Laundry-VR ™

The cutting-edge professional digital tool to transform laboratory washing results

into consumer-centric discoveries, insights and brand associations

It transforms limited laboratory washing results into ample washing results in diverse market relevant visual scenarios, where garments are washed, stored or used; with the highest practical fidelity. Transform lab data into new consumer's perception data.

It quantifies consumer's visual experience in diverse scenarios, including not-new white garments with three (3) different dinginess levels and five (5) not-new light-coloured garments.

It includes two or more (2+) 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 includes the Stain Removal Index in Virtual Realities (SRI-VR), the most advanced representation of cleanliness perception considering garment irregularities/textures in virtual visual scenarios.

It includes the most intuitive and modern interactive graphs in the web, which is applied on spectral radiance factors, three-dimensional uniform colour spaces, indexes vertical bars, continuous and categorical heat maps, in order to sustain the most comprehensive understanding of cleanliness, whiteness and colour-care results.

It combines cutting-edge concepts in fluorescent spectroscopy, colour appearance models, colour reproduction and WebXR technologies. It is the most advanced scientifically-based digital tool in the detergency field.

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

Laundry-VR tool is part of all comparative tests conducted in the CONSUMERTEC lab. It is also offered at no additional cost to laboratories using CONSUMERTEC´s dingy monitors to process lab washing results.

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

What input data do I need from my detergency laboratory?

• Two (2) spectral data from each fabric surface before and after some washing process, without and with UV cutting filter, if your lab has a spectrophotometer with the two filters option, or

• One (1) spectral data from each fabric surface before and after some washing process, could be without or with UV cutting filter, if your lab has a spectrophotometer with no UV filter option, or
  

• One (1) set of colorimetric values like L*, a*, b* from each fabric surface before and after some washing process, if your lab has only a colorimeter, or
  

• One (1) set of tristumulus values like X, Y, Z from each fabric surface before and after some washing process, if your lab has only a colorimeter, or
  

• Contact us to see other options or to clarify specific needs.
  

See the entrance page of Laundry-VR

Interested to experiment a demo? Contact Us!

Frequently Asked Questions (FAQs):

What means from lab to consumer?

What is the new SRI-VR?

What are the terms and definitions in the web app?

Frequently Asked Questions (FAQs)

From the input lab data, Laundry-VR generates new data representing consumers' visual perception of benefits and non-benefits in cleanliness, whiteness, and colour care. To be close to the actual visual scenarios, it is essential to consider not-new, white, and light-coloured garments. Remanent stained areas will be perceived differently in relation to the context pending of the garment colour, i.e., in yellowed fabrics, yellowish remanent stained areas will be perceived as cleaner than in other coloured garments.

Laundry-VR can generate those complex visual scenarios and, more importantly, quantify cleanliness using the Stain Removal Index between the remanent stained area and its surroundings. Below, you will see some examples of images from the web application.

Contact us to see a demo or to transform your lab readings.

The new Stain Removal Index in Virtual Realities (SRI-VR) is the unique feature of Laundry-VR. It combines cleanliness lab readings with consumer-relevant garment's irregularities/textures and dinginess levels for the first time in the industry. Now shadows, creases, bottoms, seams, and redeposition of body and other soils are considered to quantify cleanliness perception.

The new SRI-VR aligns with the final perception of cleanliness in actual clothes and scenarios when consumers evaluate washing results. The last values are between 2 to 5 units higher than the original lab readings.

How is it calculated? Four (4) different consumer-relevant garments are rendered, each with a different stain location and dinginess level. The remanent stained area and its proximal field are characterised by the appropriate RGB values, which are transformed to colorimetric attributes and then the SRI is calculated. See the below graphs.

1. Reach more sustainable goals. In the current paradigm of non-consumer relevant detergency studies (consumer irrelevant test monitors, lab protocols and readings), new sustainable and environmentally friendly materials appear costly and have less performance. Changing the paradigm to a more consumer-centric approach using tools like SRI-VR will uncover sustainable options that offer enough performance to consumers with no detriments in the brand’s margins.
2. Expand the cleanliness related brand' claims. New evidence appears when using consumer-relevant dingy monitors capable of being nearly thoroughly washed in the first wash in combination with SRI-VR, which quantify the level of stain remotion as consumers perceive in the actual garment visual environment. Consumers will probably link more brands’ claims to tangible benefits.
3. Balance of ingredients and margins improvements. Evaluating formulation’s ingredients with a consumer-centric approach helps balance product’s margins in two ways: prescinding of materials with no consumer's benefit, and including materials with apparently low consumer benefit. In the end, reaching the final purpose to balance business profits with consumer's benefits perception in actual diverse and flexible regional laundry scenarios.

Graph 01. Mud stain on socks:

Contact us to see a demo or to transform your lab readings.

• 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