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Laundry-VR ™

Professional digital tool to process, analyse and visualise

laboratory washing results in consumer-relevant terms

“The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.”
Marcel Proust, 1923


Shows what consumers see when evaluating washing results in diverse visual scenarios, where garments are washed, stored or used.

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

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



Is this for me or my team?

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

  • Yes, if you or your team are responsible to 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 do I have to do? Quick start

0. Define the data to be processed (this is the most critical step!):

  • Data from cleanliness, whiteness or colour-care study? What columns are present? Spectral or not? Is some surrounding part of the study? First time user? Not sure how to prepare a table? Please Contact Us

1. Basic steps in Laundry-VR Web App:

  • Register and Login or just Login if you are a registered user

  • Request a Access Key with some number of registrations. Purchase by yourself or Contact Us

  • Register a new Project

  • Click on "Work on Project" and upload the table

  • Select visual scenario parameters

  • Process the uploaded table. It will take several minutes, between 2-3 to 20-30 minutes pending of its size

2. Once the "Project Outputs" button is present on the projects list:

  • Click on the "Project Outputs" button

  • To analyse washing results to discover claims, click on Claim Discovery button and interact with the graph

  • To analyse washing results to balance formulation cost, click on Formulation Cost button and interact with the graph

  • How do consumer see this washing results? Click on the corresponding button to see virtual realities according to the display you are using

3. Now you and your team can make sound and confident decisions!



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


More Frequently Asked Questions (FAQs):

What is the current conventional scenario in laundry brands and products innovation? What is our approach?

Does it represent the actual world? Is that validated?

What is the history of CONSUMERTEC in the area? Are Laundry-VR credible?

What does CLVVR change in your detergency lab?

How CLVVR positively influences your business?

What means virtual R&D in consumer relevant terms?

Are cleanliness, whiteness and colour-care relative perceptions?

Why numerical models to represent consumer visual perceptions?




Frequently Asked Questions (FAQs)


What is the current conventional scenario in laundry brands & products innovation? What is our approach?

Conventional innovation practices are based on 100% physical experimentation at lab conditions, using protocols far away from the ample diversity and variability found in world´s consumer laundry realities. Due to time and cost limitations, teams have owed to sacrifice consumer relevancy. Therefore, innovation environments at incumbent companies has become the most favourable condition to end up with costly products with poor performance in front of consumer eyes. The end results have been non-balanced value equations for consumers and companies.

So, innovation-driven organisations/teams at incumbent companies are under intense pressure, between rising consumer expectation, more product´s margins, and unpredictable moves by agile attackers in several local and regional markets. Innovators are being asked to reinvent themselves moving away from rigidly sequenced process, strict division of responsibilities and narrow focus on internal innovation; finally they are urged to create new value equations for companies and consumers, shortening the concept-to-product time frame.

Recently it has been stated that our industry key topics and priorities are largely based on data, to develop data strategy for product innovation, helping the industry understand consumer behaviour, market trends and product performance. For some, now it is about taking the laundry industry a place on the digital stage!

Our approach is a radical change using digital technologies very upstream innovation process to visualise large amount of detergency data. First, using digital technologies to process a minimum new physical experimentation or to generate new large simulated data based on previous experimentation, and second, using digital technologies to implement immersive virtual environments visually experienced in very affordable stand alone headset virtual reality displays. Business stake holders will have the opportunity to experience consumer benefits perception and product´s cost implication in advance.



Does it represent the actual world? Is that validated?

First of all, Laundry-VR is not the result of conventional statistical modelling. It is a scientific analytic approach to visualise data.

The core of the technology are analytical models i.e. models based on current scientific knowledge about how things happen, in the fields of optical and fluorescence spectroscopy of surfaces, spectroradiometry of source of illuminations at real environments, the biology of consumer visual perception, and the current validated digital technologies to create physical-based renderings. In other words, Laundry-VR collects data or generate data, using scientific principles, and visualise them; it is not a statistical estimation with some level of correlation.

Other similar technologies to visualise scientific results has been developed and are in use in several fields, like life-sciences, biology sciences, molecular visualisation tool for proteins. It has been predicted that virtual reality and augmented reality could become standard lab tool over the next five year or so.

More on this topic in: Matthews, D., SCIENCE GOES VIRTUAL, Nature Vol 557 3 May 2018, 127-128



What is the history of CONSUMERTEC in the area? Are Laundry-VR and CLVVR credible?
... we have more than 30 years in the area with several unique innovations

The unique CLVVR algorithms, embedded in Laundry-VR, are the result of CONSUMERTEC, formerly DETERTEC, ample expertise in the worldwide laundry market. They are grounded in the following:

