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Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

Laboratory investigation; a journal of technical methods and pathology
In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancemen...

Rapid and non-destructive spectroscopic method for classifying beef freshness using a deep spectral network fused with myoglobin information.

Food chemistry
A simple, novel, rapid, and non-destructive spectroscopic method that employs the deep spectral network for beef-freshness classification was developed. The deep-learning-based model classified beef freshness by learning myoglobin information and ref...

Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning.

IEEE transactions on medical imaging
One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM imag...

Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air.

Analytical chemistry
Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ mann...

Deep learning for species identification of bolete mushrooms with two-dimensional correlation spectral (2DCOS) images.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Bolete is well-known and widely consumed mushroom in the world. However, its medicinal properties and nutritional are completely different from one species to another. Therefore, the consumers need a fast and effective detection method to discriminat...

Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms.

Scientific reports
Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problemati...

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning.

Sensors (Basel, Switzerland)
Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the...

Deep learning protocol for improved photoacoustic brain imaging.

Journal of biophotonics
One of the key limitations for the clinical translation of photoacoustic imaging is penetration depth that is linked to the tissue maximum permissible exposures (MPE) recommended by the American National Standards Institute (ANSI). Here, we propose a...

A machine learning workflow for raw food spectroscopic classification in a future industry.

Scientific reports
Over the years, technology has changed the way we produce and have access to our food through the development of applications, robotics, data analysis, and processing techniques. The implementation of these approaches by the food industry ensure qual...

Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics.

Expert review of molecular diagnostics
INTRODUCTION: Raman and Infrared spectroscopic techniques are being used for the analysis of different types of cancers and other biological molecules. It is possible to identify cancers from normal tissues both in fresh and fixed tissues. These tech...