北京典型耕作土壤养分的近红外光谱分析

为研究土壤养分含量分布信息,以从北京郊区一块试验田采集的72个土壤样品为试验材料,应用傅里叶变换近红外光谱技术分析了土样的全氮、全钾、有机质养分含量和pH值。采用偏最小二乘法(PLS)对光谱数据与土壤养分实测值进行回归分析,建立预测模型,以模型决定系数(R2)、校正标准差(RMSECV)、预测标准差(RMSEP)和相对分析误差(RPD)作为模型精度的评价指标。结果表明,利用该模型与光谱数据对土壤全氮、全钾、有机质养分含量和pH值进行预测,结果与实测数据具有较好的一致性,最高决定系数R2达到0.9544。偏最小二乘回归方法建立的养分预测模型能准确地对北京地区褐土土质全氮、有机质、全钾和pH值4种养分进行预测。     

花生籽仁蔗糖含量近红外模型构建及在高糖品种培育中的应用

含糖量是决定和影响花生食用品质和加工特性的重要指标,蔗糖含量占成熟花生籽仁总糖量的90%以上,建立蔗糖含量的高效检测技术,有助于加快高蔗糖甜味食用型花生品种培育进程。本研究利用蔗糖含量差异显著的185份花生材料,利用近红外仪(波长范围1100~2500 nm),配合小样品杯,扫描和采集自然干燥籽仁的近红外光谱,采用液相色谱(HPLC)结合标准曲线法测定试验材料的蔗糖含量,利用偏最小二乘法(partial least squares,PLS)构建了花生籽仁蔗糖含量的近红外定标模型,模型的决定系数R2=0.962,均方差为0.383。利用20份材料对模型进行外部验证,预测值和化学值的决定系数达0.947,表明该模型可较好地预测蔗糖含量,可以高效地测定杂交早期世代的单株花生蔗糖含量。利用该模型在“吉花02-1-4×中花26”杂交后代中发掘出6份含糖量7%以上、油酸78%以上、含油量48%以下,且农艺性状优良的食用花生新品系。     

Rainforest litter quality and chemical controls on leaf decomposition with near‐infrared spectrometry

Plant‐litter chemical quality is an important driver of many ecosystem processes, however, what actually constitutes high‐ or low‐quality litter (chemical potential for fast and slow decomposition, respectively) is often interpreted by the indices available. Here, near‐infrared spectroscopy (NIRS) was used to explore leaf‐litter chemical quality and the controls on decomposition in the tropical rainforest region of north Queensland Australia. Leaf‐litter samples from litterfall collections and litterbag studies were used. NIRS was used to calibrate the chemical compositions of the material (N, P, C, Mg, Ca, acid detergent fiber, acid detergent lignin, α‐cellulose, and total phenolics) from a smaller sample set covering the spectral range in the full set of samples. Calibrations were compared for both separate (local) and combined models, for litterbags, and litterfall. Coefficients of determination (r²) in the local models ranged from 0.88 (litterbag Mg) to 0.99 (litterfall N), with residual prediction deviation ratios > 3 for all constituents except Mg (≈ 2.5). Mass loss in the litterbags was strongly related to the NIR spectra, with model r²'s of 0.75 (in situ leaves) and 0.76 (common control leaf). In situ decomposability was determined from modeling the initial NIR spectra prior to decomposition with litterbag exponential‐decay rates (model r² of 0.81, n = 85 initial samples). A best subset model including litter‐quality, climate, and soil variables predicted decay better than the NIR decomposability model (r² = 0.87). For litter quality alone the NIR model predicted decay rate better than all of the best predictive litter–chemical quality indices. The decomposability model was used to predict in situ decomposability in the litterfall samples. The chemical variables explaining NIR decomposability for litterfall were initial P, C, and phenolics (linear model r² = 0.80, n = 2471). NIRS is a holistic technique that is just as, if not more accurate, than litter–chemical quality indices, when predicting decomposition and decomposability, shown here in a regional field study.     

