Near-infrared spectroscopy for predicting chemical composition and classifying local chicken meat

Near-infrared (NIR) spectroscopy was tested to predict meat quality and classify chicken parts, sex, and diet in a local chicken breed. Sixty chicks (both sexes) were reared under identical conditions until 120 days, then assigned to a low-lipidic (LL, 3.85% EE, ether extract) and a high-lipidic diet (HL, 9.49% EE). At 150 days, breast and thigh samples were collected, and physical traits –pH and color– and proximate composition –moisture, crude protein (CP), ether extract (EE), and ash– were determined using wet chemistry methods. Samples were scanned intact, ground, and freeze-dried with a benchtop NIR spectrometer (1100-2500 nm), and prediction models were developed on both fresh and dry matter bases. External validation (70/30% split) was applied for pH, yellowness, EE, and ash, whereas cross-validation was performed separately for breast and thigh for redness, lightness, moisture, and CP. Model performance was evaluated using the coefficient of determination in validation (R²VAL) and cross-validation (R²CV), and the ratio of performance to deviation (RPD). Physical traits and ash showed poor predictability (R²VAL and R2CV < 0.68; RPD < 1.74), while chemical traits, particularly EE in freeze-dried samples, achieved excellent prediction (R²VAL > 0.95; RPD > 3). Partial Least Squares-Discriminant Analysis enabled perfect discrimination of breast and thigh (100%), high accuracy for sex (> 89%), but limited discrimination for diet (< 67%). Overall, NIR spectroscopy demonstrated its potential as a rapid, non-destructive tool for predicting relevant chemical quality traits such as protein content and EE enhancing traceability in local poultry production systems.