Machine learning enabled assessment of the vacuum freeze-drying of the kiwifruit

• To develop a neural network to accurately assess the freeze drying of the kiwifruit. • Using feature scoring to translate drying characteristics into trainable data. • The use of Generative Adversarial Network for augmenting VFD data. • Bayesian surrogate hyper-parameter tuning for optimization of the DNN model. • Explainable artificial intelligence to interpret the DNN predictions. Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century. Vacuum freeze-drying (VFD), though invented over a hundred years ago, remains one of the most advanced drying techniques, known for sustainably drying perishable products while maintaining quality indices and morphological properties comparable to their fresh state. The performance of the VFD system is sensitive to the operating conditions and features of the drying product which is assessed using experimental and/or numerical methods. However, the qualitative aspects of the dried product are not predictable. In this context, the present study aims to create a deep neural framework (DNF) that predicts the performance of a Vacuum Freeze Drying (VFD) system for kiwifruit, based on its morphology and nutritional value under varying conditions. This involves translating the fruit’s morphological features into trainable data and using a Generative Adversarial Network (GAN) to create diverse, unlabeled datasets. The framework is optimized using Gaussian Process (GP) for hyper-parameter tuning, focusing on minimizing errors like mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The maximum MSE of 1.243 is found in the prediction of rehydration rate, followed by color (0.725), energy consumption (0.426), moisture content (0.379), texture (0.320), sensory (0.250), and Brix (0.215), respectively. The maximum MAE and MAPE values are recorded 0.833 and 32.99 % while the minimum is observed 0.368 and 7.019 % in the case of rehydration rate and Brix, respectively. Overall, the R 2 value was computed 0.863 which is reasonable for the quality assessment of kiwifruit dried by the VFD system.