Quantitative analysis of Cd based on the stress effect of minerals in rice by laser-induced breakdown spectroscopy.

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.

Online identification and classification of Gannan navel oranges with Cu contamination by LIBS with IGA-optimized SVM.

Elements such as minerals and heavy metals play important roles in the nutrition and safety of agricultural products. It is necessary to develop rapid, online, real-time and in situ methods for monitoring the substances in farm products. Gannan navel oranges are a unique variety of fruit, which may be affected by Cu pollution due to abundant copper mines and other factors. An online identification and classification system based on laser-induced breakdown spectroscopy (LIBS) was developed to detect possible Cu residue in Gannan navel oranges. First, transmission and classification equipment for Gannan navel oranges was built. Second, an LIBS detection module was designed. Finally, a software system for the whole online detection platform was developed based on the C# programming language. The series of operations for the online detection system, which includes the loading, transmission, detection and classification of orange samples, can be controlled. Since the navel orange has an elliptical shape, the LIBS detection module was designed with a long focal length to reduce the influence of fruit plane size fluctuation. The long focal length was optimized to 698 mm, and the depth of field was ±6 mm. Furthermore, a parameter optimization model using a support vector machine (SVM) based on an improved genetic algorithm (IGA) is proposed to improve the classification effect of Gannan navel oranges. This model avoids the over-learning or under-learning caused by improper parameter selection in the regression prediction of SVM. The IGA is used to optimize the penalty parameter c and the kernel parameter g of SVM. LIBS spectral data from two types of navel orange samples with and without Cu contamination were selected as test datasets, and the classification results were compared with those of the standard genetic algorithm-support vector machine (GA-SVM). The investigation showed that the IGA-SVM can provide better classification of navel oranges based on analysis of the LIBS spectral data, and the classification accuracy can reach 98%, which provides significant guidance for the use of LIBS to quickly realize online screening of heavy metals in agriculture products.

Online Detection of Impurities in Corn Deep-Bed Drying Process Utilizing Machine Vision.

Online detection of impurities content in the corn deep-bed drying process is the key technology to ensure stable operation and to provide data support for self-adapting control of drying equipment. In this study, an automatic approach to corn image acquisition, impurity classification and recognition, and impurities content detection based on machine vision technology are proposed. The multi-scale retinex with colour restore (MSRCR) algorithm is utilized to enhance the original image for eliminating the influence of noise. HSV (Hue, saturation, value) colour space parameter threshold is set for image segmentation, and the classification and recognition results are obtained combined with the morphological operation. The comprehensive evaluation index is adopted to quantitatively evaluate the test results. Online detection results show that the comprehensive evaluation index of broken corncobs, broken bracts, and crushed stones are 83.05%, 83.87%, and 87.43%, respectively. The proposed algorithm can quickly and effectively identify the impurities in corn images, providing technical support and a theoretical basis for monitoring impurities content in the corn deep-bed drying process.

Diagnosis of HLB-asymptomatic citrus fruits by element migration and transformation using laser-induced breakdown spectroscopy.

Huanglongbing (HLB) is one of the most devastating bacterial diseases in citrus growth and there is no cure for it. The mastery of elemental migration and transformation patterns can effectively analyze the growth of crops. The law of element migration and transformation in citrus growth is not very clear. In order to obtain the law of element migration and transformation, healthy and HLB-asymptomatic navel oranges collected in the field were taken as research objects. Laser-induced breakdown spectroscopy (LIBS) is an atomic spectrometry technique for material component analysis. By analyzing the element composition of fruit flesh, peel and soil, it can know the specific process of nutrient exchange and energy exchange between plants and the external environment, as well as the rules of internal nutrient transportation, distribution and energy transformation. Through the study of elemental absorption, the growth of navel orange can be effectively monitored in real time. HLB has an inhibitory effect on the absorption of navel orange. In order to improve the detection efficiency, LIBS coupled with SVM algorithms was used to distinguish healthy navel oranges and HLB-asymptomatic navel oranges. The classification accuracy was 100%. Compared with the traditional detection method, the detection efficiency of LIBS technology is significantly better than the polymerase chain reaction method, which provides a new means for the diagnosis of HLB-asymptomatic citrus fruits.

