AIMC Topic: Seeds

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Food defect detection technologies based on deep learning and prospects in detection of unsound wheat kernels.

Food chemistry
With rising concerns over global food security and quality pressures and the rapid advancement of agricultural intelligence, wheat quality detection demands higher efficiency, accuracy, and automation. Unsound wheat kernels, which adversely affect fl...

Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.

Scientific reports
This study presents a novel framework for adaptive optimization of electromagnetic vibration parameters in corn seed treatment using multi-objective deep learning approaches. A hybrid CNN-LSTM network architecture was developed to process heterogeneo...

Proteomics coupled machine learning-innovative approach in geographical origin authentication of green Coffea arabica.

Food chemistry
The geographical authentication of green specialty coffee is an economically sensitive analytical task that is not yet fully resolved. We used an innovative combination of proteomic profiling with linear discriminant analysis for the authentication o...

Integration of pre-trained GRU and molecular docking for virtual screening of quinoa seed derived ACE inhibitory peptides: An innovative prediction strategy.

Food chemistry
Recent developments in AI, particularly deep learning, are sparking a revolution in bioactive peptide discovery. This study introduced a pretrained Gated Recurrent Unit model (Pre-GRU) to predict the IC values of ACE inhibitory peptides derived from ...

Predicting the composition of multiple soybean varieties from whole and ground seeds using Fourier transform near-infrared spectroscopy (FT-NIRS) and machine learning.

Food chemistry
Soybean is being increasingly included in human diets, highlighting the importance of determining its composition. Although Fourier-Transform Near-Infrared Spectroscopy (FT-NIRS) has become a promising technology, currently used models remain limited...

Facile phyto-mediated synthesis of ternary CuO/MnO/ZnO nanocomposite using Nigella Sativa seeds extract: characterization,antimicrobial, and biomedical evaluations.

Scientific reports
The phyto-synthesis of ternary CuO/ MnO/ZnO nanocomposite was achieved by the utilization of an eco-friendly, straightforward approach that involved the extract of Nigella sativa seeds. Our ternary nanocomposite appears to include equal amounts of Cu...

Construction of an Automated Removal Robot for the Natural Drying of Cacao Beans.

Sensors (Basel, Switzerland)
Cacao producers often obtain low-quality beans due to the poor manual drying process. This study proposes the construction of an automated prototype robot for the removal during natural drying of cacao beans at Cooperativa Agraria Allima Cacao Ltd., ...

Unleashing the nutritional potential of Brassica microgreens: A case study on seed priming with Vermicompost.

Food chemistry
Microgreens constitute ready-to-eat functional foods, being rich sources of phytonutrients and phytochemicals. Because of their short life cycle, seed priming is a promising strategy to fortify their functional outcome. Vermicompost was applied as se...

Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning.

Food research international (Ottawa, Ont.)
In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-...

Rapid classification of Camellia seed varieties and non-destructive high-throughput quantitative analysis of fatty acids based on non-targeted fingerprint spectroscopy combined with chemometrics.

Food chemistry
Camellia oil is a high-quality vegetable oil rich in unsaturated fatty acids (FAs), with quality standardization challenged by the diversity of Camellia seed varieties. This study compared spectroscopy techniques (Near-Infrared [NIR] vs Mid-Infrared ...