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

IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...

You might also read

Related Articles

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

Sort by

Same author

High-resolution LA-ICP-MS imaging combined with attention-enhanced residual network reveal cadmium-induced metal distribution heterogeneity and toxicity in Mytilus galloprovincialis.

Journal of hazardous materials·2026

Same author

Tunable BIC metamaterials with Dirac semimetals.

Nanophotonics (Berlin, Germany)·2025

Same author

Disease burden of non-communicable diseases attributable to high body mass index in China and its provinces, 1990-2021: A systematic analysis for the Global Burden of Disease Study 2021.

Chinese medical journal·2025

Same author

Integrative Transcriptomic and Genomic Analysis Reveals the Regulatory Mechanisms Underlying Semen Quality Stability in Ganders.

Poultry science·2025

Same author

FALCON-Net: A hybrid fuzzy-attentive LSTM-convolutional neural network architecture for high-precision classification of steel alloys via femtosecond laser-ablation spark-induced breakdown spectroscopy.

Analytica chimica acta·2025

Same author

Tunable terahertz broadband absorber based on elliptical graphene ribbon metamaterials.

Applied optics·2025

Search research articles

Related Experiment Video

Updated: Mar 24, 2026

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

Target-enhanced double-pulse LIBS coupled with feature-fused CNN for mechanistic and interpretable coffee origin

Xiaoyong He¹, Kaiqiang Que^1,2, Tingrui Liang¹

¹School of Telecommunications Engineering & Intelligentization, Dongguan University of Technology, Dongguan 523808, China.

Food Chemistry: X

|March 23, 2026

Summary

This study introduces a new method combining Potassium-assisted Double-Pulse Laser-Induced Breakdown Spectroscopy (DP-LIBS) with a Feature-Fused Convolutional Neural Network (CNN) for accurate coffee origin authentication. The novel approach achieves 99% accuracy in identifying coffee provenance, outperforming traditional methods.

Keywords:

1D Grad-CAM++Coffee origin authentication Double-pulse LIBS Feature-fused CNN SHAP Trace element analysis

More Related Videos

A Two-Step Pyrolysis-Gas Chromatography Method with Mass Spectrometric Detection for Identification of Tattoo Ink Ingredients and Counterfeit Products

A Two-Step Pyrolysis-Gas Chromatography Method with Mass Spectrometric Detection for Identification of Tattoo Ink Ingredients and Counterfeit Products

Published on: May 22, 2019

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

Related Experiment Videos

Last Updated: Mar 24, 2026

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

A Two-Step Pyrolysis-Gas Chromatography Method with Mass Spectrometric Detection for Identification of Tattoo Ink Ingredients and Counterfeit Products

A Two-Step Pyrolysis-Gas Chromatography Method with Mass Spectrometric Detection for Identification of Tattoo Ink Ingredients and Counterfeit Products

Published on: May 22, 2019

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

Area of Science:

Analytical Chemistry
Food Science
Spectroscopy

Background:

Authenticating coffee's geographical origin is crucial to prevent economically motivated adulteration.
Rapid trace-element analysis in complex organic matrices like coffee is challenging.
Existing methods often struggle with accuracy and speed in complex samples.

Purpose of the Study:

To develop a novel synergistic framework for precise coffee traceability.
To integrate Potassium-assisted Double-Pulse Laser-Induced Breakdown Spectroscopy (DP-LIBS) with a Feature-Fused Convolutional Neural Network (CNN).
To enable autonomous extraction of spatial-spectral patterns for enhanced authentication.

Main Methods:

Utilized Potassium-assisted orthogonal Double-Pulse Laser-Induced Breakdown Spectroscopy (DP-LIBS) with a KHCO₃ solid target for enhanced signal sensitivity.
Developed a Feature-Fused CNN architecture by concatenating normalized spectral data with statistical descriptors.
Implemented a dual-interpretability strategy using SHAP analysis and 1D Grad-CAM++ for decision logic elucidation.

Main Results:

Achieved a superior classification accuracy and F1-score of 99.00% for coffee traceability.
Outperformed traditional algorithms like XGBoost (95.75%), PLS-DA (92.50%), RF (86.50%), and KNN (75.75%).
Demonstrated robustness with high precision (>94%) even under severe noise interference (30 dB SNR).

Conclusions:

The Feature-Fused CNN model effectively recognizes synergistic covariance of trace elements (Fe, Cr, Cu, K) for superior coffee provenance verification.
The integrated DP-LIBS and CNN framework offers a robust and mechanically interpretable strategy for food authentication.
This approach significantly advances the capability for rapid and precise trace-element analysis in complex organic matrices.