Quality assessment and classification of Persicae Semen based on HPLC-UV fingerprint

2014 ◽  
Keyword(s):  
Quality Assessment ◽  
Persicae Semen

A Study on Technology-LED Solutions for Fruit Grading to Address Post-Harvest Handling Issues of Horticultural Crops

2020 ◽  
pp. 203-221
Author(s):  
Arun Kumar R. ◽  
Vijay S. Rajpurohit ◽  
Sandeep Kautish
Keyword(s):  
Quality Assessment ◽  
Value Addition ◽  
Research Gaps ◽  
Major Phase ◽  
Comprehensive Review ◽  
Post Harvest ◽  
Horticultural Crops ◽  
Fruit Grading ◽  
Labor Requirements

The reduction of post-harvest losses and value addition of the horticultural corps has attained the higher priority of the current research works. Grading is the major phase in post-harvest handling. Presently grading is done on the basis of observation and through experience. Various drawbacks associated with such manual grading are subjectivity, tediousness, labor requirements, availability, inconsistency, etc. Such problems can be alleviated by incorporating automation in the process. Researchers round the clock are working towards the development of technology-driven solutions in order to grade/sort/classify various agricultural and horticultural produce. With the motto of helping the researchers in the field of grading and quality assessment of fruits and other horticulture products, the present work endeavors the following major contributions: (1) a precise and comprehensive review on technology-driven solutions for grading/sorting/classification of fruits, (2) major research gaps addressed by the researchers, and (3) research gaps to be addressed.

scholarly journals Using benthic diatoms for estimating lake ecological quality: Comparing different taxonomic resolution

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Aldo Marchetto ◽  
Tommaso Sforzi
Keyword(s):  
Quality Assessment ◽  
Taxonomic Resolution ◽  
Nutrient Level ◽  
Ecological Quality ◽  
The European Union ◽  
Biological Communities ◽  
Weighted Averages ◽  
Specific Index

The Water Framework Directive asks to all Member States of the European Union to classify the ecological quality of significant waterbodies on the basis of the biological communities they host. One of the biological communities that must be used for the ecological quality assessment is the periphytic community, mainly composed by diatoms. In Italy, diatom-based lake quality assessment is performed using a specific index, named EPI-L, based on the method of weighted averages. For each species, a trophic score and an indicator weight were calculated.  In order to reduce the complexity of the lake quality assessment, we calibrated a variant of EPI-L, using diatoms genera instead of species, and we compared the performance of these two variants in terms of correlation with the nutrient level and of different classification of each lake.

scholarly journals Quality assessment and variety classification of seed‐used pumpkin by‐products: Potential values to deep processing

2019 ◽  
Vol 7 (12) ◽  
pp. 4095-4104 ◽  
Author(s):  
Qinqin Chen ◽  
Ying Lyu ◽  
Jinfeng Bi ◽  
Xinye Wu ◽  
Xin Jin ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Deep Processing ◽  
Variety Classification ◽  
By Products

Image Distortion Classification towards Quality Assessment Based on Tri-Training and Natural Scene Statistics

2014 ◽  
Vol 602-605 ◽  
pp. 3526-3531
Author(s):  
Han Jie Xu
Keyword(s):  
Quality Assessment ◽  
Supervised Learning ◽  
Image Distortion ◽  
Natural Scene ◽  
Natural Scene Statistics ◽  
Classification Approach ◽  
Nature Scene

An image distortion classification approach towards quality assessment is presented in this paper based on tri-training and natural scene statistics. At first, the semi-supervised learning of tri-training is employed to carry out the classification of different distortion images by the combination of labeled images with unlabeled images. Then the method of nature scene statistics is used to extract features of distortion images so as to lay a well foundation for effective classification. Through the synthetical integration of tri-training and nature scene statistics, a well effect of classification can be achieved. A series of experiment results show the performance advantages of the presented algorithm.

scholarly journals AUTOMATIC WATER OBJECTS CLASSIFICATION AND NATURAL WATER QUALITY ASSESSMENT

2015 ◽  
Vol 2 ◽  
pp. 56 ◽  
Author(s):  
Mikhail Zobkov
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Natural Water ◽  
Trophic State ◽  
Water Quality Assessment ◽  
Water Quality Evaluation ◽  
Water Object ◽  
Total Phosphorous ◽  
Classification Parameter

Assessment of water quality and classification of water object plays significant role in an environmental and ecology study. Water quality evaluation by hydrochemical parameters is fairly difficult and required a long period of time. Automatic expert system was created to solve this problem. Automatization of objects classification and quality assessment for humus zone based on Karelian water bodies research data are presented in this study. Automation algorithms of the surface water geochemical classification based on the principal chemical transactions was obtained during research. Classification based on implicit scaling data by classification parameter. Alkalinity, pH, huminity, Fecom and total phosphorous were chosen as the main classification parameters. For classification by alkalinity were used alkalinity and pH, for huminity classification were used coefficient of huminity – Hum? Color OD?C Mn and Fecom, for trophic state were used huminity class and total phosphorous concentration. The water objects distribution by huminity, alkalinity and trophic state was obtained and basic geochemical classes were picked out. Natural water quality was assessed as combination of geochemical classes. Results of research presented as maps and trends of geochemical classes and natural water quality distribution over the area of Republic of Karelia.

scholarly journals Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2360 ◽  
Author(s):  
Zahra Salimi ◽  
Birte Boelt
Keyword(s):  
Sugar Beet ◽  
Quality Assessment ◽  
Multispectral Imaging ◽  
Seed Quality ◽  
Mechanical Damage ◽  
Derived Data ◽  
Damage Types ◽  
Potential Tool ◽  
Vision Technique

The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.

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