Feasibility of Near-Infrared Spectroscopy in the Classification of Pig Lung Lesions

Abstract

Respiratory diseases significantly affect intensive pig farming, causing production losses and increased antimicrobial use. Accurate classification of lung lesions is crucial for effective diagnostics and disease management. The integration of non-destructive and rapid techniques would be beneficial to enhance overall efficiency in addressing these challenges. This study investigates the potential of near-infrared (NIR) spectroscopy in classifying pig lung tissues. The NIR spectra (908–1676 nm) of 101 lungs from weaned pigs were analyzed using a portable instrument and subjected to multivariate analysis. Two distinct discriminant models were developed to differentiate normal (N), congested (C), and pathological (P) lung tissues, as well as catarrhal bronchopneumonia (CBP), fibrinous pleuropneumonia (FPP), and interstitial pneumonia (IP) patterns. Overall, the model tailored for discriminating among pathological lesions demonstrated superior classification performances. Major challenges arose in categorizing C lungs, which exhibited a misclassification rate of 30% with N and P tissues, and FPP samples, with 30% incorrectly recognized as CBP samples. Conversely, IP and CBP lungs were all identified with accuracy, precision, and sensitivity higher than 90%. In conclusion, this study provides a promising proof of concept for using NIR spectroscopy to recognize and categorize pig lungs with different pathological lesions, offering prospects for efficient diagnostic strategies.