谱技术分析的流程;总结并归纳了其在土壤、植物生理、农产品品质检测、凋落物分解方面的研究应用,指出高光谱技术与遥感成像技术结合在生态监测研究中的优势;归纳了高光谱技术应用中面临的问题,并希望高光谱技术在生态学领域研究中得到更广泛的应用。     

近红外光谱技术在稻米特性检测中的应用(综述)

近红外光谱技术是一种新型的检测分析技术,广泛应用于农业、林业、工业、医药以及食品等多个行业领域。文章综述近红外光谱技术在稻米特性检测中的应用概况,包括对大米淀粉、蛋白质和脂肪酸等营养物质的测定,大米糊化特性、粘稠度和食味特性的分析,水稻生长过程中氮、磷、钾和其他营养元素含量的分析,育种研究与品种鉴别,病害、重金属等有害物质以及其他方面。同时,指出该技术在当前检测应用中存在的一些问题,并针对目前发展趋势展望该技术的前景。     

基于近红外漫反射光谱快速测定淫羊藿蛋白质含量

采用近红外漫反射光谱技术对淫羊藿(Epimedium)的蛋白质含量进行快速且无损检测。近红外漫反射光谱经二阶导数处理、标准多元离散校正及主成分分析聚类处理后, 采用改进最小二乘法回归得到的定标模型预测效果最佳, 定标决定系数、交互验证标准差及交互验证相关系数分别为0.923、0.554和0.717。近红外光谱分析法的测定结果与用凯氏定氮法所得结果无显著差异, 两种方法测定值的相关性较高(R²=0.933 9)。重复性实验表明, 近红外光谱分析法的相对标准偏差为0.937%。该研究首次采用近红外光谱分析法测定了8种淫羊藿的蛋白质含量。该方法简便、精确, 在淫羊藿资源开发利用和药材质量控制方面具有参考意义。     

豌豆品质性状近红外模型建立及区域差异分析

探讨了傅立叶变换近红外光谱技术(FT-NIRS)检测豌豆蛋白质、淀粉、脂肪和总多酚含量的可行性。用化学方法测定190份豌豆种质的蛋白质、淀粉、脂肪以及总多酚含量,采集其子粒与粉末的近红外光谱,采用偏最小二乘法(PLS)分别建立两种光谱与成份含量预测模型。豌豆粉末模型结果优于子粒模型,其中蛋白质和淀粉的粉末模型的预测残差(RPD)为5.88、5.82,相关系数r2达到0.99、0.99,具有很好的预测性能。对其中产地信息详细明确的150份豌豆种质的品质性状与产地进行两步聚类分析,明确得到3种类型,其特点分别为:类群1低蛋白质含量,类群2高总多酚含量,类群3高蛋白质、高淀粉和高脂肪含量。进一步分析了豌豆品质性状随播种期、经度、纬度、海拔高度的变化情况。结果表明,近红外光谱技术可对豌豆种质资源的部分品质性状进行快速筛选鉴定,聚类分析结论、地理坐标与播期对豌豆种质主要品质性状的影响规律,都可为收集高品质性状豌豆种质资源提供可靠依据。     

水果也能做“体检”

不用切开也知道苹果是否病变、糖度如何,这是一项将可见／近红外光谱和X射线成像技术用于水果检测的技术,就像过安检系统一样简单。水果果实具有一定的光学特性,可见／近红外光谱则是根据光能损耗反馈来判定果实内部信息。从正常苹果与水心病苹果的透射光谱图可以比较出,苹果的光密度随水心病的严重程度逐渐减少。     

近红外光谱技术在食品产地溯源中的应用进展

食品产地溯源是食品安全追溯制度的重要工作。近红外光谱技术(near infrared spectroscopy,NIRS)作为一种兼具快速、简便、不破坏试样、分析过程无试剂消耗等优点的新兴绿色检测技术,近年来被逐步应用于食品产地溯源的研究中。简要介绍了应用于食品产地溯源研究中近红外光谱技术常用的化学计量学技术及软件平台,同时概述了近年来该技术在国内外食品产地溯源中的研究进展,分析了在目前产地溯源研究中的优势和存在的问题,以期为近红外光谱溯源技术的进一步发展提供参考。     

