An investigation of an instrument‐based method for assessing colour fastness to light of photochromic textiles

Aldib, Mohanad

doi:10.1111/cote.12156

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…The rate of colour change is a useful step in analysis, independent of the way the BWS classification system was organized. The perspective of rate of colour change is not restricted to microfading tests, it could be used to help with schemes developed for photochromic textiles as well as for standard light‐fastness tests .…”

Section: Resultsmentioning

confidence: 99%

“…On the basis of the classification of samples with BW categories lightning guidelines were developed for collections of artworks with the aim of preventing light‐induced colour change . An alternative approach to determine the light fastness has been presented by Aldib . Here colour differences between undyed and dyed BWS are used to classify the light fastness.…”

Section: Introductionmentioning

confidence: 99%

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A classification system to enhance light‐fastness data interpretation based on microfading tests and rate of colour change

Prestel

2017

Coloration Technology

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In general light‐fastness tests are carried out by comparing the colour change of a sample to the colour change of Blue Wool Standards in the same illumination conditions. With the microfading technique, developed to test the light fastness of a tiny test area on objects of cultural value, this comparison is made by absolute colour change after exposure. This article presents a new approach to enhance data interpretation of light‐fastness tests by the use of the rate of colour change. The procedure is combined with the conventional classification process using absolute colour change. The new classification system with the rate of colour change was applied to 44 samples, 13 of them were classified in more sensitive Blue Wool categories when analysing the rate of colour change. The perspective of rate of colour change helps to enhance data interpretation especially of samples showing fading characteristics different from the light‐fastness standards.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A classification system to enhance light‐fastness data interpretation based on microfading tests and rate of colour change

Prestel

2017

Coloration Technology

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“…The light fastness rate of the sample is linearly interpolated between the color difference values and rates from the Blue Scale. To evaluate the light fastness for photochromic fabrics, Aldib 12 proposed a new method. In his proposed method, a criterion called the reduction percentage is used.…”

Section: Introductionmentioning

confidence: 99%

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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Dynamic monitoring of thermally assisted assembly of colloidal crystals

et al. 2017

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An investigation of an instrument‐based method for assessing colour fastness to light of photochromic textiles

Cited by 8 publications

References 7 publications

A classification system to enhance light‐fastness data interpretation based on microfading tests and rate of colour change

A classification system to enhance light‐fastness data interpretation based on microfading tests and rate of colour change

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

Dynamic monitoring of thermally assisted assembly of colloidal crystals

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