注水肉无损检测技术现状与发展趋势分析

针对目前市场上注水肉现象加重问题,有必要研究注水肉的检测技术。首先对注水肉的传统检测方法进行简要论述,分析总结了传统方法的弊端;然后对现阶段肉品含水率的4种主要无损检测方法:生物电阻抗法、核磁共振方法、超声波法和近红外光谱法的研究现状及特点进行了总结分析,着重叙述了近红外光谱技术在肉品含水率及品质检测中的研究情况及技术特点,提出了未来技术发展方向。     

近红外多组分分析中异常样本识别方法

近红外光谱分析中,异常样本的存在严重影响定标模型的预测性能和适配性。基于 X / Y 联合的ODXY异常样本识别和剔除方法,提出并证明了一种专用于多组分分析的MODXY异常样本识别方法。实验采用80组玉米近红外光谱数据,利用不同异常样本识别方法剔除异常样本后建立玉米含水率、含油率、蛋白质含量和淀粉含量4种组分的偏最小二乘预测模型,采用预测均方差和决定系数作为评价指标比较所建模型的性能,检验MODXY方法在多组分分析中的异常样本识别能力。实验结果表明：在近红外多组分分析中,MODXY方法在大多数情况下具有更好的异常样本识别能力;MODXY方法和ODXY方法均有一定的适用范围,它们更适合于相应组分化学值的相对标准偏差较大的情况。     

基于梨枣轻微损伤的可见/近红外光谱判别研究

利用可见/近红外光谱技术对梨枣轻微损伤的分类判别建模方法进行研究。分别采用PLS-LDA(线性)和LS-SVM(非线性)建立判别模型,分析比较不同预处理方式和建模波段对模型精度的影响。结果表明:经9点平滑预处理后的短波近红外(780~1100nm)PLS-LDA模型判别效果最佳,校正集和预测集的正确识别率分别达到97.8%和96.7%。     

近红外光谱在转基因农产品检测中的研究进展

简述了近年来近红外光谱分析技术在农产品和食品定量分析中的应用,重点介绍了近红外光谱分析在转基因植株筛选以及转基因和非转基因农产品分类和识别等方面的研究现状,比较和分析了近红外光谱与传统的转基因农产品检测方法的特点,并展望了近红外光谱分析技术在转基因农产品检测研究中的发展前景。     

多组分相关分析技术在近红外检测中的应用研究

采用信号分析理论中的相关分析技术,将三组分的涤纶-棉-毛样品与二组分的棉-毛样品进行相关分析,提取出三组分样品中的棉-毛信息,并对毛的含量进行定量分析,在31个样品中选22个样品建立了基于偏最小二乘(PLS)方法的NIR(Near Infrared,NIR)校正模型,对余下的9个样品进行了预测分析。结果表明,借助相关分析算法,毛含量的预测相对均方差和相关系数分别从3.5575、0.9855提高到2.7835、0.9876。试验证明,多组分相关分析技术可以在复杂混合样品中提取出待测组分的近红外光谱信息,为建立较高精度的校正模型创造了条件。     

Near-Infrared Spectroscopy Coupled with Chemometrics Tools: A Rapid and Non-Destructive Alternative on Soil Evaluation

Near-infrared (NIR) spectroscopy is a rapid, non-destructive and accurate technique for analyzing a wide variety of samples, thus, the growing interest of using this technique in soil science. The objective of this study was to evaluate the potential of NIR spectroscopy to predict organic carbon (OC), total nitrogen (TN), available phosphorus (P) and available potassium (K) in the soil. NIR spectra from 20 cm³ of soil samples were acquired on the range of 750 to 2500 nm in diffuse reflectance mode, resolution of 16 cm^?1 and 64 scans. Eight models of calibration/validation were constructed. Calibration and validation models showed that the predictive potential of NIR varied with the specific soil property (OC, TN, P and K) under evaluation and according to the methodology employed in the model construction (cross-validation or test set). Good prediction models were obtained for OC and TN content based on the statistical parameters. Test set methodology was able to predict soil OC, TN, P, and K better than cross-validation methodology.     

