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IR Frequency Region: Fingerprint Region

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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...

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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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Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with

Jean Frederic Isingizwe Nturambirwe¹, Eslam A Hussein², Mattia Vaccari^1,2,3

¹Eresearch Office, DVC Research and Innovation, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa.

Foods (Basel, Switzerland)

|January 8, 2023

Summary

Variable selection in near-infrared spectroscopy effectively identifies key wavelengths for classifying apple bruising. This approach simplifies models and enables practical, rapid quality assessment of horticultural products.

Keywords:

apples baseline bruise damage defect classification feature reduction machine learning model optimisation quality control uncertainty quantification variable selection

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Area of Science:

Agricultural Science
Spectroscopy
Machine Learning

Background:

High-dimensional spectroscopy data present challenges for practical modeling in production settings.
Wavelength collinearity in spectral data can lead to redundant variables and noise, complicating model interpretation.
Variable selection is crucial for developing efficient and understandable models for biological systems.

Purpose of the Study:

To identify optimal wavelengths for classifying bruise damage in apples using near-infrared (NIR) spectroscopy.
To compare the performance of different machine learning algorithms with variable selection for apple quality assessment.
To develop a practical, open-source framework for multi-spectral applications in fresh produce grading.

Main Methods:

Utilized non-contact NIR spectroscopy (800-2500 nm) to collect spectral data from three apple cultivars.
Employed six machine learning classification algorithms and two variable selection methods.
Identified key wavelengths clustered around 900 nm, 1300 nm, 1500 nm, and 1900 nm.

Main Results:

Selected wavelengths around 900, 1300, 1500, and 1900 nm were found to be most relevant for distinguishing bruised from non-bruised apples.
Linear regression and support vector machine models using up to 40 selected wavelengths achieved high precision (0.79-0.86).
Performance of selected wavelength models was comparable to models using the full spectral range.

Conclusions:

Variable selection significantly enhances the practicality and interpretability of spectroscopy data for horticultural product quality assessment.
The developed framework supports the creation of multi-spectral applications for rapid grading of apples based on mechanical damage.
The methodology can be adapted for defect detection in other fresh produce items.