1Department of Informatics and Technology, Faculty of Engineering, Informatics and Architecture, European University of Tirana, Street Xhanfize Keko, Kompleksi Xhura, Tirana, 1000, Albania
2Department of Natural Applied Sciences, Faculty of Professional Studies, University “Aleksandër Moisiu” Durrës, Durrës, Albania
3Department of Chemistry, Faculty of Natural Sciences, University of Tirana, Blvd Zog 1, 25/1, Tirana, 1001, Albania
*Corresponding Author: Dritan Topi, Department of Chemistry, Faculty of Natural Sciences, University of Tirana, Blvd Zog 1, 25/1, Tirana, 1001, Albania, Email: dritan.topi@unitir.edu.al
Online Published on 10 March, 2026.
Wheat quality and safety are crucial for global food security and regional agricultural economies. Conventional analytical methods for assessing grain composition and quality-while accurate-are often time-consuming, destructive and resource-intensive. In contrast, near-infrared spectroscopy (NIR) has emerged as a rapid, non-destructive alternative that provides detailed chemical and physical insights. Recent advancements in machine learning have enhanced NIR’s analytical capabilities by enabling the development of predictive models that accurately categorize, quantify and monitor grain quality parameters. These models-spanning from linear regression methods to intricate neural networks-enable researchers to discern significant patterns from spectral data, even amidst noise or overlapping signals.
This study used NIR spectroscopy and machine learning models to evaluate wheat quality across multiple Albanian regions during the 2023 harvest. NIR spectra were preprocessed using standard normalization and smoothing techniques. Machine learning models-including PLSR, SVM and random forest-were trained to predict key quality traits such as protein content and moisture. Model performance was validated using cross-validation metrics (RMSE, R2, accuracy), demonstrating the potential of NIR-ML integration for rapid, non-destructive grain assessment.
Protein content ranged from 9.6% to 15.0%, gluten from 19.4% to 37.5% and starch from 66.3% to 71.5%, indicating consistent nutritional profiles across samples. Sedimentation index values (21.1-57.8 cm3) and moisture levels (9.8-12.5%) confirmed compliance with EU food safety standards. The integration of NIR spectroscopy with machine learning enabled rapid, non-destructive prediction of wheat quality traits, supporting scalable decision-making in agri-food systems.
Albania agriculture, Machine learning, Near-infrared spectroscopy, Non-destructive analysis, Small-grain cereals, Wheat quality
