https://doi.org/10.17113/ftb.64.04.26.9553

Article in press

Cross-Market Non-Destructive Quality Assessment of Arumanis Mangoes Using Genetic Algorithm–Optimized Ensemble Models and Low-Cost AS7265x UV–Vis–NIR Spectroscopy

Susanto B. Sulistyo¹*, Desta L. Fatmala¹, Dini Y. Putri¹, Muhammad R. Nursyahpratama¹, Krissandi Wijaya¹, Purwoko H. Kuncoro¹, Arief Sudarmaji¹, Pepita Haryanti² and Hasan M. Kadhim³

¹Department of Agricultural Engineering, Faculty of Agriculture, Jenderal Soedirman University, Jl. Dr. Soeparno 63, Purwokerto 53122, Indonesia

²Department of Food Technology, Faculty of Agriculture, Jenderal Soedirman University, Jl. Dr. Soeparno 63, Purwokerto 53122, Indonesia

³Department of Electrical Engineering, College of Engineering, Mustansiriyah University, Abi Talib Street, Baghdad 10052, Iraq

Copyright © 2026 This is a Diamond Open Access article published under CC-BY licence. Copyright remains with the authors, who grant third parties the unrestricted right to use, copy, distribute and reproduce the article as long as the original author(s) and source are acknowledged.

Food Technol. Biotechnol. 2026; 64(4).

Article history:

Received: 23 December 2025

Accepted: 8 November 2026

Keywords:

mango quality assessment; multispectral sensor; genetic algorithm-optimised committee machine; artificial neural network; linear regression; support vector regression

The content of this publication has not been approved by the United Nations and does not reflect the views of the United Nations or its officials or Member States.

Summary:

Research background. Arumanis mango is a high-value tropical fruit whose market acceptance depends primarily on internal quality attributes. However, reliable quality grading remains difficult because Arumanis is a green-mature cultivar that shows minimal external colour changes during ripening. Moreover, most existing non-destructive sensing studies rely on controlled experimental conditions, high-cost instruments, or homogeneous sample sources, with limited evaluation of model robustness across heterogeneous market environments.

Experimental approach. This research evaluates a low-cost, portable, and non-destructive quality assessment framework combining an AS7265x UV-Vis-NIR multispectral sensor with a genetic algorithm-optimised committee machine (GA-CM) to predict physicochemical parameters of Arumanis mangoes. A total of 200 samples were collected from both modern and traditional markets to capture variability in maturity levels and postharvest handling practices. Non-destructive spectral reflectance data were acquired at 18 wavelengths (410–940 nm), followed by destructive reference measurements of total soluble solids, total sugar, vitamin C, pH, and water content. Three base learners, e.g. linear regression (LR), support vector regression (SVR), and artificial neural network (ANN), were developed and integrated using two ensemble strategies: simple averaging and GA-based weighted averaging. Model performance was assessed using the coefficient of determination (R²) and mean squared error (MSE).

Results and conclusions. The results demonstrate that mangoes from different market typologies exhibit similar spectral trends but differ in reflectance magnitude, reflecting variations in maturity and postharvest conditions and yielding a robust, heterogeneous dataset. Among individual models, ANN and SVR significantly outperformed LR, with ANN showing superior capability in capturing non-linear relationships between spectral signatures and internal quality attributes. Ensemble modelling further enhanced prediction accuracy, with the GA-CM consistently outperforming both individual learners and simple averaging by optimally weighting base models. High predictive performance was achieved across all quality parameters, with an average R² of approximately 0.97.

Novelty and scientific contribution. This study provides a novel contribution by introducing a GA-optimised ensemble framework that leverages complementary modelling strengths under low spectral resolution conditions. By validating the system across mixed supply chains rather than controlled environments, the proposed approach demonstrates strong generalisation capability and practical applicability for real-world, non-destructive quality assessment of Arumanis mango.