Non-Contact Evaluation of Pigs' Body Temperature Incorporating Environmental Factors.

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs' internal body temperature.

Establishment of validated models for non-invasive prediction of rectal temperature of sows using infrared thermography and chemometrics.

Rectal temperature is an important physiological indicator used to characterize the reproductive and health status of sows. Infrared thermography, a surface temperature measurement technology, was investigated in this study to explore its feasibility in non-invasive detection of rectal temperature in sows. A total of 124 records of rectal temperature and surface temperature in various body regions of 99 Landrace × Yorkshire crossbred sows were collected. These surface temperatures together with ambient temperature, ambient humidity, and wind speed in pig pens were correlated with the real rectal temperature of sows to establish rectal temperature prediction models by introducing chemometrics algorithms. Two types of models, i.e., full feature models and selected feature models, were established by applying the partial least squares regression (PLSR) method. The optimal model was attained when 7 important features were selected by LARS-Lasso, where correlation coefficients and root mean squared errors of calibration were 0.80 and 0.30 °C, respectively. Particularly, the validity and stability of established simplified models were further evaluated by applying the model to an independent prediction set, where correlation coefficients and root mean squared errors for prediction were 0.80 and 0.35 °C, respectively. The validation of established models is scarce in previous similar studies. Above all, this study demonstrated that introduction of chemometrics methodologies would lead to more reliable and accurate model for predicting sow rectal temperature, thus the potential for ensuring animal welfare in a broader view if extended to more applications.

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology.

In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.