scholarly journals Metabolomics fingerprint of Philippine coffee by SPME-GC-MS for geographical and varietal classification

2020 ◽  
Vol 134 ◽  
pp. 109227
Author(s):  
Emelda A. Ongo ◽  
Giuseppe Montevecchi ◽  
Andrea Antonelli ◽  
Veronica Sberveglieri ◽  
Fortunato Sevilla III
Keyword(s):  
Varietal Classification

scholarly journals Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5010 ◽  
Author(s):  
Németh ◽  
Balazs ◽  
Daood ◽  
Kovacs ◽  
Bodor ◽  
...  
Keyword(s):  
Electronic Tongue ◽  
Storage Conditions ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sensory Profile ◽  
Linear Discriminant ◽  
Analytical Instruments ◽  
Varietal Classification ◽  
And Storage

Grafting by vegetables is a practice with many benefits, but also with some unknown influences on the chemical composition of the fruits. Our goal was to assess the effects of grafting and storage on the extracted juice of four orange-fleshed Cantaloupe type (Celestial, Donatello, Centro, Jannet) melons and two green-fleshed Galia types (Aikido, London), using sensory profile analysis and analytical instruments: An electronic tongue (E-tongue) and near-infrared spectroscopy (NIRS). Both instruments are known for rapid qualitative and quantitative food analysis. Linear discriminant analysis (LDA) was used to classify melons according to their varieties and storage conditions. Partial least square regression (PLSR) was used to predict sensory and standard analytical parameters. Celestial variety had the highest intensity for sensory attributes in Cantaloupe variety. Both green and orange-fleshed melons were discriminated and predicted in LDA with high accuracies (100%) using the E-tongue and NIRS. Galia and Cantaloupe inter-varietal classification with the E-tongue was 89.9% and 82.33%, respectively. NIRS inter-varietal classification was 100% with Celestial variety being the most discriminated as with the sensory results. Both instruments, classified different storage conditions of melons (grafted and self-rooted) with high accuracies. PLSR showed high accuracy for some standard analytical parameters, where significant differences were found comparing different varieties in ANOVA.

Simultaneous quantification of caffeine and chlorogenic acid in coffee green beans and varietal classification of the samples by HPLC-DAD coupled with chemometrics

2018 ◽  
Vol 25 (29) ◽  
pp. 28748-28759 ◽  
Author(s):  
Silvia De Luca ◽  
Eleonora Ciotoli ◽  
Alessandra Biancolillo ◽  
Remo Bucci ◽  
Andrea D. Magrì ◽  
...  
Keyword(s):  
Chlorogenic Acid ◽  
Green Beans ◽  
Simultaneous Quantification ◽  
Varietal Classification

Varietal Classification of Young Red Wines in Terms of Chemical and Colour Parameters

1996 ◽  
Vol 70 (2) ◽  
pp. 173-180 ◽  
Author(s):  
Luis Almela ◽  
Sebastián Javaloy ◽  
José A. Fernández-López ◽  
José M. López-Roca
Keyword(s):  
Red Wines ◽  
Varietal Classification ◽  
Colour Parameters

Application of image texture analysis for varietal classification of barley

2012 ◽  
Vol 26 (1) ◽  
pp. 81-90 ◽  
Author(s):  
P. Zapotoczny
Keyword(s):  
Discriminant Analysis ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Image Texture ◽  
Image Texture Analysis ◽  
Texture Parameters ◽  
Varietal Classification ◽  
Variable Reduction ◽  
Barley Kernel

Application of image texture analysis for varietal classification of barleyThis paper presents the results of a study into the use of the texture parameters of barley kernel images in varietal classification. A total of more than 270 textures have been calculated from the surface of single kernels and bulk grain. The measurements were performed in four channels from a 24 bit image. The results were processed statistically by variable reduction and general discriminant analysis. Classification accuracy was more than 99%.

scholarly journals Research on Germination Prediction and Varietal Classification for Rice Seed Quality

2019 ◽  
Vol 9 (1) ◽  
pp. 3956-3959
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Seed Quality ◽  
Germination Rate ◽  
Rice Seed ◽  
Image Processing Techniques ◽  
Varietal Classification ◽  
Processing Techniques

In agriculture most of the task done manually by experienced persons. They made decision on the basis of what they feel and see. The prediction result also not giving expected results. For getting the best yield the selection of quality seed is mandatory. But the manual analysis cannot assure the best quality seed. Rice Seed quality estimation can be done by considering the textural features of rice seed image. For this we are going to propose Digital Image processing Techniques to classify and grade the quality of the seed. There are number of digital image processing techniques proposed for classifying the variety of seed and predicting the germination rate of seed. In this paper we are going to summarize the hardware setup, varieties, features extracted, methods or algorithms used and result they obtained. In future we are going to propose a simple grading system for the rice seed quality system can be used by formers.

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