Ferreira, A. C. Silva

Lopes, V. V.

Martins, R.

English

Repositório Institucional da Universidade Católica Portuguesa

R. Martins, V. V. Lopes, A. C. Silva Ferreira !acferreira@esb.ucp.pt  !Universidade Católica Portuguesa, Porto, PORTUGAL!WinePAT : Facing the Complexity	of Grape Quality Management and 	Delivering an High-throughput Device	http://www.winepat.net/!1. Spectroscopy – Definitions	2. Spectroscopy – Holistic Measurement of Metabolism	3. Complexity and Information Processing	4. Spectroscopy Case Study : Hyperspectral monitoring	The electromagnetic spectra is classified into types according to the frequency . . .	Cosmic	 Gamma	 X	UV	 IR	 Micro	 UHF	Short	Medium	Long	Radio	The visible portion of electromagnetic spectra visible is called light and can be detected by the human eye (380-750 nm).	Ultra violet	 Infrared	Near	 Mid	 Far	1	 400	 750	 2500	 16000	 1000000	 nm	Vis	vis-SWNIR	Intensity !(photon/sec)!wavelenght (nm)!Spectral Information :	Physical properties :	•   Light difraction	•   Particules, Probe distance	•   Scattering	wavelenght (nm)!Chemical properties : “frequency changes”	•    Absortion	•    Fluorescence 	•    Ressonance	Spectral Information :	C = 0!C = N!C = C!C , N , 0!O - H, N - H!C - H!- H!= != ! (C - C, C - O, C - N)!- != ! = !stretching vibration!bending vibration!"   The different features (peaks) in the spectra are result Soil	Plant	Jackson and Lombard,1993	"   Texture!"   Water Regime!"   Nutrients!"   Rooting depth!"   Temperature!"   Light!"   Genotype!"   Pest Control!"   Soil Management!"   Training System!"   Harvest Date!Climate	MACRO!"   Latitude!"   Altitude!"   Relief!MICRO !•   Aspect!•  Leaf & Cluster TºC!Central Zone!malic acid!sugar!Intermediate Zone!tartaric acid!sugar!Peripheral Zone!astringency!potassium!acidity!sugar!oxidases!Photonics : Molecular Information!(light)!Information!= I (0)!(light + Grape) !Information!= I (0g)!Complex Fingerprinting :	•   Convoluted Information	•   Advanced Computational Signal Processing, Feature and Pattern Recognition	Central Zone	malic acid	sugar	Intermediate Zone	tartaric acid	sugar	Peripheral Zone	astringency	potassium	acidity	sugar	oxidases	Metabolic Information : Holistic	“Target Metabolites”	Glucose! Fructose! Malic Acid! Tartaric Acid!Lutein!beta-carotene! Pheophytin A! Pheophytin B!luteína	β,β-caroteno	neocromo	neoxantina	violaxantina	flavoxantina	luteína 5,6-epóxido	zeaxantina	β-criptoxantina	 equinenona	Aroma Precursors : Carotenoids	Why Carotenoids in grapes, musts and wines	GUEDES DE PINHO, SILVA FERREIRA, M. MENDES PINTO, GOMEZ BENITEZ, J, e HOGG, T.; Determination of carotenoid profiles in grapes, musts and fortified wines from Douro varieties of Vitis vinifera. J. Agric. Food Chem, 2001, 49, 11, 5484-5488.	Why Carotenoids in grapes, musts and wines	Irrigation	days of maturation!ug / Kg !of Grapes!Sum of carotenoids!Effect of sunlight on the grape carotenoid 	. . . higher levels in grapes protected from direct sunlight exposure than in those exposed to direct sunlight  . . .	Sunshine!Shade!Effect of altitude on the grape carotenoid 	ug / Kg !of Grapes!Sum of carotenoids! ... Altitude strongly affect climatic conditions due to it direct impact on TºC, humidity, and other environment factors that affect grape maturity . . .	