Abstract:
Aims: To develop a rapid, non-invasive method for predicting Hass avocado maturity using nearinfrared diffuse reflectance spectroscopy (NIR-DRS) combined with machine learning algorithms,
and to identify the optimal NIR wavelength range for accurate dry matter content prediction.
Study Design: An experimental design involving spectral data collection from Hass avocados and
the development of machine learning models for dry matter prediction.
Methodology: Spectral data from 200 Hass avocados were collected using near-infrared diffuse
reflectance spectroscopy (900-2500 nm). To improve the quality of the spectral data and reduce
noise, standard normal variate was used to correct for scattering and remove unwanted variability in the spectral data. PCA was then performed to reduce the dimension of the spectral data while
retaining the most significant variance. Following preprocessing, machine learning models,
including Convolutional Neural Networks (CNN), were trained to predict dry matter content, and the
optimal wavelength range was determined for accurate prediction.
Results: The CNN model demonstrated superior performance for dry matter prediction with R² of
0.91 in the testing set. The wavelength range of 1000-1500 nm was identified as optimal, offering
accurate predictions while reducing computational complexity. This range shows potential for
developing cost-effective NIR devices for real-time maturity assessment.
Conclusion: NIR spectroscopy combined with machine learning offers a non-invasive, accurate
method for predicting avocado dry matter, with potential applications for quality control in the
avocado industry. The findings demonstrate that focusing on specific wavelength ranges can lead to
more affordable and efficient NIR solutions.
