近红外透射光谱技术测定黍稷蛋白含量的研究(英文)

[目的]探索快速测定完整黍稷籽粒蛋白含量的方法。[方法]采用近红外光谱分析技术建立数学模型并进行预测,比较原始透射光谱经导数处理结合不同回归算法对模型的影响。[结果]分别经一阶和二阶导数处理后利用偏小二乘法和改进的偏小二乘法,4 种方法的分析效果相近,最优的是一阶导数结合改进的偏最小二乘回归法,黍稷蛋白定标模型的定标相关系数(RSQ)为0.880 6,定标标准误差(SEC)为0.342 4,交互定标标准误差(SECV)为 0.375 1,外部预测标准误差(SEP)为 0.454。[结论]以完整黍稷籽粒为样品所建立的蛋白 NITS 模型,可以用于黍稷蛋白含量的快速检测。     

利用近红外透射光谱技术测定小麦籽粒硬度的研究

以2001和2002年度583份小麦样品为材料，用近红外透射光谱仪（NITS）对小麦籽粒硬度进行分析，比较了偏最小二乘法和多元线性回归两种算法和未经导数处理、一阶导数处理、二阶导数处理3种光谱变量转换方式的分析结果。表明，两种算法中偏最小二乘法优于多元线性回归算法，3种处理方式中一阶导数处理效果最好，其定标集和预测     

Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model

Evapotranspiration (ET) can be derived from satellite data using surface energy balance principles. METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) is one of the most widely used models available in the literature to estimate ET from satellite imagery. The Simplified Surface Energy Balance (SSEB) model is much easier and less expensive to implement. The main purpose of this research was to present an enhanced version of the Simplified Surface Energy Balance (SSEB) model and to evaluate its performance using the established METRIC model. In this study, SSEB and METRIC ET fractions were compared using 7 Landsat images acquired for south central Idaho during the 2003 growing season. The enhanced SSEB model compared well with the METRIC model output exhibiting an r² improvement from 0.83 to 0.90 in less complex topography (elevation less than 2000 m) and with an improvement of r² from 0.27 to 0.38 in more complex (mountain) areas with elevation greater than 2000 m. Independent evaluation showed that both models exhibited higher variation in complex topographic regions, although more with SSEB than with METRIC. The higher ET fraction variation in the complex mountainous regions highlighted the difficulty of capturing the radiation and heat transfer physics on steep slopes having variable aspect with the simple index model, and the need to conduct more research. However, the temporal consistency of the results suggests that the SSEB model can be used on a wide range of elevation (more successfully up 2000 m) to detect anomalies in space and time for water resources management and monitoring such as for drought early warning systems in data scarce regions. SSEB has a potential for operational agro-hydrologic applications to estimate ET with inputs of surface temperature, NDVI, DEM and reference ET.     

用Shenk's方法提高芥酸NIR模型预测精度

在光谱测量的过程中,由于光谱仪器本身的精度和测量环境等因素,经常会出现光谱的漂移、线性或非线性改变。以基于近红外光谱分析建立菜籽油中芥酸预测模型为例,将多元校正Shenk's算法用于修正同台仪器测量的光谱差异,大大提高了校正模型的预测精度。结果表明,对于光谱修正后再建模预测,预测均方差从2.263下降到1.569,平均相对误差从4.6%下降到3.218%,相关系数也由0.780提高到了0.913,模型的预测精度显著提高。     

应用近红外光谱技术分析稻米蛋白质含量

以稻谷、米粒、米粉3种形态的样品，应用近红外光谱技术(NIRS)和偏最小二阶乘法(PLS)，建立了6个稻米蛋白质含量近红外光谱数学模型，并对模型预测结果的准确性进行了评价。结果表明，糙米蛋白质含量的稻谷、糙米粒和糙米粉近红外光谱预测模型校正决定系数(R_C²)分别为0.893、0.971和0.987，校正标准差(RMSEC)分别为0.507、0.259和0.183；精米蛋白质含量的稻谷、精米粒和精米粉近红外光谱预测模型R_C²分别为0.897、0.984和0.986，RMSEC分别为0.497、0.186和0.190。模型内部交叉验证分析表明，预测糙米蛋白含量的稻谷、糙米粒和糙米粉模型内部交叉验证决定系数(R_CV²)分别为0.865、0.962和0.984，内部验证标准差(RMSECV)分别为0.557、0.290和0.205；预测精米蛋白含量的稻谷、精米粒和精米粉的模型R_CV²分别为0.845、0.951和0.979，RMSECV分别为0.594、0.316和0.233。模型外部验证分析表明，预测糙米蛋白含量的稻谷、糙米粒和糙米粉近红外光谱模型外部验证决定系数(R_V²)分别为0.683、0.801和0.939，外部验证标准差(RMSEV)为0.962、0.799和0.434；预测精米蛋白含量的稻谷、精米粒和精米粉近红外光谱的模型R_V²分别为0.673、0.921和0.959，RMSEV为0.976、0.513和0.344。用米粉建立的近红外光谱预模型准确性最高，米粒次之，基于稻谷的预测模型准确性相对较低；内部交叉验证和外部验证表明，近红外光谱分析技术与化学分析方法一致性较好，且能保证样品的完整性，在水稻优质育种和稻米品质分析中具有广泛的应用价值。     

