Rapid Identification of Kudzu Powder of Different Origins Using Laser-Induced Breakdown Spectroscopy

Abstract

The rapid identification of kudzu powder of different origins is of great significance for studying the authenticity identification of Chinese medicine. The feasibility of rapidly identifying kudzu powder origin was investigated based on laser-induced breakdown spectroscopy (LIBS) technology combined with chemometrics methods. The discriminant models based on the full spectrum include extreme learning machine (ELM), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN) and random forest (RF), and the accuracy of models was more than 99.00%. The prediction results of KNN and RF models were best: the accuracy of calibration and prediction sets of kudzu powder from different producing areas both reached 100%. The characteristic wavelengths were selected using principal component analysis (PCA) loadings. The accuracy of calibration set and the prediction set of discrimination models, based on characteristic wavelengths, is all higher than 98.00%. Random forest and KNN have the same excellent identification results, and the accuracy of calibration and prediction sets of kudzu powder from different producing areas reached 100%. Compared with the full spectrum discriminant analysis model, the discriminant analysis model based on the characteristic wavelength had almost the same discriminant effects, and the input variables were reduced by 99.92%. The results of this research show that the characteristic wavelength can be used instead of the LIBS full spectrum to quickly identify kudzu powder from different producing areas, which had the advantages of reducing input, simplifying the model, increasing the speed and improving the model effect. Therefore, LIBS technology is an effective method for rapid identification of kudzu powder from different habitats. This study provides a basis for LIBS to be applied in the genuineness and authenticity identification of Chinese medicine.

Rapid Determination of Wood and Rice Husk Pellets’ Proximate Analysis and Heating Value

“…Extreme learning machine (ELM) is a recently developed machine learning algorithm based on single-hidden layer feedforward neural networks (SLFNN), and it runs in a simpler and easier way than traditional neural network methods [42,43]. Output weights can be obtained by randomly initializing input weights and hidden layer biases in global optimization [44,45].…”

Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network

“…Plant Sci. 11:821. doi: 10.3389/fpls.2020.00821 Zhao, Y., Guindo, M. L., Xu, X., Sun, M., Peng, J., Liu, F., et al (2019). Deep learning associated with laser-induced breakdown spectroscopy (LIBS) for the prediction of lead in soil.…”

“…After that, the plasma is created with the vaporization and excitation of the sample (Li et al, 2019). The emitted spectra from the plasma are collected for multi-element analysis (Liu et al, 2019;Wang et al, 2020). So far, LIBS technology has been widely used in qualitative and quantitative analysis in agricultural products such as rice (Luo et al, 2020), psoralea corylifolia seeds (Dhar et al, 2013), cucurbit seeds (Singh et al, 2017), coffee beans (Song et al, 2017), soybean seeds (Gamela et al, 2020;Larios et al, 2020), and grape seeds (He et al, 2020).…”

High-Sensitivity Determination of Nutrient Elements in Panax notoginseng by Laser-induced Breakdown Spectroscopy and Chemometric Methods

“…Laser-induced breakdown spectroscopy (LIBS) is a novel atomic technique that exhibits the advantages of fast analytical speed, multi-element analysis, minimal sample preparation, and high efficiency [18,19]. These attractive features have attracted researchers to provide laboratory analysis to explore the potential for LIBS to be a polyvalent monitoring and analysis tool for elements’ detection [20], and especially nutritive elements in plant materials [21]. Braga et al applied LIBS to analyze micronutrients in pellets of plant materials, and the results proved that LIBS technique combined with partial least-squares (PLS) is robust for elements determination [22].…”