Green (unroasted) coffee is one of the most traded agricultural commodities in the world. The Arabica (Coffea arabica L.) and Robusta (Coffea canephora Pierre ex A. Froehner) species are the two main types of coffees for commercial production. In general, Arabica coffee is known to have better quality in terms of sensory characteristics; thus, it has a higher market value than Robusta coffee. Accurate differentiation of green beans of the two species is, therefore, of commercial interest in the coffee industry. Using the newly developed single nucleotide polymorphism (SNP) markers, we analyzed a total of 80 single green bean samples, representing 20 Arabica cultivars and four Robusta accessions. Reliable SNP fingerprints were generated for all tested samples. Unambiguous differentiation between Robusta and Arabica coffees was achieved using multivariate analysis and assignment test. The SNP marker panel and the genotyping protocol are sufficiently robust to detect admixture of green coffee in a high-throughput fashion. Moreover, the multilocus SNP approach can differentiate every single bean within Robusta and 55% of Arabica samples. This advantage, together with the single-bean sensitivity, suggests a significant potential for practical application of this technology in the coffee industry.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1093/jaocint/qsz002 | DOI Listing |
Pathogens
December 2024
Department of Inorganic and Analytical Chemistry, Faculty of Chemistry, Rzeszów University of Technology, Powstańców Warszawy 6, 35-959 Rzeszów, Poland.
and are challenging to differentiate using methods such as phenotyping, 16S rRNA sequencing, or protein profiling through matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) due to their close relatedness. This study explores the potential for identifying and by incorporating reference spectra of metabolite profiles, obtained via surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) employing gold nanoparticles (AuNPs), into the Bruker Biotyper database. Metabolite extracts from and cells were prepared using liquid-liquid extraction in a chloroform-methanol-water system.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Oceanography and Spatial Information, China University of Petroleum East China-Qingdao Campus, Qingdao 266580, China.
Salt marsh vegetation in the Yellow River Delta, including (), (), and (), is essential for the stability of wetland ecosystems. In recent years, salt marsh vegetation has experienced severe degradation, which is primarily due to invasive species and human activities. Therefore, the accurate monitoring of the spatial distribution of these vegetation types is critical for the ecological protection and restoration of the Yellow River Delta.
View Article and Find Full Text PDFSensors (Basel)
January 2025
National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China.
Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China's agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE) and plant senescence reflectance index (PSRI).
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Sciences, Xi'an Technological University, Xi'an 710021, China.
A solution to address the issues of environmental light interference in Remote Photoplethysmography (rPPG) methods is proposed in this paper. First, signals from the face's region of interest (ROI) and background noise signals are simultaneously collected, and the two signals are processed by a differential to obtain a more accurate rPPG signal. This method effectively suppresses background noise and enhances signal quality.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
This paper presents an automated method for solving the initial structure of compact, high-zoom-ratio mid-wave infrared (MWIR) zoom lenses. Using differential analysis, the focal length variation process of zoom lenses under paraxial conditions is investigated, and a model for the focal power distribution and relative motion of three movable lens groups is established. The particle swarm optimization (PSO) algorithm is introduced into the zooming process analysis, and a program is developed in MATLAB to solve for the initial structure.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!