Abstract: A personal cleansing composition includes a surfactant system, where the surfactant system contains from about 0.1% to about 5% of a fatty acyl isethionate surfactant and from about 0.5% to about 40% of a co-surfactant. The composition is free of alkyl sulfate or alkyl ether sulfate type of surfactants. The composition also contains from about 0.05% to about 5% of a of a non-ionic surfactant comprising a fatty alkanolamide, from about 0.01% to about 2.0% of a hydrophobically modified ethoxylated methyl glucoside, and from about 0.01% to about 1.0% of a preservative. The pH of the composition is from about 5.5 to about 7.

Abstract: A personal cleansing composition includes from about 1% to about 15% of an anionic surfactant by weight of the composition, wherein the anionic surfactant comprises sodium laureth(n) sulfate (SLEnS), wherein n is the average moles of ethoxylation, wherein n equals to 1, 2 or 3; from about 0.01% to about 1% of an organic acid having a pKa greater than about 2.7, or a cosmetically acceptable salt thereof, or a mixture thereof, by weight of the composition; and wherein the pH of the personal cleansing composition is from about 5.0 to about 6.5.

Abstract: The present disclosure relates to a system and method for providing textile information and visualizing the same. The method for determining a damage level of a textile includes: receiving an image of at least a part of the textile; receiving information about a fabric type of the at least a part of the textile; analyzing the image by using a machine learning method so as to identify a fabric attribute of the at least a part of the textile; determining a severity value associated with the identified fabric attribute by using the machine learning method according to the received image, the identified fabric attribute and the fabric type; and determining the damage level of the textile based on the determined severity value.