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Simulating Senses
Dr. Beena Rai
Principal Scientist & Head
Physical Sciences Research
Tata Consultancy Services, Pune
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TCS Research and InnovationEngineering Technology
centers
Engineeringservice
providers
ISV Softwarepartners
Tier 1 Suppliers
Captive Centers
Customer Eco-System
21+ TCS
Innovation Labs(Research Area/Programs)
Center of Excellence
Co-Innovation Network
- 29 Start-ups &
6 VC Partners
20+ Standard
Setting Bodies
Industry solutions, ConnectedPlatforms, Social, Big Data
30+ Academic
Institutions
40+ ISV,
Semiconductor Platform Providers
30+ Industrial Alliances
3
Physical SciencesEngaged in cutting-edge
materials research leveraging
enormous opportunity unleashed
by digital revolution impacting
the complete life cycle of
materials (Discovery-
Development-Deployment-
Recycle)
Focus Areas
Materials
Informatics
Biological System: in-
silico Model/Digital Twin Chemical
Formulations
Battery
Materials
Photovoltaic
materials E-waste
Recycling
Materials & Manufacturing
Processes Modeling &
Simulation
Domain Expertise
Design &
Optimization
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Today’s Agenda
Touch Taste Sight HearSmell
Senses
Organs
Applications
Transdermal Drug delivery
Personal care formulations
Artificial skin/robo skin
Flavouring additives
Molecular mechanism of taste perception
Quantifying taste
AI/ML models for food quality evaluation
Monitoring food freshness
Sensing enabled IoTsolutions
Design of fragrances
Discovery, design and testing of fragrances
Understanding odour perception
Ultrasonic sensors for non destructive estimation of food firmness
Image Source: https://www.visiblebody.com/learn/nervous/five-senses
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Human Skin Digital Twin
Touch Taste Sight HearSmell
Senses
Organs
Applications
Transdermal Drug delivery
Personal care formulations
Artificial skin/robo skin
Flavouring additives
Molecular mechanism of taste perception
Quantifying taste
AI/ML models for food quality evaluation
Monitoring food freshness
Sensing enabled IoTsolutions
Design of fragrances
Discovery, design and testing of fragrances
Understanding odour perception
Ultrasonic sensors for non destructive estimation of food firmness
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Cosmetics• Cosmetics market is expected to
garner $429.8 B by 2022#
• Global skin care is ~$ 120 B USD
Pharma• Transdermal drug delivery ~$35 B • CAGR of 9.3% over the period of 2017-2023*• 1/3 drugs involved in clinical trial are for
delivery through skin
Wound1/Burn2 CareMarket worth
$22.01 B by 20221
$ 2.23 B by 20212
Issues & Challenges
• Differential penetration is required across skin• EU regulation (76/768/EEC,Feb,2003)
prohibits the use of animal or animal derived substances for the development and testing of cosmetics and pharmaceutical ingredients
• Avoidance of pain• Compliance issues related to injections• Possibility to control drug release over long time• Avoiding the first-pas metabolism, bypassing the
GI tract (oral route)• Improved bio-availability
Benefits
• Skin barrier (Stratum Corneum)• Inter and intra personal variability • Very few transdermal drug formulations
have been approved by FDA
Issues & Challenges
• Skin grafting is time challenging• Slow wound healing• Infection and pain• Hypertrophic scarring
Issues & Challenges
• Help to perfume, clean, change theappearance, protect, keep in good conditionand correct body odours
• New age in skincare preparations –the age ofCosmeceuticals
Benefits
Digital intervention for in-silico design & testing Rapid development of drugs and cosmetics
Reduced time & cost to market
*Business wire, Jan 2018#Allied Market Research, July 20161Marketandmarket, 2016
Skin Care Overview
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Skin Functions & Complexity
