## 1 Introduction  
Over the past decade, there has been a significant increase in food-related health issues, such as obesity and diabetes [1; 2; 3], affecting billions of the global population [4; 5; 6]. To help people manage these conditions, self-management is becoming increasingly important for maintaining health and wellness. The Centers for Disease Control and Prevention (CDC) recommend that individuals record their intakes of daily calorie and nutrients, such as carbohydrates, proteins, and fats (CPF), which are essential for managing weight and blood sugar levels [7].  
To facilitate the recording of food intake for individuals, various mobile applications have been proposed to estimate the calorie (Cal) and CPF contents of meals [8; 9; 10; 11; 12; 13; 14; 15]. Most of these apps use a smartphone camera to visually assess the food and estimate the nutrients based on image recognition technology [16; 17; 18; 19; 20]. While the camera-based approaches offer convenience and accessibility, they often struggle to provide accurate estimations for cooked food or meals with complex ingredients. To address the limitations, alternative approaches using near-infrared spectroscopy (NIR) have gained attention. The NIR identifies macronutrients based on the absorption characteristics of the chemical bonds (C-H, O-H and N-H) in the near-infrared region. Each macronutrient (CPF) has unique absorption peaks at specific wavelengths in the NIR spectrum. This makes NIR a promising solution for analyzing a wide variety of foods [21; 22; 23; 24; 25]. In particular, deep learning models based on a supervised approach excel at generating proposed outputs from specific inputs, making them highly effective for analyzing Cal and CPF values from the NIR signal of food.  
Most studies using NIR cameras [26; 27; 28; 29; 30; 31; 32] have focused on developing regression models to predict nutrient values such as Cal and CPF. However, these approaches often overlook the uncertainty associated with the reliability of the prediction result. In this case, for individuals who are highly sensitive to specific foods, even minor inaccuracies in calorie estimation can lead to severe consequences. Therefore, it is essential to reduce the uncertainty in the prediction model to ensure their safety. Additionally, a notification system is necessary to inform users when a prediction result may be unreliable due to high uncertainty, enabling them to decide whether to trust the estimate.  
In this paper, we propose a novel approach to food nutrient prediction that aims to reduce model uncertainty and improve prediction accuracy. The proposed model achieves these improvements by leveraging the natural relationship between Cal and CPF values, which are related by the fact that their values should ideally be equal. As prediction errors cause deviations between these values, this difference forms the basis for a reliability metric that allows for the model to autonomously discard unreliable predictions. Based on that, we introduce a metric for measuring the uncertainty of prediction based on this deviation and formulate an uncertainty-driven objective function designed to minimize the uncertainty and improve the accuracy. Furthermore, the proposed objective function does not require additional parameters other than those needed for food nutrient prediction, thereby eliminating the need for extra architectures or multimodal layers. Consequently, our approach enables the development of a simpler and more efficient model.  
In summary, our main contributions are as follows:  
* Uncertainty-Driven Prediction and Rejection. We introduce an uncertainty assessment metric that allows for the model to autonomously reject predictions deemed unreliable due to high uncertainty.
* Object Function for Uncertainty Reduction. We propose a specialized loss function that reduces uncertainty in predictions by focusing more on reliable data and less on uncertain data during training, which enhances model performance and robustness.