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.

Laser-induced breakdown spectroscopy (LIBS) is a promising alternative to conventional methods in classifying citrus huanglongbing (HLB). Mature citrus fruits with similar features were picked and divided into healthy and HLB-asymptomatic groups. LIBS spectra and images were collected by focusing a laser on fresh fruit surfaces without sample preparation. The pH value and soluble solids content of juice as the indicators of acidity and sugar were detected, and the content of Ca, Zn, and K in peel and pulp was analyzed. The characteristic lines from LIBS spectra were extracted by continuous wavelet transform and principal component analysis (PCA). The t -test of these indicators displayed significant difference between the two groups. Fisher discriminant analysis and multilayer perception neural network (MLP) were applied to identify the disease. The classification accuracy reached 100% by PCA-MLP. The results show that LIBS can realize in situ detection of citrus HLB fruits.

Cadmium (Cd) is a highly toxic heavy metal that can accumulate in the food chain, posing a significant threat to human health. One of the key food sources through which Cd is often observed is rice. Therefore, determining heavy metals in rice is essential to assess the risk status of rice. Laser-induced breakdown spectroscopy (LIBS) has the advantages of simple sample preparation and fast analysis, which is expected to achieve real-time and rapid detection of rice. In this work, 40 naturally matured rice samples growing from the area that is possibly contaminated with Cd were collected to determine the Cd reference content in rice by graphite furnace atomic absorption spectroscopy as recommended by the Chinese National Standard. LIBS spectral acquisition and analysis are adopted as well. The Cd characteristic spectral lines were selected to predict the Cd content directly using PCA, PLSR, and ELM models, and the coefficient of determination (R²) of the models' training and prediction sets was 0.9278, 0.8920; 0.9036, 0.9771; 0.7940, and 0.8409, respectively. Further, based on the Cd stress effect in rice, the spectra of elements Mn, Mg, K, and Na with highly significant and significant correlation with Cd were selected and coupled with the Cd characteristic spectra to form a new matrix of the same size for quantitative analysis. Based on the stress effect, R² of models' training and prediction sets was improved to 0.9786, 0.9753; 0.9395, 0.9900; 0.9798, and 0.9927, respectively. It is demonstrated that combining the stress effect when using LIBS for quantitative analysis of Cd in rice reduces the overfitting and further improves the model's prediction accuracy. This work indicates that using LIBS combined with suitable mathematical models to predict the Cd content of naturally matured rice based on stress effects in rice is feasible. It is promising to evaluate the safety of rice by analyzing LIBS spectra.

To predict the nutrition and safety of agricultural products by laser-induced breakdown spectroscopy (LIBS), heavy metal Cd in rice was selected as an analytical target. Mature rice grain samples from 40 growing geographical areas around Poyang Lake were picked on-site and processed by grinding to obtain the edible rice. The content of Cd in rice samples was determined by graphite furnace atomic absorption spectrometry, and the rice pellets were detected by LIBS. The risk intake was estimated by the target hazard quotient and Chinese National Standard. Moreover, the samples were classified as clean, slight, and severe ones according to evaluation. The content of Cd was predicted by analyzing LIBS spectra coupled with the partial least square (PLS) model. The correlation coefficients ( R 2 ) reached 0.9036 and 0.9771 for the training and prediction sets, respectively, and the root mean square errors were 0.0487 and 0.027, respectively. It denotes that the PLS model has a higher prediction ability especially after LIBS spectra were processed by smoothing and multiplicative scatter correction. For the clean, slight, and severe rice samples, the LIBS intensity ratio between minerals Mg, K, Na, Si, and Mn to Ca was compared. The ratio was decreased in all samples as Cd stress increased. Correlation analysis results show that Mn displayed a highly significant negative correlation with Cd stress, while Mg, K, and Na displayed a significant negative correlation with Cd stress. The relationship between Si and Cd did not reach a significant level. This work indicated that it was feasible to use LIBS combined with a suitable data process to predict Cd content and the effect of Cd stress on minerals in rice. It is promising to evaluate the nutrition and safety of food products by analyzing LIBS spectra.

As societal concern for environmental and public health issues intensifies, precisely monitoring harmful heavy metal concentrations in aquatic systems has become a critical scientific endeavor. Although laser-induced breakdown spectroscopy (LIBS) allows for the simultaneous detection of multiple elements, its sensitivity for tracing metal in water remains inadequate. This work investigates the enhancement of LIBS signals for chromium (Cr) and lead (Pb) detection of micro/nanostructures on aluminum surfaces, generated by femtosecond laser irradiation. These structures effectively reduce the coffee-ring effect, increasing plasma temperature and electron density. At a concentration of 100 µg/L, the spectral intensities of Cr and Pb increased by 378% and 307%, respectively. Consequently, this reduced the detection limits to 6.33 µg/L for Cr and 2.53 µg/L for Pb. Additionally, a partial least squares regression (PLSR) model was employed to quantitatively analyze the concentrations of Cr and Pb in water, achieving correlation coefficients (R²) of 0.997, 0.981 for Cr, and 0.995, 0.977 for Pb, in the training and prediction sets, respectively. This work demonstrates that integrating micro/nanostructures induced by femtosecond lasers on aluminum surfaces with the PLSR model substantially improves the detection sensitivity for trace amounts of Cr and Pb in water, offering new avenues for environmental monitoring and research.