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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
06/06/2019 |
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Actualizado : |
14/08/2019 |
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Tipo de producción científica : |
Poster |
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Autor : |
PEZARD, J.; FERNANDEZ, P.; PEREYRA, S.; QUINCKE, M.; SAINT-PIERRE, C.; SINGH, P.K.; AZZIMONTI, G. |
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Afiliación : |
AgroParisTech, Paris, France.; PETER DENNIS FERNANDEZ GRAF, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA ANTONIA PEREYRA CORREA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA SAINT-PIERRE, International Maize and Wheat Improvement Center (CIMMYT), El Batán, México.; PAWAN K. SINGH, International Maize and Wheat Improvement Center (CIMMYT), El Batán, México.; GUSTAVO AZZIMONTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Adapting automated image analysis to breeding programs constraints for the characterization of the resistance to leaf rust and other diseases. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
In: Proceedings of the International Cereal Rusts and Powdery Mildew Conference (ICRPMC): Skukuza, South Africa, 23-26 september 2018. |
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Idioma : |
Inglés |
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Contenido : |
Description:
Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and positively with the VS measured for the three diseases. Host responses estimated by AIA were the same as determined visually, (error<5%). AIA was fast, a mean of 214 leaves/hour analyzed, taking into account the adjustments of color thresholds and the validation of AIA. However, the time to prepare and scan the leaves was higher than the VS: a mean of 205 lines could be scanned per person/day while a mean of 402 lines per person/day could be visually scored. Adjustments to the scan methodology are being carried out to enhance the speed at this step. Nevertheless, AIA can be a performing alternative to VS in limited panels or mapping populations that undergo QTL analysis, where precise measurements of quantitative resistance variables are required to detect QTL with moderate effects and QTL interactions. MenosDescription:
Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and posit... Presentar Todo |
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Palabras claves : |
INIA-CIMMYT; PLATAFORMA FENOTIPADO DE TRIGO; RUST DISEASE; WHEAT. |
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Thesagro : |
ENFERMEDADES DE LAS PLANTAS; Trigo. |
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Asunto categoría : |
H20 Enfermedades de las plantas |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13100/1/PosterPezardetalICRPMC2018.pdf
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Marc : |
LEADER 03183nam a2200253 a 4500
001 1059824
005 2019-08-14
008 2018 bl uuuu u00u1 u #d
100 1 $aPEZARD, J.
245 $aAdapting automated image analysis to breeding programs constraints for the characterization of the resistance to leaf rust and other diseases.$h[electronic resource]
260 $aIn: Proceedings of the International Cereal Rusts and Powdery Mildew Conference (ICRPMC): Skukuza, South Africa, 23-26 september 2018.$c2018
520 $aDescription: Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and positively with the VS measured for the three diseases. Host responses estimated by AIA were the same as determined visually, (error<5%). AIA was fast, a mean of 214 leaves/hour analyzed, taking into account the adjustments of color thresholds and the validation of AIA. However, the time to prepare and scan the leaves was higher than the VS: a mean of 205 lines could be scanned per person/day while a mean of 402 lines per person/day could be visually scored. Adjustments to the scan methodology are being carried out to enhance the speed at this step. Nevertheless, AIA can be a performing alternative to VS in limited panels or mapping populations that undergo QTL analysis, where precise measurements of quantitative resistance variables are required to detect QTL with moderate effects and QTL interactions.
650 $aENFERMEDADES DE LAS PLANTAS
650 $aTrigo
653 $aINIA-CIMMYT
653 $aPLATAFORMA FENOTIPADO DE TRIGO
653 $aRUST DISEASE
653 $aWHEAT
700 1 $aFERNANDEZ, P.
700 1 $aPEREYRA, S.
700 1 $aQUINCKE, M.
700 1 $aSAINT-PIERRE, C.
700 1 $aSINGH, P.K.
700 1 $aAZZIMONTI, G.