Detection model of the plasma spectrum based on the polarization recognition rate induced by a low energy density laser.

Laser-induced breakdown spectroscopy (LIBS) is a fast recognition method for heavy-metal detection. The recognition rate of the characteristic spectrum is related to the laser-induced energy. In order to analyze the polarization characteristics of plasma and the improvement mechanism of recognition rate under the condition of low energy density, a detection model of polarization recognition rate was established by exploring the intensity formula of discrete spectral data. At the Brewster angle, the LIBS and discrete spectral data of Cd and Cu elements induced by five energies were measured in combination with the polarization spectrometric path. According to the model of polarization recognition rate, the optimization effect of the polarization separation approach on the plasma spectra of heavy-metal elements has been clarified, and the recognition rate of discrete spectra induced by low energy density has been improved. This study shows that the increase of laser energy is helpful to the recognition of characteristic spectral lines. Under the same energy induction, the polarization recognition rate of heavy-metal elements is larger, and this characteristic is more obvious under low energy density. This model not only improves the recognition rate of the plasma spectrum but also greatly reduces the requirement of laser energy and the damage of the medium surface, which is a more effective nondestructive testing technology.

Prediction of heavy metal Cd and stress on minerals in rice by analysis of 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 (R2) 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.

Isolation and identification of new source of bacteriocin-producing Lactobacillus plantarum C010 and growth kinetics of its batch fermentation.

The control of food-borne pathogens and spoilage organisms in meat and related products is urgently needed. Bacteriocins produced by lactic acid bacteria (LAB) are promising natural food preservatives. In this study, six bacteriocin-producing bacteria were screened from soil and fresh cow dung. Pseudomonas koreensis PS1, a specific spoilage organism from spoiled chilled pork, was used as the indicator bacteria. From the analyses, the strain C010 was selected due to its high yield, broad spectrum, and subculture stability. Through morphological, biochemical, and 16S rDNA gene sequence analysis, this strain was identified as Lactobacillus plantarum. Crude bacteriocin extracted from the cell-free supernatant (CFS) of L. plantarum C010 was stable under high temperature, ultraviolet radiation, and protease attack (pepsin, trypsin, and proteinase K). The kinetics of bacterial growth and bacteriocin production by L. plantarum C010 were analyzed during batch fermentation. Bacteriocin was produced throughout the logarithmic growth phase, and the Leudeking-Piret model characterized the synthesis of bacteriocins. The present study indicates that this novel bacteriocin produced by bacteria is a promising option for reducing spoilage microorganisms and can be widely used as a bio-preservative in meat and other foods.

Effects of exogenous AHLs on the spoilage characteristics of Pseudomonas koreensis PS1.

Some specific spoilage organisms (SSO) respond to the presence of exogenous N-acyl-homoserine lactones (AHLs) through the quorum sensing (QS) system to modulate their spoilage characteristics. To explore the effect of exogenous AHLs on the spoilage characteristics of Pseudomonas koreensis PS1 from spoiled chilled pork, four kinds of AHLs were added to the liquid medium to analyze their effect on the cell growth and spoilage characteristics of P. koreensis PS1, and N-hexanoyl-l-homoserine lactone (C6-HSL) was added to evaluate its effect on spoilage characteristics of P. koreensis PS1 inoculated in fresh chilled pork. The results showed that the addition of low concentrations of C6-HSL (10 µmol/L) to the liquid medium could remarkably promote the protease activity, lipase activity, and biofilm formation of P. koreensis PS1 (p < 0.05), and more than 30 µmol/L C6-HSL could significantly increase the cell density (p < 0.05). Furthermore, the addition of 10 µmol/L C6-HSL into fresh chilled pork could increase the lipase and protease activities of P. koreensis PS1. The enzyme activity accelerated the decomposition of total protein, total fat, and total sugar, and led to an increase in putrescine, tyramine, cadaverine, and total volatile basic nitrogen (TVB-N) content in chilled pork during the storage at 4°C. PRACTICAL APPLICATION: The infestation of chilled pork with SSO may be a challenge for the meat industry. In this study, exogenous AHLs were found to have a positive effect on the spoilage of chilled pork. The elimination of the QS phenomenon of bacteria should be considered when looking for ways to prolong the preservation of chilled pork.

