首页> 外文期刊>New Zealand Journal of Crop and Horticultural Science >Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops.
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Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops.

机译:使用近红外光谱快速预测热带块根和块茎作物中的总氮、矿物质、糖和淀粉。

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摘要

The objective of the present study was to test the robustness of near infrared spectroscopy (NIRS) for the prediction of total N content in underground storage organs across a diverse range of root and tuber crop varieties. Overall, 1096 accessions (acc.) from five different species (cassava = 112 acc., cocoyam =117 acc., sweet potato = 225 acc., taro = 306 acc. and yams = 266 acc.) were chemically analysed for total N and minerals, as well as starch, sugars and cellulose. For validation of the models, 178 samples composed of the same five different species were collected in farmers' fields, at random. All spectra were taken over the wavelength range of 350-2500 nm. Partial least-squares (PLS1) regression technique was used to develop predictive models. Their comparison with the chemical values allowed the establishment of equations of calibration. In terms of predictive performance, the equation for total N should be considered as very good with a r2predof 0.93 (SEP = 0.87). Minerals presented low r2predof 0.62 (SEP = 1.05). Starch and sugars presented r2predof 0.77 and 0.86, respectively (SEP = 3.2 and 1.82). Cellulose could not be satisfactorily predicted with a low r2cv(0.57) for the calibration. The r2pred values of total N, starch and sugars are high enough to allow good estimates of their contents, confirming the interest of NIRS for predicting rapidly these major compounds. Potential applications are discussed
机译:本研究的目的是测试近红外光谱(NIRS)在预测各种块根和块茎作物品种地下储存器官中总氮含量的稳健性。总体而言,对来自五个不同物种的1096份种质(木薯=112个累积,椰芋=117个累积,甘薯= 225个累积,芋头= 306个累积,山药= 266个累积)的总氮和矿物质以及淀粉、糖和纤维素进行了化学分析。为了验证模型,在农民的田地里随机收集了178个由相同的五种不同物种组成的样本。所有光谱均在350-2500nm的波长范围内采集。采用偏最小二乘法(PLS1)回归技术开发预测模型。它们与化学值的比较允许建立校准方程。在预测性能方面,当 r2predof 0.93 (SEP = 0.87) 时,总氮的方程应被认为是非常好的。矿物的 r2predof 为 0.62 (SEP = 1.05)。淀粉和糖的 r2predof 分别为 0.77 和 0.86 (SEP = 3.2 和 1.82)。在低r2cv(0.57)的校准下,无法令人满意地预测纤维素。总氮、淀粉和糖的r2pred值足够高,可以很好地估计其含量,这证实了近红外光谱对快速预测这些主要化合物的兴趣。讨论了潜在的应用

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