快速检测牛奶中尿素和过氧化氢的化学计量模型

M. Basak, M. Uddin, Raunaq Jahan, Aa Rana, M. Karim
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引用次数: 1

摘要

利用傅里叶变换红外(FTIR)光谱数据建立化学计量模型,建立了一种简单、经济有效的测定牛奶中尿素和过氧化氢掺假的方法。牛奶样本购自一家奶牛场(孟加拉国达卡南巴纳斯里),并添加了不同浓度的尿素和过氧化氢。所有样品的光谱数据均采用ATR-FTIR分光光度计采集。获取光谱数据后,利用乘散校正(MSC)和savitzky-golay导数等变换技术对光谱数据进行预处理。比较主成分回归(PCR)和偏最小二乘回归(PLSR)方法的相对预测误差和回收率(%)。与PCR相比,PLSR的预测效率更高,尿素和过氧化氢的R2值分别为99%和95%。用该方法对6个品牌的商品牛奶样品进行了评价,样品中100毫升牛奶中尿素含量为21.66 ~ 44.73 mg,过氧化氢含量为1.62 ~ 2.86 mg。该方法可方便地用于牛奶的质量评价。[j]。工业再生,56(1),1-8,2021
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chemometric model for rapid detection of urea and hydrogen peroxide in milk
A simple and cost effective method has been developed for determination of adulteration in milk with urea and hydrogen peroxide by using chemometric modeling with Fourier Transform Infrared (FTIR) spectroscopic data. Milk samples were purchased from a dairy farm (South Banasree, Dhaka, Bangladesh) and spiked at different concentrations of urea and hydrogen peroxide. Spectral data of all samples were collected using ATR-FTIR spectrophotometer. After acquisition of spectral data, they were preprocessed with transformation techniques such as multiplicative scatter correction (MSC) and savitzky-golay derivative. The predictive performance of principal component regression (PCR) and partial least-square regression (PLSR) methods were assessed by relative prediction errors and recoveries (%) were compared . PLSR shows better prediction efficiencies over PCR with R2 value 99% for urea and 95% for hydrogen peroxide.Six brands of commercial milk samples have been evaluated by this method and the samples contain 21.66-44.73 mg urea and 1.62-2.86 mg hydrogen peroxide in 100 ml milk. This method can be easily used in the quality assessment of milk. Bangladesh J. Sci. Ind. Res.56(1), 1-8, 2021
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