Liu Lingzhi, Arnold Mark A
Department of Chemistry and Optical Science Technology Center, University of Iowa, Iowa City, IA 52242, USA.
Anal Bioanal Chem. 2009 Jan;393(2):669-77. doi: 10.1007/s00216-008-2475-0. Epub 2008 Nov 14.
Near-infrared spectroscopy offers the potential for direct in situ analysis in complex biological systems. Chemical selectivity is a critical issue for such measurements given the extent of spectral overlap of overtone and combination spectra. In this work, the chemical basis of selectivity is investigated for a set of multivariate calibration models designed to quantify glucose, glucose-6-phosphate, and pyruvate independently in ternary mixtures. Near-infrared spectra are collected over the combination region (4,000-5,000 cm(-1)) for a set of 60 standard solutions maintained at 37 degrees C. These standard solutions are composed of randomized concentrations (0.5-30 mM) of glucose, glucose-6-phosphate, and pyruvate. Individual calibration models are constructed for each solute by using the partial least-squares (PLS) algorithm with optimized spectral range and number of latent variables. The resulting standard errors are 0.90, 0.72, and 0.32 mM for glucose, glucose-6-phosphate, and pyruvate, respectively. A pure component selectivity analysis (PCSA) demonstrates selectivity for each solute in these ternary samples. The concentration of each solute is also predicted for each sample by using a set of net analyte signal (NAS) calibration models. A comparison of the PLS and NAS calibration vectors demonstrates the chemical basis of selectivity for these multivariate methods. Selectivity of each PLS and NAS calibration model originates from the unique spectral features associated with the targeted analyte. Overall, selectivity is demonstrated for each solute with an order of sensitivity of pyruvate > glucose-6-phosphate > glucose.
近红外光谱法为复杂生物系统中的直接原位分析提供了可能性。鉴于泛音光谱和组合光谱的光谱重叠程度，化学选择性是此类测量中的一个关键问题。在这项工作中，针对一组旨在独立定量三元混合物中葡萄糖、6-磷酸葡萄糖和丙酮酸的多元校准模型，研究了选择性的化学基础。在37摄氏度下，对一组60种标准溶液在组合区域（4000 - 5000 cm⁻¹）收集近红外光谱。这些标准溶液由随机浓度（0.5 - 30 mM）的葡萄糖、6-磷酸葡萄糖和丙酮酸组成。通过使用具有优化光谱范围和潜变量数量的偏最小二乘法（PLS）算法，为每种溶质构建单独的校准模型。葡萄糖、6-磷酸葡萄糖和丙酮酸的所得标准误差分别为0.90、0.72和0.32 mM。纯组分选择性分析（PCSA）表明了这些三元样品中每种溶质的选择性。还通过使用一组净分析物信号（NAS）校准模型对每个样品中每种溶质的浓度进行预测。PLS和NAS校准向量的比较证明了这些多元方法选择性的化学基础。每个PLS和NAS校准模型的选择性源自与目标分析物相关的独特光谱特征。总体而言，证明了每种溶质的选择性，灵敏度顺序为丙酮酸>6-磷酸葡萄糖>葡萄糖。
