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The exploration and analyses of chemical components in (extra‐)terrestrial geological materials (such as asteroids and meteorites) are insightful in modern research. Laser‐induced breakdown spectroscopy (LiBS) is a popular method for analyzing the chemical attributes of geologic samples—which scientists use to study and understand planetary bodies and their complex histories. In the literature, several machine learning models that produce high‐accuracy predictions have been proposed. In our work, we compared the performances of such models in predicting elemental abundances on a certain spectroscopic dataset. Models included partial least squares (PLS), extreme gradient boost machines (XGB), neural networks, and linear models. In our results, we showed how PLS and XGB are superior in terms of high predictive power, their ability to generalize, and their reasonably efficient runtimes. In addition, we proposed Ensemble models that aggregate predictions of top‐tier models and observed that they can be desirable. We intend to gain better understanding of how these models perform in predicting elemental compositions on specific spectrum (LIBS) datasets. In this paper, we compare several machine learning models to identify those who performed best in a specific spectroscopic datasets. The models investigated include partial least squares, Lasso regression, support vector regression, artificial and convolutional neural networks, and extreme gradient boosting machines. We then propose an Ensemble model aggregating the predictions of the top‐tier, models and we have found that it performs significantly well. [ABSTRACT FROM AUTHOR]

Laser‐induced breakdown spectroscopy (LIBS) is a new type of elemental analytical technology with the advantages of real‐time, online, and noncontact as well as enabling the simultaneous analysis of multiple elements. It has become a frontier analytical technique in spectral analysis. However, the issue of how to improve the accuracy of qualitative and quantitative analyses by extracting useful information from a large amount of complex LIBS data remains the main problem for the LIBS technique. Chemometrics is a chemical subdiscipline of multi‐interdisciplinary methods; it offers advantages in data processing, signal analysis, and pattern recognition. It can solve some complicated problems that are difficult for traditional chemical methods. In this paper, we reviewed the research progress of chemometrics methods in LIBS for spectral data preprocessing as well as for qualitative and quantitative analyses in the most recent 5 years (2012‐2016). Laser‐induced breakdown spectroscopy (LIBS) is a new type of elemental analytical technology with the advantages of without complex sample preparation, rapid and remote detection. However, the issue of how to improve the accuracy of qualitative and quantitative analyses by chemometrics methods remains the main problem for LIBS technique. The research progress of chemometrics methods in LIBS was reviewed in the most recent 5 years (2012‐2016). [ABSTRACT FROM AUTHOR]

This study developed a reliable laser‐induced breakdown spectroscopy (LIBS) screening approach capable of detecting GSR in just a few minutes with minimal damage to the sample, high specificity, and sensitivity. Moreover, a novel micro‐sampling method was developed to gather three‐dimensional data of the simultaneous occurrence of IGSR markers from a discrete space. The method is capable of micro‐spatial chemical analysis from just two laser shots fired at an area of 100‐μm diameter. The performance of the micro‐spot method is compared with our previously published bulk‐line method. Superior accuracy, spatial information of IGSR distribution in the sample, and a less invasive sampling are some of the advantages of the newly proposed method. A benefit afforded by this approach is the use of the universal hand's collection method currently used by practitioners, while leaving over 99% of the stub left unaltered for further analysis. Machine learning algorithms were used for the classification of samples derived from shooters' hands versus nonshooters hands, based on their LIBS spectrochemical data. Four different approaches—critical threshold, logistic regression, naïve Bayes, and neural networks—were applied to examine the performance and accuracy of two different ablation patterns (micro‐spot and bulk‐line mode). A validation set of 326 samples originated from 51 nonshooters and 56 known shooters resulted in an overall accuracy between 87% and 100%, depending on the ablation pattern and the type of prediction model applied. The incorporation of this rapid screening and statistical decision‐making approach could offer more efficient case management in firearm‐related investigations. A LIBS method capable of documenting the distribution of IGSR markers on substrates dabbed from an individual's hands was developed. The 3D data include simultaneous information about the analyte characteristic wavelengths, their respective intensity, and x‐y location coordinates within the sample. Logistic regression, naïve Bayes, and neural network machine learning models provided effective predictive methods, with classification accuracy better than 93.7% for a validation set of 326 authentic samples collected from shooters and background populations. [ABSTRACT FROM AUTHOR]