This document discusses modern food processing technologies that can help increase value addition and exports of Indian agricultural products. It describes emerging technologies like pulse electric field processing, high pressure processing, ultrasound, and nano-technologies that can help improve food quality and safety while minimizing negative impacts on flavors and textures. The document also outlines various applications of these technologies like increasing shelf life, extracting bioactive compounds, and controlling foodborne pathogens. Overall, it promotes adoption of advanced processing methods for Indian commodities to boost agricultural competitiveness and meet growing domestic food demands.

1. MMooddeerrnn FFoooodd PPrroocceessssiinngg TTeecccchhnnoollooggiieess--
AA SSuurrvviivvaall KKiitt ffoorr IInnddiiaann AAggrriiccuullttuurree
DDrr.. RR..TT.. PPaattiill
FFoorrmmeerr DDiirreeccttoorr,, CCIIPPHHEETT,, LLuuddhhiiaannaa
CChhaaiirrmmaann && EEDD,, BBeenneevvoollee ffoorr PPHHTT,,
BBhhooppaall

2. IInnddiiaann PPeerrssppeeccttiivvee
 Value addition to fruits and vegetables only 2% and to
food grain 7%
 Export market share <1.5% mainly of primary
processed goods with low price realization
 Indian produce have unique aroma, flavour, taste,
nutritional properties and health benefits, such as
Jamun, Bel fruit, Aonla, Pomegranate, Custard Apple
etc.
 Increase in export potential of Indian processed foods
and to meet aspirations of growing middle class
highest quality and food safety issues need
modernization
 Emerging Post Harvest Technologies need to be
adopted for indigenous commodities involving proper
engineering principles……

3. SSttaattuuss ooff FFoooodd PPrroocceessssiinngg IInndduussttrriieess
Size of food market in India - Rs. 8,60,000 Crores
Primarily processed food market – Rs. 2,80,000
crore
Value added processed food market – Rs. 1,80,000
crore
Investment during the 10th plan was about Rs.
62,105 Crores
Industry growth rate during the last 10th plan was
about 7.14% against GDP of 6.2%
Investment required during next ten years – Rs.
1,10,000
crores
FFoooodd QQuuaalliittyy aanndd SSaaffeettyy IIssssuueess aarree PPrriimmee IImmppoorrttaanntt

4. DDrriivveerrss ooff RR&&DD iinn PPoosstt
HHaarrvveesstt SSeeccttoorr
o Preference for Natural, Fresh-like,
Minimally processed, Nutritious,
Preservative-free and safe Foods
o Environmental Sustainability
o Improved understanding of dietary
requirements
o Breeding of processable varieties

5. PPrreesseenntt MMeetthhooddss ooff EEnnhhaanncciinngg
SShheellff LLiiffee//PPrroocceessssiinngg
 Pasteurization (milk, juice, honey etc.)
 Sterilization (milk, juice, etc.)
 Canning
 Hurdle Technology
 Addition of chemicals as preservative
 Drying/Dehydration
 Irradiation
Most of these are thermal techniques, detrimental to
the preservation of natural flavour and texture

6. EEmmeerrggiinngg TTeecchhnnoollooggiieess ffoorr FFoooodd
PPrroocceessssiinngg
 Pulse electric field
 High pressure processing
 Ultrasound
 Cryogenic processing
 Supercritical fluids including water
 Ohmic heat processing
 Irradiation
 Nano-technology
 Extrusion processing
 Microwave and Infrared Processing
 Nutrigenomics

7. MMiiccrroowwaavvee HHeeaatt PPrroocceessssiinngg
•Effective for inactivating enzymes, reduced indirect heating
requirement and water use
•Result in improved product flavour, colour, texture and nutritive
value.

8. OOhhmmiicc HHeeaatt PPrroocceessssiinngg
•Alternating electrical current is passed through a food sample.
•Internal energy generation in foods.
•Produces an inside-out heating pattern at different
frequencies than MW.
•Uniformly heats foods with different densities.

9. MMiiccrroonniissaattiioonn
•Short time exposure of electromagnetic radiation at a wavelength
of 1.8-3.4 mm,
•Promotes internal heating and increased digestibility
•Instantized product due to increased ability to uptake of water.
•Starch is gelatinized, seed microstructure becomes more
penetrable and thus short cooking times.

