Assessment of Frozen Stored Silver Carp Surimi Gel Quality Using Synthetic Data-Driven Machine Learning (SDDML) Model

TL;DR

This study uses machine learning to improve the quality of surimi gels made from frozen Silver Carp, an invasive species, by finding optimal food additive combinations.

Contribution

A synthetic data-driven machine learning model is introduced to optimize surimi gel quality from frozen Silver Carp with limited experimental data.

Findings

Short-term frozen storage (<2 months) improves surimi gel-forming ability, while long-term storage (>2 months) degrades it.

Transglutaminase at 0.1 wt% effectively mitigates the negative effects of frozen storage on surimi gel quality.

The random forest-based SDDML model outperformed other algorithms in predicting gel quality attributes with high accuracy (R2 0.871–0.889).

Abstract

The invasive Silver Carp (Hypophthalmichthys molitrix) in North America represents a promising resource for surimi production; however, its gel formability deteriorates significantly during frozen storage. This study investigated the deterioration of gel properties in Silver Carp surimi over six months of frozen storage, and showed that short-term frozen storage (<2 months) was beneficial for surimi gel-forming ability, while extended frozen storage (>2 months) tended to have detrimental effects. The adverse effect of long-term frozen storage could be mitigated via using food additives (e.g., manufactured microfiber, transglutaminase, and chicken skin collagen), among which transglutaminase was the most effective. Transglutaminase at a relatively low level (0.1 wt%) could effectively negate frozen storage’s effects, and produced surimi gel with quality attributes (e.g., gel strength, hardness, and chewiness) at levels comparable to those from fresh fish samples. To assess the effects of the addition of various food additives for quality improvement, a synthetic data-driven machine learning (SDDML) approach was developed. After testing multiple algorithms, the random forest model was shown to yield synthetic data points that represented experimental data characteristics the best (R2 values of 0.871–0.889). It also produced improved predictions for gel quality attributes from control variables (i.e., additive levels) compared to using experimental data alone, showing the potential to overcome data scarcity issues when only limited experimental data are available for ML models. A synthetic dataset of 240 data points was shown to supplement the experimental dataset (60 points) well for assessment of the Frozen Silver Carp (FSC) surimi gel quality attributes. The SDDML method could be used to find optimal recipes for generating additive profiles to counteract the adverse effects of frozen storage and to improve surimi gel quality to upgrade underutilized invasive species to value-added food products.

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