Assessment of fat depots for several goat body parts is an expensive and time-consuming task requiring a trained technician. Therefore, the establishment of models to predict fat depots based on data requiring simpler and easier procedures, such as ultrasound measurements, that could be carried out in vivo, would be a major advantage. An interesting alternative to the use of multiple linear regression models is the use of partial least squares or artificial neural network models because they allow the establishment of one model to simultaneously predict different fat depots of interest. In this work, the applicability of these models to simultaneously predict 7 goat fat depots (subcutaneous fat, intermuscular fat, total carcass fat, omental fat, kidney and pelvic fat, mesenteric fat, and total body fat) was investigated. Although satisfactory correlation and prediction results were obtained using the multiple partial least squares model (cross-verification and validation R^sup 2^ and standard prediction error values between 0.66 and 0.98 and 247 and 2,168. respectively), the best global correlation and prediction performances were achieved with the multiple radial basis function artificial neural network (verification and validation R^sup 2^ and standard prediction error values between 0.82 and 0.96 and 304 and 1,707, respectively). These 2 multiple models allowed correlating and predicting simultaneously the 7 goat fat depots based on the goat BW and on only 2 ultrasonic measures (lumbar subcutaneous fat between fifth and sixth vertebrae and the fat depth at the third sternebra). Moreover, both multiple models showed better results compared with those obtained with multiple linear regression models proposed in previous work. [PUBLICATION ABSTRACT]
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