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Spectrum Analysis

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Predicting leaf nitrogen content in wolfberry trees by hyperspectral transformation and machine learning for precision agriculture.

PloS one
Leaf nitrogen content (LNC) is an important indicator for scientific diagnosis of the nutrition status of crops. It plays an important role in the growth, yield and quality of wolfberry. This study aimed to develop new spectral indices (NSIs) and con...

Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes.

Applied spectroscopy
Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus thro...

Towards high-performance deep learning architecture and hardware accelerator design for robust analysis in diffuse correlation spectroscopy.

Computer methods and programs in biomedicine
This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to gener...

Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics: Feasibility Studies With Murine Models.

Journal of biophotonics
Collagen, a key structural component of the extracellular matrix, undergoes significant remodeling during carcinogenesis. However, the important role of collagen levels in breast cancer diagnostics still lacks effective in vivo detection techniques t...

Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.

Environmental science and pollution research international
Leveraging hyperspectral data across various domains yields substantial benefits, yet managing many spectral bands and identifying the essential ones poses a formidable challenge. This study identifies the most relevant bands within a hyperspectral d...

Insights into the characteristics and toxicity of microalgal biochar-derived dissolved organic matter by spectroscopy and machine learning.

The Science of the total environment
Microalgal biochar has potential applications in various fields; however, there is limited research on the properties and risks of microalgal biochar-derived dissolved organic matter (MBDOM). This study examined how different pyrolysis temperatures (...

Microfluidic Optical Aptasensor for Small Molecules Based on Analyte-Tuned Growth of Gold Nanoseeds and Machine Learning-Enhanced Spectrum Analysis: Rapid Detection of Mycotoxins.

ACS sensors
Natural toxins, mainly small molecules, are a category of chemical hazards in agri-food systems that pose threats to both public health and food security. Current standard methods for monitoring these toxins, predominantly based on liquid chromatogra...

Integration of spectroscopic techniques and machine learning for optimizing Phaeodactylum tricornutum cell and fucoxanthin productivity.

Bioresource technology
The development of sustainable and controlled microalgae bioprocesses relies on robust and rapid monitoring tools that facilitate continuous process optimization, ensuring high productivity and minimizing response times. In this work, we analyse the ...

A noninvasive blood glucose detection method with strong time adaptability based on fuzzy operator decision fusion and dynamic spectroscopy characteristics of PPG signals.

Analytical methods : advancing methods and applications
PPG signals are a new means of non-invasive detection of blood glucose, but there are still shortcomings of poor time adaptability and low prediction accuracy of blood glucose quantitative models. Few studies discuss prediction accuracy in the case o...