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Registro original : |
INIA La Estanzuela (LE) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
07/02/2023 |
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Actualizado : |
07/02/2023 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
FARIÑA, L.; BOIDO, E.; ARES, G.; GONZALEZ, N.; LADO, J.; CURBELO, R.; ALMEIDA, L.; MEDINA, K.; CARRAU, F.; DELLACASSA, E, |
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Afiliación : |
LAURA FARIÑA, Área de Enología y Biotecnología de Fermentaciones, Departamento de Ciencia y Tecnología de los Alimentos, Facultad de Química, Universidad de la República, Av. General Flores 2124, 11800 Montevideo, Uruguay; EDUARDO BOIDO, a Área de Enología y Biotecnología de Fermentaciones, Departamento de Ciencia y Tecnología de Los Alimentos, Facultad de Química, Universidad de la República, Av. General Flores 2124, Montevideo, 11800, Uruguay; GASTÓN ARES, Sensometría y Ciencia Del Consumidor, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, By Pass de Rutas 8 y 101 s/n, Canelones, Pando, 91000, Uruguay; NOELA GONZALEZ, Área de Enología y Biotecnología de Fermentaciones, Departamento de Ciencia y Tecnología de Los Alimentos, Facultad de Química, Universidad de la República, Av. General Flores 2124, Montevideo, 11800, Uruguay; JOANNA LADO LINDNER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROMINA CURBELO, Laboratorio de Biotecnología de Aromas, Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Av. General Flores 2124, Montevideo, 11800, Uruguay; LUCÍA ALMEIDA, Área de Enología y Biotecnología de Fermentaciones, Departamento de Ciencia y Tecnología de Los Alimentos, Facultad de Química, Universidad de la República, Av. General Flores 2124, Montevideo, 11800, Uruguay; KARINA MEDINA, Área de Enología y Biotecnología de Fermentaciones, Departamento de Ciencia y Tecnología de Los Alimentos, Facultad de Química, Universidad de la República, Av. General Flores 2124, Montevideo, 11800, Uruguay; FRANCISCO CARRAU, Área de Enología y Biotecnología de Fermentaciones, Departamento de Ciencia y Tecnología de Los Alimentos, Facultad de Química, Universidad de la República, Av. General Flores 2124, Montevideo, 11800, Uruguay; EDUARDO DELLACASSA, Laboratorio de Biotecnología de Aromas, Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Av. General Flores 2124, 11800 Montevideo, Uruguay. |
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Título : |
Solid phase microextraction for the characterization of food aroma and particular sensory defects. (Chap.6) |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
In: ACS Symposium Series, 2023, Volume 1433, Pages 299 - 325. Flavors and Fragrances in Food Processing: Preparation and Characterization Methods. Balakrishnan P., Gopi S. (editors). doi: https://doi.org/10.1021/bk-2022-1433.ch006 |
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Serie : |
(ACS Symposium Series; Volume 1433). |
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ISSN : |
0097-6156 |
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DOI : |
10.1021/bk-2022-1433.ch006 |
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Idioma : |
Inglés |
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Notas : |
Chapter book history: Publication Date (Web):December 28, 2022 -- Corresponding author: Dellacassa, E.; Laboratorio de Biotecnología de Aromas, Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Av. General Flores 2124, Montevideo, Uruguay; email:edellac@fq.edu.uy -- Publisher:
American Chemical Society -- Volume editors: Balakrishnan P., Gopi S., ADSO Naturals India, Bangalore, Balakrishnan P., Gopi S., Curesupport Netherlands, Deventer. -- |
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Contenido : |
ABSTRACT.- Solid Phase Microextraction or SPME was created to facilitate faster sample preparation, both in the laboratory and wherever the sampling site is located. Solid phase microextraction (SPME) was developed by Pawliszyn's group in 1990 as a solvent-free technique on the basis of adsorption-absorption theory. SPME is based on the principle that analytes are distributed between the sample matrix and the fiber coating. The fiber is built of fused silica and covered with a sorbent (polymeric materials identical to those used as stationary phase in gas chromatography columns). The transport of the analytes from the sample matrix to the fiber begins when the fiber comes into contact with the sample. The analytes are then desorbed by temperature or with an organic solvent. The extraction is complete and satisfactory when the analyte has reached an equilibrium concentration of distribution between the sample and the fiber. Even being experimentally a non-exhaustive extractive technique (it is an equilibrium), SPME has been rapidly adopted as a simple, miniaturized, and green technique, which combines sampling, extraction, concentration, cleanup and sample introduction in a single step. These characteristics transformed SPME in one of the most used techniques for different applications related to analytical chemistry. In this chapter, we will present different number of examples by which SPME focuses in the characterization of both food aroma and frequent odor defects.. © 2023 American Chemical Society. All rights reserved. MenosABSTRACT.