AIMC Topic: Flour

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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 Jan 7, 2025
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...
Trichothecenes Flour Machine Learning Algorithms Spectroscopy, Near-Infrared Food Contamination Triticum
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Lightweight deep learning algorithm for real-time wheat flour quality detection via NIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 22, 2024
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...
Spectroscopy, Near-Infrared Deep Learning Food Analysis Neural Networks, Computer Algorithms Triticum Quality Control Flour Food Quality
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Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.

PloS one Oct 22, 2024
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 ...
Neural Networks, Computer Particle Size Flour Calibration
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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 Oct 5, 2024
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)...
Algorithms Spectroscopy, Near-Infrared Support Vector Machine Discriminant Analysis Food Contamination Flour Least-Squares Analysis Fagopyrum
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Non-invasive prediction of maca powder adulteration using a pocket-sized spectrophotometer and machine learning techniques.

Scientific reports May 7, 2024
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...
Spectrophotometry Flour Macau Powders Glycine max Machine Learning Food Contamination Zea mays Spectroscopy, Near-Infrared
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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 Mar 5, 2024
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...
Flour Least-Squares Analysis Triticum Spectroscopy, Near-Infrared Deep Learning Algorithms
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Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

Journal of the science of food and agriculture Jul 31, 2023
BACKGROUND: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, n...
Neural Networks, Computer Amylose Spectroscopy, Near-Infrared Plant Tubers Flour Dioscorea
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Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jun 19, 2023
Rapid detection of wheat flour grade played an important role in the food industry. In this work, hyperspectral technology was used to detect five types of wheat flour. An analysis model was established based on the reflectance of samples at 968 ∼ 25...
Support Vector Machine Least-Squares Analysis Flour Triticum Algorithms Spectroscopy, Near-Infrared
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Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble.

Sensors (Basel, Switzerland) Jul 4, 2019
Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and...
Flour Artificial Intelligence Food-Processing Industry Support Vector Machine Machine Learning Random Allocation Algorithms Hordeum Image Processing, Computer-Assisted
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Protease inhibitors in various flours and breads: Effect of fermentation, baking and in vitro digestion on trypsin and chymotrypsin inhibitory activities.

Food chemistry Dec 18, 2016
In this study trypsin (TIA) and chymotrypsin inhibitory (CIA) activities were determined in the extracts of wheat, rye mix, mixed cereals and, whole wheat flours and, breads made with these flours. In addition, effects of fermentation, baking and in ...
Cooking Chymotrypsin Fermentation Bread Digestion Secale Trypsin Flour Triticum Protease Inhibitors Edible Grain
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