AIMC Topic: Carbon Isotopes

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Estimating weaning duration from incremental dentine δ15N and δ13C using a sequence-based LSTM neural network: A deep learning framework for bioarchaeological applications.

PloS one
The estimation of weaning duration from incremental dentine δ15N and δ13C values offers insights into health, nutrition, and demography in past populations. In this study, we developed a novel machine learning approach to estimate weaning duration us...

Stable isotope profiling and machine learning for determining cocoa origin, ingredient composition, and cocoa content in Brazilian chocolates.

Food chemistry
Chocolate is a versatile product with flavours ranging from sweet to bitter. Cocoa, from a C3-photosynthetic plant, is its main raw material, while sugar from sugarcane (C4-metabolism) is commonly used. We analyzed the δ13C and δ15N composition of Br...

Unsupervised machine learning for mass spectrometry imaging data analysis with isotope labeling.

The Analyst
Mass spectrometry imaging (MSI) has emerged as a powerful tool for spatial metabolomics, but untargeted data analysis has proven to be challenging. When combined with isotope labeling (MSI), MSI provides insights into metabolic dynamics with high sp...

From NMR to AI: Fusing H and C Representations for Enhanced QSPR Modeling.

Journal of chemical information and modeling
The ability to predict log  directly from spectral patterns marks a conceptual shift in cheminformatics. In this work, we demonstrate that H and C NMR spectra, computationally generated from molecular structures and transformed into machine learning-...

Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy.

Nature communications
Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our current understanding of cell homeostasis and metabolism, including how cells utilize nutrients, comes ...

Beef Traceability Between China and Argentina Based on Various Machine Learning Models.

Molecules (Basel, Switzerland)
Beef, as a nutrient-rich food, is widely favored by consumers. The production region significantly influences the nutritional value and quality of beef. However, current methods for tracing the origin of beef are still under development, necessitatin...

Combining stable isotopes and multi-elements with machine learning chemometric models to identify the geographical origins of Tetrastigma hemsleyanum Diels et Gilg.

Food chemistry
Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum) is an edible plant with considerable medicinal properties, the quality of which varies depending on its origin. Therefore economically motivated adulteration has emerged. So there is an urgent ne...

Deep learning insights into spatial patterns of stable isotopes in Iran's precipitation: a novel approach to climatological mapping.

Isotopes in environmental and health studies
Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerou...

Using a deep learning prior for accelerating hyperpolarized C MRSI on synthetic cancer datasets.

Magnetic resonance in medicine
PURPOSE: We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets.

Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis.

Food research international (Ottawa, Ont.)
In addressing the generalization issue faced by data-driven methods in food origin traceability, especially when encountering diverse input variable sets, such as elemental contents (C, N, S), stable isotopes (C, N, S, H and O) and 43 elements measur...