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Related Experiment Video

Updated: Jun 14, 2025

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

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Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality

Jiwen Ren¹, Yuming Xiong¹, Xinyu Chen²

¹School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.

Sensors (Basel, Switzerland)

|August 29, 2024

Summary

This summary is machine-generated.

Deep learning (DL) models significantly improve near-infrared spectroscopy (NIRS) analysis accuracy compared to shallow learning (SL). A novel Gramian angular difference field and convolutional neural network (G-CACNN) model demonstrates superior robustness and noise resistance for NIRS applications.

Keywords:

Gramian angular difference field convolutional neural networks coordinate attention near-infrared spectroscopy robust model

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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

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Published on: June 28, 2016

Area of Science:

Analytical Chemistry
Chemometrics
Spectroscopy

Background:

Near-infrared spectroscopy (NIRS) analysis success relies on precise calibration models.
Shallow learning (SL) algorithms struggle with spectral data complexity and noise, limiting NIRS applications.
Deep learning (DL) offers potential for improved feature extraction from limited spectral samples.

Purpose of the Study:

To evaluate the robustness and effectiveness of NIRS calibration models.
To compare the performance of SL, consensus learning (CL), and DL methods.
To propose and validate a novel G-CACNN model for NIRS discriminant analysis.

Main Methods:

Utilized discriminant analysis on wheat kernels and Yali pears datasets.
Compared partial least squares discriminant analysis (PLS-DA) with DL and CL models.
Developed a Gramian angular difference field and coordinate attention convolutional neural network (G-CACNN) model.

Main Results:

DL and CL models showed less sensitivity to spectral preprocessing than SL.
The proposed G-CACNN model achieved high accuracy (98.48% and 99.39%) in discriminant tasks.
G-CACNN demonstrated superior robustness and noise resistance compared to other models.

Conclusions:

Deep learning significantly enhances NIRS analysis accuracy and robustness.
The G-CACNN model provides a powerful and noise-resistant approach for NIRS applications.
This study advances NIRS calibration model development for broader applicability.