Dynamics of luminescence characteristics of Haematococcus lacustris cultures in different cultivation conditions

Luminescence ◽  
2022 ◽  
Author(s):  
Vladislav E. Erokhin ◽  
Galina S. Minyuk ◽  
Alla P. Gordienko ◽  
Sergey V. Kapranov
Keyword(s):  
Cultivation Conditions ◽  
Haematococcus Lacustris ◽  
Luminescence Characteristics

Changes of Arabinoxylan Content in Malting Barley under Different Environments and Cultivation Conditions

2013 ◽  
Vol 39 (9) ◽  
pp. 1674
Author(s):  
Xiao-Qin ZHANG ◽  
Da-Wei XUE ◽  
Guo-Ping ZHANG
Keyword(s):  
Malting Barley ◽  
Cultivation Conditions

Influence of Planting Density on Root Spatio-Temporal Distribution of Different Types of Maize under High-Yielding Cultivation Conditions

2013 ◽  
Vol 38 (7) ◽  
pp. 1286-1294 ◽  
Author(s):  
Zong-Xin LI ◽  
Yuan-Quan CHEN ◽  
Qing-Cheng WANG ◽  
Kai-Chang LIU ◽  
Wang-Sheng GAO ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Planting Density ◽  
Cultivation Conditions ◽  
Different Types ◽  
Spatio Temporal

Unraveling the Role of Crystallization Dynamics on Luminescence Characteristics of Perovskite Light‐Emitting Diodes

2021 ◽  
pp. 2100023
Author(s):  
Kong‐Chao Shen ◽  
Jing‐Kun Wang ◽  
Yang Shen ◽  
Yan‐Qing Li ◽  
Ming‐Lei Guo ◽  
...  
Keyword(s):  
Light Emitting Diodes ◽  
Light Emitting ◽  
Crystallization Dynamics ◽  
Luminescence Characteristics

Computational Studies on the Materials Combining Graphene Quantum Dots and Pt Complexes with Adjustable Luminescence Characteristics

2021 ◽  
Vol 60 (3) ◽  
pp. 1480-1490
Author(s):  
Shi-Ping Wang ◽  
Yuan Li ◽  
Zhi-Xiang Zhang ◽  
Yu Zhang ◽  
Yu Wang ◽  
...  
Keyword(s):  
Quantum Dots ◽  
Graphene Quantum Dots ◽  
Computational Studies ◽  
Luminescence Characteristics

scholarly journals Cultivation Conditions Change Aroma Volatiles of Strawberry Fruit

Horticulturae ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 81
Author(s):  
Yunduan Li ◽  
Yuanyuan Zhang ◽  
Xincheng Liu ◽  
Yuwei Xiao ◽  
Zuying Zhang ◽  
...  
Keyword(s):  
Volatile Compounds ◽  
Major Contributor ◽  
Fragaria Ananassa ◽  
Strawberry Fruit ◽  
Cultivation Conditions ◽  
Soil Culture ◽  
Aroma Volatiles ◽  
Ms Analysis ◽  
Significant Difference ◽  
Strawberry Cultivars

Volatile compounds principally contribute to flavor of strawberry (Fragaria × ananassa) fruit. Besides to genetics, cultivation conditions play an important role in fruit volatile formation. Compared to soil culture as control, effects of substrate culture on volatile compounds of two strawberry cultivars (‘Amaou’ and ‘Yuexin’) were investigated. GC-MS analysis revealed significant difference in volatile contents of ‘Amaou’ strawberry caused by substrate culture. No significant effect was observed for cultivar ‘Yuexin’. For ‘Amaou’ strawberry from soil culture produced higher volatile contents compared with substrate culture. This difference is contributed by high contents of esters, lactones, ketones, aldehydes, terpenes, hydrocarbons, acids, furans and phenols in ‘Amaou’ strawberry fruit from soil culture. Furanones, beta-linalool, trans-Nerolidol and esters are major contributor to strawberry aroma, whose contents are higher in soil culture planted fruit when compared to substrate culture. Moreover, strawberry fruit from soil culture had higher transcripts related to volatile biosynthesis were observed, including FaQR, FaOMT, FaNES1, FaSAAT and FaAAT2.

scholarly journals Self-Configuring CVS to Discriminate Rocket Leaves According to Cultivation Practices and to Correctly Attribute Visual Quality Level

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1353
Author(s):  
Michela Palumbo ◽  
Bernardo Pace ◽  
Maria Cefola ◽  
Francesco Fabiano Montesano ◽  
Francesco Serio ◽  
...  
Keyword(s):  
Fruits And Vegetables ◽  
Irrigation Management ◽  
Classification Performance ◽  
Quality Level ◽  
Cultivation Conditions ◽  
Computer Vision Systems ◽  
Soilless Cultivation ◽  
Fertilization Level ◽  
Non Destructive

Computer Vision Systems (CVS) represent a contactless and non-destructive tool to evaluate and monitor the quality of fruits and vegetables. This research paper proposes an innovative CVS, using a Random Forest model to automatically select the relevant features for classification, thereby avoiding their choice through a cumbersome and error-prone work of human designers. Moreover, three color correction techniques were evaluated and compared, in terms of classification performance to identify the best solution to provide consistent color measurements. The proposed CVS was applied to fresh-cut rocket, produced under greenhouse soilless cultivation conditions differing for the irrigation management strategy and the fertilization level. The first aim of this study was to objectively estimate the quality levels (QL) occurring during storage. The second aim was to non-destructively, and in a contactless manner, identify the cultivation approach using the digital images of the obtained product. The proposed CVS achieved an accuracy of about 95% in QL assessment and about 65–70% in the discrimination of the cultivation approach.

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