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Laundry-VR â„¢

A professional digital tool to process, analyse, visualise and communicate

laboratory washing results in consumer-relevant terms


It shows what consumers see when evaluating washing results in diverse visual scenarios, where garments are washed, stored or used—validated and verified.

It includes perceptual thresholds to represent how consumers discriminate satisfaction in cleanliness, whiteness and colour-care.

It works for different regional consumers around the world, different laundry experiences, different cultures.

It combines cutting-edge concepts in fluorescent spectroscopy, colour appearance models, colour reproduction and WebXR technologies.

In blending with VR-Clicks (the digital tool to collect consumer responses), they offer the most modern and advanced approach to represent the interaction between consumers with products and products' performance in relevant visual scenarios.



Is this for my team or me?

  • Yes, if you or your team are responsible for deciding about brand claims based on washing performance. Which ones? How to support them?

  • Yes, if you or your team are responsible for formulation design, formulation cost, margins, selection of materials/ingredients, balance consumer benefits with laundry technologies in the area of cleanliness, whiteness and colour-care



See the entrance page of Laundry-VR

Interested to experiment a demo? Contact Us!

Interested to get a free trial with your lab data? Contact Us!



Transform lab data to new consumer's perception data:



Frequently Asked Questions (FAQs):

What are the terms and definitions in the web app?



Frequently Asked Questions (FAQs)


Laundry-VR terms and definitions
  • Modality. Adopted whites in the visual scenarios. Options:
    • PROPER. The adopted white is the original white fabric, before it becomes the dingy surrounding in stained dingy monitors
    • STANDARD, BRIGHT, BLUE or GREEN. Different white fabrics without or with shading dyes, selected as adopted whites in relevance to some specific laundry market
  • Source of illumination - Categorical observer. Combination of three parameters: observer field of view, source of illumination in the visual scenario, categorical observer age. Options:
    • FV_2 or FV_10. Field of view: 2 degrees for cleanliness studies and 10 degrees for whiteness or colour-care studies
    • ILLCFL. Source of illumination: compact fluorescent lamp. Nearly no presence of UV light
    • ILLD65. Source of illumination: exterior daylight, equivalent to D65. Moderate relative UV light
    • ILLID65. Source of illumination: interior daylight, equivalent to ID65. Low relative UV light
    • ILLD150. Source of illumination: exterior daylight, equivalent to D150. High relative UV light
    • 32, 42 or 62. Observer age. Photoreceptor status in the retina
  • Cleanliness sensitivity or Stain Removal Indexes. Numerical functions to calculate cleanliness perception
    • SRI_STw. Cleanliness perception with balanced sensitivity to differences on lightness, hue and chroma, using the selected adopted white, between the surrounding and the remanent stained area
    • SRI_HUw. Cleanliness perception with more sensitivity to differences in yellowness than lightness and chroma, using the selected adopted white, between the surrounding and the remanent stained area
    • SRI_CRw. Cleanliness perception with more sensitivity to differences in chroma than lightness and any particular hue, using the selected adopted white, between the surrounding and the remanent stained area
    • SRI_LIw. Cleanliness perception with more sensitivity to differences in lightness than chroma and any particular hue, using the selected adopted white, between the surrounding and the remanent stained area
  • Whiteness sensitivity or Whiteness Indexes. Numerical functions to calculate whiteness perception
    • WI_STw. Whiteness perception with more sensitivity to blue violetish perception, very low sensitivity to lightness perception and no sensitivity at all of greenish - reddish opponency perception, using the selected adopted white. Similar to CIE whiteness index
    • WI_GEw. Whiteness perception with more sensitivity to greenish perception, low sensitivity to lightness perception and low sensitivity to blue violetish perception, using the selected adopted white
    • WI_VOw. Whiteness perception with more sensitivity to reddish perception, low sensitivity to lightness perception and low sensitivity to blue violetish perception, using the selected adopted white
    • WI_LGw. Whiteness perception with more sensitivity to lightness perception, more sensitivity to unique blue perception and low sensitivity to greenish - reddish perception, using the selected adopted white
  • Colour-care sensitivity or Colour Change Indexes. Numerical functions to calculate colour change perception
    • DE_76w. Colour change perception with balanced sensitivity to differences in lightness, hue and chroma, using the selected adopted white, between coloured surfaces before and after some washing/drying process. Similar to DE76
    • DE_STw. Colour change perception with balanced sensitivity to differences in lightness, hue and chroma, using the selected adopted white, between coloured surfaces before and after some washing/drying process. Similar to DE200 but with more sensitivity.
  • Perceptual discrimination thresholds for pairwise performance comparisons. Statistical test: Practical equivalence test with thresholds (TOST test). Default values:
    • CLEANLINESS. A difference between SRI means of more than 2 for Superior and between 1 and 2 for Superior Trend. The opposite for Inferior and Inferior Trend. Parity for no differences between means
    • WHITENESS. A difference between WI means of more than 4 for Superior and between 2 and 4 for Superior Trend. The opposite for Inferior and Inferior Trend. Parity for no differences between means
    • COLOUR-CARE. A difference between DE means of more than 4 for Superior and between 2 and 4 for Superior Trend. The opposite for Inferior and Inferior Trend. Parity for no differences between means





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