School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, China.
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Oct 15;240:118573. doi: 10.1016/j.saa.2020.118573. Epub 2020 Jun 2.
It is of great significance to detect the components of turbid solutions using hyperspectral imaging technology in analytical chemistry. To solve the problems including complex computations and poor interpretations in previous researches, this study proposed a novel quantitative detection model based on contour extraction and ellipse fitting for turbid solutions. A wedge-shaped sample reservoir was firstly designed to increase the dimensions of light spot information. Subsequently, the visual features of the spot were extracted from their hyperspectral images using ellipse fitting. Partial least squares regression was performed for the concentrations of Intralipid-20% and the ellipse eigenvectors, and it gave a good prediction ability with the correlation coefficient (Rp) of 0.98 and the root-mean-square error (RMSEP) of 0.07%. Experimental results indicate that ellipse fitting model shows excellent performances in more reasonable interpretation, better stability, less computation, clearer visualization effect and lower requirements for data acquisition process, compared with conventional light intensity model and abstract textural features model. It can be concluded that using ellipse fitting method based on hyperspectral imaging to detect compositions of complex mixed solutions is a potential progress.
在分析化学中，使用高光谱成像技术检测混浊溶液的成分具有重要意义。为了解决先前研究中存在的计算复杂和解释困难的问题，本研究提出了一种基于轮廓提取和椭圆拟合的混浊溶液定量检测新模型。首先设计了一个楔形样品池，以增加光斑信息的尺寸。然后，使用椭圆拟合从光斑的高光谱图像中提取光斑的视觉特征。对 Intralipid-20%的浓度和椭圆特征向量进行偏最小二乘回归，得到了良好的预测能力，相关系数（Rp）为 0.98，均方根误差（RMSEP）为 0.07%。实验结果表明，与传统的光强模型和抽象纹理特征模型相比，椭圆拟合模型在更合理的解释、更好的稳定性、更少的计算、更清晰的可视化效果和对数据采集过程的要求更低方面表现出优异的性能。可以得出结论，基于高光谱成像的椭圆拟合方法用于检测复杂混合溶液的成分是一种潜在的进展。