J. van Smeden et al. Biochimica et Biophysica Acta. 2014 Norlen et al. 2012, J. of Investigative DermatologyRoberts et. Al. 2013. Advanced Drug Delivery Reviews https://www.medicalnewstoday.com/articles/262881.php
More than 300 types of CER and FFA are present in human skin
Transport Barrier
Healthy Collagen
Denatured Collagen
Mechanical Strength
Epidermis – Chemical barrier
Dermis – Mechanical support
Hypodermis – Thermal barrier
8 Gupta R and Rai B. J .Phys. Chem. B. 2015, 119, 11643−11655J. van Smeden et al. Biochimica et Biophysica Acta 1841 (2014) 295–313
Skin Barrier Function
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Transport Model Development
Gupta R and Rai B. J .Phys. Chem. B. 2015, 119, 11643−11655
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Phase Transition temperature : 370 K – 380 K
Moore, D. J. Phys. Chem. B. 1997, 101, 8933-8940Shah et al. J. Lipid Res. 1995, 36, 1936-1944 & 1945-1955. Gupta R and Rai B.J .Phys. Chem. B. 2015 119 (35), pp 11643–11655
CER chains in hexagonal packing
Model Validation: Structural properties of lipid layer
• Below the phase transition temperature CHOL reduces the order parameter and gives fluidity to bilayer
• CHOL Increases the order parameter above the phase transition temperature
Notman et al. Biophys. J. 2007, 93, 2056–2068Dmitry et al. (BBA)- Biomembranes. 2004, 1666, 142-157
Ratio in figures stands for molar ratio of CER:CHOL:FFA
11Alwarawrah et al. J Phys Chem B. 2010, 114(22), 7516-7523Marrink et al. J. Phys. Chem. B. 2001, 105, 6122-6127
Chol. sits in between CER and FFA
Gupta R and Rai B.J .Phys. Chem. B. 2015 119 (35), pp 11643–11655
CHOL gives rigidity to layer and FFAprovides the flexibility
Model Validation: Structural properties of lipid layer
Ratio in figures stands for molar ratio of CER:CHOL:FFA
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Model Validation: Variation in ceramide constituents
Gupta R and Rai B.J .Phys. Chem. B. 2015 119 (35), pp 11643–11655
Alwarawrah et al. J Phys Chem B. 2010, 114(22), 7516-7523
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Multiscale Model: Molecular to Macro
Kishore Gajula, Rakesh Gupta , D B Sridhar and Beena Rai, Journal of Chemical Information and Modeling. 2017.
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Bringing in Complexity: Personalized Models
Rogers et al. Archives of dermatological research. 1996
• Model of various ceramides are developed and tested with experimental data
• The model can capture demographic variation
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Case StudiesFormulation Design (Safety and Efficacy)
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1. In-silico design of safe formulations
Rakesh Gupta, Beena Rai and Samir Mitragotri. ACS JPC B. 2019 (in review)
• Ethanol extracts the skin lipids • Extent of extraction depends upon its
concentration• The optimum ethanol concentration could
be obtained using in silico model • Both structural and thermodynamics
properties are calculated to quantify the extent of extraction
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2. In-silico Screening of Permeation Enhancers
a) geraniol, b) geranic acid, c) isopropyl palmitate, d) monoolein, e) limonene, f) n-octyl
pyrrolidone, g) palmitic acid, h) oleic acid and i) undecanoic acid. Rakesh G, D Sridhar, Beena R and Samir M. Nature Sci. Rep. 2018
ER (enhancement ratio): It is a
measure of the extent of the SC
disruption or (permeability)
enhancement. Quantitatively, ER is
defined as skin conductivity at the end
of 24 h, when incubated with a
particular formulation, normalized to
conductivity at time 0s.
The experimental parameter (ER) and simulation parameter reverse
of order parameter (1/<s>) are compared for 10 permeation
enhancers
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3. Optimum Concentration of Active/s in Formulation
Gupta R and Rai B. RSC Nano Scale. 2017Matins et al. Engineering in Life Sciences. 2017
• The optimum concentration of fullerene in aqueous formulation is determined using in-silico model.
