Evaluating the liquid egg white freshness: Development, mechanistic analysis and application of a novel electrochemical method-based rapid detection technique

Globally, liquid eggs are gradually replacing shell eggs as the primary fresh eggs in market, yet no techniques for assessing their freshness. This study aims to establish E-HU (electrochemical Haugh units) models for detecting LEW's (liquid egg white) freshness: a Nonlinear regression model (M-1) for predicting Haugh units, and a Logistic regression model (M-2) for predicting freshness grade. Both models were validated, mechanistically analyzed, and applied. Validation: M-1 passed joint hypothesis test, regression coefficient test, correlation coefficient test (R2: 0.92), and accuracy test (92.9%). M-2 passed categorical hypothesis test (chi 2: 72.8), goodness-of-fit test (PR2: 0.93), and accuracy test (89.1%, for unfresh LEW: 95.2%), indicating E-HU model is reasonable. Mechanistic analysis: FT-IR and particle size demonstrate changes in proteins' structure (side chains, beta-turns, and aggregated beta-sheets increased; intramolecular beta-sheets and random coils decreased). Water activity, contact angle (86.0 degrees- 64.4 degrees to 91.0 degrees-68.3 degrees), surface tension (47.1-45.0 to 54.1-49.9 mN & sdot;m 1 ), LF-NMR (relaxation time T1: 383 to 333 ms; T2: 542-581 to 440-506 ms), and magnetic resonance imaging, indicate changes in hydration capacity. Briefly, proteins' structural changes and water migration during storage alter the LEW's electrochemical properties. Application (10 varieties): M-1 has high accuracy (brown-shelled eggs: 68.4%). M-2 has higher accuracy (total: 80.0%; pink-shelled eggs: 75.0%; brown-shelled eggs: 100%). In summary, a novel non-destructive and rapid detection technology (E-HU) was developed, demonstrating high accuracy in predicting Haugh unit and freshness grade of LEW. This innovation fills a gap in liquid egg detection technology and offers a valuable reference for future research.