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首页> 外文期刊>Food Science & Nutrition >Applicability of near‐infrared reflectance spectroscopy (NIRS) for determination of crude protein content in cowpea (Vigna unguiculata) leaves
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Applicability of near‐infrared reflectance spectroscopy (NIRS) for determination of crude protein content in cowpea (Vigna unguiculata) leaves

机译：近红外反射光谱法（NIRS）用于测定cow豆（Vigna unguiculata）叶片中粗蛋白含量的适用性

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AbstractThere is uncertainty on how generally applicable near-infrared reflectance spectroscopy (NIRS) calibrations are across genotypes and environments, and this study tests how well a single calibration performs across a wide range of conditions. We also address the optimization of NIRS to perform the analysis of crude protein (CP) content in a variety of cowpea accessions (n = 561) representing genotypic variation as well as grown in a wide range of environmental conditions in Tanzania and Uganda. The samples were submitted to NIRS analysis and a predictive calibration model developed. A modified partial least-squares regression with cross-validation was used to evaluate the models and identify possible spectral outliers. Calibration statistics for CP suggests that NIRS can predict this parameter in a wide range of cowpea leaves from different agro-ecological zones of eastern Africa with high accuracy (R2cal = 0.93; standard error of cross-validation = 0.74). NIRS analysis improved when a calibration set was developed from samples selected to represent the range of spectral variability. We conclude from the present results that this technique is a good alternative to chemical analysis for the determination of CP contents in leaf samples from cowpea in the African context, as one of the main advantages of NIRS is the large number of compounds that can be measured at once in the same sample, thus substantially reducing the cost per analysis. The current model is applicable in predicting the CP content of young cowpea leaves for human nutrition from different agro-ecological zones and genetic materials, as cowpea leaves are one of the popular vegetables in the region.
机译：摘要目前尚不确定在基因型和环境中一般适用的近红外反射光谱（NIRS）校准如何，这项研究测试了单个校准在各种条件下的性能如何。我们还解决了NIRS的优化问题，以分析代表基因型变异并在坦桑尼亚和乌干达的各种环境条件下生长的各种of豆种质（n = 561）中的粗蛋白（CP）含量。将样品提交NIRS分析，并建立预测性校准模型。修改后的带有交叉验证的偏最小二乘回归用于评估模型并确定可能的光谱离群值。 CP的校准统计数据表明，NIRS可以高精度地预测来自东非不同农业生态区的of豆叶片中的该参数（R 2 cal = 0.93;交叉验证的标准误差= 0.74）。当从代表光谱变化范围的样本中开发出一套校准套件时，NIRS分析将得到改善。我们从目前的结果得出结论，该技术是测定非洲cow豆叶片样品中CP含量的化学分析方法的一种很好的替代方法，因为NIRS的主要优势之一是可以测量大量化合物同时在同一样品中进行分析，从而大大降低了每次分析的成本。由于cow豆叶是该地区最受欢迎的蔬菜之一，因此当前模型可用于预测来自不同农业生态区和遗传材料的用于人类营养的年轻human豆叶的CP含量。

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