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Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra.

Dharma Raj Pokhrel¹, Panmanas Sirisomboon¹, Lampan Khurnpoon²

¹Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

Sensors (Basel, Switzerland)

|June 10, 2023

Summary

Machine learning algorithms, particularly neural networks, show superior performance in classifying Monthong durian pulp using near-infrared (NIR) spectra compared to Partial Least Squares Discriminant Analysis (PLS-DA). This advancement aids in quality control for durian pulp.

Keywords:

Monthong durian NIR spectroscopy Partial Least Squares Discriminant Analysis (PLS-DA)dry matter content machine learning multivariate classification algorithms neural network soluble solid content

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Area of Science:

Agricultural Science
Analytical Chemistry
Data Science

Background:

Accurate classification of durian pulp quality is crucial for production and storage.
Near-infrared (NIR) spectroscopy offers a non-destructive method for analyzing fruit pulp composition.
Multivariate classification algorithms are employed to interpret complex spectral data.

Purpose of the Study:

To compare the classification performance of Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning (ML) algorithms.
To classify Monthong durian pulp based on Dry Matter Content (DMC) and Soluble Solid Content (SSC) using NIR spectra.
To identify optimal spectral preprocessing techniques for enhanced classification accuracy.

Main Methods:

Collected and analyzed 415 Monthong durian pulp samples.
Acquired inline near-infrared (NIR) spectra for each sample.
Preprocessed spectra using five techniques: MA+SNV, SG+SNV, SG+MN, SG+BC, SG+MSC.
Applied PLS-DA and machine learning algorithms, including a wide neural network.

Main Results:

The Savitzky-Golay with Standard Normal Variate (SG+SNV) spectral preprocessing yielded the best results for both algorithms.
The optimized wide neural network achieved the highest classification accuracy of 85.3%.
The PLS-DA model achieved an overall classification accuracy of 81.4%, lower than the ML model.

Conclusions:

Machine learning algorithms, specifically neural networks, demonstrate superior potential for classifying durian pulp quality using NIR spectroscopy compared to PLS-DA.
The SG+SNV preprocessing method is effective for enhancing classification accuracy.
These findings support the application of ML algorithms in the quality control of durian pulp production and storage.