To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explo...To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.展开更多
农产品标准不仅是衡量农产品安全的尺度,也是农产品安全监管的重要依据,当前农产品标准信息并没有得到系统性的关联划分与复用。针对此问题,该研究依据标准化文件的起草规范设计了农产品标准信息本体规则,在现有的农产品标准文件及相关...农产品标准不仅是衡量农产品安全的尺度,也是农产品安全监管的重要依据,当前农产品标准信息并没有得到系统性的关联划分与复用。针对此问题,该研究依据标准化文件的起草规范设计了农产品标准信息本体规则,在现有的农产品标准文件及相关词条数据基础上,为半结构化数据设计了正则包装器;为非结构化文本提出了一个基于依存句法分析的农产品领域开放关系抽取模型(Open Relation Extraction Model In Agricultural Products Field, OREM-AF),实现了该领域知识的自动抽取。结果表明该研究设计的包装器在提取半结构化数据信息时,准确率与F1值均在95%以上;提出的OREM-AF模型在农产品语料上准确率达74.22%、F1值为75.12%,在通用语料上准确率达84.51%、F1值为75.43%,抽取结果均好于基于依存句法分析的其他模型。依托抽取数据构建了农产品标准领域知识图谱,并在知识图谱的相互关联网络上进行了标准社区挖掘,挖掘出的关联标准知识能够为农产品标准监管提供辅助分析支撑。展开更多
基金Supported by the National Key R&D Program of China(2023YFD2101001)National Natural Science Foundation of China(32202144,61807001)。
文摘To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
文摘农产品标准不仅是衡量农产品安全的尺度,也是农产品安全监管的重要依据,当前农产品标准信息并没有得到系统性的关联划分与复用。针对此问题,该研究依据标准化文件的起草规范设计了农产品标准信息本体规则,在现有的农产品标准文件及相关词条数据基础上,为半结构化数据设计了正则包装器;为非结构化文本提出了一个基于依存句法分析的农产品领域开放关系抽取模型(Open Relation Extraction Model In Agricultural Products Field, OREM-AF),实现了该领域知识的自动抽取。结果表明该研究设计的包装器在提取半结构化数据信息时,准确率与F1值均在95%以上;提出的OREM-AF模型在农产品语料上准确率达74.22%、F1值为75.12%,在通用语料上准确率达84.51%、F1值为75.43%,抽取结果均好于基于依存句法分析的其他模型。依托抽取数据构建了农产品标准领域知识图谱,并在知识图谱的相互关联网络上进行了标准社区挖掘,挖掘出的关联标准知识能够为农产品标准监管提供辅助分析支撑。