• The simulation predictions were confirmed by experimental study on pig skin by Matins et al.
Gupta R and Rai B. RSC Nano Scale. 2017
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4. Nanoparticles Design and Testing Framework
Rakesh Gupta and Beena Rai. Nanoscale, 2018, 1-12.
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4.1 Nanoparticle formulation stability
Neutral
Charged
D Sridhar, Rakesh Gupta and Beena Rai. RSC PCCP. 2018
a 10 % b 20 %c 40 % d 80 %e 100 %
• Based on PMF, stability of the nanoparticles is determined
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4.2 Nanoparticle Permeation: Effect of Size
Huang et.al. Biomaterials. 2010. Gupta R and Rai B. J. Phys. Chem.B.2016
Self-Healing • The skin permeability of gold nanoparticle decreaseswith increase in their size
• The gold nanoparticle create local defects in the skinlipid layer which are temporary and the layer gets self-healed once nanoparticle crosses the layer
t = 3µsNP PermeationNP Permeation
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Labouta et al. Nanoscale, 2011, 3, 4989–4999
Gupta R and Rai B. Nature Scientific Report 2017
4.3 Nanoparticle Permeation: Surface Chemistry
• The neutral nanoparticle easily cross the lipid layer whereas the charged nanoparticle adsorb on the skin layer
• The permeability of charged nanoparticle are much lower as compared to neutral one
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4.4 Nanoparticle Permeation: Surface Pattern
• The nanoparticle of various surface pattern (mixed ofhydrophobic and hydrophilic beads) are screened basedon free energy of permeation across the layer using insilico skin model
• The binding of protein on screened nanoparticle ischecked and further utilized for co-delivery application
• Nanoparticle having 2:1 (hydrophobic:hydrophilic)homogenous surface pattern was found to be the bestcandidate for co-delivery application
Rakesh Gupta and Beena Rai. Nanoscale. 2018
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5. Electroporation of Skin
Rakesh Gupta and Beena Rai. Langmuir 2018
Pore stabilization Pore Resealing Drug Permeation
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Skin Mechanical Model
(Nils Krueger et al, Skin Research and Technology, 2011)
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Hemlatha J et al. Skin Physiochemical model. J. Biomechanics. (In review)
Distensibility behavior of skin Collagen degradation leading to hysteretic behavior of skin
Skin Mechanical Model
PHYSIOCHEMICAL MODEL OF SKIN LAYER/S FOR RAPID DESIGN AND TESTING OF FORMULATIONS. 18/01/2019. Application No: 201921002217. India
Exp: (Yu et al, Nature Materials, 2016)
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Take Away & Way Forward
PHYSIOCHEMICAL MODEL OF SKIN LAYER/S FOR RAPID DESIGN AND TESTING OF FORMULATIONS. 18/01/2019. Application No: 201921002217. India
Deeper layer models Combining transport and mechanical model Extending mechanical model in 2D and 3D Capturing demographic variation in models Development of complex formulations and testing Understanding of transport of macromolecules
through skin
• Skin models at various length scale could be built based on applications and requirements
• The demographic variation in skin composition could be captured
• Given the structural properties of a drug molecule, its release profile from skin SC can be obtained
• The in-silico model can be personalized based on the skin composition
• The influence of drug/formulation on skin mechanical properties (related to wrinkle formation) could be captured
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Design of Tastants
Touch Taste Sight HearSmell
Senses
Organs
Applications
Transdermal Drug delivery
Personal care formulations
Artificial skin/robo skin
Flavouring additives
Molecular mechanism of taste perception
Quantifying taste
AI/ML models for food quality evaluation
Monitoring food freshness
Sensing enabled IoTsolutions
Design of fragrances
Discovery, design and testing of fragrances
Understanding odour perception
Ultrasonic sensors for non destructive estimation of food firmness
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*https://www.marketsandmarkets.com/PressReleases/food-flavors.asp# https://www.businesswire.com/news/home/20170301005073/en/Global-Sugar-free-Food-Beverages-Market-Size-ReachImage source : https://www.flexibledietinglifestyle.com/artificial-sweeteners/https://www.newsclick.in/generic-prescribing-medicines-diktats-are-not-substitute-sound-public-policy
Flavour Industry: Market
The food flavours current market is estimated to be
USD 13.56 billion and is projected to reach USD
17.10 billion by 2023, at a CAGR of 4.8% during the
forecast period.
