Early pregnancy diagnosis of rabbits: A non-invasive approach using Vis-NIR spatially resolved spectroscopy

Abstract

Pregnancy diagnosis is essential for rabbit's reproductive management. The early identification of non-pregnant rabbits allows for earlier re-insemination, increases the service rate, and reduces the laboring interval in commercial operations. The objective of this study was to establish the feasibility of using a Vis-NIR spatially resolved spectroscopy for diagnosing pregnancy in female rabbits. A total of 141 female rabbits, including 67 pregnant female rabbits (PRs) and 74 non-pregnant female rabbits (NPRs), were measured spectrally between 350 and 1000 nm with different source-detector distances (SDD). Different preprocessing methods were used to transform and enhance the spectral signal. A partial least squares-discriminant analysis (PLS-DA) classification model of the original and preprocessed spectra was established. The highest accuracy of the calibration set and prediction set was 91.75% and 86.05%, respectively. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were used to select characteristic wavelengths from the variables of VIP > 1 (Variable importance in projection),and four classification models were established based on selected wavelengths, including PLS-DA, support vector machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes. SPA-SVM was the optimal classification model, the sensitivity, specificity, and accuracy of the validation set and prediction set were 93.18%, 94.44%, 93.88%, 86.96%, 90.00%, 90.69% respectively. The results showed that Vis-NIR spatially resolved spectroscopy combined with classification models could discriminate the PRs and NPRs.