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首页> 外文期刊>Analytical and Bioanalytical Chemistry >Use of NIRS technology with a remote reflectance fibre-optic probe for predicting mineral composition (Ca, K, P, Fe, Mn, Na, Zn), protein and moisture in alfalfa
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Use of NIRS technology with a remote reflectance fibre-optic probe for predicting mineral composition (Ca, K, P, Fe, Mn, Na, Zn), protein and moisture in alfalfa

机译:使用NIRS技术和远程反射光纤探头预测苜蓿中的矿物质成分(Ca,K,P,Fe,Mn,Na,Zn),蛋白质和水分

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In the present work we study the use of near-infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for the analysis of major (Ca, K, P) and minor (Fe, Mn, Na, Zn) elements, protein and moisture in alfalfa. The method allows immediate analysis of the alfalfa without prior sample treatment or destruction through direct application of the fibre-optic probe on ground samples in the case of the mineral composition and on-ground and compacted (baled) samples in the case of protein and humidity. The regression method employed was modified partial least-squares (MPLS). The calibration results obtained using samples of alfalfa allowed the determination of Ca, K, P, Fe, Mn, Na and Zn, with a standard error of prediction (SEP(C)) and a correlation coefficient (RSQ) expressed in mg/kg of alfalfa of 1.37 × 103 and 0.878 for Ca, 1.10 × 103 and 0.899 for K, 227 and 0.909 for P, 103 and 0.948 for Fe, 5.1 and 0.843 for Mn, 86.2 and 0.979 for Na, and of 1.9 and 0.853 for Zn, respectively. The SEP(C) and RSQ values (in %) for protein and moisture in ground samples were 0.548 and 0.871 and 0.150 and 0.981, respectively; while in the compacted samples they were 0.564 and 0.826 and 0.262 and 0.935, respectively. The prediction capacity of the model and the robustness of the method were checked in the external validation in alfalfa samples of unknown composition, and the results confirmed the suitability of the method.
机译:在当前的工作中,我们研究使用近红外光谱(NIRS)技术和远程反射光纤探头来分析主要元素(Ca,K,P)和次要元素(Fe,Mn,Na,Zn) ,苜蓿中的蛋白质和水分。该方法可以立即对苜蓿进行分析,而无需事先进行样品处理或通过在矿物成分的情况下将光纤探针直接应用于地面样品,而在蛋白质和湿度较大的情况下将地面和压实(打包)样品直接应用而不会造成破坏。所采用的回归方法是修正的偏最小二乘(MPLS)。使用苜蓿样品获得的校准结果允许测定Ca,K,P,Fe,Mn,Na和Zn,预测标准误差(SEP(C))和相关系数(RSQ)以mg / kg表示苜蓿的钙含量分别为1.37×103 和0.878,钾含量分别为1.10×103 和0.899,磷含量分别为227和0.909,铁含量分别为103和0.948,锰含量为5.1和0.843,锰含量分别为86.2和0.979 ,对于Zn分别为1.9和0.853。地面样品中蛋白质和水分的SEP(C)和RSQ值(%)分别为0.548和0.871和0.150和0.981。而在压实样品中,它们分别为0.564和0.826以及0.262和0.935。在外部验证中对未知成分的苜蓿样品进行了模型预测能力和方法的鲁棒性验证,结果证实了该方法的适用性。

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