Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

You might also read

Related Articles

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

Sort by
Same author

Multi-Wavelength Machine Learning for High-Precision Colorimetric Sensing.

Sensors (Basel, Switzerland)·2026
Same author

Genetic Information Enhances a Novel Non-Invasive Steatosis Index and Outperforms Existing Steatosis Indices.

Liver international : official journal of the International Association for the Study of the Liver·2026
Same author

Dual-Channel Microfluidic Photoionization Detector.

Analytical chemistry·2025
Same author

Development of a Miniaturized, Automated, and Cost-Effective Device for Enzyme-Linked Immunosorbent Assay.

Sensors (Basel, Switzerland)·2025
Same author

Sensitive Bioassay with an Ultralarge Dynamic Range via Microlaser Ensemble Quenching.

ACS sensors·2025
Same author

Consolidated Microscale Interferon-γ Release Assay with Tip Optofluidic Immunoassay for Dynamic Parallel Diagnosis of Tuberculosis Infection.

Analytical chemistry·2025
Same journal

A Coumarin-Based Probe for Sequential ON-OFF-ON Detection of Cu<sup>2+</sup> and Biothiols: Naked-Eye Detection, Smartphone RGB Readout and In Vivo Imaging.

Biosensors·2026
Same journal

Electropolymerized Molecularly Imprinted Polymers Supported on Carbon-Based Materials for (Bio)sensing: Direct and Indirect Detection Strategies.

Biosensors·2026
Same journal

Progress in (Photo)electrochemical Biosensors for the Detection of Amyloid-Beta Oligomer.

Biosensors·2026
Same journal

Design and Simulation of Lamotrigine Intermittent Release from a Subcutaneous Implant with an Enzymatic Biosensor Based on Clinical Data.

Biosensors·2026
Same journal

Prediction of Chronic Kidney Disease Based on Simulated Serum Analysis by Vibrational Spectroscopy.

Biosensors·2026
Same journal

AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.

Biosensors·2026
See all related articles

Related Experiment Video

Updated: May 10, 2026

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
08:12

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing

Published on: March 13, 2013

13.2K

Accuracy Enhancement in Refractive Index Sensing via Full-Spectrum Machine Learning Modeling.

Majid Aalizadeh1,2,3,4, Chinmay Raut5, Morteza Azmoudeh Afshar6

  • 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Biosensors
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning framework analyzes full spectra for refractive index sensing. Titanium nanorods show superior performance in predicting refractive index changes compared to silicon nanorods.

Keywords:
index sensorlinear regressionmachine learningmean squared errormeta-grating

More Related Videos

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

7.0K
ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

3.0K

Related Experiment Videos

Last Updated: May 10, 2026

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
08:12

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing

Published on: March 13, 2013

13.2K
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

7.0K
ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

3.0K

Area of Science:

  • Nanophotonics
  • Machine Learning
  • Spectroscopy

Background:

  • Refractive index sensing is crucial for biosensing applications.
  • Meta-grating structures offer tunable optical properties for sensing.
  • Machine learning can enhance the analysis of complex spectral data.

Purpose of the Study:

  • To develop and evaluate a full-spectrum machine learning framework for refractive index sensing.
  • To compare the performance of titanium and silicon nanorod meta-gratings for sensing.
  • To investigate the impact of spectral features on model accuracy.

Main Methods:

  • Simulated absorption spectra from titanium and silicon nanorod meta-gratings.
  • Extraction of 80 principal components from spectral data.
  • Application of linear regression and five-fold cross-validation.
  • Analysis of TE and TM polarized light.

Main Results:

  • Titanium nanorods demonstrated significantly higher accuracy (up to 8128-fold improvement) due to broadband intensity changes.
  • Silicon nanorods showed more limited gains because of spectral nonlinearity.
  • Full-spectrum linear models outperformed single-feature models, especially for intensity-modulated sensors.
  • Data-driven analysis identified optimal single-wavelength predictors.

Conclusions:

  • Full-spectrum machine learning is effective for refractive index sensing.
  • Titanium nanostructures are highly promising for advanced biosensing applications.
  • Spectral shape and linearity significantly influence the performance of machine learning models in sensing.