In situ diagnosis of mature HLB-asymptomatic citrus fruits by laser-induced breakdown spectroscopy.

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.

Optimization of copper detection based on polarization-resolved laser-induced breakdown spectroscopy.

In order to obtain stable spectral data of copper plasma, a detection platform of polarization-resolved laser-induced breakdown spectroscopy (PRLIBS) was built. The PRLIBS characteristic function of copper was constructed by combining the spectral path of plasma discrete spectrum and contact spectrum. The system can not only measure the original data, but also obtain the polarization information in the spectral data. By analyzing the extraction method of spatial polarized light information, the characteristic model of S-wave intensity information in PRLIBS was derived. The results show that in the decay process of plasma energy, the anisotropy of plasma recombination under local thermal equilibrium makes the number of deflected particles of atoms and electrons different in unit time, which leads to the polarization of radiation. The polarization characteristics of the plasma spectrum decreased with the increase of laser energy density. The S-wave was very active, and the polarization of continuous media was much stronger than that of discrete line emission. The advantages were helpful to obtain more stable characteristic peak signals. As a plasma element identification method, PRLIBS makes up for the deficiency of plasma detection technology, and can provide a scientific basis for the safety and non-destructive detection of heavy metals.

Detection model of copper based on polarization degree induced by low-energy density laser.

In order to analyze the mechanism of plasma polarization characteristics and the improvement effect of spectral signal-to-back ratio, the intensity formulas of continuous spectrum and discrete spectrum were derived by exploring the path of the radiation spectrum. At the Brewster angle, the model of polarization degree was established based on the measured spectral data to identify the radiation intensity of plasma. The experimental results showed that the polarization characteristics of the background and discrete spectrum were both observed in the plasma spectrum of a copper element, and there were obvious differences in polarization degree and vibration direction. Moreover, cadmium and chromium were used to verify the detection model. It was found that the characteristic signals of the polarization spectrum were more than the effective peaks in laser-induced breakdown spectroscopy, and the variation trend was relatively gentle. The model retained the effective information in the continuum spectrum and fully explored the basic polarization mechanism of plasma. The measured data were not only convenient to observe the characteristic signal peaks of elements, but also greatly improved the recognition effect. This method could extract effective information of illumination plasma under the condition of low incident light intensity and reduce the damage of medium surface, which is a more effective nondestructive testing technology.

Rapid identification of rice species by laser-induced breakdown spectroscopy combined with pattern recognition.

Laser-induced breakdown spectroscopy (LIBS) combined with pattern recognition was proposed to discriminate rice species. LIBS spectra in the range of 210-480 nm wavelength from 11 different rice species were collected and preprocessed. Principal component analysis was applied to extract the characteristic variables from LIBS spectral data. Three pattern recognition methods, discriminant analysis, radial basis function neural network, and multi-layer perceptron neural network (MLP) were performed to compare the precision in identifying rice species. The results showed that the performance of the MLP model was better. The average identification rate of rice species reached 100% and 97.9% in the training and test sets, respectively, with MLP. The highest and lowest percentages for correct identification were 100% for early indica rice, Huai rice 5, Yan japonica 6, Lian japonica 8, Xuhan 1, Lvhan 1, Sheng rice 16, Yang japonica 687, and Fenghan 30, and 77.8% for Wuyu japonica rice in test sets. The overall results demonstrate that LIBS combined with MLP could be utilized to rapidly discriminate rice species.

Identification of Huanglongbing-infected navel oranges based on laser-induced breakdown spectroscopy combined with different chemometric 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 10×10-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.

Detection of heavy metal Cd in polluted fresh leafy vegetables by laser-induced breakdown spectroscopy.

