To improve the accuracy of laser-induced breakdown spectroscopy (LIBS) in the classification of tea leaves, a modified adaptive mutation probability for genetic algorithm (GA) was proposed to optimize support vector machines (SVM). The implementation process of the GA-SVM algorithm was discussed, and the key parameters were analyzed. The penalty factor and kernel function parameters in SVM were optimized by GA. The spectral line intensities of Mg (279.55 nm), Mn (279.83 nm), Mg (280.27 nm), CN (0–0) (388.34 nm), Ca (393.37 nm), Al (396.15 nm), Ca (396.84 nm), C₂ (0–0) (516.45 nm), Fe (517.46 nm) and K (766.49 nm) compared to C (247.86 nm) were selected as the analysis indexes according to the differences of LIBS spectra. LIBS spectra pre-processed by multiple scattering correction (MSC) with the optimal input feature were used to construct the GA-SVM model for different tea species. The results showed that the average correct classification rate was 99.73% in the training set. In addition, the average accuracy was 98.40% in the test set. The classification accuracy of the improved GA-optimized SVM was obviously higher than that of cross validation-support vector machine (CV-SVM) and particle swarm optimization-support vector machine (PSO-SVM). This work demonstrates that the GA-SVM can avoid the blindness of parameter selection and can effectively increase the accuracy of tea classification. The results indicated that LIBS is an effective technology in identificating tea leaves; moreover, it has a real-time, rapid and reliable measurement prospect.

LIBS in agricultural product sample preparation methods and agricultural product quality and safety analysis applications are reviewed. The advantages as well as challenges of LIBS in agricultural applications are discussed.

In order to improve the stability and prediction accuracy of a Laser Induced Breakdown Spectroscopy (LIBS) quantitative analysis model a chemometrics method named PSO-SVM (particle swarm optimization-support vector machine) is proposed for the quantitative prediction of heavy metal chromium (Cr) in pork by analyzing the spectral data obtained by LIBS. The usable spectral region from 424.77 to 429.99 nm was identified. The normalized preprocessing of spectra was employed. Also, the spectral dimension was reduced through principal component analysis (PCA). The PSO algorithm was applied to select the SVM hyper-parameters including the regularization and kernel parameters. The calibration model of heavy metal Cr in pork was established using the optimum hyper-parameters obtained by PSO-SVM. The performance of this PSO-SVM model was compared with a synergy interval partial least squares (siPLS) model and a multilayer perceptron artificial neural network (MLP-ANN). The feasibility of these three methods was examined on the test sample set. The experimental results showed that the proposed method (PSO-SVM) achieved better prediction accuracy robustness compared with the other two quantitative analysis methods. The calibration results of siPLS and MLP-ANN models are similar, and the fitting degree of the siPLS modeling effect is not high, while the MLP-ANN verification results are not ideal. For the PSO-SVM model, the correlation coefficients (R²) of the calibration and validation sets were higher than 0.99. The mean absolute percentage error (MAPE) of the calibration set is 0.0792%, and the MAPE of the validation set is 1.9395%. The RMSE values of the calibration and validation sets were 0.0245% and 0.6961%, respectively, which were much lower than the other two models. This investigation displayed that the PSO-SVM algorithm exhibited excellent model robustness and prediction accuracy. There is definite practical significance and application value in the monitoring of meat safety by analyzing LIBS data coupled with PSO-SVM methods.

In order to realize rapid identification of Gannan navel oranges infected by Huanglongbing (HLB), a full optical diagnostic method of laser-induced breakdown spectroscopy (LIBS) was proposed. All navel oranges were collected from Ganzhou, Jiangxi, China, and samples contain healthy and HLB-infected navel oranges. The LIBS spectra of the plasma plume were collected directly from the epidermis of these navel oranges. The navel orange LIBS spectra in the wavelength range of 200–1050 nm were pretreated with smoothing and multiple scatter correction; on the basis of $10\times 10$-fold cross validation, a random forest (RF) model based on continuous wavelet transform (CWT) and principal component analysis (PCA) were analyzed to identify the navel orange of HLB. The results showed that the PCA-RF and CWT-RF models coupled with suitable methods in preprocessing data can identify HLB-infected navel oranges. The average accuracy obtained from the CWT-RF model was 96.86% in the training set and 97.45% in the test set; the average accuracy by the PCA-RF model was 97.64% in the training set and 97.89% in the test set. The overall results demonstrate that LIBS combined with CWT-RF or PCA-RF, as a valuable analytical tool, could be used for HLB-infected navel orange identification.

Meat can be polluted by heavy metals during the feeding of livestock and processing of meat. It is necessary to monitor the concentration of toxic metals in meat. The element chromium (Cr) in pork was selected as a determination target for this work. Fresh pork was polluted in a Cr solution to create a different content level, then dried and pressed into pellets to eliminate the effect of water and improve the stability and sensitivity of laser-induced breakdown spectroscopy (LIBS). The spectra of pressed pellets were collected at optimized LIBS experimental parameters. After that, the real content of samples was obtained by atomic absorption spectroscopy. Characteristic lines Cr I 425.43, Cr I 427.48, and Cr I 428.97 were verified, and a model comparing the LIBS intensity of the line peak and the actual concentration of Cr was constructed. The results showed that the model has better predicted precision and accuracy, especially applying the line Cr I 425.43 for calibration. This work makes it obvious that LIBS has food safety potential for detecting residue of heavy metals in meat.