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Accurate recognition of nutritional components in food is crucial for dietary management and health monitoring. Current methods often rely on traditional chemical analysis techniques, which are time-consuming, require destructive sampling, and are not suitable for large-scale or real-time applications. Therefore, there is a pressing need for efficient, non-destructive, and accurate methods to identify and quantify nutrients in food. In this study, we propose a novel deep learning model that integrates EfficientNet, Swin Transformer, and Feature Pyramid Network (FPN) to enhance the accuracy and efficiency of food nutrient recognition. Our model combines the strengths of EfficientNet for feature extraction, Swin Transformer for capturing long-range dependencies, and FPN for multi-scale feature fusion. Experimental results demonstrate that our model significantly outperforms existing methods. On the Nutrition5k dataset, it achieves a Top-1 accuracy of 79.50% and a Mean Absolute Percentage Error (MAPE) for calorie prediction of 14.72%. On the ChinaMartFood109 dataset, the model achieves a Top-1 accuracy of 80.25% and a calorie MAPE of 15.21%. These results highlight the model's robustness and adaptability across diverse food images, providing a reliable and efficient tool for rapid, non-destructive nutrient detection. This advancement supports better dietary management and enhances the understanding of food nutrition, potentially leading to more effective health monitoring applications.

Human milk vitamin content is an important indicator to evaluate the nutritional composition of human milk. This paper investigates the influence of maternal and infant factors on the dynamics of human milk vitamin content. A total of 147 mother-infant pairs from 3 different cities (north-south distribution) in China were selected and 9 major vitamins were measured in 332 human milk samples. The three vitamins (vitamin A, β-carotene, and pantothenic acid) showed significant downward trends with lactation period (| r | > 0.3, p < 0.05). The lactation period factor could explain the negative variation of vitamin A (21.2%) and pantothenic acid (9.5%). The factors of lactation period and oils intake could jointly explain variations of β-carotene (11.8%). (Registration number: NCT02658500).