Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

2D NMR: Overview of Heteronuclear Correlation Techniques

2D NMR: Overview of Heteronuclear Correlation Techniques

Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Nutrient fluxes are underestimated in the Qinghai Lake Basin, the largest endorheic basin of the Asian water tower.

Water research·2026

Same author

Atomic modulation of Na<sup>+</sup> transport channels to enhance rate and cycling performance of tunnel-type sodium manganese oxides.

Chemical science·2026

Same author

Three-Principal-Substrate SERS Profiling Enables Reliable Screening of Serum Biomarkers: A General Approach.

Analytical chemistry·2026

Same author

Variation in plant phenological changes and contributions by climatic drivers across six grassland and desert ecosystems in China.

International journal of biometeorology·2026

Same author

A Low-Cost Computational Spectrometer Based on a Trained Sparse Base Matrix.

Sensors (Basel, Switzerland)·2025

Same author

Cervical cancer screening by biomarker-free Serum-SERS technique: A three-principal-substrate approach.

Biosensors & bioelectronics·2024

Search research articles

Related Experiment Video

Updated: Jul 1, 2025

Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes

Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes

Published on: December 4, 2009

A Neural Network Computational Spectrometer Trained by a Small Dataset with High-Correlation Optical Filters.

Haojie Liao¹, Lin Yang^1,2, Yuanhao Zheng¹

¹Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao 266237, China.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

This study introduces a neural network computational spectrometer that achieves high accuracy using a small dataset and high-correlation optical filters. This approach overcomes challenges in traditional spectrometer design and real-time measurements.

Keywords:

computational spectrometer neural network small training dataset spectra reconstruction

More Related Videos

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Published on: October 20, 2019

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

Related Experiment Videos

Last Updated: Jul 1, 2025

Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes

Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes

Published on: December 4, 2009

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Published on: October 20, 2019

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

Area of Science:

Spectroscopy
Optical Engineering
Machine Learning

Background:

Computational spectrometers offer portable in situ analysis but face challenges in filter design and reconstruction accuracy.
Current methods require non-correlated filters and efficient algorithms for real-time applications.

Purpose of the Study:

To develop a neural network computational spectrometer (NNCS) that performs accurately with a small dataset and high-correlation optical filters.
To challenge the conventional reliance on large datasets and non-correlated filters in NNCS.

Main Methods:

A novel distribution law was identified for large datasets to extract a small, representative training set.
A fully connected neural network was designed for spectral reconstruction.
Thin film filters were employed as the encoding layer.

Main Results:

The NNCS demonstrated high performance in simulations and experiments using a small dataset and high-correlation filters.
The trained neural network successfully reconstructed spectra, validating the approach.

Conclusions:

This work presents a viable method for computational spectrometers utilizing high-correlation filters and limited training data.
The findings offer a new paradigm for designing efficient and accurate portable spectrometers.