scholarly journals Evaluation of Cup Profile for Post-harvest in Coffee Variety Castillo from Cauca Department

2021 ◽  
Author(s):  
Andrés Felipe Solis Pino ◽  
Carlos Andres Anacona Bambague ◽  
Bibiana Montoya ◽  
Efren Venancio Ramos Cabrera
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Coffea Arabica ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Statistical Techniques ◽  
Key Factors ◽  
Fermentation Time ◽  
Post Harvest

The post-harvest processes of coffee are widely accepted as key factors in determining the quality of the product. In the Cauca department, Southwestern Colombia, this stage is carried out empirically by farmers in the region, using old methods that do not assure consistent quality. This study proposes to determine the best post-harvest temperature and time conditions for coffee produced in the region. For this purpose, we carried the fermentation and honey process out on different coffee samples of the Coffea Arabica species of the Castillo variety. Subsequently, the cup profile quality of the coffee samples was determined by a sensory evaluation by experts. Finally, we applied descriptive statistical techniques to the resulting data and principal component analysis and hierarchical cluster analysis to find similarities between the samples. The results suggest that the honey process gets better evaluations in the cup profile over any condition of temperature and fermentation time.

scholarly journals Evaluation of Cup Profile for Postharvest in Coffee Variety Castillo from Cauca Department

2021 ◽  
Author(s):  
Andrés Felipe Solis Pino ◽  
Carlos Andres Anacona Bambague ◽  
Bibiana Montoya ◽  
Efren Venancio Ramos Cabrera
Keyword(s):  
Cluster Analysis ◽  
Sensory Evaluation ◽  
Hierarchical Cluster Analysis ◽  
Coffea Arabica ◽  
Hierarchical Cluster ◽  
Statistical Techniques ◽  
Fermentation Condition ◽  
Key Factors ◽  
Post Harvest

It is widely accepted that the post-harvest processes of coffee are key factors in determining the final quality of the product. In the department of Cauca in Colombia, this stage is carried out empirically by the farmers of the region, using old methods that do not assure consistent quality. We propose a study to determine the best conditions of temperature and time in post-harvest for the coffee produced in the region. For this purpose, we carried the fermentation and honey process out on different samples of coffee of the Coffea Arabica species of the Castillo variety. Subsequently, the quality of the samples was determined through sensory evaluation by experts. Finally, descriptive statistical techniques applied to the resulting data, and component and hierarchical cluster analysis to find similarities between the samples. The results suggest that the honey process gets better evaluations in the cup profile over any fermentation condition.

Evaluation of Cup Profile for Postharvest in Coffee Variety Castillo from Cauca Department

2021 ◽  
Author(s):  
Andrés Felipe Solis Pino ◽  
Carlos Andres Anacona Bambague ◽  
Bibiana Montoya ◽  
Efren Venancio Ramos Cabrera
Keyword(s):  
Cluster Analysis ◽  
Sensory Evaluation ◽  
Hierarchical Cluster Analysis ◽  
Coffea Arabica ◽  
Hierarchical Cluster ◽  
Statistical Techniques ◽  
Fermentation Condition ◽  
Key Factors ◽  
Post Harvest

It is widely accepted that the post-harvest processes of coffee are key factors in determining the final quality of the product. In the department of Cauca in Colombia, this stage is carried out empirically by the farmers of the region, using old methods that do not assure consistent quality. We propose a study to determine the best conditions of temperature and time in post-harvest for the coffee produced in the region. For this purpose, we carried the fermentation and honey process out on different samples of coffee of the Coffea Arabica species of the Castillo variety. Subsequently, the quality of the samples was determined through sensory evaluation by experts. Finally, descriptive statistical techniques applied to the resulting data, and component and hierarchical cluster analysis to find similarities between the samples. The results suggest that the honey process gets better evaluations in the cup profile over any fermentation condition.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

scholarly journals Multivariate characteristics of selected grass varieties for seed production

2019 ◽  
Vol 55 (No. 2) ◽  
pp. 83-86
Author(s):  
Marzena Iwańska ◽  
Danuta Martyniak ◽  
Marcin Martyniak ◽  
Dariusz Gozdowski
Keyword(s):  
Principal Component Analysis ◽  
Growth Rate ◽  
Cluster Analysis ◽  
Seed Production ◽  
Plant Resistance ◽  
Hierarchical Cluster Analysis ◽  
Multivariate Analyses ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Festuca Rubra

Data were obtained in a field experiment carried out at Plant Breeding and Acclimatization Institute Radzikow (central Poland) in 2009–2011. The aim of this study was a multivariate evaluation of 13 advanced lines and cultivars of Festuca rubra, taking into account traits important in seed production. Eleven traits of the grasses and plant resistance to diseases were evaluated. On the basis of multivariate analyses, i.e. hierarchical cluster analysis and principal component analysis, groups of varieties were separated and described, relationships between the traits were evaluated as well. The traits with the biggest influence on multivariate diversity of examined varieties were correlated with the first principal component i.e. height of plants, seeds yield, growth rate of plants, leaf width and time to beginning of earing.  

scholarly journals Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

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