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Determination of protein concentration in raw milk by mid-infrared Fourier transform infrared/attenuated total reflectance spectroscopy

机译:中红外傅里叶变换红外/衰减全反射光谱法测定生乳中的蛋白质浓度

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This study investigates the potential use of attenuated total reflectance spectroscopy in the mid-infrared range for determining protein concentration in raw cow milk. The determination of protein concentration is based on the characteristic absorbance of milk proteins, which includes 2 absorbance bands in the 1500 to 1700 cm(-1) range, known as the amide I and amide II bands, and absorbance in the 1060 to 1100 cm(-1) range, which is associated with phosphate groups covalently bound to casein proteins. To minimize the influence of the strong water band (centered around 1640 cm(-1)) that overlaps with the amide I and amide II bands, an optimized automatic procedure for accurate water subtraction was applied. Following water subtraction, the spectra were analyzed by 3 methods, namely simple band integration, partial least squares (PLS) and neural networks. For the neural network models, the spectra were first decomposed by principal component analysis (PCA), and the neural network inputs were the spectra principal components scores. In addition, the concentrations of 2 constituents expected to interact with the protein (i.e., fat and lactose) were also used as inputs. These approaches were tested with 235 spectra of standardized raw milk samples, corresponding to 26 protein concentrations in the 2.47 to 3.90% ( weight per volume) range. The simple integration method led to very poor results, whereas PLS resulted in prediction errors of about 0.22% protein. The neural network approach led to prediction errors of 0.20% protein when based on PCA scores only, and 0.08% protein when lactose and fat concentrations were also included in the model. These results indicate the potential usefulness of Fourier transform infrared/attenuated total reflectance spectroscopy for rapid, possibly online, determination of protein concentration in raw milk.
机译:这项研究调查了在中红外范围内使用衰减全反射光谱法确定生牛奶中蛋白质浓度的潜在用途。蛋白质浓度的确定基于乳蛋白的特征吸光度,其中包括2个在1500至1700 cm(-1)范围内的吸收带,称为酰胺I和酰胺II带,以及1060至1100 cm处的吸光度(-1)范围,与共价结合酪蛋白的磷酸基团相关。为了最大程度地减少与酰胺I和酰胺II谱带重叠的强水谱带(以1640 cm(-1)为中心)的影响,应用了一种用于精确减水的优化自动程序。减水后,通过3种方法分析光谱,即简单带积分,偏最小二乘(PLS)和神经网络。对于神经网络模型,首先通过主成分分析(PCA)分解光谱,而神经网络输入为光谱主成分评分。另外,预期与蛋白质相互作用的2种成分(即脂肪和乳糖)的浓度也用作输入。对这些方法进行了235次标准化原料乳样品的光谱测试,对应于2.47至3.90%(每体积重量)范围内的26种蛋白质浓度。简单的积分方法导致非常差的结果,而PLS导致约0.22%蛋白质的预测误差。仅基于PCA分数时,神经网络方法导致蛋白质预测误差为0.20%,而当模型中还包括乳糖和脂肪浓度时,预测误差为0.08%。这些结果表明傅里叶变换红外/衰减全反射光谱法可用于快速(可能在线)测定生乳中蛋白质的浓度。

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