Testing two NIRs instruments to predict chicken breast meat quality and exploiting machine learning approaches to discriminate among genotypes and presence of myopathies

Abstract

To discriminate among three poultry meat types (hybrid broiler, hybrid broiler affected by breast myopathies, and slow-growing native breed), and to predict the proximate and the amino acid (AA) composition of breast meat, two NIRs (Near-Infrared) instruments operating between 850 and 2500 nm coupled with chemometric algorithms and Machine Learning (ML) approaches, were tested. The Partial Least Square Discriminant Analysis was performed for genotype identification, resulting in a Mathew Correlation Coefficient (MCC) ranging from 0.61 to 1.00, according to the spectra pretreatments and instrument adopted. The Partial Least Square Regression allowed reaching a high cross-validation determination coefficient (R2cv) for crude protein (0.98) and ether extract (0.99), while only three AA (aspartic acid, alanine and methionine) reached R2cv > 0.55. The latter predictions were successfully used to discriminate between genotypes using Factorial Discriminant Analysis, with an MCC ranging from 0.67 to 0.95. Overall, both tested NIRs instruments allowed to determine the chemical composition of fresh and freeze-dried chicken meat. In this sense, a significant improvement of NIRs data interpretability was achieved thanks to the use of ML algorithms, as it was possible to discriminate the chemical composition of meat depending on the genetic group and the presence of breast myopathies.