TY - JOUR
T1 - FRN-LIBS
T2 - A laser-induced breakdown spectroscopy approach based on a feedforward residual fusion network for accurate geographical origin tracing of yellowhorn
AU - Bai, Yu
AU - Zhang, Suyalatu
AU - Zhang, Dacheng
AU - Wang, Shaoyi
AU - Zhou, Kunpeng
AU - Wang, Dexin
AU - Ha, Ribala
AU - Wang, Jun
AU - Bai, Jinfeng
AU - Bao, Gerile
AU - Bao, Jinhua
AU - Zhang, Gaolong
AU - Qu, Weiwei
AU - Huang, Meirong
N1 - Publisher Copyright:
© 2027 World Scientific Publishing Company.
PY - 2026
Y1 - 2026
N2 - The geographical origin of plants exerts a significant influence on their chemical compositions and quality. However, the spectral features of samples grown under similar habitats tend to converge remarkably, which compromises the accuracy of geographical origin tracing. Laser-induced breakdown spectroscopy (LIBS) offers distinct advantages of rapid and nondestructive elemental analysis, yet it is constrained by plasma fluctuations and matrix effects, making it challenging to accurately discriminate samples with analogous compositions. Deep learning, by contrast, provides an effective tool for extracting nonlinear features from high-dimensional spectral data. In this study, we propose a LIBS analysis system based on a Feedforward Residual Fusion Network (FRN), termed FRN–LIBS, for the geographical origin tracing of yellowhorn. Yellowhorn samples were collected from five regions around Tongliao, and 500 sets of LIBS spectra within the wavelength range of 220–880nm were acquired. Principal Component Analysis (PCA) was performed to extract 353 principal components with a cumulative contribution rate of 90%, which were then used as the input of the models. The performance of the FRN model integrated with residual modules was compared with that of the conventional Feedforward Neural Network (FNN). The results demonstrate that the accuracy, precision, recall and F1-score of the FRN model all reached approximately 94%, representing an improvement of around 6% compared with the FNN model. This study confirms that the residual fusion mechanism can enhance the characterization of nonlinear features in LIBS spectra and effectively suppress plasma fluctuations and matrix effects. The proposed FRN–LIBS system provides a versatile and accurate modeling framework for high-dimensional spectral recognition, as well as an efficient and reliable technical solution for the geographical origin tracing of plants.
AB - The geographical origin of plants exerts a significant influence on their chemical compositions and quality. However, the spectral features of samples grown under similar habitats tend to converge remarkably, which compromises the accuracy of geographical origin tracing. Laser-induced breakdown spectroscopy (LIBS) offers distinct advantages of rapid and nondestructive elemental analysis, yet it is constrained by plasma fluctuations and matrix effects, making it challenging to accurately discriminate samples with analogous compositions. Deep learning, by contrast, provides an effective tool for extracting nonlinear features from high-dimensional spectral data. In this study, we propose a LIBS analysis system based on a Feedforward Residual Fusion Network (FRN), termed FRN–LIBS, for the geographical origin tracing of yellowhorn. Yellowhorn samples were collected from five regions around Tongliao, and 500 sets of LIBS spectra within the wavelength range of 220–880nm were acquired. Principal Component Analysis (PCA) was performed to extract 353 principal components with a cumulative contribution rate of 90%, which were then used as the input of the models. The performance of the FRN model integrated with residual modules was compared with that of the conventional Feedforward Neural Network (FNN). The results demonstrate that the accuracy, precision, recall and F1-score of the FRN model all reached approximately 94%, representing an improvement of around 6% compared with the FNN model. This study confirms that the residual fusion mechanism can enhance the characterization of nonlinear features in LIBS spectra and effectively suppress plasma fluctuations and matrix effects. The proposed FRN–LIBS system provides a versatile and accurate modeling framework for high-dimensional spectral recognition, as well as an efficient and reliable technical solution for the geographical origin tracing of plants.
KW - LIBS
KW - feed-forward residual fusion network (FRN)
KW - origin identification
KW - yellowhorn
UR - https://www.scopus.com/pages/publications/105036603930
U2 - 10.1142/S0129183127500756
DO - 10.1142/S0129183127500756
M3 - 文章
AN - SCOPUS:105036603930
SN - 0129-1831
JO - International Journal of Modern Physics C
JF - International Journal of Modern Physics C
M1 - 2750075
ER -