Detection of Dispersibility and Bulk Density of Instant Whole Milk Powder Based on Residual Network

To address the problems of the traditional international standard methods for milk powder quality detection such as subjectivity and lag, this study proposed a rapid method for the detection of the dispersibility and bulk density of milk powder based on residual network (ResNet). The dataset used in this study included 499 particle distribution images taken for 10 groups of instant whole milk powder samples under a 10 × optical microscope. Initially, these sample groups were tested for dispersibility and bulk density using the international standard methods, and classified into different levels of dispersibility and bulk density based on the test results. Subsequently, these microscopic images were used to train the ResNet to facilitate effective classification of different samples. Ultimately, the classification results were used to predict the dispersibility, loose density, and tapped density of instant whole milk powder. Additionally, this study compared the predictive performance of different deep learning models, including ResNet, EfficientNetV2, and Swin Transformer. The results indicated that the deep learning model based on ResNet 152 exhibited the best performance in predicting the dispersibility, loose density, and tapped density of instant whole milk powder, with accuracy rates of 97.50%, 98.75%, and 95.00%, respectively for the test set. The exceptional performance of these deep learning models in milk powder quality detection not only proves that this method can predict the dispersibility and bulk density of milk powder in real time and accurately, but also provides a new technological approach for online quality detection of milk powder.