- marketsandmarkets*
Global Sugar-free Food and Beverages Market Size
to Reach USD 72.37 Billion by 2021
- Technavio#
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https://myersdetox.com/complete-list-of-artificial-sweeteners/Gaudette, Nicole J., and Gary J. Pickering. "The efficacy of bitter blockers on health-relevant bitterants." Journal of Functional Foods 4.1 (2012): 177-184.https://www.prnewswire.com/news-releases/senomyx-announces-approval-of-bittermyx-bb68-and-sweetmyx-sr96-flavor-ingredients-in-china-300320418.htmlhttp://www.who.int/news-room/fact-sheets/detail/diabetes
Artificial sweeteners
• Acesulfame K
• Aspartame
• Cyclamate
• Saccharin
• Sucralose
• Alitame
Bitter blockers
• β-cyclodextrin (β-CYCLO)
• homoeriodictyol sodium salt (HED)
• zinc sulphate monohydrate (ZnSO4)
• magnesium sulphate (MgSO4)
• carboxymethylcellulose sodium salt
Diabetes has risen from 108 million in 1980 to 422 million in 2014
Search for new Tastants is on
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Taste Perception: Molecular Mechanism
https://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0072592/ American Journal of Clinical Nutrition 90(3):747S-752S. 2009
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SYSTEM AND METHOD FOR DISCOVERY, DESIGN, CLASSIFICATION AND TESTING OF PLURALITY OF TASTANTS. Indian Patent Application. 201921004696
In-Silico Design of Tastants
Atomic, shape, topological, geometrical, electrotopological
QSAR models Genetic Function
Approximation Artificial Neural Network
Compounds + biological activity
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• Number of sweet molecules - 488
• Sweetness Index (SI) - 0.2 to 220000
Structure Building
• Structure stored in a SMILE
• Optimized using Universal forcefield
• RMSD – 0.1 Kcal/mol
Dataset Preparation
Data Compilation
Descriptor Generation
• 564 descriptors generated
• Descriptors – topological, constitutional,
geometric, electronic, etc.
Goel, A., Gajula, K., Gupta, R. and Rai, B., 2018.. Food chemistry, 253, pp.127-131.
QSAR Model of Sweetness
Data Pretreatment
• Removal of constant or nearly constant features
• Removal of features having correlation greater than 0.85
• Normalization of features
• Feature selection - ensemble technique (lasso, MLR, SVM,
RF) – 17 features
32 outliers detected
Number of molecules in refined dataset - 455
Statistical Analysis
GFA
ANN
R2=0.86Rtest
2=0.83
R2=0.889Rtest
2=0.831
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Way Forward
SYSTEM AND METHOD FOR DISCOVERY, DESIGN, CLASSIFICATION AND TESTING OF PLURALITY OF TASTANTS. Indian Patent Application. 201921004696
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Fragrance Design
Touch Taste Sight HearSmell
Senses
Organs
Applications
Transdermal Drug delivery
Personal care formulations
Artificial skin/robo skin
Flavouring additives
Molecular mechanism of taste perception
Quantifying taste
AI/ML models for food quality evaluation
Monitoring food freshness
Sensing enabled IoTsolutions
Design of fragrances
Discovery, design and testing of fragrances
Understanding odour perception
Ultrasonic sensors for non destructive estimation of food firmness
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Understanding Structure Odor Relationships (SOR)
• Global fragrance market is expected to reach $70 billion by 2022*
• Fragrances are formulated for different applications• Soaps, detergents, cosmetics, toiletries etc.
