Abstract:
Determining the optimum harvest maturity is crucial for ensuring high avocado fruit quality after
harvesting. Currently, maturity is assessed using invasive methods that involve dissecting the fruit to
measure markers such as dry matter (DM), moisture content (MC), or mesocarp oil (CM). These
methods are time-consuming and require laboratory facilities and trained personnel. Therefore, rapid,
non-invasive methods are needed. This study established a fast method to assess avocado maturity
without damaging the fruit, employing diffuse reflectance spectroscopy and machine learning
algorithms. Spectral data from 200 Hass avocados were recorded over the 900-2500 nm wavelength
range using a NIR Quest spectrometer. To establish a reference, dry matter content was determined
through a conventional destructive method. The spectral data were converted to absorbance and
preprocessed using first-order and second-order derivatives, Savitzky-Golay filtering, standard normal
variate (SNV) transformations, and multiplicative scatter correction (MSC). Machine learning models,
including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Support Vector
Machines (SVM), and Random Forest (RF), were employed to predict dry matter content. In this study,
a CNN model trained on raw spectral data for the full range 1000-2350nm achieved superior
performance, with a coefficient of determination (R²) value of 0.94, a root mean square error of
prediction (RMSEP) of 1.52, and a mean absolute error (MAE) of 1.17. The narrower 1000-1500 nm
range performed comparatively well, with an R² value of 0.94, RMSEP of 1.58, and MAE of 1.19,
indicating it is a strong candidate for accurate predictions with potential for cost-effective NIR
instruments. This range offers good accuracy and potential for creating cost-effective NIR instruments.
These findings indicate that targeted NIR spectroscopy, combined with advanced machine learning, can
accurately predict avocado maturity without damaging the fruit. The developed approach provides a
non-destructive, rapid, and objective alternative to traditional laboratory-based methods, reducing the
need for destructive sampling and minimizing post-harvest losses. This contributes to improved supply
chain efficiency, consistent fruit quality, and enhanced competitiveness in both local and international
avocado markets.