Spectrophotometrical characteristics in the near infrared region in beech (<Emphasis Type="Italic">Fagus crenata</Emphasis>) and pine (<Emphasis Type="Italic">Pinus densiflora</Emphasis>) litters at the various decomposing stages

We examined the relationships between the absorptional characteristics in the near infrared region and the chemical changes of decomposing beech (Fagus crenata) and pine (Pinus densiflora) litters. Spectra as well as the concentrations of chemical substances approached each other and converged with decomposition, although both initial characteristics differed markedly between beech and pine. This indicated that the fundamental chemical structures were almost the same, although their organochemical composition differed. Specific absorption bands for lignin, polysaccharide, and protein were identified at 2,140 and 1,670 nm, 2,270, 1,720, 1,590, and 1,216 nm, and 2,350 nm, respectively. Absorbance at 1,670 nm, peculiar band of aromatics, showed a positive correlation with lignin concentration, which suggested the relative increment of aromatics due to condensed lignin in decomposing litters. Absorbance at 2,140 nm, characterized as the C–H bond in HRC = CHR, showed a negative correlation with lignin concentration, which suggested the decrements of some structures such as side-chains in lignin polymers unrelated to aromatics. Absorbance at 2,270, 1,720, and 1,216 nm, specified to O–H/C–O/C–H bonds in saccharide, might reflect the change of polysaccharide during decomposition because they showed a positive correlation to polysaccharide concentration. In the same way, absorbance at 2,350 nm, identified to the C–H/CH₂ bonds in protein, showed a negative correlation to nitrogen concentration in decomposing litters, which might indicate that the C–H/CH₂ bonds in protein decreased with decomposition due to microbial consumption of carbon in protein. Our findings suggested the possibility that the spectral changes indicate the litter digestibility during decomposition and that also explain the compositional change in decomposing litters.     

基于NIR和GIS的甘蔗含糖量与种植条件关系研究

为监测较大区域内甘蔗产量及含糖量的时间、空间变化情况,了解地区、环境及土壤成分与甘蔗含糖量的关系,为甘蔗栽培提供指导,以日本琉球群岛南大东岛甘蔗栽培区为对象,用NIR方法测定甘蔗含糖量及其他成分含量,采用GIS技术建立地块位置、甘蔗产量、含糖量及P、K含量和土壤K含量数据库。在此基础上,建立3个栽培年度甘蔗含糖量、单产与甘蔗汁P、K含量的空间分布图,发现甘蔗含糖量与降水量及种植区水环境密切相关。对甘蔗含糖量及P、K含量和土壤K含量的相关分析表明,甘蔗含糖量与甘蔗汁P含量呈正相关,而与甘蔗汁和土壤K含量均呈负相关。     

基于田间原位土壤含水量估测的可见/近红外光谱建模方法

为实时、准确地获取原位土壤含水量信息,利用可见/近红外光谱技术,分别使用全局偏最小二乘(PLS)建模、局部PLS建模方法,对田间原位土壤含水量进行快速估测。结果表明:全局PLS模型中,其建模集的决定系数(R~2)、交叉验证均方根误差(RMSECV)分别为0.943和1.750%,检验集的决定系数(R~2)、预测均方根误差(RMSEP)分别为0.956和1.260%。局部PLS模型中,分别比较了选取定标子集的2种方法(欧氏距离法和马氏距离法),采用欧氏距离法和马氏距离法选取定标子集进行建模的R~2值分别为0.974和0.979,RMSEP值分别为0.976%和0.943%。因此,将可见/近红外光谱技术应用到田间原位含水量测量是可行的,其中,使用局部建模方法的效果优于全局建模。     

近红外技术(NIR)在小麦商品粮收购中的应用研究

利用近红外谷物品质分析仪 (Perten DA910 0 ) ,对陕西省某县 6 2 4户农民 2 0 0 0年夏季上缴粮库的公购粮进行现场品质分析。结果认为 :人工主观评定的商品粮等级与其蛋白质含量、水分含量、硬度、沉淀值、烘烤体积无显著相关性 ;近红外谷物品质分析仪 (Perten DA910 0 )完全能够满足粮库验粮的需要 ;小麦籽粒硬度、蛋白质含量是商品粮收购定级的重要指标