Grain drying technology is a core process for ensuring food quality, extending storage life, and improving processing adaptability. With the continuous growth of global food demand and the increasing requirements for food quality and energy efficiency, traditional drying technologies face multiple challenges. This paper reviews six major grain drying technologies, comprising hot air drying, microwave drying, infrared drying, freeze drying, vacuum drying, and solar drying. It provides an in-depth discussion of the working principles, advantages, and limitations of each technology, and analyzes their performance in practical applications. In response to challenges such as high energy consumption, uneven drying, and quality loss during the drying process, the paper also explores the research progress of several hybrid drying systems, such as microwave–hot air drying combined systems and solar–infrared drying systems. Although these emerging technologies show significant potential in improving drying efficiency, energy saving, and maintaining food quality, their high costs, scalability, and process stability still limit large-scale applications. Therefore, future research should focus on reducing energy consumption, improving drying precision, and optimizing drying system integration, particularly by introducing intelligent control systems. This would maximize the preservation of food quality while improving the system’s economic efficiency and sustainability, promoting innovation in food production and processing technologies, and further advancing global food security and sustainable agricultural development.

Food contaminants, including harmful microbes, pesticide residues, heavy metals and illegal additives, pose significant public health risks. While traditional detection methods are effective, they are often slow and require complex equipment, which limits their application in real-time monitoring and rapid response. Surface-enhanced Raman scattering (SERS) technology has gained widespread use in related research due to its hypersensitivity, non-destructibility and molecular fingerprinting capabilities. In recent years, Au-Ag bimetallic nanoparticles (Au-Ag BNPs) have emerged as novel SERS substrates, accelerating advancements in SERS detection technology. Au-Ag BNPs can be classified into Au-Ag alloys, Au-Ag core–shells and Au-Ag aggregates, among which the Au-Ag core–shell structure is more widely applied. This review discusses the types, synthesis methods and practical applications of Au-Ag BNPs in food contaminants. The study aims to provide valuable insights into the development of new Au-Ag BNPs and their effective use in detecting common food contaminants. Additionally, this paper explores the challenges and future prospects of SERS technology based on Au-Ag BNPs for pollutant detection, including the development of functional integrated substrates, advancements in intelligent algorithms and the creation of portable on-site detection platforms. These innovations are designed to streamline the detection process and offer guidance in selecting optimal sensing methods for the on-site detection of specific pollutants.

The structural integrity of poultry eggs is essential for food safety, economic value, and hatchability. External eggshell quality—measured by thickness, strength, cracks, color, and cleanliness—is a key criterion for grading and sorting. Traditional assessment methods, although simple, suffer from subjectivity, low efficiency, and destructive nature. In contrast, recent developments in nondestructive testing (NDT) technologies have enabled precise, automated, and real-time evaluation of eggshell characteristics. This review systematically summarizes state-of-the-art NDT techniques including acoustic resonance, ultrasonic imaging, terahertz spectroscopy, machine vision, and electrical property sensing. Deep learning and sensor fusion methods are highlighted for their superior accuracy in microcrack detection (up to 99.4%) and shell strength prediction. We further discuss emerging challenges such as noise interference, signal variability, and scalability for industrial deployment. The integration of explainable AI, multimodal data acquisition, and edge computing is proposed as a future direction to develop intelligent, scalable, and cost-effective eggshell inspection systems. This comprehensive analysis provides a valuable reference for advancing nondestructive quality control in poultry product supply chains.