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SRI-VR.
What is new?
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.
Main implications:
- 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.
- 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.
- 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:

Graph02. Collar stain on shirt:

Graph 03. Armpit stain on
T-shirt:

A screenshot of the new
outputs in Laundry-VR:
Contact
us to see a demo or to
experiment with your lab
readings.
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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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