10. EExxttrruussiioonn PPrroocceessssiinngg

11. EExxttrruuddeedd PPrroodduuccttss ffrroomm CCooaarrssee CCeerreeaallss
 EExxttrruussiioonn iiss aa hhiigghh
tteemmppeerraattuurree sshhoorrtt ttiimmee
pprroocceesssseedd ttoo pprroodduuccee
eexxppaannddeedd & tteexxttuurreedd
pprroodduuccttss
 CCIIPPHHEETT hhaass mmooddiiffiieedd tthhee llooww
ccoosstt ccoolllleett eexxttrruuddeerr ttoo
pprroodduuccee eexxppaannddeedd rreeaaddyy ttoo
eeaatt ssnnaacckkss uussiinngg rriiccee aanndd
ppuullssee bbrrookkeennss..
 TThhee ccoosstt ooff tthhee uunniitt iiss RRss 22
LLaakkhh,, ccaappaacciittyy 2255 kkgg//hh,, ccoosstt
ooff aacccceessssoorriieess RRss.. 22 llaakkhh aanndd
tthhuuss eeaarrnn aabboouutt mmiinniimmuumm ooff
RRss.. 5500,,000000 ppeerr mmoonntthh

12. BBiiootteecchhnnoollooggyy
Fermenter of 30 litre capacity with
controls for temperature, pH, DO and
CO2 monitoring installed at CIPHET,
Ludhiana

13. BBiioopprroocceessssiinngg TTeecchhnnoollooggiieess
ffoorr CCrroopp RReessiidduueess
 EEffffiicciieenntt pprroocceessss ffoorr pprroodduuccttiioonn ooff
eetthhaannooll
 PPrroodduuccttiioonn ooff eennzzyymmeess ssuucchh aass
cceelllluullaassee,, xxyyllaannaassee,, ppeeccttiinnaassee aanndd
pprrootteeaassee
 PPrroodduuccttiioonn ooff iinndduussttrriiaallllyy iimmppoorrttaanntt
pprroodduuccttss ssuucchh aass cciittrriicc aacciidd
 SSiinnggllee CCeellll PPrrootteeiinn ((SSCCPP)) pprroodduuccttiioonn

14. SSmmaarrtt aanndd BBiioo--ddeeggrraaddaabbllee PPaacckkaaggiinngg
ooff FFooooddss
 Controlled release mechanism technology using
volatiles of plant origin for shelf-life extension
packaging
 Food specific packaging having insect-repelling and
water absorbing properties
 Smart packaging interventions for retention of volatile
and flavour in packaged food
 Time-temperature indicators and colour code
schemes for shelf-life detection of packaged foods.
 Poly-lactic acid based bio-degradable clamshell
containers and films/coatings for packaging of food
materials.
 Bio-polymer films embedded with nano-composites
(biodegradable smart packaging)

15. NNoonn--tthheerrmmaall PPrroottooccoollss ffoorr FFoooodd
PPrroocceessssiinngg aanndd PPrreesseerrvvaattiioonn ((PPEEFF&&HHPPPP))
 Enhancement of Shelf Life of Milk and RTS
Beverages
 Enhancement of diffusivity of enzymes
(xylanase, protease; etc.) and effectiveness
of modified enzymes on de-hulling and
cooking quality of pulses
 Development of extraction processes for
bioactive mixtures/ compounds from by
products and process wastes

16. HHiigghh PPrreessssuurree PPrroocceessssiinngg
•HPP can replace conventional
processes, while maintaining
safety and quality.
•Effects of HPP are generally
marked as retention of color,
flavor and fresh appearance

17. NNaannoo--tteecchhnnoollooggiieess ffoorr CCoonnttrroolllleedd
RReelleeaassee aanndd FFuunnccttiioonnaall FFooooddss
 Micro-encapsulation and nano-encapsulation of
herbal extracts and bioactive compounds
 Nano-composites for food packaging with
improved barrier or antimicrobial properties
 Nano-emulsions/delivery systems for increased
absorption of nutraceuticals and health
supplements.
 Nano-sensors for traceability and monitoring
conditions of foods during transport and storage
(Hyper homogenisation, Molecular mixing,
Multi-emulsion technology)

18. SSuuppeerrccrriittiiccaall FFlluuiidd iinn FFoooodd
PPrroocceessssiinngg
 Designer foods using super critical carbon
dioxide having desirable texture, flavours and
micro nutrients through extrusion processing
 Short-time continuous bread making extrusion
process without yeast to bread proofing
 Super critical carbon dioxide and water as
solvent for extraction of
Bio-active compounds, essential oils
Amino acids from fish, meat etc.

