Outline General concepts Instruments Applications reflectance, - - PowerPoint PPT Presentation

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Outline General concepts Instruments Applications reflectance, - - PowerPoint PPT Presentation

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Outline General concepts Instruments Applications reflectance, absorption, fluorescence Non-conventional instruments for absorption spectroscopy Spectroscopy by mobile devices Raman spectroscopy The kitchen of the

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Outline

 General concepts  Instruments  Applications

 reflectance, absorption, fluorescence

 Non-conventional instruments for absorption

spectroscopy

 Spectroscopy by mobile devices  Raman spectroscopy  The kitchen of the future

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Smartphone: the Swiss-knife of XXI century

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Smartphone – Startrek tricorder

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China and mobile phones

http://www.chinadaily.com.cn/china/2013

  • 01/25/content_16172589.htm
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Trends

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 Scenarios for smartphone-based sensors

 Passive: info retrieval only  Plug-in sensors  Embedded spectroscopy

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Passive: info retrieval only

Smartphone camera used to read QR or bar-code

QR/bar-code pic sent through internet to a data warehouse where the info is stored

Info retrieval using internet connection

This approach implies that the info requested by the consumer has been acquired and is available

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Embedded spectroscopy

+ clip-on coupling

and diffractive optics

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 White LED = source  Camera = spectrometer

 3 channels only: RGB  Added chemometric functionalities for a better exploitation of

spectroscopic info

400 450 500 550 600 650 700 750 0.002 0.004 0.006 0.008 0.01 0.012 0.014 Wavelength ( nm ) Normalized Units OPTIMO Samsung

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Embedded spectroscopy

  • utside source

Smith et alii, PLOS ONE, vol. 6, 2011, e17150

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Embedded spectroscopy

Shazam for materials........ & food Advanced prototype - 3D printed

http://store.publiclab.org/products/smartphone-spectrometer

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Embedded spectroscopy

by means of a special cover

https://fringoe.com/

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Lab in a phone

http://innovate.ee.ucla.edu/welcome.html http://www.iplaustralia.com/

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https://phonebloks.com/plan/ - http://www.dscout.com

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Smartphone sensors

 External unit with sensors

 Plug-in through socket  Blue-tooth connection for stand-alone units

http://www.mydario.com/#Device

  • J. Li et alii, IEEE Sensors Conf. 2012

http://www.sensorcon.com/sensordrone/

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Mobile spectroscopy + cloud computing

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TellSpec

http://www.tellspec.com

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SCiO

http://www.consumerphysics.com

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SCiO

http://www.consumerphysics.com

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SCiO

http://www.consumerphysics.com

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Outline

 General concepts  Instruments  Applications

 reflectance, absorption, fluorescence

 Non-conventional instruments for absorption

spectroscopy

 Spectroscopy by mobile devices  Raman spectroscopy  The kitchen of the future

20

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………… Steps towards multicomponent analysis

R2

Spectroscopy

Chemometrics

Classification maps

Library of ref. spectra / analytical data

Model for prediction of quality indicators

Validation

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Raman spectroscopy

I Raman 

Raman shift (cm-1) anti-Stokes Stokes

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  • Most of the scattered light has the same

frequency/energy as that of the incident light (scattering Rayleigh)

  • A slight fraction of the incident light

donates or receives energy to contribute to a change in the vibrational and rotational state of molecules.

  • The change in the photon energy as a

result of inelastic scattering of light with molecules is the “Raman shift”

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500 1000 1500 2000 2500 3000 1000 2000 3000 4000 5000 6000 7000 8000 Raman Shift ( cm-1 ) Power ( counts / s ) M1 M2 M3 M4 M5 M7 PL M6 M8 500 1000 1500 2000 2500 3000 200 400 600 800 1000 Raman Shift ( cm-1 ) Power ( counts / s )

Maple Syrup Honey Lizzano Honey Mielizia Crystal Honey

@ 785nm @ 1064nm

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Raman @785nm VS @1064nm

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salmon

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Raman – food fingerprints

R.M. El-Abassy et alii, JAOCS, vol. 86, 2009, pp. 507-511 C.M. McGoverin et alii, Anal. Chim. Acta, vol. 673, 2010, pp. 26-32 N.K. Afseth et alii, Anal. Chim. Acta, vol. 572, 2006, pp. 85-92

  • B. Schrader, J. Mol. Str., vol. 480-481, 1999, pp. 21-32

powder milk whole and skim

  • live oil

different brands fresh herbs

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Raman spectroscopy @ 1064 nm

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Raman spectroscopy @ 1064 nm

RamSpec-1064nm-HR BaySpec Inc., San Josè CA www.bayspec.com

Laser power: 400 mW Detector cooling: - 55°C

www.bayspec.com www.rigakuraman.com www.wysri.com www.metrohm.com

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Raman spectroscopy for honey applications: the collection of honeys from Calabria

 Distinguishing the botanic origin  Predictive models for sugar profile  Potassium as important nutraceutic

indicator

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Raman spectra

700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 0.02 0.04 0.06 0.08 0.1 0.12 Wavenumber ( cm-1 ) Normalized Units

Citrus Chestnut Acacia

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Raman band (cm‐1) Main contribution Secondary contribution 707 Fructose 821 Fructose 867 Fructose Glucose 917 Glucose Maltose 1060‐1080 Fructose Glucose 1127 Glucose Maltose 1267 Fructose Glucose 1372 Glucose Maltose 1460 Fructose Glucose

800 1000 1200 1400 1600 0.02 0.04 0.06 0.08 0.1 0.12 Wavenumber ( cm-1 ) Normalized Units

