AIMC Topic: Flour

Clear Filters Showing 1 to 10 of 15 articles

Enhanced YOLO-based framework for accurate detection and identification of common wheat impurities with distinct objects.

Scientific reports
Real-time detecting and identifying impurities in wheat grain mass is crucial for wheat storage silos, flour mills and modern combines. Depending on the detection objectives, accuracy is typically prioritized in laboratory-based applications, whereas...

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 printer control parameters for 3D printing of millet dough.

Journal of the science of food and agriculture
BACKGROUND: Three-dimensional (3D) food printing enables precise customization and intricate shapes of food materials. The influence of printer control parameters on the printing performance of millet-based dough is still underexplored. OBJECTIVE: Th...

Machine learning-combined hyperspectral imaging analysis for the non-destructive identification of wheat flours with varying gluten strengths.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study aimed to non-destructively identify wheat flours with different gluten strengths through the application of machine learning-combined hyperspectral imaging analysis. The performance of this approach was compared to conventional instrumenta...

Prediction of Deoxynivalenol contamination in wheat kernels and flour based on visible near-infrared spectroscopy, feature selection and machine learning modelling.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Contamination of wheat by the mycotoxin Deoxynivalenol (DON), produced by Fusarium fungi, poses significant challenges to the quality of crop yield and food safety. Visible and near-infrared (vis-NIR) spectroscopy has emerged as a promising, non-dest...

Lightweight deep learning algorithm for real-time wheat flour quality detection via NIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Wheat flour quality, determined by factors such as protein and moisture content, is crucial in food production. Traditional methods for analyzing these parameters, though precise, are time-consuming and impractical for large-scale operations. This st...

Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.

PloS one
In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete ...

Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms.

Food chemistry
The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS)...

Non-invasive prediction of maca powder adulteration using a pocket-sized spectrophotometer and machine learning techniques.

Scientific reports
Discriminating different cultivars of maca powder (MP) and detecting their authenticity after adulteration with potent adulterants such as maize and soy flour is a challenge that has not been studied with non-invasive techniques such as near infrared...

Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS).

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduc...