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Plant Oils

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Lightweight deep learning model for embedded systems efficiently predicts oil and protein content in rapeseed.

Food chemistry
Conventional methods for determining protein and oil content in rapeseed are often time-consuming, labor-intensive, and costly. In this study, a mobile application was developed using an optimized deep learning method for low-cost, non-destructive an...

Antibacterial effects of thyme oil loaded solid lipid and chitosan nano-carriers against Salmonella Typhimurium and Escherichia coli as food preservatives.

PloS one
OBJECTIVES: Escherichia coli and Salmonella Typhimurium are frequent causes of foodborne illness affecting many people annually. In order to develop natural antimicrobial agents against these microorganisms, thyme oil (TO) was considered as active an...

Using three-dimensional fluorescence spectroscopy and machine learning for rapid detection of adulteration in camellia oil.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Camellia oil had been widely utilized in the realms of cooking, healthcare, and beauty. Nevertheless, merchants frequently adulterated pure camellia oil with low-priced oils to cut costs. This study was aimed at identifying the authenticity of camell...

Smartphone-Assisted Nanozyme Colorimetric Sensor Array Combined "Image Segmentation-Feature Extraction" Deep Learning for Detecting Unsaturated Fatty Acids.

ACS sensors
Conventional methods for detecting unsaturated fatty acids (UFAs) pose challenges for rapid analyses due to the need for complex pretreatment and expensive instruments. Here, we developed an intelligent platform for facile and low-cost analysis of UF...

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods.

Food chemistry
Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of tradition...

Microwave detection technique combined with deep learning algorithm facilitates quantitative analysis of heavy metal Pb residues in edible oils.

Journal of food science
The heavy metal content in edible oils is intricately associated with their suitability for human consumption. In this study, standard soybean oil was used as a sample to quantify the specified concentration of heavy metals using microwave sensing te...

Methyl cellulose/okra mucilage composite films, functionalized with Hypericum perforatum oil and gentamicin, as a potential wound dressing.

International journal of biological macromolecules
There is a growing demand for the development of functional wound dressings enriched with bioactive natural compounds to improve the quality of life of the population by accelerating the healing process of chronic wounds. In this regard, a functional...

Raman spectroscopy combined with multiple one-dimensional deep learning models for simultaneous quantification of multiple components in blended olive oil.

Food chemistry
Blended vegetable oils are highly prized by consumers for their comprehensive nutritional profile. Therefore, there is an urgent need for a rapid and accurate method to identify the true content of blended oils. This study combined Raman spectroscopy...

A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine.

Food chemistry
Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classif...

Machine learning prediction of contents of oxygenated components in bio-oil using extreme gradient boosting method under different pyrolysis conditions.

Bioresource technology
This work aims to develop a prediction model for the contents of oxygenated components in bio-oil based on machine learning according to different pyrolysis conditions and biomass characteristics. The prediction model was constructed using the extrem...