Intelligent monitoring of post-processing characteristics in 3D-printed food products: A focus on fermentation process of starch-gluten mixture using NIR and multivariate analysis

The production of three-dimensional (3D)-printed food products requires not only optimal 3D-printing adaptability but also appropriate post-processing characteristics. This study aimed to use near infrared (NIR) spectroscopy to predict the rheological properties of 3D-printed dough, enabling intelligent monitoring of the dough's fermentation process. Utilizing support vector machine (SVM) classification model, the fermentation stages can be classified as under-fermentation, complete fermentation, and over-fermentation. Employing preprocessing methods with Synergy Interval Partial Least Square-Competitive Adaptive Reweighted Sampling (SIPLS-CARS) algorithm, 27, 39, 23, and 27 key wavelengths were filtered from the raw NIR spectral data, corresponding to the prediction of storage modulus (G′), loss modulus (G″), complex viscosity (η∗), and loss factor (tan δ), respectively. Quantitatively, SVM (Support Vector Machine) regression outperformed Partial Least Squares (PLS) with Rc2 values (0.95, 0.94, 0.94) and Rp2 values (0.93, 0.93, 0.94) for G′, G″, and η∗. NIR spectra-based predictive models demonstrated superior performance compared to rheo-fermentation properties models. In summary, these findings show the potential of NIR spectroscopy as a rapid tool for predicting the fermentation progress of 3D-printed doughs.