• Design methods are empirical based on experience and knowledge of experts
• Challenges:• Olfactory perception is complex and subjective
• Structurally similar molecules can show distinct odor profiles
• Classification itself can be ambiguous
Fragrance Classification
Intensity
TOP
MIDDLE
BASE
Character
FRUITY
FLORAL
WOODY
https://www.thelibraryoffragrance.eu/create-your-own-scent/
https://www.perfumerflavorist.com/fragrance/trends/Global-Fragrance-Market-to-Hit-70-Billion--469990353.html*
Vision
Wavelength of light
Hearing
Frequency of sound
Olfactory perception
???
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Earlier days – perfumes entirely made of natural products
– Restricted to wealthy
– Personal use
Chanel No 5: first fine fragrance to use synthetic organic chemicals
Synthetic ingredients advantages:
– Cost, Availability
– Consistency, Stability
– Originality
New compound likely to be fragrant –
– What will it smell like?
– How intense will its smell be?
Chemical point of view
Volatile @ ambient temperature
M.W. < 400g/mol
Weak polarity
Lipophilicity
Bio-chemical point of view
Should vaporize
Ease of approach to Nasal cavity
Solubilized within the olfactory mucus
Be the agonist of an Olfactory Receptor
General requirements of an odorant
Need for Structure - Odor Relationships
Karen J. Rassiter, Chemical Reviews, 1996, 96, 3201-3240
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Psychophysical dataset for Olfactory Perception
• Aromatic Aldehydes (Group 11) are rated to be most familiar at the same time most pleasant on the basis of average ratings from 55 Subjects
Keller et al.BMC Neuroscience (2016)
• Aromatic Acids (Group 5) are rated to be least familiar and least pleasant, presenting a starting point to develop new fragrances
• Within Aromatic Aldehydes there are 2 odorants which are the most familiar and pleasant while being most intense
• Within Aromatics acids there is 1 Odorant which is the most pleasant but not so familiar while being less intense
Charts made using TCS Proprietary software
Charts made using TCS Proprietary software
Odor Familiarity
Od
or
Ple
asan
tnes
s
Odor Intensity
(Aliphatic, Aromatics)Acids (4,5)Alcohol (6,7)Ester (8,9)Aldehyde (10, 11)Ketone (12,13)Cyclic (3)Others (14,15,16)
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Food Quality & Freshness Monitoring
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Food Wastage: Global Scenario One third of the food produced (1.6 billion tonnes) in the
world for human consumption is lost every year– About $1.2 trillion are lost– Accounts for 8% of global greenhouse gas emission(Food and Agriculture Organization (FAO))
One in nine people do not have enough food (795 million people)
UN’s Sustainable Development Goals sets a target of halving the food loss and waste by 2030
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South and
Southeast
Asia
Subsaharan
Africa
North
Africa, West
and Central
Asia
Latin
America
Europe North
AmericaFo
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Lo
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pe
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ap
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(kg
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Region
Production to retailing Consumer
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Food Loss in different countries
Food Loss in different regions
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Framework for Monitoring Food Freshness/Shelf Life
Deterioration of quality during Transportation/storage in food supply chain(Environmental conditions and time spent at each stage)
Dynamic monitoring of quality (freshness) and shelf life computation (Dynamic Pricing)
Food container
Sensors
Invasive/
Non-invasive
Models
Physics based
Data based
Kinetics based
T H M G L
Farm production
Harvesting and
packagingGrading &
SortingCentralized
Storage
Consumer
Processing Centre
Food product storage
DistributorLocal supplier
Knowledge Base
FSSI Standards, Geography specific food cultivar and environmental data, Real time food pricing
data
Freshness Shelf life Price
