Feature Reduction for the Classification of Bruise Damage to Apple Fruit Using a Contactless FT-NIR Spectroscopy with Machine Learning
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Date
2023Author
Nturambirwe, Jean Frederic Isingizwe
Hussein, Eslam
Thron, Christopher
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Spectroscopy data are useful for modelling biological systems such as predicting quality
parameters of horticultural products. However, using the wide spectrum of wavelengths is not
practical in a production setting. Such data are of high dimensional nature and they tend to result
in complex models that are not easily understood. Furthermore, collinearity between different
wavelengths dictates that some of the data variables are redundant and may even contribute noise.
The use of variable selection methods is one efficient way to obtain an optimal model, andthis was
the aim of this work. Taking advantage of a non-contact spectrometer, near infrared spectral data
in the range of 800–2500 nm were used to classify bruise damage in three apple cultivars, namely
‘Golden Delicious’, ‘Granny Smith’ and ‘Royal Gala’. Six prominent machine learning classification
algorithms were employed, and two variable selection methods were used to determine the most
relevant wavelengths for the problem of distinguishing between bruised and non-bruised fruit. The
selected wavelengths clustered around 900 nm, 1300 nm, 1500 nm and 1900 nm. The best results were
achieved using linear regression and support vector machine based on up to 40 wavelengths: these
methods reached precision values in the range of 0.79–0.86, which were all comparable (within error
bars) to a classifier based on the entire range of frequencies. The results also provided an open-source
based framework that is useful towards the development of multi-spectral applications such as rapid
grading of apples based on mechanical damage, and it can also be emulated and applied for other
types of defects on fresh produce