Untitled

Lerceteau, Estelle; Flipo, Sophie; Quiroz, James; Soria, Jorge; Pétiard, Vincent; Crouzilat, D.

doi:10.1023/a:1002993415875

Cited by 24 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Estimates of H ind revealed that the majority of the collected samples were heterozygous with few highly homozygous trees. In contrast, Lerceteau et al (1997) reported a high level of homozygous trees in two old plantations (80-100 years) in Ecuador. This indicated a low incidence of inbreed individuals in the current study and the presence of good cross-compatibilities among a greater number of founder individuals in the current study.…”

Section: Discussionmentioning

confidence: 84%

Genetic diversity and population structure of fine aroma cacao (Theobroma cacao L.) from north Peru revealed by single nucleotide polymorphism (SNP) markers

et al. 2022

View full text Add to dashboard Cite

Cacao (Theobroma cacao L.) is the basis of the lucrative confectionery industry with “fine or flavour” cocoa attracting higher prices due to desired sensory and quality profiles. The Amazonas Region (north Peru) has a designation of origin, Fine Aroma Cacao, based on sensory quality, productivity and morphological descriptors but its genetic structure and ancestry is underexplored. We genotyped 143 Fine Aroma Cacao trees from northern Peru (Bagua, Condorcanqui, Jaén, Mariscal Cáceres, and Utcubamba; mainly Amazonas Region), using 192 single nucleotide polymorphic markers. Identity, group, principal coordinate, phylogenetic and ancestry analyses were conducted. There were nine pairs of matched trees giving 134 unique samples. The only match within 1,838 reference cacao profiles was to a putative CCN 51 by a Condorcanqui sample. The “Peru Uniques” group was closest to Nacional and Amelonado-Nacional genetic clusters based on FST analysis. The provinces of Bagua and Utcubamba were genetically identical (Dest = 0.001; P = 0.285) but differed from Condorcanqui (Dest = 0.016–0.026; P = 0.001–0.006). Sixty-five (49%) and 39 (29%) of the Peru Uniques were mixed from three and four genetic clusters, respectively. There was a common and strong Nacional background with 104 individuals having at least 30% Nacional ancestry. The fine aroma of cacao from Northern Peru is likely due to the prevalent Nacional background with some contribution from Criollo. A core set of 53 trees was identified. These findings are used to support the continuance of the fine or flavour industry in Peru.

show abstract

Section: Discussionmentioning

confidence: 84%

Genetic diversity and population structure of fine aroma cacao (Theobroma cacao L.) from north Peru revealed by single nucleotide polymorphism (SNP) markers

et al. 2022

View full text Add to dashboard Cite

show abstract

“…22 EET 53 is a traditional Ecuadorian fine cocoa type called Nacional. 23 These traditional varieties or types were the only ones prior to utilization in the 1950s of germplasm from Upper Amazon expeditions seeking resistance to witches' broom, a devastating fungal disease. Scavina 6 is one of the key clones from which resistance was obtained.…”

Section: Methodsmentioning

confidence: 99%

“…Catongo is a similar type, but differs in bean color and certain genetically useful characters . EET 53 is a traditional Ecuadorian fine cocoa type called Nacional . These traditional varieties or types were the only ones prior to utilization in the 1950s of germplasm from Upper Amazon expeditions seeking resistance to witches’ broom, a devastating fungal disease.…”

Section: Methodsmentioning

confidence: 99%

Operationally Realistic Validation for Prediction of Cocoa Sensory Qualities by High-Throughput Mass Spectrometry

et al. 2010

View full text Add to dashboard Cite

The potential of analytical chemistry to predict sensory qualities of food materials is a major current theme. Standard practice is cross-validation (CV), where a set of chemical and associated sensory data is partitioned so chemometric models can be developed on training subsets, and validated on held-out subsets. CV demonstrates prediction, but is an unlikely scenario for industrial operations, where concomitant data acquisition for model development and test materials would be unwieldy. We evaluated cocoa materials of diverse provenance, and analyzed on different dates to those used in model development. Liquor extracts were analyzed by flow-injection electrospray-mass spectrometry (FIE-MS), a novel method for sensory quality prediction. FIE-MS enabled prediction of sensory qualities described by trained human panelists. Optimal models came from the Weka data-mining algorithm SimpleLinearRegression, which learns a model for the attribute giving minimal training error, which was (-)-epicatechin. This flavonoid likewise dominated partial least-squares (PLS)-regression models. Refinements of PLS (orthogonal-PLS or orthogonal signal correction) gave poorer generalization to different test sets, as did support vector machines, whose hyperparameters could not be optimized in training to avoid overfitting. In conclusion, if chemometric overfitting is avoided, chemical analysis can predict sensory qualities of food materials under operationally realistic conditions.

show abstract

“…For eight years have recorded aspects related to the productivity and good phytosanitary behavior of cocoa from the germplasm bank of the Tenguel Aroma Cocoa Center (CCAT). The germplasm bank (CCAT) is considered one of the most important in the country according (Lerceteau et al, 1997) and with great genetic richness.…”

Section: Introductionmentioning

confidence: 99%

Rescue of Cocoa National Variety: Genetic Potential of Selection

2022

pnr

View full text Add to dashboard Cite

The research focused on fine aroma cocoa is a priority for Ecuador. The objective of this work was to identify stable, high-yielding accessions. The genetic base was based on 2222 cocoa accessions from the germplasm bank of the Tenguel Aroma Cocoa Center -Ecuador. An index was applied for selecting 150 accessions based on yield and health status. A descriptive analysis, principal component analysis by the factor extraction method between the meteorological parameters and the variables evaluated, was used. A multiple regression and stability analysis was also performed using the method of Eberhart & Russell (1966) modified by considering the years as the different environments and multivariate principal component analysis. Variables related to high CV % yield ranged from 93 to 112 %. The highest yields were observed in 2003, 2004 and 2005. The flowering intensity was influenced by rainfall, temperature and relative humidity and thus by the yield of the accessions. The predictor variables for yield were healthy pods, diseased pods and flowering intensity with a correlation of 0.95, 0.33 and 0.07, respectively, and an R2 = 0.899. The following were selected for their stability and average yield per tree 2.

show abstract

Untitled

Cited by 24 publications

References 9 publications

Genetic diversity and population structure of fine aroma cacao (Theobroma cacao L.) from north Peru revealed by single nucleotide polymorphism (SNP) markers

Genetic diversity and population structure of fine aroma cacao (Theobroma cacao L.) from north Peru revealed by single nucleotide polymorphism (SNP) markers

Operationally Realistic Validation for Prediction of Cocoa Sensory Qualities by High-Throughput Mass Spectrometry

Rescue of Cocoa National Variety: Genetic Potential of Selection

Contact Info

Product

Resources

About