• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>American Journal of Analytical Chemistry >Analysis of Various Quality Attributes of Sunflower and Soybean Plants by Near Infrared Reflectance Spectroscopy: Development and Validation Calibration Models
【24h】

Analysis of Various Quality Attributes of Sunflower and Soybean Plants by Near Infrared Reflectance Spectroscopy: Development and Validation Calibration Models

机译：近红外反射光谱分析向日葵和大豆植物的各种质量属性：开发与验证校准模型

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Soybean and sunflower are summer annuals that can be grown as an alternative to corn and may be particularly useful in organic production systems for forage in addition to their traditional use as protein and/or oil yielding crops. Rapid and low cost methods of analyzing plant forage quality would be helpful for nutrition management of livestock. We developed and validated calibration models using Near-infrared Reflectance Spectroscopic (NIRS) analysis for 27 different forage quality parameters of organically grown sunflower and soybean leaves or reproductive parts. Crops were managed under conventional tillage or no-till with a cover crop of wheat before soybean and rye-crimson clover before sunflower. From a population of 120 samples from both crops, covering multiple sampling dates within the treatments, calibration models were developed utilizing spectral information covering both visible and NIR region of 61 - 85 randomly chosen samples using modified partial least-squares (MPLS) regression with internal cross validation. Within MPLS protocol, we compared nine different math treatments on the quality of the calibration models. The math treatment “2,4,4,1” yielded the best quality models for all but starch and simple sugars (r2 = 0.699 - 0.999; where the 1st digit is the number of the derivative with 0 for raw spectra, 1 for first derivative, and 2 for second derivative, the 2nd digit is the gap over which the derivative is calculated, the 3rd digit is the number of data points in a running average or smoothing, and the 4th digit is the second smoothing). Prediction of an independent validation set of 28-35 samples with these models yielded excellent agreement between the NIRS predicted values and the reference values except for starch (r2 = 0.8260 - 0.9990). The results showed that the same model was able to adequately quantify a particular forage quality of both crops managed under different tillage treatments and at different stages of growth. Thus, these models can be reliably applied in the routine analysis of soybean and sunflower forage quality for the purposes of livestock nutrient management decisions.
机译：大豆和向日葵是夏季的年度，可以作为玉米的替代品种，除了作为蛋白质和/或油的传统用途外，还可以在有机生产系统中特别有用。分析植物饲料质量的快速和低成本方法将有助于牲畜的营养管理。我们使用近红外反射光谱（NIRS）分析开发和验证了校准模型，分析了有机种植的向日葵和大豆叶或生殖部件的27种不同的饲料质量参数。在常规的耕作或No-Tillage或No-Port of Mudflower之前，在豆类和黑麦 - 深红色的三叶草之前，在常规耕作或No-Port的覆盖作物。从两种作物的120个样本中，覆盖在处理中的多个采样日期，利用覆盖61-85的可见和NIR区域的光谱信息，使用与内部修改的局部最小二乘（MPLS）回归覆盖61-85的可见和NIR区域的光谱信息交叉验证。在MPLS协议中，我们对校准模型的质量进行了比较了九种不同的数学处理。数学处理“2,4,4,1”产生了所有除淀粉和简单糖的最佳质量模型（R2 = 0.699 - 0.999;其中第1位是衍生物的数量为0对于原始光谱，首先是1衍生物和2对于第二导数，第2位是计算衍生物的间隙，第3位是运行平均值或平滑的数据点数，第4位是第二平滑）。预测与这些模型的28-35个样本的独立验证集在NIR预测值和除淀粉之外的参考值之间产生了很好的一致性（R2 = 0.8260 - 0.9990）。结果表明，相同的模型能够充分量化在不同耕作处理下管理的两种作物的特定饲料质量和生长的不同阶段。因此，为了牲畜营养管理决策，可以可靠地应用这些模型在大豆和向日葵饲料质量的常规分析中。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号