J&M Analytik AG, 8 Willy-Messerschmitt-Straße, 73457 Essingen, Germany; Samara State Technical University, Molodogvardeyskaya Street 244, 443100 Samara, Russia.
Samara State Technical University, Molodogvardeyskaya Street 244, 443100 Samara, Russia.
Talanta. 2017 May 15;167:563-572. doi: 10.1016/j.talanta.2017.02.047. Epub 2017 Feb 20.
New technique of diffuse reflectance spectroscopic analysis of milk fat and total protein content in the visible (Vis) and adjacent near infrared (NIR) region (400-995nm) has been developed and tested. Sample analysis was performed through a probe having eight 200-µm fiber channels forming a linear array. One of the end fibers was used for the illumination and other seven - for the spectroscopic detection of diffusely reflected light. One of the detection channels was used as a reference to normalize the spectra and to convert them into absorbance-equivalent units. The method has been tested experimentally using a designed sample set prepared from industrial raw milk standards with widely varying fat and protein content. To increase the modelling robustness all milk samples were measured in three different homogenization degrees. Comprehensive data analysis has shown the advantage of combining both spectral and spatial resolution in the same measurement and revealed the most relevant channels and wavelength regions. The modelling accuracy was further improved using joint variable selection and preprocessing optimization method based on the genetic algorithm. The root mean-square errors of different validation methods were below 0.10% for fat and below 0.08% for total protein content. Based on the present experimental data, it was computationally shown that the full-spectrum analysis in this method can be replaced by a sensor measurement at several specific wavelengths, for instance, using light-emitting diodes (LEDs) for illumination. Two optimal sensor configurations have been suggested: with nine LEDs for the analysis of fat and seven - for protein content. Both simulated sensors exhibit nearly the same component determination accuracy as corresponding full-spectrum analysis.
已开发和测试了一种在可见(Vis)和近邻近红外(NIR)区域(400-995nm)中用于分析牛奶脂肪和总蛋白含量的漫反射光谱分析新技术。通过具有八个 200-µm 光纤通道的探头进行样品分析,这些通道形成线性阵列。其中一根末端光纤用于照明,其他七根用于漫反射光的光谱检测。其中一个检测通道用作参考,以归一化光谱并将其转换为吸光度等效单位。该方法使用从具有广泛变化的脂肪和蛋白质含量的工业原料乳标准制备的设计样品组进行了实验测试。为了提高建模稳健性,所有牛奶样品均在三种不同的均化程度下进行了测量。全面数据分析表明,在相同测量中结合光谱和空间分辨率具有优势,并揭示了最相关的通道和波长区域。使用基于遗传算法的联合变量选择和预处理优化方法进一步提高了建模精度。不同验证方法的均方根误差对于脂肪低于 0.10%,对于总蛋白质含量低于 0.08%。根据当前的实验数据,通过计算表明,该方法中的全谱分析可以用几个特定波长的传感器测量代替,例如,使用发光二极管(LED)进行照明。已经提出了两种最佳传感器配置:对于脂肪分析使用九个 LED,对于蛋白质含量使用七个 LED。两个模拟传感器的组分测定精度与相应的全谱分析几乎相同。