Detection of Chicken Meat Freshness Using NIR spectroscopy and Machine Learning Algorithms

Oblitas-Cruz J.; Uriarte-Valdivia J.; Rodríguez-León A. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.535

Resumen

Chicken meat freshness is a critical quality and safety indicator that requires rapid and non-destructive detection methods. This study aimed to develop a spectral classification model to identify chicken freshness using NIR spectroscopy and machine learning algorithms. A total of 180 spectra (1100-2495 nm) were collected and labeled as “fresh” (days 1-3) or “spoiled” (days 4-5). After Savitzky-Golay smoothing and SNV correction, five models (SVM, LASSO, Ridge, Elastic Net, Random Forest) were trained using stratified cross-validation. The LASSO model achieved the best performance with 97.2% accuracy and AUC = 0.977, with no false negatives in the spoiled class, followed by SVM (94.4%). Results demonstrate the effectiveness of regularized linear architectures for detecting chemical changes associated with spoilage. This approach provides a rapid, non-invasive, and accurate tool for freshness monitoring in poultry production chains.

Instituciones: Universidad Nacional de Cajamarca; Universidad Privada del Norte