In seeking a novel method with the ability of green analysis in monitoring toxic heavy metals residue in fresh leafy vegetables, laser-induced breakdown spectroscopy (LIBS) was applied to prove its capability in performing this work. The spectra of fresh vegetable samples polluted in the lab were collected by optimized LIBS experimental setup, and the reference concentrations of cadmium (Cd) from samples were obtained by conventional atomic absorption spectroscopy after wet digestion. The direct calibration employing intensity of single Cd line and Cd concentration exposed the weakness of this calibration method. Furthermore, the accuracy of linear calibration can be improved a little by triple Cd lines as characteristic variables, especially after the spectra were pretreated. However, it is not enough in predicting Cd in samples. Therefore, partial least-squares regression (PLSR) was utilized to enhance the robustness of quantitative analysis. The results of the PLSR model showed that the prediction accuracy of the Cd target can meet the requirement of determination in food safety. This investigation presented that LIBS is a promising and emerging method in analyzing toxic compositions in agricultural products, especially combined with suitable chemometrics.

Simultaneous analysis of Cr and Pb in contaminated pork by laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) as a rapid and green method was used to detect heavy metals Cr and Pb in pork contaminated in the lab. The laser-induced plasma was generated by a Q-switched Nd:YAG laser, and the LIBS signal was collected by a spectrometer with a charge-coupled device detector. The traditional calibration curves (CC) and multivariate partial least squares (PLS) algorithm were applied and compared to validate the accuracy in predicting the content of heavy metals in samples. The results demonstrated that the correlation coefficient of CC is poor by the classical univariate calibration method, so the univariate calibration analysis cannot effectively serve the quantitative purpose in analyzing heavy metals' residue in pork with a complex matrix. The analysis accuracy was improved effectively by the PLS method, and the correlation coefficient is 0.9894 for Cr and 0.9908 for Pb. The concentration of Cr and Pb in samples from a prediction set was obtained using the PLS calibration method, and the average relative errors for the 21 samples in the prediction set are lower than 6.53% and 7.82% for Cr and Pb, respectively. The investigated results display that the matrix effect would be reduced effectively during the quantitative analysis of pork by a LIBS-combined PLS model, and the predictive accuracy would be improved greatly compared to traditional univariate analysis.

[Study on the Enhancement Intensity of Cd in Rice with Microwave-Assisted Laser-Induced Breakdown Spectroscopy].

As food safety problem has become the focus of attention all over the world, green detection methods of the contaminants in food is in accordance with the sustainable development of environment. Heavy metal pollutant Cd element in rice was used as the object of study in this work, laser induced breakdown spectroscopy (LIBS) and microwave assisted laser induced breakdown spectroscopy (MA-LIBS) were utilized to detect the blank and laboratory polluted rice samples respectively. The characteristic line of Cd Ⅰ 228.802 nm was employed as analytical line to discuss the enhancement effect of plasmas emission intensity for the analytical line of target element. Meanwhile, the actual concentration of Cd in rice was measured by anodic stripping voltammetry. The result displayed that LIBS can just detect the plasmas signals of the sample which contained 13.69 µg·g-1 cadmium for the laboratory polluted rice samples which concentration range from 2.16 to 13.69 µg·g-1, however, in the same experimental conditions, MA-LIBS can detect the plasmas signals of Cd in all of the contaminated rice samples successfully, and compared with LIBS, the plasmas emission intensity of Cd element was enhanced from 9 to 27 times. The results showed that the plasmas emission intensity of Cd element in rice can be enhanced effectively by MA-LIBS, and the detection sensitivity can be effectively improved.

Quantitative analysis of chromium in potatoes by laser-induced breakdown spectroscopy coupled with linear multivariate calibration.