1	13	2	3	4	5	6	7	8	9	10	 12	11	14	15	17	 16	19	18	20	15’	14’	13’	12’	9’	10’	11’	 7’	8’	5’	6’	 2’	3’	4’	1’	20’	 19’	18’	16’	 17’	C40H56	Ionones	Damascones	C13	Megastigma skeleton	2,2,6-Trimethylcyclohexanone	       TCH	β-cyclocitral	C9	 C10	 C11	DHC	Norisoprenoids	non-Megastigma skeleton	CARLA OLIVEIRA, ANTONINA BARBOSA, A. C. SILVA FERREIRA, JOAQUIM GUERRA AND GUEDES de PINHO P. “Carotenoid Profile in High Resolution vis-SWNIR 	Imaging	“Non Target Metabolites”	What is Hyperspectral Imaging?	What is Hyperspectral Imaging?	"    Spacial Information for each time,	"    Time Course Resolution,	•   Each point of the field is a spectra,	•   Allows high discrimination,	•  Maturation Monitoring... 	‏Movie Making... ! Imaging : vis-SWNIR	What is Hyperspectral Imaging?	19- July	260 - A gust	23- August	30time!Space![ X ]!Global Matrix!X and Y Axis: GPS coordinates	Wavelength: 3650 variables from 200 to 1200nm	Time Course : Heuristic Sampling ...	[ X ]!Global!Matrix! =! +!Signal Processing : Relevant Decomposition	Signal	Relevant Information for Analysis	Relevant !Matrix!Information	[ X ]!Error !Matrix!Noise	[ X ]![ X ]! =! +!Signal Processing : Filtering Physical Information	Relevant !Matrix!Information	[ X ]!Chemical	[ X ]!Physical	"    Light diffraction	"    Particles	"    Probe distance	"    Scattering	Relevant Information for Grape Maturation	[ X ]!Chemical	Metabolic	Fingerprint	Supervised Signal 	Decomposition	Signal Processing	Blind Signal 	Decomposition	SVD	PLS	[ X ]!Chemical	 What Relevant Information Discriminates between the “metabolic fingerprints” ?	“Metabolic Fingerprinting”	19-July	26-July	09-August	30-August	23-August	[ X ]!Chemical	The Metabolic	Fingerprints	“Metabolic Fingerprinting”	Classification	Independent Information	Dependent Information	Independent	 Independent Information - Relevant Variance	Feature Space Coordinates	Relevant Variance Imaging	19-July	26-July	09-August	30-August	23-August	[ X ]!Chemical	 Correlation Spectra & Chemical Composition	The Metabolic	Fingerprints	Relevant Metabolites:	Glucose	Organic Acids	Lutein	beta - Carotenes	Clorophyll	Pheophytins	Antocyanins	“Combined fraction”	Correlated	Variance	Metabolic	Composition	[Y]! Correlation Spectra & Chemical Composition	Glucose! Fructose! Malic Acid! Tartaric Acid!Lutein! Pheophytin A! Pheophytin B!beta-carotene!PLS models!Correlated	Variance	x	[Y]!Rendering a Co-Variance Image	for Managing Grape Maturation !!!	Correlated	Feature Space	Coordinates	 Correlation Spectra & Chemical Composition	Co-variance	Coordinates	Glucose!vis-SWNIR : Metabolic Imaging	0 %	 100 %	19-July	26-July	09-August	30-August	23-August	vis-SWNIR : Metabolic Imaging	Malic acid!0 %	 100 %	19-July	26-July	09-August	30-August	23-August	0 %	 100 %	vis-SWNIR : Metabolic Imaging	beta-Carotene!19-July	26-July	09-August	30-August	23-August	Combined !Fraction Linalool!vis-SWNIR : Metabolic Imaging	0 %	 100 %	19-July	26-July	09-August	30-August	23-August	"    Non destructive Spectroscopy techniques coupled with signal processing are ideal tools for management of “grape quality” ;	"   Non targeted approach enables the “glass to vineyard” sensory perspective ;	3.  Hyperspectral monitoring allows optimization and consistence in final wine quality.	

WinePAT : Facing the Complexity of Grape Quality Management and Delivering an High-throughput Device

https://repositorio.ucp.pt/bitstream/10400.14/6020/1/com_nac_2009_ESB_100_Silva%20Ferreira_Ant%c3%b3nio%20C%c3%a9sar_03.pdf

WinePAT : Facing the Complexity of Grape Quality Management and Delivering an High-throughput Device

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Repositório Institucional da Universidade Católica Portuguesa