The poor spectral stability of laser-induced breakdown spectroscopy (LIBS) seriously affects its analytical performance, which is a key obstacle to its further development. To overcome this challenge, an improved spectral standardization method based on plasma image-spectrum fusion (ISS-PISF) was proposed in this study. This method, for the first time, considers and quantifies the influence of plasma morphology on spectral intensity based on the line-integrated intensity formula of LIBS spectra. It recognizes that the spectral fluctuations mainly stem from variations in total number density, plasma temperature, electron number density, and plasma morphology. Therefore, ISS-PISF innovatively utilizes easily accessible features from plasma images and spectra to eliminate the influence of these four plasma parameters, thereby improving the spectral stability and analytical performance of LIBS. To validate the effectiveness of this method, the spectra of aluminum alloy samples obtained under complex detection conditions simulated by varying laser energy and defocusing amount were analyzed. After correction by ISS-PISF, the R² for Mg I 516.73 nm, Mn II 294.92 nm, and Si I 288.16 nm improved to 0.990, 0.976, and 0.961, and the average RMSE of the validation set decreased by 46.154%, while the average STD of the validation set decreased by 37.405%. These experimental results indicate that this study provides a simple, effective, and physically supported spectral standardization method, which contributes to the further promotion and application of LIBS.

Laser-induced breakdown spectroscopy (LIBS) was suitable for the identification of meat species due to fast and less sample preparation. However, the problem of low accuracy rate of the recognition model caused by improper selection of training set samples by random split has severely restricted the development of LIBS in meat detection. Sample set portioning based on the joint x–y distance (SPXY) method was applied for dividing the meat spectra into a training set and a test set. Then, the five kinds of meat samples (shrimp, chicken, beef, scallop, and pig liver) were classified by the support vector machine (SVM). With the random split method, Kennard–Stone method, and SPXY method, the recognition accuracies of the SVM model were 90.44%, 91.95%, and 94.35%, respectively. The multidimensional scaling method was used to visualize the results of the sample split for the interpretation of the classification. The results showed that the identification performance of the SPXY method combined with the SVM model was best, and the accuracy rates of shrimp, chicken, beef, scallop, and pig liver were 100.00%, 100.00%, 100.00%, 78.57%, and 92.00%, respectively. Moreover, to verify the broad adaptability of the SPXY method, the linear discriminant analysis model, the K-nearest neighbor model, and the ensemble learning model were applied as the meat species identification model. The results demonstrated that the accuracy rate of the classification model can be improved with the SPXY method. In light of the findings, the proposed sample portioning method can improve the accuracy rate of the recognition model using LIBS.

A rapid and high-precision blood cancer detection method based on Hyperspectral Image (HSI) using the forward search method for feature extraction to improve the accuracy of the SVM model.

At present, there is no comprehensive and systematic research on laser-induced breakdown spectroscopy (LIBS) data visualization.

Due to the effect of bagging on fruit growth, non-destructive and in situ soluble solid content (SSC) in citrus detection remains a challenge. In this work, a new method for accurately quantifying SSC in citrus using hyperspectral imaging of citrus leaves was proposed. Sixty-five Ehime Kashi No. 28 citruses with surrounding leaves picked at two different times were picked for the experiment. Using the principal components analysis combined with Gaussian process regression model, the correlation coefficients of prediction-real value by citrus and its leaves in cross-validation were 0.972 and 0.986, respectively. In addition, the relationship between citrus leaves and SSC content was further explored, and the possible relationship between chlorophyll in leaves and SSC of citrus was analyzed. Comparing the quantitative analysis results by citrus and its leaves, the results show that the proposed method is a non-destructive and reliable method for determining the SSC by citrus leaves and has broad application prospects in indirect detection of citrus.