Point-of-use sensors and machine learning enable low-cost determination of soil nitrogen

Nature Food ◽  
2021 ◽  
Author(s):  
Max Grell ◽  
Giandrin Barandun ◽  
Tarek Asfour ◽  
Michael Kasimatis ◽  
Alex Silva Pinto Collins ◽  
...  
The Analyst ◽  
2020 ◽  
Vol 145 (9) ◽  
pp. 3431-3439
Author(s):  
Estefanía Nunez-Bajo ◽  
M. Teresa Fernández-Abedul

Paper-based electrochemical platforms with coulometric readout are employed for fast and low cost determination of ascorbic acid in commercial juice samples.


2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Rute C. Martins ◽  
Ana M. Pereira ◽  
Elisabete Matos ◽  
Luisa Barreiros ◽  
António J. M. Fonseca ◽  
...  

Zinc is an essential trace element for animals in several biological processes, particularly in energy production, and it is acquired from food ingestion. In this context, a microplate-based fluorimetric assay was developed for simple, fast, and low-cost determination of zinc in pet food using 2,2′-((4-(2,7-difluoro-3,6-dihydroxy-4aH-xanthen-9-yl)-3-methoxyphenyl)azanediyl)diacetic acid (FluoZin-1) as fluorescent probe. Several aspects were studied, namely, the stability of the fluorescent product over time, the FluoZin-1 concentration, and the pH of reaction media. The developed methodology provided a limit of detection of 1 μg L−1 in sample acid digests, with a working range of 10 to 200 μg L−1, corresponding to 100–2000 mg of Zn per kg of dry dog food samples. Intraday repeatability and interday repeatability were assessed, with relative standard deviation values < 3.4% (100 μg L−1) and <11.7% (10 μg L−1). Sample analysis indicated that the proposed fluorimetric assay provided results consistent with ICP-MS analysis. These results demonstrated that the developed assay can be used for rapid determination of zinc in dry dog food.


2014 ◽  
Vol 6 (12) ◽  
pp. 4124-4129 ◽  
Author(s):  
Yanfeng Nong ◽  
Xiaoguo Ma ◽  
Shaofeng Fan ◽  
Yumian Yu

Ultrasonic-assisted extraction with high performance liquid chromatography was used for rapid and low-cost determination of melamine in soil and sediment.


Author(s):  
P. Agrafiotis ◽  
D. Skarlatos ◽  
A. Georgopoulos ◽  
K. Karantzalos

Abstract. The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.


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