Rapid identification of ginseng origin by laser induced breakdown spectroscopy combined with neural network and support vector machine algorithm

Based on laser-induced breakdown spectroscopy and machine learning algorithms, ginseng origin identification model is established by principal component analysis algorithm combined with back-propagation (BP) neural network and support vector machine algorithm to analyze and identify ginseng from five different origins in northeast China (Daxinganling, Ji'an, Hengren, Shizhu, and Fusong). The experiment collects a total of 657 groups of laser-induced breakdown spectral data from five origins of ginseng at 200-975 nm, reduces the background continuous spectrum of the original spectral data by moving window smoothing method, labels the ginseng LIBS spectral elements according to the American NIST atomic spectral database. Eight characteristic spectral lines of 7 elements Mg, Ca, Fe, C, H, N and O are selected for principal component analysis according to characteristic spectral selection conditions. The cumulative contribution rate of the first three principal components of the original spectral data reaches 92.50%, which represents a large amount of information about the original ginseng LIBS spectrum, and the samples show a good aggregation and classification in the principal component space. After dimension reduction, the first three principal components are randomly selected in a ratio of 2 to 1 and divided into 438 test sets and 219 training sets, which are used as the input values of the classification algorithm. The experimental results show that the principal component analysis combined with the BP neural network algorithm and support vector machine algorithm can correctly identify 217 and 218 spectra of 219 spectra of the test set respectively, and the average recognition rate is 99.08% and 99.5% respectively. The modeling time of BP neural network is 11.545 s shorter than that of the support vector machine. Both models misjudged Ji'an Ginseng as Shi zhu ginseng, and the reason for this misjudgment is that the normalized intensity of H and O under Ca element ion emission spectrum are similar due to the proximity of Ji 'an to Shi Zhu in geographical environment. The study presented here demonstrates that laser-induced breakdown spectroscopy combined with machine learning algorithm is a useful technology for rapid identification of ginseng origin and is expected to realize automatic, real-time, rapid and reliable discrimination.