Rapid identification of the geographical origins of crops using laser-induced breakdown spectroscopy combined with transfer learning

Lin, Peng; Wen, Xuelin; Ma, Shixiang; Liu, Xinchao; Xiao, Renhang; Gu, Yifan; Chen, Guanghai; Han, Yuxing; Dong, Daming

doi:10.1016/j.sab.2023.106729

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Xu et al [17] realized the rapid identification of citrus huanglongbing (HLB) by combining LIBS with near-infrared spectroscopy. Lin et al [18] used deep learning to assist LIBS in identifying the geographical origin of crops so as to achieve food traceability. Sun et al [19] used LIBS combined with a machine learning algorithm to identify the original source of Angelica dahurica medicinal herbs.…”

Section: Introductionmentioning

confidence: 99%

Rapid and High-Performance Analysis of Total Nitrogen in Coco-Peat Substrate by Coupling Laser-Induced Breakdown Spectroscopy with Multi-Chemometrics

Lu,

Wang,

et al. 2024

Agriculture

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Nitrogen is an important nutrient element for crop growth. Rapid and accurate acquisition of nitrogen content in cultivation substrate is the key to precise fertilization. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the total nitrogen (TN) of coco-peat substrate. A LIBS spectrum acquisition system was established to collect the spectral line signal of samples with wavelengths ranging from 200 nm to 860 nm. Synergy interval partial least squares (Si-PLS) algorithm and elimination of uninformative variables (UVE) algorithm were used to select the spectral data of TN characteristic lines in coco-peat substrate. Univariate calibration curve and partial least squares regression (PLSR) were used to build mathematical models for the relationship between the spectral data of univariate characteristic spectral lines, full variables and screened multi-variable characteristic spectral lines of samples and reference measurement values of TN. By comparing the detection performance of calibration curves and multivariate spectral prediction models, it was concluded that UVE was used to simplify the number of spectral input variables for the model and PLSR was applied to construct the simplest multivariate model for the measurement of TN in the substrate samples. The model provided the best measurement performance, with the calibration set determination coefficient (RC2) and calibration set root mean square error (RMSEC) values of 0.9944 and 0.0382%, respectively; the prediction set determination coefficient (RP2) and prediction set root mean square error (RMSEP) had values of 0.9902 and 0.0513%, respectively. These results indicated that the combination of UVE and PLSR could make full use of the variable information related to TN detection in the LIBS spectrum and realize the rapid and high-performance measurement of TN in coco-peat substrate. It would provide a reference for the rapid and quantitative assessment of nutrient elements in other substrate and soil.

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Section: Introductionmentioning

confidence: 99%