Digital Twins of Food productsDyn
amic
co
ntr
ol
of
envi
ron
men
tal
con
dit
ion
s
Rea
l tim
e p
red
icti
on
T: Temperature
H: Relative humidity
M: Moisture
G: Gases (O2,C2H4, CO2)
L:Light
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Modular framework for multimodal sensing andsynchronized monitoring of food items
Novel framework for multimodal sensing and monitoring of perishable commodities, 201821040783 (Indian patent application)
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Case Studies1. Freshness of banana2. Shelf life estimation of potato
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1. Sensing & Monitoring – Fresh Banana
Food FreshnessModel
O2
Good
OK
Bad
System and method for monitoring and quality evaluation of perishable food items. Indian Patent application201921004783
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White sprouts Green sprouts - Solanine
Dark Light
Shrinkage
Hot conditionsHigh humidity
Fungal growth
High sugar in potato → Brown chips (Acrylamide – carcinogen)
Reducing sugar 2.3%Reducing sugar 0.5%
Effect of storage conditions on the food quality
Parameter
Weight loss
Compo-sition
ToxinSprouting
Colour
Shelf life defining attributes
Sugar, Starch, Protein, Fats, Vitamins
Flavour
Moisture
2. Shelf life estimation of potato
Long term storage in cold conditions
• Potato is the world's fourth-largest food crop, following maize, wheat and rice
• India is 3rd largest producer of potato in the world
- Yearly production of 25 million tonnes
- Low export (Only 0.5% of global potato market) and low processing
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Shelf life prediction based on the weight loss data
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22
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20
40
60
80
100
10 15 20 25
Shel
f lif
e (d
ays)
Shelf life criteria (Weight loss (%) )
Inside Chamber Temp 10 °C & 90% RH
Outside Chamber Temp Avg 29.9 °C & 38.4% RH
Experimental data and kinetic model Shelf life prediction
*Potatos has previous storage history of 2 months
0
2
4
6
8
10
12
14
16
0 20 40 60 80 100
Cum
ula
tive
wei
ght
loss
(%
)
Number of days (#)
Potato Atlanta Avg Temp 29 °C & 60% RH
Predicted (Zero order (1-5 day) & First order (5day onwards)
Shelf life estimation of potato
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15
20
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40
60
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120
140
A B C D
Sh
elf
lif
e (
da
ys)
Storage conditions (Temp & RH)
5 10 15 20
Storage conditions A -10 °C (Freezer)
B 10 °C & 40 % RH (Freeze)
C 29 °C & 60 % RH (Ambient)
D 35 °C & 40 % RH (Incubator)
0 10 20 30 40 50 60
0
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Time (days)
Weig
ht
loss
(%
)
25 C & RH 50%
25 C & RH 60%
25 C & RH 70%
25 C & RH 90%
Shelf life estimation of potato: effect of process parameters
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Shelf Life Prediction: Optimum Environmental Condition
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Way Forward – Farm to Market
Smart and efficient sensing technology in supply chain
Reduce food loss
Sustainable economy
Visibility & Accountability
Assisting local food producers
Ensuring and retaining nutritional value
Personalized food
Natural ripening of climacteric fruits Smart Packaging
Image Source: https://www.raconteur.net/sustainability/smart-packaging-brings-challenges-to-privacy-in-the-homeImage Source: https://www.raconteur.net/sustainability/smart-packaging-brings-challenges-to-privacy-in-the-home
Food Composition Assessment
Image Source: https://www.slideshare.net/CorVerdouw/20150624-tp-organics-req-smart-farming-food-security-v1
Smart Supply Chain
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AR/VR
Summary & Road Ahead
GSR
Electro-Chemical
Hyperspectral camera
Ultrasonic
Gases
Data
Touch
Taste
Sight
Sound
Smell
ScienceDigital Twin
Digital Thread
Image Source: https://www.inc.com/james-paine/how-blockchain-is-disrupting-supply-chain-management.html
https://www.networkworld.com/article/3280225/internet-of-things/
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