Colour difference analysis of lightfastness for some direct dyes

Ibrahim, Moustafa; Shakra, S.

doi:10.1016/0144-2880(85)90016-8

Cited by 5 publications

(1 citation statement)

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“…Instead of S.D.C 2 standards, he used a series of red pigment standards and printed them on paper. 5 Shakra et al 6,7 proposed an instrumental light fastness assessment method to link the color differences of some exposed vat dyes and direct dyes and their corresponding visual assessments. An instrumental method for the light fastness evaluation was also examined by Teraji et al [8][9][10] They analyzed the color change of the Blue Scale specimens exposed to carbon-arc light, and a formula for the relation between exposure time and the color change was proposed by them.…”

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A neural network‐based instrumental method for light fastness assessment with dataset validation

Karami

Izadan

Hamadani

2020

Color Research & Application

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A novel instrumental method for the light fastness assessment has been established. The accuracy of the visual method is affected by undesirable constraints, such as the different severity and the complications due to off-tone color change of the specimens. Thus, it was desired to develop an instrumental method of the light fastness assessment. In this regard, a neural network-based instrumental method of the light fastness assessment was developed. First, a proper light fastness panel was prepared, and then the visual light fastness assessments of experienced and inexperienced observers were collected. The accuracy and repeatability of the visual assessment results from these two groups of the observers were analyzed using different statistical criteria. The statistical analysis has shown that the mean of three trials of the inexperienced observers can be combined with the mean of the results obtained by experienced. Thus, all the results from the inexperienced and experienced observers were used to prepare a valid dataset for training a neural network. Different neural network structures trained with the prepared valid dataset. Among all of the implemented structures, the most accurate neural network structure is the one with one hidden layer and a 3-7-1 structure. The root mean square error and correlation coefficients of proposed 3-7-1 NN are 0.32 and 0.975, respectively, for the test sets. According to these results and the results from the comparison of the instrumental and visual assessment of the light fastness, it was concluded that the proposed neural network can be used for the instrumental light fastness assessment.

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confidence: 99%