Application of Photoacoustic Spectroscopy in Quality Assessment of Agricultural and Forestry Products

doi:10.12133/j.smartag.SA202505026

Abstract

Abstract:

[Significance] 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. [Progress] In the specific applications of agricultural and forestry product testing, PAS has demonstrated practical value in multiple aspects. In seed testing, researchers have not only established quantitative relationship models between photoacoustic signals and seed viability but 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. [Conclusions and Prospects] 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.

Key words: photoacoustic spectroscopy, quality of agricultural and forestry products, non-destructive evaluation, progress in application, constraints

CLC Number:

TS255.1

XIE Weijun, CHEN Keying, QIAO Mengmeng, WU Bin, GUO Qing, ZHAO Maocheng. Application of Photoacoustic Spectroscopy in Quality Assessment of Agricultural and Forestry Products[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202505026.

Figures/Tables 7

Fig. 1

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Table 1

Application of photoacoustic spectroscopy in grain and oil quality analysis

技术方法	应用领域	研究对象	检测目标形态	数据处理方法	核心目标	波长范围	功率	光调制频率	特征波长范围
光声光谱^［49］	谷物质量评估	玉米	固态	感官评价相关性分析	关联光吸收特性与消费者感知颜色	350~700 nm	——	17 Hz	350~670 nm
紫外-可见光声光谱^［50］	谷物成分分析	红高粱	粉末	F检验、t检验	替代传统化学法测定酚类化合物	200~800 nm	——	18 Hz	285 nm 335 nm
傅里叶变换红外光声光谱^［51］	谷物黄曲霉素检测	玉米	固态	专家系统	玉米黄曲霉侵染检测	4 000~600 cm^-1	140W	——	3 400~3 360 cm^-1 2 853~2 923 cm^-1 3 200~2 200 cm^-1
傅里叶变换红外光声光谱^［52］	谷物营养成分分析	小麦	粉末	主成分分析、多变量校准模型	测定小麦粉中的总蛋白和湿面筋	4 000~500 cm^-1	——	——	4 000~2 300 cm^-1 2 300~1 600 cm^-1 1 600~400 cm^-1
光声光谱^［53］	谷物光学系数测定	玉米	固体	波长和谷物状态、厚度和光学不透明数据分析	确定玉米种的光吸收系数	320~700 nm	——	——	633 nm
光声光谱^［54］	谷物病原体检测	小麦	固态	特征峰识别	快速诊断小麦携带的病原菌污染	200~800 nm	——	18 Hz	285和335 nm
紫外-可见光声光谱^［55］	谷物质量评估	玉米	固态	主成分分析	区分不同产地玉米的营养品质差异	270~500 nm	——	17 Hz	325～395 nm
光声光谱^［56］	谷物质量评估	玉米	固态	方差分析	谷物反射率测量、物理特性和养分含量	270~500 nm	——	——	290 nm 350 nm
光声光谱^［57］	谷物质量评估	豆类	固态	相关性分析	优化激光参数以提升谷物储藏性能	270~500 nm	——	17 Hz	470 nm
光声光谱^［58］	谷物评估	玉米	固态	非辐射弛豫模型	研究种子色素分子能量衰减过程	350、470和650 nm	700 W	17~50 Hz	350 nm
光声光谱^［59］	谷物质量检测	大麦	固态	光学吸收系数分析 + 方差分析	评估激光处理对谷物微生物的抑制作用	650 nm	27.4 mW	17 Hz	650 nm
光声光谱^［60］	谷物分类	玉米	固态	标准差分析、一阶导数处理	提高颜色差异玉米的检测灵敏度	325~800 nm	——	17 Hz	325～670 nm
多频光声光谱^［61］	种子深度剖面分析	玉米	固态	频率扫描热扩散模型	无损获取玉米内部结构信息	330~800 nm	——	17~50 Hz	350～800 nm
光声光谱^［62］	谷物健康监测	玉米	固态	方差分析	分析激光处理对色素合成的生物刺激效应	400~500 nm	27.4 mW	17 Hz	471～478 nm
光声光谱^［63］	谷物健康管理	大麦	固态	微生物计数统计分析	验证激光处理对种子真菌污染的抑制作用	650 nm	27.4 mW	——	650 nm
光声光谱^［64］	谷物生物物理学	玉米	固态	温度-时间曲线分析	研究激光辐照对玉米种子表面温度的影响	650 nm	27.4 mW	——	650 nm
光声显微镜^［65］	谷物老化评估	小麦/玉米种子	固态	热成像分析	评估谷物老化对内部结构的影响	330~800 nm	——	1 Hz	——
光热电显微镜^［66］	谷物检测	玉米	固态	直方图核密度估计	区分种子晶体与粉质结构	650 nm	100 mW	1 Hz	——
光声显微镜^［67］	谷物结构分析	玉米	固态	数据密度分布分析	优化热成像分辨率与深度敏感性	405、532和650 nm	5 mW	17 Hz	405 nm
光声光谱^［68］	油品质量检测	柯拜巴油	液态	光谱特征峰与浓度关联分析	分析油品在不同稀释条件下的光学特性	0.18~4.00 μm	700 W	——	0.23 和2.72 μm
傅里叶变换红外光声光谱^［69］	营养成分分析	油菜籽	固态	独立成分分析+偏最小二乘	快速无损测定氮含量	4 000~500 cm⁻¹	——	——	1747、2 858和2 927 cm⁻¹
光声光谱^［70］	油品质量检测	大豆油	液态	主成分分析、Stacking集成模型	快速无损识别油品变质程度	0.18~4.00 μm	700 W	——	0.23 和1.40 μm

Table 1

Fig. 6

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