Laser-induced breakdown spectroscopy (LIBS) coupled with the linear multivariate regression method was utilized to analyze chromium (Cr) quantitatively in potatoes. The plasma was generated using a Nd:YAG laser, and the spectra were acquired by an Andor spectrometer integrated with an ICCD detector. The models between intensity of LIBS characteristic line(s) and concentration of Cr were constructed to predict quantitatively the content of target. The unary, binary, ternary, and quaternary variables were chosen for verifying the accuracy of linear regression calibration curves. The intensity of characteristic lines Cr (CrI: 425.43, 427.48, 428.97 nm) and Ca (CaI: 422.67, 428.30, 430.25, 430.77, 431.86 nm) were used as input data for the multivariate calculations. According to the results of linear regression, the model of quaternary linear regression was established better in comparing with the other three models. A good agreement was observed between the actual content provided by atomic absorption spectrometry and the predicted value obtained by the quaternary linear regression model. And the relative error was below 5.5% for validation samples S1 and S2. The result showed that the multivariate approach can obtain better predicted accuracy than the univariate ones. The result also suggested that the LIBS technique coupled with the linear multivariate calibration method could be a great tool to predict heavy metals in farm products in a rapid manner even though samples have similar elemental compositions.

[Influence of Spectral Pre-Processing on PLS Quantitative Model of Detecting Cu in Navel Orange by LIBS].

Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data ptetreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0. 992 8, 3. 43 and 3. 4 respectively, the average relative error of prediction model is only 5. 55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIBS.

[Effect of Characteristic Variable Extraction on Accuracy of Cu in Navel Orange Peel by LIBS].

Heavy metals pollution in foodstuffs is more and more serious. It is impossible to satisfy the modern agricultural development by conventional chemical analysis. Laser induced breakdown spectroscopy (LIBS) is an emerging technology with the characteristic of rapid and nondestructive detection. But LIBS' s repeatability, sensitivity and accuracy has much room to improve. In this work, heavy metal Cu in Gannan Navel Orange which is the Jiangxi specialty fruit will be predicted by LIBS. Firstly, the navel orange samples were contaminated in our lab. The spectra of samples were collected by irradiating the peel by optimized LIBS parameters. The laser energy was set as 20 mJ, delay time of Spectral Data Gathering was set as 1.2 micros, the integration time of Spectral data gathering was set as 2 ms. The real concentration in samples was obtained by AAS (atom absorption spectroscopy). The characteristic variables Cu I 324.7 and Cu I 327.4 were extracted. And the calibration model was constructed between LIBS spectra and real concentration about Cu. The results show that relative error of the predicted concentrations of three relational model were 7.01% or less, reached a minimum of 0.02%, 0.01% and 0.02% respectively. The average relative errors were 2.33%, 3.10% and 26.3%. Tests showed that different characteristic variables decided different accuracy. It is very important to choose suitable characteristic variable. At the same time, this work is helpful to explore the distribution of heavy metals between pulp and peel.

[Determination of Cu in Shell of Preserved Egg by LIBS Coupled with PLS].

In this work, the content of copper in the shell of preserved eggs were determined directly by Laser induced breakdown spectroscopy (LIBS), and the characteristics lines of Cu was obtained. The samples of eggshell were pretreated by acid wet digestion, and the real content of Cu was obtained by atomic absorption spectrophotometer (AAS). Due to the test precision and accuracy of LIBS was influenced by a serious of factors, for example, the complex matrix effect of sample, the enviro nment noise, the system noise of the instrument, the stability of laser energy and so on. And the conventional unvariate linear calibration curve between LIBS intensity and content of element of sample, such as by use of Schiebe G-Lomakin equation, can not meet the requirement of quantitative analysis. In account of that, a kind of multivariate calibration method is needed. In this work, the data of LIBS spectra were processed by partial least squares (PLS), the precision and accuracy of PLS model were compared by different smoothing treatment and five pretreatment methods. The result showed that the correlation coefficient and the accuracy of the PLS model were improved, and the root mean square error and the average relative error were reduced effectively by 11 point smoothing with Multiplicative scatter correction (MSC) pretreatment. The results of the study show that, heavy metal Cu in preserved egg shells can be direct detected accurately by laser induced breakdown spectroscopy, and the next step batch tests will been conducted to find out the relationship of heavy metal Cu content in the preserved egg between the eggshell, egg white and egg yolk. And the goal of the contents of heavy metals in the egg white, egg yolk can be knew through determinate the eggshell by the LIBS can be achieved, to provide new method for rapid non-destructive testing technology for quality and satety of agricultural products.