19. IIrrrraaddiiaattiioonn TTeecchhnnoollooggiieess ffoorr
PPrroocceessssiinngg aanndd PPrreesseerrvvaattiioonn
Standardize the irradiation protocols for retention of
properties and shelf-life enhancement
Optimized irradiation protocol suitable for
 Sprouting inhibition,
 Delay in ripening and senescence,
 Insect disinfestation,
 Pasteurisation/sterilisation of processed and raw foods,
 Reduction in anti-nutritional factors and
 Improved keeping quality of food grains and flours

20. IIrrrraaddiiaattiioonn
•Gamma - irradiation reduces antinutritional factors
•Reduces the phytic acid content and flatulence causing
oligosaccharides in leguminous crops
•Helps improved keeping quality of food grains and flours

21. BBiioosseennssoorrss ffoorr RRaappiidd aanndd PPrreecciissee
QQuuaalliittyy AAsssseessssmmeenntt
Principle: A biosensor is a probe that integrates
a biological component, such as a whole
bacterium or a biological product (e.g., an
enzyme or antibody) with an electronic
component to yield a measurable signal.
 Characterization of bio-indicators in relation to
quality of food and its interaction with electro-mechanical
sensing system.
 Biosensor based online monitoring system

22. Use ooff AArrttiiffiicciiaall IInntteelllliiggeennccee iinn tthhee
CCoonntteexxtt ooff FFoooodd PPrroocceessssiinngg
• In artificial intelligence the tolerance for imprecision
and uncertainty is exploited to achieve tractability,
lower cost, high Machine Intelligence Quotient
(MIQ) and economy of communication
• Artificial intelligence makes use of multivalued or
fuzzy logic
• Artificial intelligence can deal with ambiguous and
noisy data
• Artificial intelligence can yield approximate
answers but good enough to solve the practical
problems of trade

23. AArrttiiffiicciiaall IInntteelllliiggeennccee
•Artificial intelligence can be used to model and analyse very
complex problems where conventional methods have not
been able to produce cost-effective, analytical, or complete
solutions.
•In agricultural and biological engineering, researchers and
engineers have developed methods to analyse the operation
of food processing.
•Fuzzy Logic (FL),
•Artificial Neural Networks (ANNs),
•Genetic Algorithms (Gas),
•Bayesian Inference (BI),
•Decision Tree (DT), and
•Support Vector Machines (SVMs)

24. CCoommpplleexx FFoooodd PPrroocceessss CCoonnttrrooll
· A single sensory property like color or texture can be linked individually to
several dimensions recorded by the human brain.
· The food industry works with non-uniform, variable raw materials that, when
processed, should shaped into a product that satisfies a fixed standard.
· The process control of foods are highly non-linear and variables are coupled.
· In addition to the temperature changes during a heating or cooling process,
there are biochemical (nutrient, color, flavor, etc.) or microbial changes that
should be considered.
· The moisture in food is constantly fluctuating either loss or gain throughout
the process which can affect the flavor, texture, nutrients concentration and
other properties.
· Other properties of foods such as density, thermal and electrical conductivity,
specific heat, viscosity, permeability, and effective moisture diffusivity are
often a function of composition, temperature, and moisture content, and
therefore keep changing during the process.
· The system is also quite non-homogeneous and hence detailed input data are
not available.
· Often, irregular shapes are present.

25. AANNNN ffoorr CCrriissppnneessss ooff SSnnaacckk FFooooddss
• The crispness was evaluated by
acoustic testing.
• The acoustic patterns were
generated by crushing the snack
samples with a pair of pincers
• The inputs for training the NNs
comprised 102 amplitudes of
sound signals in 0–7 kHz
frequency range at the intervals of
about 69 Hz with crispness grades
as outputs
• Probabilistic (PNN) models
showed good performance in
classifying the snack foods into
four grades of crispness.
• The prediction accuracy of models
ranged approximately from 96 to
98%
Plot of average amplitude of acoustical signal spectrum for
different moisture content of Pringles potato chip samples

26. DDeetteeccttiioonn ooff PPllaanntt DDiisseeaasseess
• EElleeccttrroonniicc nnoossee iinnccoorrppoorraattiinngg
AAII wwaass uusseedd ttoo ddeetteecctt ppllaanntt
ddiisseeaassee ccaauusseedd bbyy GGaannooddeerrmmaa
bboonniinneennssee ffuunngguuss aaffffeeccttiinngg ooiill
ppaallmm..
• TThhee eelleeccttrroonniicc nnoossee,,
CCyyrraannoossee 332200,, hhaass tthhee ffrroonntt
eenndd sseennssoorrss aanndd aarrttiiffiicciiaall
nneeuurraall nneettwwoorrkkss ffoorr ppaatttteerrnn
rreeccooggnniittiioonn..
• TThhee ssyysstteemm wwaass aabbllee ttoo
ddiiffffeerreennttiiaattee hheeaalltthhyy aanndd
iinnffeecctteedd ooiill ppaallmm wwiitthh aa hhiigghh
rraattee ooff aaccccuurraaccyy..

