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利用 Kubelka-Munk 和 Allen-Goldfinger 反射模型估算染料浓度:性能比较。

Estimation of dye concentration by using Kubelka-Munk and Allen-Goldfinger reflective models: comparing the performance.

机构信息

Department of Color Physics, Institute for Color Science and Technology, Tehran, Iran.

Department of Textile Engineering, Textile Chemistry and Fiber Sciences, Amirkabir University of Technology, Tehran, Iran.

出版信息

Sci Rep. 2023 Feb 3;13(1):2019. doi: 10.1038/s41598-023-29264-x.

Abstract

If the relationship between the reflectance function (K/S) and dye concentration (C) is known, the color of the dyed textile (R) and C could be predicted from each other. In the present work, the concentration value estimated from the reflectance data using two reflective models, i.e. the Kubelka-Munk and the Allen-Goldfinger is compared. First, the Allen-Goldfinger model was run by using the absorption coefficient of dyes in fiber, i.e. the unit k/s values instead of that in the solution. The results showed that the replacement of the unit k/s for the Beer-Lambert absorption coefficient in the Allen-Goldfinger model causes lower error in the prediction of the spectral reflectance factor as well as the dye concentration. However, this model did not lead to better results. Then, an inverse form was used to estimate the concentration of dyes from the corresponding spectral reflectance. Consequently, it was observed that the Kubelka-Munk model is still a more reliable method while benefiting from more simplicity than the Allen-Goldfinger model. The analysis of errors showed that the results deeply depend on different factors such as the applied concentration range as well as the dye spectral adsorption behavior.

摘要

如果已知反射率函数 (K/S) 与染料浓度 (C) 之间的关系,则可以相互预测染色纺织品 (R) 和 C 的颜色。在本工作中,使用两种反射模型,即 Kubelka-Munk 和 Allen-Goldfinger 对反射率数据进行了浓度值估计,并对其进行了比较。首先,Allen-Goldfinger 模型通过使用纤维中染料的吸收系数(即单位 k/s 值)代替溶液中的吸收系数进行了运行。结果表明,在 Allen-Goldfinger 模型中用单位 k/s 代替 Beer-Lambert 吸收系数会导致光谱反射率和染料浓度的预测误差降低。然而,该模型并未带来更好的结果。然后,从相应的光谱反射率中使用逆形式来估计染料的浓度。因此,观察到 Kubelka-Munk 模型仍然是一种更可靠的方法,同时比 Allen-Goldfinger 模型更简单。误差分析表明,结果很大程度上取决于不同的因素,如应用的浓度范围以及染料的光谱吸收行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e78/9898285/e9fdf80a0d6d/41598_2023_29264_Fig1_HTML.jpg

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