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The quality and safety of agricultural products are of paramount importance in ensuring the health of the food supply chain. Additionally, the composition and trace elements in agricultural products significantly influence their quality and nutritional value. Therefore, the need for rapid and accurate analysis techniques for agricultural product composition is particularly crucial. In the current landscape of evolving compositional analysis technologies, Laser-Induced Breakdown Spectroscopy (LIBS) technology is emerging as a promising analytical tool with broad applications in agricultural product testing. Its characteristics of being rapid, real-time, and capable of simultaneous detection of multiple elements provide an efficient and reliable means for assessing the quality, monitoring safety, and tracing the origin of agricultural products. This technology is expected to play a significant role in controlling and managing the agricultural industry chain and can offer consumers safer and healthier agricultural products. This paper provides an overview of the research status and recent developments of LIBS technology in agricultural product testing applications in recent years. Based on the current research landscape, challenges and opportunities of applying LIBS technology in fields such as agricultural product quality and safety assessment, soil analysis, assessment of crop nutrition, detection of plant diseases, and identification of agricultural product varieties have been evaluated. Moreover, recommendations for further expanding the application of LIBS technology in the agricultural sector are proposed.
The quality and safety of agricultural products are of paramount importance in ensuring the health of the food supply chain. Additionally, the composition and trace elements in agricultural products significantly influence their quality and nutritional value. Therefore, the need for rapid and accurate analysis techniques for agricultural product composition is particularly crucial. In the current landscape of evolving compositional analysis technologies, Laser-Induced Breakdown Spectroscopy (LIBS) technology is emerging as a promising analytical tool with broad applications in agricultural product testing. Its characteristics of being rapid, real-time, and capable of simultaneous detection of multiple elements provide an efficient and reliable means for assessing the quality, monitoring safety, and tracing the origin of agricultural products. This technology is expected to play a significant role in controlling and managing the agricultural industry chain and can offer consumers safer and healthier agricultural products. This paper provides an overview of the research status and recent developments of LIBS technology in agricultural product testing applications in recent years. Based on the current research landscape, challenges and opportunities of applying LIBS technology in fields such as agricultural product quality and safety assessment, soil analysis, assessment of crop nutrition, detection of plant diseases, and identification of agricultural product varieties have been evaluated. Moreover, recommendations for further expanding the application of LIBS technology in the agricultural sector are proposed.
The economic value and consumer acceptance of Pu-erh tea heavily depend on the production year. The present study aims to evaluate the potential of utilizing laser-induced breakdown spectroscopy (LIBS) in conjunction with chemometric models to identify Pu-erh raw tea from various production years. The research utilizes tea leaves from a common source in 2008, 2013, and 2018 as the analytical samples. One hundred spectral datasets were collected for each type of tea, and these datasets are randomly partitioned into cross-validation and test sets in a 3:2 ratio. Subsequently, by utilizing threshold peak finding to extract features from the baseline-corrected LIBS spectrum, 21 spectral datasets are identified and input into LDA, SVM, EML, and KNN classification models for analysis. Results demonstrate that the LDA model achieves superior performance in identifying tea leaf years, attaining a recognition rate of 98.75%. Additionally, the average recognition rate of the other three algorithms in three-classification tasks exceeds 90%. Overall, this study confirms the feasibility and effectiveness of utilizing LIBS in conjunction with machine learning algorithms for discriminating Pu-erh raw tea originating from different production years.
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