传统蛋制品无损检测技术及装备研究进展OA

Preserved and salted eggs have been the most favorite and representative traditional food products in China,because of their flavor,texture,and nutrition.However,raw materials,pickling environments,processing,and storage parameters have frequently led to unstable product quality during industrial production.Individual variation should also be taken into account.Common quality attributes include shell cracking,internal liquefaction,insufficient yolk oil exudation,uneven gel formation,and microbial deterioration.Traditional quality evaluation can rely on sensory assessment,candling inspection,knocking sound analysis,and physicochemical measurements.Nevertheless,strong subjectivity,low detection efficiency,destructive sampling,and low repeatability cannot fully meet the requirements of large-scale,standardized production and intelligent manufacturing.Consequently,there is an increasing demand for rapid,objective,and reliable quality evaluation in the traditional egg product industry in modern agriculture.This review aims to summarize the research progress of nondestructive detection technologies and detection equipment for traditional egg products.Cultural significance and economic value of preserved eggs and salted eggs were first introduced,followed by an overview of the pickling and quality evaluation indices.Attention was paid to the physicochemical transformations during pickling,including salt diffusion,alkaline penetration,protein denaturation,gel network formation,yolk oil release,moisture migration,and internal structural reconstruction.Feature extraction and quality identification depended on the sensory quality,nutritional properties,and storage stability of traditional egg products.Recent advances in nondestructive detection technologies were reviewed,including machine vision,spectral analysis,acoustic and mechanical detection,and emerging intelligent sensing technologies.Machine vision technology was widely employed for external quality inspection,including crack detection,shell defect recognition,texture analysis,and color feature extraction.High-throughput quality assessment was achieved by integrating image processing and deep learning.Spectroscopic techniques,such as near-infrared spectroscopy,hyperspectral imaging,Raman spectroscopy,and ultraviolet-visible spectroscopy,were also used to identify characteristics related to protein degradation,lipid migration,moisture distribution,and chemical compositions,particularly for internal quality.Acoustic and mechanical detections were used to evaluate shell integrity,internal viscosity,and structural stability using vibration signals,impact responses,and elastic properties.Furthermore,advanced technologies including electrical property-based techniques,chemical sensor technology,terahertz imaging technology,and nuclear magnetic resonance have gradually been introduced into traditional egg product detection,improving detection accuracy,automation level,and adaptability under complex industrial conditions.Practical challenges were then proposed for current nondestructive detection technologies,including insufficient robustness of detection models,unstable signal acquisition under variable pickling conditions,limited online detection,and the lack of unified quality grading standards.It was often required to integrate artificial intelligence,intelligent sensing,digital image processing,and multimodal data fusion to realize more accurate,adaptive,and real-time quality assessment.In addition,fully automated and multidimensional standardized grading can be expected to promote intelligent quality control and industrial upgrading.Nondestructive detection technologies can transform traditional egg product inspection from empirical judgment to data-driven intelligent analysis,particularly for national food safety,production efficiency,and product quality improvement.The findings can also provide data support for the sustainable and high-quality nature of the traditional egg product industry.