The quality assessment of agricultural and forestry products is a core process in ensuring food safety and enhancing product competitiveness. Traditional detection methods suffer from drawbacks such as sample destruction, expensive equipment, and poor adaptability. As an innovative analytical technique combining optical and acoustic detection principles, photoacoustic spectroscopy technology (PAS) overcomes the limitations of conventional detection techniques that rely on transmitted or reflected optical signals through its unique light-thermal-acoustic energy conversion mechanism. With its non-contact, high-sensitivity, and multi-form adaptability characteristics, PAS has been increasingly applied in the quality assessment of agricultural and forestry products in recent years, providing a new solution for the simultaneous detection of internal and external quality in these products.

In the specific applications of agricultural and forestry product testing, PAS has demonstrated practical value in multiple aspects. In seed testing, researchers have established quantitative relationship models between photoacoustic signals and seed viability also achieved dynamic assessment of seed health by monitoring respiratory metabolic gases (e.g., CO₂ and ethylene). In fruit and vegetable quality analysis, PAS can capture characteristic substance changes during ripening. In the quality control of grain and oil products, Fourier-transform infrared PAS technology has been successfully applied to the rapid detection of protein content in wheat flour and aflatoxin in corn. In food safety monitoring, PAS has achieved breakthrough progress in heavy metal residue detection, pesticide residue analysis, and food authenticity identification.

Despite its evident advantages, PAS technology still faces multiple challenges in practical implementation. Technically, the complex matrix of agricultural and forestry products causes non-uniform generation and propagation of photoacoustic signals, complicating data analysis. And environmental noise interference (e.g., mechanical vibrations, temperature fluctuations) compromises detection stability, while spectral peak overlap in multi-component systems limits quantitative analysis accuracy. Equipment-wise, current PAS systems remain bulky and costly, primarily due to reliance on imported core components like high-power lasers and precision lock-in amplifiers, severely hindering widespread adoption. Moreover, the absence of standardized photoacoustic databases and universal analytical models restricts the technology's adaptability across diverse agricultural products. Looking forward, PAS development may focus on these key directions. Firstly, multi-technology integration by combining with Raman spectroscopy, near-infrared spectroscopy, and other sensing methods to construct multidimensional data spaces for enhanced detection specificity. Moreover, miniaturization through developing chip-based detectors via micro-electromechanical technology, replacing conventional solid-state lasers with vertical-cavity surface-emitting lasers (VCSELs), and adopting 3D printing for integrated photoacoustic cell fabrication to significantly reduce system size and cost. Furthermore, intelligent algorithm innovation with incorporating advanced deep learning models like attention mechanisms and transfer learning to improve interpretation of complex photoacoustic spectra. As these technical bottlenecks are progressively overcome, PAS is poised to establish a quality monitoring network spanning the entire "field-to-market" chain—from harvesting to processing/storage to distribution —thereby transforming agricultural quality control from traditional sampling-based methods to intelligent, standardized, full-process monitoring. This will provide technical support for food safety assurance and agricultural industry advancement.