This study was conducted to evaluate the feasibility of using near-infrared spectroscopy (NIRS) to predict beef consumers’ perceptions. Photographs of 200 raw steaks were taken, and NIRS data were collected (transmittance and reflectance). The steak photographs were used to conduct a face-to-face survey of 400 beef consumers. Consumers rated beef color, visible fat, and overall appearance, using a 5-point Likert scale (where 1 indicated “Dislike very much” and 5 indicated “Like very much”), which later was simplified in a 3-point Likert scale. Factor analysis and structural equation modeling (SEM) were used to generate a beef consumer index. A partial least square discriminant analysis (PLS-DA) was used to predict beef consumers’ perceptions using NIRS data. SEM was used to validate the index, with root mean square errors of approximation ≤0.1 and comparative fit and Tucker–Lewis index values <0.9. PLS-DA results for the 5-point Likert scale showed low prediction (accuracy < 42%). A simplified 3-point Likert scale improved discrimination (accuracy between 52% and 55%). The PLS-DA model for purchasing decisions showed acceptable prediction results, particularly for transmittance NIRS (accuracy of 76%). Anticipating beef consumers’ willingness to purchase could allow the beef industry to improve products so that they meet consumers’ preferences.