近红外光谱分析技术及其在农业研究中的应用

介绍了近红外光谱分析技术原理、特点及发展概况,论述了该技术在作物品质育种、品种资源鉴定与分类、生物样品的非破坏测定、作物抗虫性鉴定等方面的应用。并对近红外分析技术的应用前景进行了展望。     

秦王川灌区田间土壤水分特征曲线与导水率的标定

运用相似性原理和标定理论，对秦王川灌区的土壤水分特征曲线和非饱和导水率进行了标定。通过分析认为，该标定结果对这一灌区的土壤有较好的代表性，可供水分运动研究之用     

Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents

Near infrared reflectance (NIR) spectroscopy has been used successfully to measure soluble solids in apple fruit. However, for practical implementation, the technique needs to be able to compensate for fruit temperature fluctuations, as it was observed that the sample temperature affects the near infrared reflectance spectrum in a non-linear way. Temperature fluctuations may occur in practice because of varying weather conditions or improper conditioning of the fruit immediately after harvest. Two techniques were found well suited to control the accuracy of the calibration models for soluble solids with respect to temperature fluctuations. The first, and most practical one, consisted of developing a global robust calibration model to cover the temperature range expected in the future. The second method involved the development of a range of temperature dedicated calibration models. The drawback of the latter approach is that the required data collection is very large. When no precautions are taken, the error on the soluble solids content reading may be as large as 4%brix.     

Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen

Dynamic simulations models may enable for farmers the evaluation of crop and soil management strategies, or may trigger crop and soil management strategies if they are used as warning systems, e.g. for drought risks and for nutrients shortage. Predictions by simulation models may differ from field observations for a variety of reasons, and such deviations can be revealed instantly by traditional or by new field monitoring techniques. The objective of this study was to improve simulation results by integrating remote sensing observations during the growing season in the simulation (i.e. run-time calibration). The Rotask 1.0 simulation model was used as it simulates daily interactions between climate (radiation temperature, vapour pressure, wind speed, precipitation), soils (water holding capacities, soil organic matter dynamics, evaporation) and crops (light interception, dry matter production, nitrogen uptake, transpiration). Various run-time calibration scenarios for replacing simulated values by remotely observed values were tested. For a number of times in the growing season, simulated values of leaf area index (LAI) and canopy nitrogen contents were replaced with values estimated from remote sensing. Field experiments were carried out in the Netherlands in 1997 (validation) and 1998 (calibration) with potato variety Bintje. Destructive field samplings were performed to follow LAI and canopy nitrogen development in the growing season. Remote sensing observations at canopy level were taken by CropScan™ equipment, covering the electromagnetic spectrum between 460–810 nm in eight spectral bands. LAI and canopy nitrogen were monitored at various moments throughout the growing season by relating them with vegetation indices (VI) that were calculated from the combination of specific remote sensing bands. The results of this study show that run-time calibration of mechanistic simulation models may enhance simulation accuracy, depending on the method how additional information is integrated. It is advised to synchronize dry matter balances and internal nitrogen balances in accordance with adjustments to observed calibration variables (in this case LAI and canopy nitrogen content). It is shown an integrated approach follows the actual crop–soil system more closely, which is helpful for specific crop management and precision agriculture in general. Run-time calibration with variables that can be estimated from remote sensing observations gives more accurate simulation results of variables that can not be observed directly, e.g. the evolution of soil inorganic nitrogen contents. High frequencies of remote sensing observations and interpolation in between them, allow reconstructing the evolution of LAI and canopy nitrogen contents to be integrated in the simulation, thereby increasing simulation accuracy of other model variables.     

高油玉米群体油分、蛋白质和淀粉含量近红外分析模型的构建

以BHO高油玉米F2∶3家系为材料,应用主成分空间和傅里叶变换近红外光谱分析技术,采用偏最小二乘回归法(PLS),建立了测定高油玉米子粒的油分、蛋白质和淀粉含量的近红外校正模型。预处理分别采用一阶导数 矢量归一化、一阶导数 多元散射校正及直线相减等方法,主成分维数分别为5、9、9。验证分析表明,所建立的油分、蛋白质和淀粉含量的校正模型的校正和预测效果最好,其校正决定系数(R2cal)分别为0.950、0.973、0.976,交叉验证决定系数(R2cv)和外部验证决定系数(R2val)在0.918～0.948,各项误差(RMSEE、RMSECV、RMSEP)在0.305%～0.721%。结果表明,所建立的高油玉米完整子粒品质性状三成分模型的准确度和精确度均较高,可以满足高油玉米群体大量样品无损品质分析的需要。     