Durum wheat quality prediction in Mediterranean environments: From local to regional scale

Durum wheat is one of the most important agricultural crops in the Mediterranean area. In addition to yield, grain quality is very important in wheat markets because of the demand for high-quality end products such as pasta, couscous and bulgur wheat. Grain quality is directly affected by several agronomic and environmental factors. Our objective is to determine the general principles underlying how, in Mediterranean environments, grain protein content (GPC) is affected by these factors and provide a system model with high predictive ability. We initially evaluated the capability of the Delphi system to simulate GPC in the major Italian supply basins (Basilicata, Capitanata, Marche, Tuscany) for 9 years (1999–2007) a month ahead of harvesting and we then analyzed relations between Delphi system errors and selected environmental variables during flowering and grain filling stages. The results were evaluated on the basis of regression with observed GPC, while errors were calculated performing a linear correlation analysis with environmental variables. The model showed a high capability to reproduce the inter-annual variability, with important year to year differences, with better performance in the southern study areas (Basilicata and Capitanata). In this study the highest overestimation occurred in conjunction with the year (2004) characterized by the lowest quality in terms of GPC, lowest average temperature in May and highest yield production for the whole study period.     

Comparison of empirical and partly deterministic methods of time domain reflectometry calibration, based on a study of two tropical soils

The use of time domain reflectometry for the determination of volcanic soils' moisture content in tropical areas has shown the necessity to set up an accurate calibration. We chose to test the most representative calibration models on two tropical volcanic soils coming from two different climatic areas of the Réunion Island. The empirical model of Topp et al. [Topp, G.C., Davis, J.L., Annan, A.P., 1980. Electromagnetic determination of soil water content: measurements in coaxial transmission lines. Water Resources Research, 16 (3) 574–582.] was found to offer the best accuracy/simplicity trade-off for simulations with a cambisol, with an accuracy of ±2.46 dielectric constant points (or about ±2.7% for a water content close to 45%). In contrast, it gave poorer results for analysis of the andosol containing the greatest quantity of amorphous minerals (±5.74 dielectric constant points). The partly deterministic models were found to be better for this latter application, including that of Ansoult et al. [Ansoult, M., De Backer, L.W., Declercq, M., 1985. Statistical relationship between apparent dielectric constant and water content in porous media. Soil Sci. Soc. Am. J., 49, 47–50.] which gave the best accuracy (±3.84 dielectric constant points). For both soil materials studied, there was only a slight gain of accuracy by introducing a specific polynomial calibration determined in the laboratory: ±1.72 and ±3.46 dielectric points for the cambisol and the andosol, respectively. Choices of models to be used should thus be based on the type of soil to be analysed and accuracy requirements.     

Cone and seed production from stone pine (<Emphasis Type="Italic">Pinus pinea</Emphasis> L.) stands in Central Range (Spain)

Natural regeneration of stone pine (Pinus pinea L.) stands in the Central Range of Spain can be difficult to attain. The success of this regeneration is affected by factors such as the total amount of seed available, the short dispersal ability of the pinyon, the conditions required for germination, specific problems associated with the establishment, early survival of the seedlings due to severe summer heat and drought, competition for water and mineral resources and grazing damage. This study focuses on seed availability. The amount of available seeds depends on the number and size of the cones and the number of viable pinyons within the cones. In stone pine stands, both variables show great year to year variability. Both cone and seed production, for a given year, are also conditioned by the vigour and health of the tree, its size, the condition and attributes of the stand and the loss of seed through pests or predation. In this study, the main factors which influence cone and pinyon production are identified and a multivariate model to predict annual cone and viable seed production is developed. To consider the correlation among observations coming from the same tree, stand or year, random components are included in the model. The multivariate random structure allows for future calibration of the model for a given year from a small additional sample of observations. It is important to know the total amount of viable seed produced in a stand for a given year since regeneration cuttings for Stone pine should be concentrated in high crop years.     

近红外光谱分析中定标集样品挑选方法研究

介绍了目前常见的几种定标集样品挑选方法：含量梯度法、Duplex法和Kennard-Stone法,并提出了新的挑选方法：GN距离法.该方法以全局距离来界定定标集样品范围,以邻域距离来剔除相似样品,根据不同的全局和邻域距离组合挑选出定标集样品建模,根据计算所得最小交叉验证误差SECV来确定最合理的定标集样品.通过实例,讨论比较了上述4种方法优缺点.结果表明：GN距离法能够在保持原始样品集覆盖范围的基础上,适量剔除异常样品,GN距离法挑选出的定标集所建模型具有较低的模型复杂度、较高的相关系数和较好的模型预测能力.     

基于双目视觉技术的猪生长监测系统标定模

针对养猪生产中对猪体生长监测的需求,设计了基于双目视觉的猪生长监测系统的软、硬件,实现了基于非线性摄像机模型双目视觉系统的标定算法.根据摄像机成像原理建立了基于最小二乘法的空间点坐标检测算法.利用标定和检测算法,从标定板图像数目、标定板位置、旋转角度3个方面对系统的标定模式进行了研究.结果表明:利用19幅以上标定板图像能够得到稳定的标定结果;不同位置的标定板图像对检测精度影响较大,应当在全视场内采集标定板图像;标定板的旋转角度对检测精度影响不明显,但是旋转角度增大不利于标定点的完全提取.