Microwave l-band (1730 MHz) accurately estimates the relative water content in poplar leaves. A comparison with a near infrared water index (R1300/R1450)

In this study the estimation of reflectivity at 1730 MHz (l-band), measured with a microwave digital cordless telephony (DCT) patch antenna, is presented as an easy-to-handle and non-destructive new method to assess the relative water content (RWC) of poplar leaves and filter discs at different levels of dehydration. The accuracy of this new method has been contrasted with the R₁₃₀₀/R₁₄₅₀ index, determined by a portable near infrared (NIR) spectrometer. The close correlations found between RWC and the reflectivity at a frequency of 1730 MHz, both for filters and leaves, indicate that microwave determinations are rather independent of the physical properties of the material analysed. On the contrary, the differences found between poplar leaves and leaf filters in the relationships established between RWC and the R₁₃₀₀/R₁₄₅₀ index demonstrate a strong influence of the properties of the material in NIR reflectance measurements, specifically as they relate to changes in leaf thickness during dehydration. It should be noted that the amount of energy received by the leaf for the microwave technique (0.1 mW) was much lower than that received for the measuring of the R₁₃₀₀/R₁₄₅₀ index (2.5 W). Moreover, R-square coefficients were higher for microwaves than for the R₁₃₀₀/R₁₄₅₀ index. The use of a technologically simple, low cost and portable device, based on a microwave DCT patch antenna, could yield a solid support for the development of a commercial apparatus enabling the determination of plant water status under field conditions.     

基于近红外光谱和机器视觉融合技术的板栗缺陷检测

为提高合格和缺陷板栗分级检测识别精度,提出了近红外光谱和机器视觉的多源信息融合技术的板栗缺陷检测方法。试验以湖北京山板栗为试验对象,利用BP神经网络方法建立了基于近红外光谱、机器视觉和多源信息融合技术的板栗分级检测模型。试验结果表明,3种识别模型对对训练集板栗回判率分别为96.25%、96.67%和97.92%;对测试集板栗的识别率为86.25%、83.75%和90.00%。基于近红外光谱和机器视觉的多源信息融合技术进行板栗分级检测的方法是可行的,融合模型较单独采用机器视觉技术或近红外光谱分析技术建立模型的识别率均有显著提高。     

近红外光谱法快速鉴定干海参产地

该文采用近红外漫反射光谱法,对来自4个不同产地的96个干刺参样品,进行了产地鉴别分析。研究表明：在5 000～4 000 cm-1 波段,干海参样品具有丰富的光谱结构,且反映出了由于海参生长环境不同带来的差异性;对该波段的一次微分光谱数据进行主成分聚类（PCCA）计算,可成功实现对其产地的鉴定分析。该研究为动物性海洋食品的产地快速鉴别分析提供了新方法。     

基于近红外光谱的未知类别样品聚类方法

在近红外光谱分析中,针对大量样品参与建模时,需将样品集进行分类,以减少样品光谱变异范围,提高近红外模型的预测准确度。本文以来自中国各地的222份小麦样品为例,在未知样品组分含量和类别归属的前提下,结合样品的近红外光谱信息,采用基于试探的未知类别的样品聚类方法（最邻近规则法和最大最小距离算法）对样品集分类。其中,最邻近规则法在阈值T为1.9时,最大最小距离算法在阈值为样品间的最大距离的1/2时,分类建模指标均优于未分类所建模型。从分类实现过程和结果可以看出：基于试探的未知类别的样品聚类方法中无需多次训练,且对未知类别的样品集无需事先确定分类数目,但需要确定分类阈值,阈值不同,则分类结果会随之改变。研究为近红外建模过程中未知类别样品的分类提供了一种参考方法。     

基于MODIS数据的长江三角洲地区近地表气温遥感反演

近地表气温是一个重要的气候参数,为了给农业研究提供空间上连续的气温信息,以长江三角洲为研究区,根据MODIS地表温度和NDVI数据运用温度-植被指数方法反演了2005年全年的气温,并通过进一步去除温度-植被指数空间窗口的残余云和水体信息扩大了该方法的通用范围.最后利用气象站点观测气温数据对遥感反演值进行了精度验证,分析...     

平板目标与二维粗糙介质分形海面复合电磁散射近场特性研究

采用二维连续带限分形函数模拟粗糙海面,研究了与其上方金属平板的近场复合电磁散射,将目标分化为微小面元,依据远场条件,运用Kirchhoff近似法导出了复合散射的计算公式,并给出了数值计算结果,进一步将其推至远场,并同远场计算结果进行比较;讨论了复合散射截面随入射角、入射频率及平板尺寸的变化关系。     

MET玉米浸种剂的增产效果研究

1985～1987年玉米浸种剂(MET)通过九个县(区)累积面积达30.2万余亩的试验、示范表明,MET比当前推广的0.05％硫酸锌浸种增产显著,且效果稳定。用MET浸种玉米比清水浸种平均每亩增产58.6±4.6kg,增产率为5.9～27.9％,平均14.6％。使用MET可提早出苗1～2天,根系发达,叶片叶绿素a,b含量高。中后期根系活力强,穗三叶面积大,使光合作用增强,显著提高千粒重16.2±5.6g,减少秃顶率,增加穗粒数30±10粒,使成熟期提前2～3天。而且使用方便,成本低,经济效益显著。