不同原理土壤水分传感器的室内外标定方法及对比测试研究

土壤水分是地表水循环的关键环节,也是农林系统的重要状态指标。土壤水分观测系统的研制有利于土壤水分的有效观测,从而及时掌握其时空动态变化规律。当前存在多种土壤水分传感器,如常见的基于不同工作原理的频域传感器(Frequency domain spec-troscopy,FDS)和电感传感器等,其成本、土壤含水量-信号曲线以及影响因素均有较大差异。选用国产的FDS传感器,研制了信号采集和长期存储的数据采集器,制成了低成本土壤水分观测系统。同时设计了基于不同试验条件与水分含量的室内控制试验和室外现场同步观测试验,将研制系统的观测结果与美国HOBO土壤水分观测系统的观测结果、环刀取样测试结果进行了比对分析。结果表明:土壤水分含量较低时,研制的土壤水分观测系统观测结果与实际值较接近,优于HOBO观测结果,且适用于瞬时速测和定点长期观测。HOBO传感器在不同环境下表现更为稳定,观测结果值域变化范围较小。盐度对2种土壤水分传感器测量结果均有重要影响。     

Using Interlaboratory Proficiency Data to Guide NIR/MIR Calibrations

There is growing interest in the use of near-range and/or midrange infrared (IR) diffuse reflectance spectroscopy (NIR and MIR) as nondestructive alternatives to chemical testing of soils. This trend is supported by research on how best to correlate IR spectral data with results obtained by conventional laboratory measurements. While for soils there is growing interest in developing local and national calibrations using “legacy” data, the proven analytical performance of provider laboratories now and earlier, the moisture status of reported results, and the method of soil preparation warrant greater attention. Examples for soil carbon (C) and total soil nitrogen (N) from Australasian interlaboratory proficiency testing across multiple years from 1993 are provided to demonstrate the magnitude of past and present measurement uncertainties, including the effects of method and different concentrations. The evidence is sufficient to require those commissioned to develop NIR and MIR calibrations to subject their prototype calibrations to external peer review by participating in credible, independent interlaboratory proficiency testing programs for ≥12 months, including checks on soil moisture status and possible effects of sample preparation. To rate as credible for most uses, the prototype results should be within the interquartile range for each sample and ideally there should be no outliers and few stragglers. Across the period of assessment (1993–2008), users of Walkley and Black organic C and Kjeldahl digestion for total soil N (Kjeldahl method does not measure total N, but most of the organic N plus an undetermined proportion of nitrate and nitrate present in the sample; quantitative inclusion of both requires a modification of the Kjeldahl procedure) declined as use of furnace technologies for soil C and N increased linearly. There is a strong case to commission two or three well-performing and experienced laboratories to reanalyze samples in “legacy” soil collections prior to finalizing predictive relationships with NIR/MIR spectra for the same samples.     

联合收获机籽粒损失监测传感器性能标定试验

设计了由升降平台、升降驱动装置、给料装置、传感器安装平台等构成的籽粒损失监测传感器标定试验台;选取饱满小麦籽粒、不饱满小麦籽粒和不同长度茎秆等物料运用标定试验台对籽粒损失监测传感器在不同安装高度及不同安装角度情况下进行实验室内标定.室内试验表明,针对含水率不同的小麦样品籽粒损失监测传感器的测量误差能限制在4.8％以内;根据室内试验标定结果确定了籽粒损失监测传感器在监测夹带损失时的安装位置,田间试验表明,夹带损失最大监测误差为3.40％.     

基于样本挑选和不同偏最小二乘方法的近红外光谱玉米淀粉组分校正模型的研究

[目的]获得精度高、鲁棒性强的玉米近红外光谱淀粉组分检测模型。[方法]用一阶导数和Savitzky．Golay平滑对玉米1300～2298nlTl近红外光谱进行预处理,而后分别以RS（random sampling）、KS（Kennard Stone）、Duplex、SPXY（sample set partitioning based on joint x-y distance）方法选取最佳校正集样本集合,最后分别用PLS（Partial Least Squares）、iPLS（intervalPLS）和siPLS（synergy interval PLS）方法建立校正模型。[结果]采用sPXY方法选取有代表性的校正集合样本,以siPLS方法所建立的近红外光谱玉米淀粉组分校正模型最优,校正样本集合中r为0．9917,RMSECV为n1073,预测样本集合中r达到了0．9944,RMSEP为0．0814。[结论]SPXY-siPLS方法建立的近红外光谱玉米淀粉组分校正模型,不但可以减小参与建模的数据规模．而且缩短了运算时间．预测能力和精度也均得到提高。     

校正变换矩阵法用于多组分食用色素的同时测定研究

[目的]对胭脂红、苋菜红、日落黄3种食用色素进行不经分离的同时测定。[方法]将校正变换矩阵法与可见分光光度法相结合,使用交叉验证法选择主因子数建立了校正模型。[结果]胭脂红的回收率在94.98%～113.69%,苋菜红的回收率在79.25%～104.96%,日落黄的回收率在93.22%～109.27%,13个样品中3种组分的表观预测误差（AET）分别为0.392 72、0.272 930、.200 80。[结论]校正变换矩阵法用于混合食用色素不经分离同时测定是可行的,其预测准确性与偏最小二乘回归法没有显著性差异。