近红外漫反射技术测定烤烟烟叶中的茄尼醇

为研究烟草茄尼醇的近红外光谱快速检测技术,采用偏最小二乘法(PLS),选择合适的预处理方法,建立了烤烟烟叶中茄尼醇的近红外光谱漫反射定量校正模型,模型交叉验证均方差RMSECV为0.108,外部验证的预测均方差RMSEP为0.0822,平均相对偏差为5.3%,说明模型可以对茄尼醇进行快速准确地预测。并通过有标转移法成功地将模型转移到其它仪器上,实现了烟草茄尼醇在不同实验室的快速检测。     

近红外光谱技术在烟草行业中的应用进展

介绍了近红外光谱分析技术在烟草常规化学成分分析、烟气分析、卷烟配方设计、不同产地模式识别和真假烟鉴别等方面的应用,认为近红外光谱分析技术在烟草行业中正扮演着越来越重要的角色,而且具有十分广阔的应用前景.     

基于可见/近红外高光谱成像技术的西甜瓜糖度检测

提出了以不同品种的西瓜、甜瓜为研究对象,利用可见光/近红外成像高光谱技术分析不同糖度的西瓜、甜瓜的光谱差异及西瓜、甜瓜糖度在可见光/近红外波段范围的响应。研究表明应用成像高光谱技术检测西甜瓜糖度具有可行性,为进一步研究不同水果糖度高精度模型奠定基础。     

FT-NIRS技术应用于稻米直链淀粉含量分析研究

运用近红外光谱快速分析技术,使用偏最小二乘法建立了近红外光谱和水稻糙米直链淀粉含量的数学模型,并进行糙米直链淀粉含量预测.结果表明糙米近红外光谱与其直链淀粉含量具有良好的相关性,决定系数r2=0.8429,最大绝对误差4.82%,平均误差2.30%.该方法在不破坏样品的前提下快速分析水稻直链淀粉含量,可用于稻种资源的快速鉴定,对于水稻优质育种及其相关研究具有重要意义.     

Ecological applications of near infrared reflectance spectroscopy – a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance

Many ecological studies rely heavily on chemical analysis of plant and animal tissues. Often, there is limited time and money to perform all the required analyses and this can result in less than ideal sampling schemes and poor levels of replication. Near infrared reflectance spectroscopy (NIRS) can relieve these constraints because it can provide quick, non-destructive and quantitative analyses of an enormous range of organic constituents of plant and animal tissues. Near infrared spectra depend on the number and type of CH, NH and OH bonds in the material being analyzed. The spectral features are then combined with reliable compositional or functional analyses of the material in a predictive statistical model. This model is then used to predict the composition of new or unknown samples. NIRS can be used to analyze some specific elements (indirectly – e.g., N as protein) or well-defined compounds (e.g., starch) or more complex, poorly defined attributes of substances (e.g., fiber, animal food intake) have also been successfully modeled with NIRS technology. The accuracy and precision of the reference values for the calibration data set in part determines the quality of the predictions made by NIRS. However, NIRS analyses are often more precise than standard laboratory assays. The use of NIRS is not restricted to the simple determination of quantities of known compounds, but can also be used to discriminate between complex mixtures and to identify important compounds affecting attributes of interest. Near infrared reflectance spectroscopy is widely accepted for compositional and functional analyses in agriculture and manufacturing but its utility has not yet been recognized by the majority of ecologists conducting similar analyses. This paper aims to stimulate interest in NIRS and to illustrate some of the enormous variety of uses to which it can be put. We emphasize that care must be taken in the calibration stage to prevent propagation of poor analytical work through NIRS, but, used properly, NIRS offers ecologists enormous analytical power. Received: 10 October 1997 / Accepted: 12 May 1998     

A rapid and sensitive method for the evaluation of cereal grains in bioethanol production using near infrared reflectance spectroscopy

The potential of near infrared spectroscopy (NIRS) for determining fermentable substance and also ethanol yield in wheat, rye and triticale grains as a feedstock for fuel ethanol production was investigated. The total sample set contained 480 samples of 10 types of wheat, 24 types of triticale and 6 types of rye, which were grown on 10 locations in Germany from 2006 to 2008. Samples were scanned by NIRS as whole or ground grains, and their reference values regressed against different spectral transformations by partial least squares regression (PLS-1). Ground grains were scanned on a dispersive NIR-Spectrometer. Whole grains were scanned on a diode array NIR-Spectrometer. Principal Component Analysis (PCA) revealed that samples could be classified by crop season, growth location and species. It is shown that near infrared spectroscopy is an appropriate and useful tool for prediction of biofuel yield for both industry and research.     

