AIMC Topic: Sugars

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Evaluation of conditional treatment effect of salt stress on tomato sugar content using causal machine learning: A pilot study.

PloS one
Exposing tomatoes to salt stress has been reported to increase the fruit sugar content (°Brix); however, the causal impact of this treatment under varying environmental conditions remains unclear. In this pilot study, a causal inference analysis was ...

Ultraviolet-visible spectral characterization and ANN modeling of aqueous sugar solutions: Clinical and environmental perspectives.

PloS one
The characterization of aqueous sugar solutions using optical techniques offers a non-invasive, rapid, and reagent-free approach for concentration monitoring in both analytical and environmental contexts. In this study, aqueous D-glucose solutions at...

Grape sugar content prediction with multispectral alignment and improved residual network.

Scientific reports
Sugar content is a crucial indicator of grape ripeness and grading, and developing non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms. Spectroscopy, which can detect the chemica...

A machine learning approach fusing multisource spectral data for prediction of floral origins and taste components of Apis cerana honey.

Food research international (Ottawa, Ont.)
This study explores the use of near-infrared (NIR), mid-infrared (MIR), and Raman spectral fusion for the rapid prediction of floral origins and main taste components in Apis cerana (A. cerana) honey. Feature-level fusion with the partial least squar...

ATR-FTIR spectroscopy and machine/deep learning models for detecting adulteration in coconut water with sugars, sugar alcohols, and artificial sweeteners.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresenta...

An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning.

Food research international (Ottawa, Ont.)
Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid ...

Evaluation of mouse behavioral responses to nutritive versus nonnutritive sugar using a deep learning-based 3D real-time pose estimation system.

Journal of neurogenetics
Animals are able to detect the nutritional content of sugar independently of taste. When given a choice between nutritive sugar and nonnutritive sugar, animals develop a preference for nutritive sugar over nonnutritive sugar during a period of food d...

Prediction of ethanol fermentation under stressed conditions using yeast morphological data.

Journal of bioscience and bioengineering
A high sugar concentration is used as a starting condition in alcoholic fermentation by budding yeast, which shows changes in intracellular state and cell morphology under conditions of high-sugar stress. In this study, we developed artificial intell...

A non-destructive methodology for determination of cantaloupe sugar content using machine vision and deep learning.

Journal of the science of food and agriculture
BACKGROUND: To determine the maturity of cantaloupe, measuring the soluble solid content (SSC) as the indicator of sugar content based on the refractometric index is commonly practised. This method, however, is destructive and limited to a small numb...

Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time.

Food chemistry
This study aims to investigate the changes in physical and chemical properties of edamame during bean development and apply a spectroscopy-based machine learning (ML) technique to determine optimal harvest time. The edamame harvested at R5 (beginning...