Citrus Chestnut Acacia

800 1000 1200 1400 1600 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 Wavenumber ( cm-1 ) Output ( conts / ms ) Concentration = 20% w/w

Sucrose Fructose Glucose Maltose

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Distinguishing the botanic origin

800 1000 1200 1400 1600 0.02 0.04 0.06 0.08 0.1 0.12 Wavenumber ( cm-1 ) Normalized Units

Citrus Chestnut Acacia

PCA + LDA + KNN

  • 2
  • 1

1 2

  • 2.5
  • 2
  • 1.5
  • 1
  • 0.5

0.5 1 1.5 DF 1 DF 2

h23 h28 h32 ta tc h14 h30 h31 h27 h19 h25 h26 h21

Citrus Chestnut Acacia

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Sugar profile

glucose fructose 250 300 350 400 450 Sugar content ( mg / g ) DS-Maltose DS-TIKN Total TS 10 20 30 40 50 60 Sugar content ( mg / g )

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Results of PLS predictive models for sugars & potassium

Analyte

RMSEC R2 (cal) RMSECV R2 (val)

SUGARS

Monosaccharides (mg/g) Glucose 7,3 0,96 11 0,92 Fructose 5,5 0,89 7,6 0,82 Disaccharides (mg/g) Maltose 3,5 0,83 5,3 0,66 Trehalose+Isomaltose +Kojibiose+Nigerose 2,3 0,91 3,6 0,83 Trisaccharides (mg/g) Erlose+Isomaltotriose +Panose 2,6 0,89 3,9 0,80

POTASSIUM (g/g)

0,3 0,97 0,5 0,94

R2 = 1

A.G. Mignani et alii, IEEE-JLT, 2016

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Raman fingerprints of blueberry juices

Brix and Carbohydrates

Parameter and model results Carbohydrates BRIX degrees RMSEC 0,80 g/hg 0,97% RMSCV 0,97 g/hg 1,1% R2 (cal) 0,887 0,9 R2 (val) 0,840 0,88

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Mycotoxins in wheat flour

DON – Raman spectra and predictive model

Parameter Calibration Cross‐validation (LOO) RMSE (ppb) 313 357 R squared 0,72 0,65

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4 levels of contamination: 1) < 20 ppb 2) 100‐500 ppb 3) 500‐1000 ppb 4) > 1000 ppb

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500 1000 1500 2000 0.1 0.2 0.3 0.4 0.5 Wavenumber ( cm-1 ) Normalized Units

DON < 500 DON >= 500

DON < 400 g/Kg DON > 400 g/Kg

  • 1
  • 0.5

0.5 1 1.5

  • 0.6
  • 0.4
  • 0.2

0.2 0.4 PC 1 PC 2 KNN decision border ( K = 3 )

14 14 14 14 14 14 14 14 20 20 20 20 20 20 20 20 21 21 21 21 21 21 21 21 28 28 28 28 28 28 28 28 29 29 29 29 29 29 29 29 30 30 30 30 30 30 30 30 33 33 33 33 33 33 33 33 86 86 86 86 86 86 86 86 89 89 89 89 89 89 89 89 90 90 90 90 90 90 90 90 91 91 91 91 91 91 91 91 98 98 98 98 98 98 98 98

DON < 500 DON >= 500

DON < 500 ppb DON >= 500 ppb

  • 0.5

0.5 1

  • 0.4
  • 0.2

0.2

  • 0.2

0.2

89 89 89 89

PC 1

98 89 98 98 98 98 98 98 91 89 98 89 14 91 86 91 14 89 86 86 86 14 28 86 14 14 90 91 28 28 14 28 28 90 28 14 86 28 28 29 91 90 14 90 90 30 30 30 30 29 29 90 91 29 29 20 30 30 20 29 91 29 30 20 20 86 90 20 21 91 20 21 21 20 20 21 86 21 21 21 30 21 90 33 29 33 33 33 33 33 33 33

PC 2 PC 3

DON < 500 DON >= 500

DON < 500 ppb DON >= 500 ppb DON < 400 g/Kg DON > 400 g/Kg

DON < 400 g/Kg DON > 400 g/Kg

Mycotoxins in wheat flour

DON – Raman spectra and predictive model

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Outline

 General concepts  Instruments  Applications

 reflectance, absorption, fluorescence

 Non-conventional instruments for absorption

spectroscopy

 Spectroscopy by mobile devices  Raman spectroscopy  The kitchen of the future

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Home farming

http://www.nextnature.net/2009/10/food- design-in-the-21th-century/

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Home farming @ CES 2019

https://www.alamy.com/indoor-soil-free-gardens-with-herb-plants-and-vegetables-producing-food-on- display-at-the-consumer-electronics-show-ces-in-las-vegas-nv-usa-image221407345.html

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Digital gastronomy

http://www.nextnature.net/2010/01/digital- gastronomy/ http://www.nextnature.net/2010/05/nano- product-the-food-printer/

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Digital gastronomy

https://www.naturalmachines.com/foodini/

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Curiosity and gadgets

http://situscale.com/ https://nimasensor.com/

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The Internet of Things - IoT

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The kitchen of the future

http://www.digitaltrends.com/home/heck-internet-things-dont-yet/

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The fridge of the future – a family hub

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http://koreabizwire.com/from-ai-to-iot-home-appliances-get-tech-treatment/99404

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The fridge of the future – a community hub

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https://pulsenews.co.kr/view.php?year= 2017&no= 565012

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What’s next in food

?? Edible electronics ?? ?? Edible photonics ??

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https://www.youtube.com/watch?v= oaaHLu77pQc