## 3 Related Works and Baselines  
The studies related to the mobile food nutrient analysis can be categorized into a food-intake recommendation system, camera-based vision analysis system, and NIRs-based food analysis system. A food recommendation system [10; 15] helps users plan meals by suggesting meal timings and diets based on their health status. By entering details like height and weight, it generates a customized diet plan and recommends food within a specific calorie range. These systems are particularly useful for managing conditions like obesity and diabetes through personalized nutrition advice. Other studies [11; 14] have developed systems that use IoT technology to accurately monitor food intake, allowing for more precise diet management by tracking what is actually consumed. Recent studies [9; 16] have integrated machine learning algorithms to enhance the personalization and accuracy of these recommendations by analyzing health data and eating patterns. This ensures that dietary advice adjusts to the user's changing health needs and preferences. Camera-based methods [15; 34; 35] are the most widely researched approach to food recognition and calorie estimation. This approach involves taking photos of food with a mobile camera, identifying the food item, and displaying its calorie content based on data retrieved from a food database. This approach has gained particular popularity due to their simplicity and convenience. From a user-experience perspective, it is easy to capture and log the nutritional content of a meal just by snapping a photo with one's own smartphone. Additionally, the RGB-based method is relatively straightforward to develop, using image recognition technology to identify foods, connect to a dataset, and pull up the relevant nutritional information. However, despite being easy to use, this method struggles with accurately estimating the nutritional values of cooked food and meals with different recipes. This is mainly due to the limitations of the database, which only provides data for the stored food names and cannot account for foods not already in the system. To address these limitations of RGB cameras, applications using mobile NIR technology are being developed. NIR cameras can capture the chemical characteristics derived from nutritional bonds, making them more accurate for estimating nutrients in a wide variety of foods [21; 22; 23; 24]. Most studies using NIR camera [21; 22; 26; 27; 28; 29; 30; 31; 32] have focused on using prediction models to estimate values such as Cal and CPF, but they often overlook the uncertainty in these predictions. This uncertainty is crucial for individuals who are highly sensitive to specific foods as even small inaccuracies can have serious consequences. To tackle this, a previous study [31] proposed a neural network model with a multi-modal structure to reduce uncertainty. The proposed model consists of the two components that are the reconstruction for analyzing the food's NIR image and the prediction for estimating the nutrient values. The idea is that if a major error occurs in analyzing the food's NIR image, the predicted nutrient values cannot be trusted either, as both predictions are connected in the root layer of the neural network. Therefore, the study used errors in the NIR image analysis to reject unreliable nutrient predictions. However, this approach struggles to balance learning between analyzing the food's NIR image and predicting the nutrients. The reconstruction component requires a significantly larger number of terminal nodes compared to those needed for nutrient estimation. Consequently, during the training phase, the accumulated loss from the reconstruction nodes surpasses the loss from the nutrient estimation nodes. This imbalance can lead the model to be more sensitive to the reconstruction process than the estimation of the nutrient values [36; 37]. We identified reference models that work in a similar environment to ours.