  • more than 30 years of conducting detergency test, using both conventional laboratory equipment and more importantly, consumer relevant washing process, including use of customised diverse machine equipments and in-house equipments to mimic hand washing process with short and long strokes, and use of indoor and outdoor (UV controlled) drying stages
  • more than 15 years in the field of advanced spectroscopy, including in-house design and fabrication of non-contact and contact spectrofluorimetric equipments based on fibre optics and CCD spectrometers
  • more than 12 years of preparation of dingy test fabrics with high relevancy to world's laundry practices including the realities of: China, India, Europe, USA, Latin America, Middle East and North Africa
  • more than 15 years using modelling techniques. Initially using the scripting capabilities of JMP/SAS software and currently with more universal languages like R and Python. Ample expertise on numerical models to represent consumer benefit perceptions like: garment cleanliness, garment whiteness, diaper yellowness, diaper bulkiness, among others
  • more than 7 years of conducting household experiential ethnography and consumer purchase behaviour experimentation, to interpret the link between consumer benefit perceptions and actions, and brand's purchases (brand's household penetration and purchase frequency)
  • fundamental understandings of the underlying sciences: radiative transfer models, fluorescence and optical spectroscopy and colour appearance models

CLVVR is a proprietary sustainable innovation hold by CONSUMERTEC because it is line with our core academic dedication: fluorescence spectroscopy and the neurobiology of perception and action. Part of this dedication has been shared with the scientific community through more than 30 presentations around the world in the last 20 years. See Presentations and Papers.



What does CLVVR change in your detergency lab?
... it focus innovation on consumer and market realities in a new way

The laundry industry as any other consumer industry has been working in the last decade looking for alternatives to experiment and innovate using virtual technologies most of the time and only validate physically under very specific relevant conditions.

CLVVR is our approach in this area and we are sure that it has the potential to change everything and forever the way we and the industry conduct detergency studies. Can you imagine a detergency study that include:

.. laundry monitors: virtually created using two or more (+2) fabric substrates, soiled or stained with two (2) different levels of more than ten (+10) different materials, with four or more (4+) dingy surrounding. All in relevance to more than ten (+10) specific laundry regions?

.. procedures: virtually washed in two or more (+2) machine or manual process, using several products/technologies at two or more (+2) concentrations, and dried indoors (no UV) and outdoors with two (2) levels of natural UV radiation. All in relevance to more than ten (+10) specific laundry regions?

.. washing results: virtually evaluated by three or more (+3) categorical observers, perceiving at three or more (+3) different visual scenarios, with a perception variability that include three or more (+3) visual sensitivity or preferences?

.. with a cost of less than 10% of the physical alternative, conducted in just a matter of minutes?

If additionally the final output is clearly understand from the perspective of finding the WHY of the final consumer perceptual response, then it is easy to understand why this approach change everything in the laundry industry.

CLVVR can predict laundry products and technologies with poor perceived consumer benefits under real conditions of use, taking into account the ample variability in the real regional laundry markets, prior to produce any prototype

CLVVR can expand and support the definition of products and technologies problems by a combination of empirical and modern analytical approaches. By this way go further than "black box experimentation" and new questions can be generated and solved

For the very first time ever in the laundry industry all participants, like suppliers and producers, have the alternative to interact in a virtual environment, with virtual prototypes and materials to change definitely innovation time and cost at both sides

CLVVR can substitute more than 80% of current laboratory physical detergency evaluation procedures, with substantial gains in consumer relevancy at a fraction of time and cost, speeding up creativity and innovation linked to the different regional laundry markets

CLVVR inputs are real or predicted data about changes of fabric's radiation and outputs are maps of consumer perceptual responses from visual evaluation of washing results, based on in-silico experimentation in a virtual laundry environment

CLVVR provides a tool kit which offers a unique opportunity to generate proprietary inputs based on previous physical experimentation. Its modular design is capable to match prior outputs from previous modelling and simulation tools or match inputs for posterior purchase behaviours models

CLVVR can be combined, in very smart ways, with physical experimentation to predict and validate consumer perceptual responses. CLLS (dingy monitors + algorithms) works together with CLVVR in this way.


How CLVVR positively influences laundry businesses?
... because consumers and scenarios occupies the centre stage in CLVVR

Laundry business is really about winning positive consumer experiences created by products and brands. Successful products in this highly competitive market are offering improved sensory experiences to demanding consumers whom perceive product benefits easily and consistently.

But nowadays competition is fierce, with an accelerated commoditisation of products and services, increasing price wars, and shrinking profit margins. The executives number one concern "sustained and steady top-line growth," can not possible be supported by existing low-productive innovation models. Today development and product introduction efforts in the consumer products industry are urged to accelerate the pace of innovation, with different cost structures, in a different time frame, being collaborative with external sources, and more importantly, are compelled to design products and technologies that offer superior perceived consumer value in line with benefits delivered by brands. In short, R&D has to develop affordable products/technologies with the ability to improve sensory experiences in consumer terms, fast and with less money, i.e. to develop better and cheaper products with a systematic focus on productivity. But, is that possible? how to execute this? ....That is the R&D challenge!!

CLVVR is in line with business models based on innovative developments focused on consumer and actual realities, and provides a competitive advantage because it reduces time and cost of the technology innovation process.