- Solid Phase Microextraction or SPME was created to facilitate faster sample preparation, both in the laboratory and wherever the sampling site is located. Solid phase microextraction (SPME) was developed by Pawliszyn's group in 1990 as a solvent-free technique on the basis of adsorption-absorption theory. SPME is based on the principle that analytes are distributed between the sample matrix and the fiber coating. The fiber is built of fused silica and covered with a sorbent (polymeric materials identical to those used as stationary phase in gas chromatography columns). The transport of the analytes from the sample matrix to the fiber begins when the fiber comes into contact with the sample. The analytes are then desorbed by temperature or with an organic solvent. The extraction is complete and satisfactory when the analyte has reached an equilibrium concentration of distribution between the sample and the fiber. Even being experimentally a non-exhaustive extractive technique (it is an equilibrium), SPME has been rapidly adopted as a simple, miniaturized, and green technique, which combines sampling, extraction, concentration, cleanup and sample introduction in a single step. These characteristics transformed SPME in one of the most used techniques for different applications related to analytical chemistry. In this chapter, we will present different number of examples by which SPME focuses in the characterization of both food aroma and frequent odor defects.. © 202... Presentar Todo |
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Palabras claves : |
Beverages; Extraction; Fibers; Food processing; Organic compounds; Volatile organic compounds. |
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Asunto categoría : |
Q01 Ciencia y tecnología de los alimentos |
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Marc : |
LEADER 03189naa a2200349 a 4500
001 1063955
005 2023-02-07
008 2023 bl uuuu u00u1 u #d
022 $a0097-6156
024 7 $a10.1021/bk-2022-1433.ch006$2DOI
100 1 $aFARIÑA, L.
245 $aSolid phase microextraction for the characterization of food aroma and particular sensory defects. (Chap.6)$h[electronic resource]
260 $c2023
490 $a(ACS Symposium Series; Volume 1433).
500 $aChapter book history: Publication Date (Web):December 28, 2022 -- Corresponding author: Dellacassa, E.; Laboratorio de Biotecnología de Aromas, Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Av. General Flores 2124, Montevideo, Uruguay; email:edellac@fq.edu.uy -- Publisher: American Chemical Society -- Volume editors: Balakrishnan P., Gopi S., ADSO Naturals India, Bangalore, Balakrishnan P., Gopi S., Curesupport Netherlands, Deventer. --
520 $aABSTRACT.- Solid Phase Microextraction or SPME was created to facilitate faster sample preparation, both in the laboratory and wherever the sampling site is located. Solid phase microextraction (SPME) was developed by Pawliszyn's group in 1990 as a solvent-free technique on the basis of adsorption-absorption theory. SPME is based on the principle that analytes are distributed between the sample matrix and the fiber coating. The fiber is built of fused silica and covered with a sorbent (polymeric materials identical to those used as stationary phase in gas chromatography columns). The transport of the analytes from the sample matrix to the fiber begins when the fiber comes into contact with the sample. The analytes are then desorbed by temperature or with an organic solvent. The extraction is complete and satisfactory when the analyte has reached an equilibrium concentration of distribution between the sample and the fiber. Even being experimentally a non-exhaustive extractive technique (it is an equilibrium), SPME has been rapidly adopted as a simple, miniaturized, and green technique, which combines sampling, extraction, concentration, cleanup and sample introduction in a single step. These characteristics transformed SPME in one of the most used techniques for different applications related to analytical chemistry. In this chapter, we will present different number of examples by which SPME focuses in the characterization of both food aroma and frequent odor defects.. © 2023 American Chemical Society. All rights reserved.
653 $aBeverages
653 $aExtraction
653 $aFibers
653 $aFood processing
653 $aOrganic compounds
653 $aVolatile organic compounds
700 1 $aBOIDO, E.
700 1 $aARES, G.
700 1 $aGONZALEZ, N.
700 1 $aLADO, J.
700 1 $aCURBELO, R.
700 1 $aALMEIDA, L.
700 1 $aMEDINA, K.
700 1 $aCARRAU, F.
700 1 $aDELLACASSA, E,
773 $tIn: ACS Symposium Series, 2023, Volume 1433, Pages 299 - 325. Flavors and Fragrances in Food Processing: Preparation and Characterization Methods. Balakrishnan P., Gopi S. (editors). doi: https://doi.org/10.1021/bk-2022-1433.ch006
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