【摘要】 粮油加工作为食品工业的核心领域，其智能化转型亟需高效、精准的品质检测技术支撑。可见/近红外光谱技术凭借快速、无损、多指标同步检测的优势，已成为粮油加工过程品质监控的核心手段。本文系统梳理近红外光谱检测技术原理、智能装备研发及光谱数据处理方法的创新进展，即：硬件层面，便携式与在线监测装备突破小型化与抗干扰技术瓶颈，实现从实验室到工业场景的跨越；算法层面，光谱预处理、变量筛选与智能建模技术的融合，显著提升了检测精度与鲁棒性；应用层面，该技术已渗透至谷物加工链水分调控、油脂精炼氧化监测等关键环节，推动质控模式向数据驱动转型。然而，模型泛化能力不足、复杂工况适应性弱及标准化体系缺失仍是当前主要的技术瓶颈。未来需通过深度迁移学习、多源信息融合与边缘计算等技术优化“算法-设备-标准”协同创新体系，以实现粮油加工全链条实时质量调控与智能化升级。更多还原

【Abstract】 As a core sector of the food industry, grain and oil processing urgently requires efficient and precise quality detection technologies to drive its intelligent transformation. Visible/Near-infrared(Vis/NIR) spectroscopy, with its advantages of rapid, non-destructive, and multi-parameter synchronous detection, has emerged as a pivotal tool for quality monitoring in grain and oil processing. This paper systematically reviewed the latest advancements in NIR detection principles, intelligent equipment development, and spectral data processing methodologies. At the hardware level, breakthroughs in miniaturization and anti-interference technologies had enabled portable and online monitoring devices to transition from laboratory research to industrial applications. Algorithmically, the integration of spectral preprocessing, variable selection, and intelligent modeling had significantly enhanced detection accuracy and robustness. In practical applications,the technology had been deployed across critical stages such as moisture regulation in grain processing chains and oxidation monitoring during oil refining, driving a shift toward data-driven quality control. However, challenges persist, including limited model generalization, weak adaptability to complex industrial environments, and the absence of standardized systems. Future advancements demanded collaborative innovation in ’algorithm-equipment-standard’ systems through deep transfer learning, multi-source information fusion, and edge computing technologies to achieve real-time quality regulation and intelligent upgrades across the entire grain and oil processing chain.更多还原