What means in consumer relevant terms?
... it means that detergency labs can include the variability of consumers and scenarios

CLVVR provides the opportunity to include consumer realities at the beginning of the innovation journey, not at the end. The virtual experimentation matrix includes:

Selection of white commercial substrate fabrics that contains fluorescent whitening agents from textile mills

Set of clean, soiled, stained and coloured dingy test fabrics according to more than ten (10) regional laundry markets. Stained areas and coloured areas include adjacent dingy fabrics

Ample selection of actual source of illumination at relevant laundry market scenarios, outdoor and indoor, natural and artificial, and mixtures, with different UV relative content and illuminance.

Consumer ages between 20 and 60 years, variable parameters for field size, lens density, macula optical density, photopigments optical density, and photopigments lambda maximum shift. Individual colorimetric observer models.

Selected different visual contexts and surrounding as a basis to perform modern numerical calculations of colour attributes

Set of colour differences sensitivities with a different degree of lightness, chroma and hue influences, in line with different sensitivities to perceive cleanliness and colour-care in the garment context

Group of whiteness preferences according to cultural and persistent white targets, in line with different lightness and colour opponency influences on whiteness perception



Are consumer perception context-relative?
... yes, cleanliness, whiteness and colour-care perception are relative terms

Visual and olfactive cleanliness, visual whiteness as well as colour-care visual perception depends on consumer realities. There is no absolute cleanliness, whiteness, freshness/cleanliness nor colour-care, all depends on consumers, environments and history.

Cleanliness perception depends on visual detection of remanent stained areas at places perceived as stained before laundry. That visual detection is conducted automatically doing a comparison between the prior stained area and its surrounding. Full remotion means that no remanent stained area is detected or the slight presence of remanent stained is nearly not perceived and accepted as clean. The level of consumer sensitivity to detect sometimes small differences depends,among others on previous laundry experiences, consumer age, garment life and significance, expected laundry performance, and importantly on the visual scenario present during visual evaluation of laundry results.

Whiteness perception, a complex relative perceptual attribute, is linked on the one hand to consumer ability to perceive surface's more basics attributes like lightness, red-green opponency and yellow-blue opponency, all under some specific visual scenario, and on the other, to consumer preference of what is considered as more white, which has roots on habits and culture. The evolution of whiteness models are linked to the evolution of the level of understanding of basic and relative perceptual attributes of colours like brightness, lightness, colourfulness, chroma, saturation and hue as well as phenomenon like chromatic adaptation and colour constancy, on the consumer side; and of light reflection and fluorescence, on the surface side. Currently the reference whites (like a recently purchased white garment) are surfaces that fluoresce usually with shading dyes, and these are relative to different world regions.

Colour-care perception has conventionally been treated as a colour change process after some washing and drying cycle. In that terms, it has been considered a relative minor problem in the laundry industry because consumers has no reference of the original colour garment. Nowadays the situation is different. Much of current coloured garments are in fact a combination of white areas with adjacent coloured areas so much of colour-care perception is a visual perception of some coloured area adjacent or surrounded by a white area, therefore is a case of colour contrast effects in which whiteness perception of the adjacent fabric affect the colour perception of the coloured area. This simple fact, easy to notice at lab, has tremendous implications for numerical calculations that needs the use of recent models to calculate colorimetric attributes taking into account adjacent surfaces.

Consumers cleanliness, whiteness, and colour-care perception are relative terms, so new numerical models have to include most of the variables of those perceptions, including: consumer age, sensitivities, visual scenarios, preferences and references.

All of these have a very important role for innovation, which is, to experiment virtually (in-silico) with large complexities, in order to find those technologies that really match the need to products capable to do the job expected by actual consumers around the world.



Why numerical models to represent consumer perceptions?
... because they can accurately represent variable perception realities

The ultimate way of consumer understanding is to represent it with a numeric function, capable to be optimised as our understanding improve. It is cyclic, better models to better understanding to better models. This is mandatory for our consumer industry due to the high level of competition and the complex consumer environment; we have to clearly understand how consumers perceive brand central benefits, like whiteness and cleaning, which in turn are key purchase intent drivers.

Marketing teams need to model consumer purchase behaviour in order to virtual simulate the influence of variables on business success or failure. Accurate perception models are the critical intermediate stage, and relevant in-vitro models act as a fulcrum in that effort.

R&D teams needs to experiment in virtual terms so as to be fast, with less costs, and to include a real matrix of relevant market elements. In the laundry market any other way is very expensive, time consuming and practically impossible to follow. Can you image a physical experimentation under five or more source of illuminations? with three or more different group of consumers with different visual conditions due to age? with four or more different group of consumers with different sensitivities and preferences concerning lightness, red-green opponency and yellow-blue opponency? with more than ten different stains and more than ten different white substrates? Probably not, but now all of this and more is possible with virtual R&D techniques. That is the purpose of CLVVR.

Why numerical modelling? The well known answer is: "I am never content until I have constructed a mathematical model of what I am studying. If I succeed in making one, I understand; otherwise I do not" William Thomson (Lord Kelvin) 1824 - 1907.




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