光谱学与光谱分析, 2019, 39 (12): 3711, 网络出版: 2020-01-07  

浑浊散射介质中被测成分线性吸光度提取与模型转换研究

Extracting Linear Attenuance of Analyte in Turbid Scattering Media and Prediction Model Transfer Based Thereon
作者单位
天津大学精密测试技术及仪器国家重点实验室, 天津 300072
摘要
近红外漫反射光谱法可用于物质成分定性、 定量分析, 具有快速、 无损检测等优点, 在食品、 制药、 环境监测、 生命科学等领域有广泛应用。 采用近红外漫反射光谱对浑浊散射介质进行成分定量分析时, 由于光散射作用, 介质吸光度随测量距离不再呈线性变化, 不同测量距离下测量模型也很难转换。 从扩散方程出发, 提出了从漫射光谱中提取线性吸光度的方法, 获得了与测量距离无关的介质光学参数——有效衰减系数的光谱, 该光谱可反映介质吸收变化, 并用于物质成分测量。 具体采用了双位置差分法, 可在线性吸光度的范围内任意选取两个光源-探测器距离测量, 通过差分获得与测量位置无关的有效衰减系数光谱, 其中测量距离可依据波段灵活地进行选择。 另外, 经理论推导可知, 差分运算的同时还大大削减了光子扩散对测量的影响, 有利于不同散射特性的介质间光谱模型的相互借鉴。 经理论计算、 蒙特卡洛模拟和实验验证, 在1 000~1 360 nm波段, 该方法适用于强散射的浑浊水溶液。 对散射系数处于较大范围(28.53~87.47 cm-1)的intralipid水溶液进行了测试, 获得了线性吸光度的光源-探测器距离范围。 进一步以葡萄糖为被测成分为例, 获得了葡萄糖在intralipid水溶液中的有效衰减系数光谱。 采用测量距离连续可调的漫反射光谱测量系统进行了实验, 测量范围位于0~0.3 cm, 测试了3种典型的介质(3%, 5%, 10%Intralipid水溶液)。 结果可知, 3种介质中葡萄糖光谱呈现一致性, 它们之间的测量模型可以方便地进行线性转换。 研究对象intralipid水溶液常作为生物组织的仿体, 其光学参数也覆盖了常见的牛奶、 果汁等被测浑浊溶液, 因此测量结果具有广泛的适应性。 综上, 该研究实现了对浑浊水溶液中物质成分线性吸光度的提取, 该方法不仅使得测量位置的选择更加灵活方便, 还有利于实现不同散射介质间模型的线性转换, 特别适用于介质成分复杂多变的场合下的测量应用, 如对人体组织、 牛奶或食品中的成分测量等。
Abstract
In near-infrared spectroscopy (NIRS) is widely applied for the qualitative or quantitative component detection in foods, medicines, pollution and biological tissues as it can provide real-time, non-invasive and continuous measurement. In the quantitative analysis of components in scattering media based on NIRS, the attenuance of the media doesn’t exhibit linearity with the source-detector separation (SDS) due to light scattering. Then measurement models at different SDSs are difficult to be applied to each other. In this paper, we propose a signal processing method based on Diffusion Equation (DE) to acquire the linear attenuance and get a SDS-independent optical parameter, i. e. effective attenuation coefficient (EAC). The EAC spectrum can be used to detect the component since the spectrum shows a comprehensive absorption of it. We apply a differential measurement on the spectra from two arbitrary SDSs to get the EAC spectrum, which will be independent to the used SDSs as long as the SDSs are in a limited linear range. The SDSs also can be flexibly selected according the different wavebands. More over, the differential can greatly reduce the influence from light diffusing in medium, and then benefit the measurement model transferring for different scattering media. We tested the method on the turbid water solutions with high scattering property by using theoretical calculation, Monte Carlo (MC) simulation and experiment. The tested solutions’ scattering coefficients were in the range of 28.53~87.47 cm-1 and the waveband was 1 000~1 360 nm. The linear range of SDS for the attenuance was acquired. Taking the glucose measurement as an example, the EAC spectra of glucose in these media were tested. Using the SDSs from the linear range, we measured glucose’s EAC spectra. The experiment system was equipped with a SDS adjustable probe to get the diffuse light from different SDSs. The spectra of three typical media, which are 3%, 5%, 10% Intralipid solutions, were compared. These glucose spectra presented high similarity and could be linearly transferred to each other. Intralipid solutions are often used as biological tissue phantoms since their optical property covers most water rich materials. And the solutions can also be used to mimic water rich food like milk, juice etc. Therefore, our study would be widely beneficial to many cases. In summary, we proposed an effective method to extract linear attenuance for diffuse spectra in turbid scattering media. This method can well improve the real use of NIRS, since it not only helps flexibly select SDSs, but also presents a convenient and linear measurement model transferring for easy varying media, such as tissues, milk, and other foods.

曹海青, 韩同帅, 刘雪玉, 刘瑾. 浑浊散射介质中被测成分线性吸光度提取与模型转换研究[J]. 光谱学与光谱分析, 2019, 39(12): 3711. CAO Hai-qing, HAN Tong-shuai, LIU Xue-yu, LIU Jin. Extracting Linear Attenuance of Analyte in Turbid Scattering Media and Prediction Model Transfer Based Thereon[J]. Spectroscopy and Spectral Analysis, 2019, 39(12): 3711.

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