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利用中红外傅里叶变换红外/衰减全反射光谱法测定原料乳中的蛋白质浓度。

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

作者信息

Etzion Y, Linker R, Cogan U, Shmulevich I

机构信息

The Interdisciplinary Program of Biotechnology, Technion-Israel Institute of Technology, Haifa, Israel.

出版信息

J Dairy Sci. 2004 Sep;87(9):2779-88. doi: 10.3168/jds.S0022-0302(04)73405-0.

Abstract

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.

摘要

本研究探讨了中红外范围内衰减全反射光谱法在测定原料牛奶中蛋白质浓度方面的潜在应用。蛋白质浓度的测定基于牛奶蛋白质的特征吸光度,其中包括1500至1700 cm⁻¹范围内的2个吸收带,即酰胺I带和酰胺II带,以及1060至1100 cm⁻¹范围内与共价结合到酪蛋白的磷酸基团相关的吸光度。为了最小化与酰胺I带和酰胺II带重叠的强水带(以1640 cm⁻¹为中心)的影响,应用了一种优化的自动程序进行精确的水扣除。水扣除后,通过3种方法对光谱进行分析,即简单谱带积分、偏最小二乘法(PLS)和神经网络。对于神经网络模型,首先通过主成分分析(PCA)对光谱进行分解,神经网络的输入是光谱主成分得分。此外,还将预期与蛋白质相互作用的2种成分(即脂肪和乳糖)的浓度用作输入。这些方法用235个标准化原料牛奶样品的光谱进行了测试,这些样品对应于2.47至3.90%(重量/体积)范围内的26种蛋白质浓度。简单积分法得到的结果非常差,而PLS法导致蛋白质预测误差约为0.22%。仅基于PCA得分时,神经网络方法导致蛋白质预测误差为0.20%,当模型中也包括乳糖和脂肪浓度时,蛋白质预测误差为0.08%。这些结果表明傅里叶变换红外/衰减全反射光谱法在快速、可能在线测定原料牛奶中蛋白质浓度方面的潜在实用性。

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