27. DDeetteeccttiioonn ooff SSppoonnggyy TTiissssuuee iinn MMaannggoo
• "spongy tissue", affects about
30% of ‘Alphonso’ mangoes
• Fruits show no external
symptoms at harvest or on
ripening but cutting reveals
internal damage which
adversely affects fruit quality.
Both fully grown green, unripe
mangoes and ripe fruits show
spongy tissue.
• A non-destructive x-ray
inspection can detect affected
mangoes.
• The method could be used for
quality control for on-line
Central Electronics Engineering
detection and separation
Research Institute (CEERI)

28. AANNNN iinn iimmaaggee rreeccooggnniittiioonn aanndd
ccllaassssiiffiiccaattiioonn ooff ccrroopp aanndd wweeeeddss
• The images were taken, Colour index values were assigned to
the pixels of the indexed image and used as ANN inputs.
• There were 80 images, 100x100 pixels, for training, and 20
images for testing. Many back propagation ANN models were
developed with different numbers of PEs in their hidden and
various output layers.
• Six different evaluation schemes for two ANN output strategies
were used.
• The performance of the ANNs was compared and the success
rate for the identification of corn was observed to be as high as
80 to 100%, while the success rate for weed classification was
as high as 60 to 80%.
• The results indicated the potential of ANNs for fast image
recognition and classification.
• Fast image recognition and classification can be useful in the
control of real-world, site-specific herbicide application.

29. IIddeennttiiffiiccaattiioonn ooff cciittrruuss ddiisseeaassee uussiinngg
ccoolloorr tteexxttuurree aanndd ddiissccrriimmiinnaanntt aannaallyyssiiss
• Color co-occurrence method (CCM) with texture based hue, saturation,
and intensity (HSI) color features in conjunction with statistical
classification algorithms were used to identify diseased and normal citrus
leaves under laboratory conditions.
• The leaf sample discriminant analysis using CCM textural features
achieved classification accuracies of over 95% for all classes.
• Although, high accuracies were achieved when using an unreduced
dataset consisting of all HSI texture features, the overall best performer
was determined to be a reduced data model that relied on hue and
saturation features.
• This model was selected due to reduced computational load and the
elimination of intensity features, which are not robust in the presence of
ambient light variation.

30. MMiilleessttoonneess ooff AAII iinn FFoooodd PPrroocceessssiinngg
2004 Brudzewski et al. Classification of milk by an electronic nose
2005 Pierna et al. Classification of modified starches
2006 Chen et al. Identification of tea varieties
2006 Onaran et al. Detection of underdeveloped hazenuts from fully
developed nuts
2006 Wang and
Paliwal
Discrimination of wheat classes
2007 Zhang et al. Differentiate individual fungal infected and healthy
wheat kernels.
2008 Fu et al. Quantification of vitamin C content in kiwifruit
2008 Kovacs et al. Prediction of different concentration classes of instant
coffee with electronic tongue
2008 Li et al. Classification of paddy seeds by harvest year
2008 Sun et al. On-line assessing internal quality of pears
2008 Wu et al. Identification of varieties of Chinese cabbage seeds
2009 Deng et al. Classification of intact and cracked eggs
2012 Jha et al. Method of determining maturity of intact mango in tree

31. RReesseeaarrcchhaabbllee IIssssuueess
• Online non destructive measurement of quality of food
grains, fruits and vegetables using NIR sensors
• Electronic nose to assess the quality and authenticity of
food products.
• Electronic tongue - for recognition (identification,
classification, discrimination), quantitative multi-component
analysis and artificial assessment of taste
and flavour of various liquids
• Affordable instrumentation for measurement of
spoilage of grain in bags and silos
• Smart labels of food packets to detect their shelf life
with automatically changing bar codes
• Simple gadgets like pH meter to detect pollutants in
drinking water
• Method to detect adulterants and harmful chemicals in
foods

32. TThhaannkk YYoouu!!!!!!
WWiitthh aann aappppeeaall ttoo ssuuppppoorrtt
ddeemmaanndd ffoorr mmaannddaattoorryy
eemmppllooyymmeenntt ffoooodd pprrooffeessssiioonnaall
iinn ffoooodd eessttaabblliisshhmmeenntt