Method for online monitoring of gel strength changes during surimi thermal processing based on hyperspectral imaging

A method for online monitoring of gel strength changes during surimi thermal processing based on hyperspectral imaging, comprising:

• acquiring raw hyperspectral data of a surimi sample during a heating process, and measuring a gel strength of the surimi sample;
• preprocessing the raw hyperspectral data;
• constructing a dataset based on preprocessed raw hyperspectral data and the gel strength;
• training and validating a partial least squares (PLS) model using the dataset to obtain a gel strength prediction model;
• monitoring the gel strength changes during the surimi thermal processing by the gel strength prediction model;
• step of preprocessing the raw hyperspectral data comprises:
• performing black-and-white correction on the raw hyperspectral data, wherein the raw hyperspectral data comprises visible and near-infrared (VNIR) and near-infrared (NIR) dual-band hyperspectral data;
• extracting region of interest (ROI) from corrected hyperspectral data;
• obtaining mean spectral reflectance of all pixels within the ROI as average spectral data of the surimi sample;
• processing the average spectral data through standard normal variable transformation, multiplicative scattering correction, first-order derivative, and second-order derivative;
• and
• before training the PLS model using the dataset, the method further comprises:
• simplifying the PLS model using variable selection algorithms comprising variable combination population analysis (VCPA), VCPA-iterative retaining information variables (VCPA-IRIV), VCPA-genetic algorithm (VCPA-GA), and iterative retaining information variables (IRIV).