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• W4385285453 abstract "An ultrasonic detection method combined with machine learning algorithms was proposed for the resilience and cohesion prediction as well as visualization of tofu during the deep-frying process. The ultrasonic data underwent preprocessing techniques including discrete wavelet transform, dimensionality reduction through PCA, and outlier handling using MCCV. Subsequently, four machine learning algorithms, namely XGBoost, Random Forest, LightGBM, and Artificial Neural Network, were employed to construct the model, and their prediction performances were compared. Results indicated that the combination of LightGBM and discrete wavelet transform yielded the best performance, with Rp2 values of 0.969 and 0.956, and RMSEP values of 1.81 and 2.11, respectively. These results satisfy the requirements for predicting the texture properties of deep-fried tofu. This method provided a novel, rapid, and efficient non-destructive approach for detecting the texture properties of deep-fried tofu, which could have practical applications in the food industry." @default.
• W4385285453 created "2023-07-27" @default.
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• W4385285453 date "2023-12-01" @default.
• W4385285453 modified "2023-10-16" @default.
• W4385285453 title "Prediction of resilience and cohesion of deep-fried tofu by ultrasonic detection and LightGBM regression" @default.
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• W4385285453 doi "https://doi.org/10.1016/j.foodcont.2023.110009" @default.
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