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Near infrared quantitative analysis of total curcuminoids in rhizomes of Curcuma longa by moving window partial least squares regression

机译:移动窗口偏最小二乘回归分析姜黄根中总姜黄素的近红外定量分析

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摘要

The present paper reports an application of moving window partial least squares regression (MWPLSR) to determine total curcuminoid content in rhizomes of Curcuma longa by near infrared (NIR) spectroscopy. The MWPLSR method was applied to original and pretreated NIR data of Curcuma longa rhizomes to select informative regions for total curcuminoids. Afterward, partial least squares (PLS) calibration models were developed and compared for each spectral region proposed by MWPLSR and the whole spectral region. The best PLS calibration model for total curcuminoids was obtained from 2nd derivative NIR spectra in the region of 2040-2486 nm. The standard error of prediction was 1.00% w/w and the ratio of prediction to deviation was 4.9 when using seven principle components. NIR spectroscopy combined with MWPLSR can lead to better calibration models with higher performance.
机译:本文报道了应用移动窗口偏最小二乘回归(MWPLSR)通过近红外(NIR)光谱法测定姜黄中根茎中总姜黄素含量的应用。将MWPLSR方法应用于姜黄根茎的原始NIR数据和预处理过的NIR数据,以选择总姜黄素含量较高的信息区域。之后,开发了偏最小二乘(PLS)校准模型,并针对MWPLSR提出的每个光谱区域与整个光谱区域进行了比较。从2040-2486 nm范围内的二阶NIR光谱获得了总姜黄素的最佳PLS校准模型。使用七个主要成分时,预测的标准误差为1.00%w / w,预测与偏差的比率为4.9。 NIR光谱与MWPLSR结合可产生具有更高性能的更好的校准模型。

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