Differentiation between carcasses from suckling lambs reared with ewe milk or milk replacers by near infrared reflectance spectroscopy of perirenal fat

Near infrared reflectance spectroscopy (NIRS) was used to discriminate between carcasses from Churra breed suckling lambs reared with ewe milk or milk replacers. Samples were scanned over the NIR spectral range (1100–2500 nm). The results showed that NIRS could be used successfully to discriminate the suckling lambs depending on milk source, with a 100% of correctly classified samples. NIRS technology would be a good method, since it is a rapid, economical and little complex method.     

In-situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation

The use of in-situ near infrared spectroscopy (NIRS) as a tool for monitoring four key analytes in a CHO-K1 animal cell culture was investigated. Previous work using on-line NIRS to monitor bioprocesses has involved its application ex-situ where the analyzer is physically outside the fermentor, or to microbial bioprocesses. This novel application of NIRS to monitor analytes within an animal cell culture using a steam sterilizable in-situ fiber optic probe is very important for furthering the use of NIRS within the bioprocessing industry. The method of calibration used to develop the models involved the use of large data sets so that all likely variation in stoichiometry was incorporated within the models. Successful models for glucose, lactate, glutamine, and ammonia were built with Standard Error of Predictions (SEP's) of 0.072 (g/L), 0.0144 (g/L), 0.308 (mM), and 0.036 (mM), respectively of the total concentration range.     

近红外光谱技术在猫脑射频热凝毁损中的应用研究

目的：探索近红外光谱（nears）技术用于立体定向靶点毁损术中实时监测的可行性。方法：利用猫脑建立体内不同毁损时间、温度下的毁损灶体积模型,通过病理检测及近红外光谱仪观察并记录脑组织靶点毁损时的NIRS尤其是优化散射系数（）的变化情况。结果：不同温度、不同时间温度点下NIRS出现特征性变化曲线。并建立时间、温度及三维模型。结论：利用NIRS实时活体在位监测猫脑射频神经核团毁损术是科学、可行的,优化散射系数是监测的良好指标,比以往单凭经验的作法更科学、更准确。     

近红外光谱技术在运动脑功能研究中的应用

近红外光谱是一种评估生物组织氧合水平的无创性光学技术,这一技术以血液动力学原理为基础,能实时监测局部脑区的动态变化。近红外光谱作为一种客观的测量工具,在人体运动科学领域广泛运用。近红外光谱技术可用于区别体能水平、监控耐力训练和抗阻训练过程特征以及考察运动中的认知活动变化。本文综述了近红外光谱技术原理及其在抗阻运动、运动中枢疲劳和运动认知领域的运用,并对近红外光谱在运动科学领域的未来研究趋向作了分析。     

A geographical traceability method for Lanmaoa asiatica mushrooms from 20 township-level geographical origins by near infrared spectroscopy and ResNet image analysis techniques

Food authenticity and traceability and climate change are key scientific issues that must be addressed in response to the food crisis in 2050. Lanmaoa asiatica mushroom is an expensive and nutritious fungi-based diets resource, it is necessary to identify its geographical origin and explore the impact of the climate on it. Thus, the purpose of this study is to establish a fast and accurate geographical traceability model based on L. asiatica mushrooms chemical information collected by near-infrared spectroscopy (NIRS) technology, and screen out key climate variables by competitive adaptive reweighted sampling (CARS) algorithm. Based on the NIRS information of L. asiatica mushrooms, two-dimensional correlation spectroscopy (2D-COS) images were generated and a residual neural network (ResNet) image recognition model was established to identify the geographical origin of L. asiatica mushrooms. The accuracy of training set and test set of ResNet model is 100%, and the loss value is 0.052, which indicates that the model is effective. In addition, the CARS algorithm was used to select the feature variables from 105 climate variables. Four important variables (February, March, and April precipitation and January minimum temperature) related to NIRS difference of L. asiatica mushroom were obtained by CARS algorithm. The results can provide a fast and accurate method for food authenticity and traceability research, and provide an innovative idea for screening key climate factors.