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Detection of vegetable proteins from soy, pea and wheat isolates in milk powder by near infrared spectroscopy

机译:近红外光谱法检测奶粉中大豆,豌豆和小麦分离物中的植物蛋白

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摘要

This work aimed to prove the feasibility of using near infrared (NIR) spectroscopy to detect vegetable proteins (soy, pea and wheat isolates) in milk powder. One hundred and fifty-five samples of genuine and adulterated milk powder (NIZO, Ede, The Netherlands) were analysed by NIR spectroscopy using an InfraAlyzer 500 (Bran+Luebbe). NIR spectra were collected at room temperature, and data were processed by Sesame Software (Bran+Luebbe) to select the most significant wavelengths. Calibrations were made on all samples in the range 0-5% of added vegetable proteins. Separate prediction and validation sets were prepared using samples not included in the calibration set. The best results were obtained by applying multiple linear regression (MLR) to the first derivative of absorbance values at five wavelengths. NIR spectroscopy proved a very effective tool for detecting the presence of vegetable proteins in milk powder on the basis of standard error of calibration (SEC), standard error of prediction (SEP) and standard error of validation (SEV) values found in this study. The calibration carve was characterised by a regression coefficient (R{sup}2) equal to 0.993 with a SEC of 0.20, a SEP of 0.23 and a SEV of 0.21.
机译:这项工作旨在证明使用近红外(NIR)光谱法检测奶粉中植物蛋白(大豆,豌豆和小麦分离株)的可行性。使用InfraAlyzer 500(Bran + Luebbe)通过近红外光谱分析了155个纯正和掺假奶粉样品(NIZO,埃德,荷兰)。在室温下收集NIR光谱,并通过Sesame Software(Bran + Luebbe)处理数据以选择最重要的波长。在添加的植物蛋白的0-5%范围内对所有样品进行校准。使用未包含在校准集中的样品准备了单独的预测和验证集。通过对五个波长的吸光度值的一阶导数应用多元线性回归(MLR),可获得最佳结果。根据本研究中发现的校准标准误差(SEC),预测标准误差(SEP)和验证标准误差(SEV)值,NIR光谱证明是检测奶粉中植物蛋白存在的非常有效的工具。校准曲线的特征在于回归系数(R {sup} 2)等于0.993,SEC为0.20,SEP为0.23,SEV为0.21。

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