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Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several realistic situations when the degree of correlation among the regressors differs. First, we show analytically that the new estimator outperforms the maximum likelihood estimator (MLE) using the mean square error (MSE) criteria. Second, using a 'simulation study, we investigate the properties in finite samples of six different suggested estimators of the shrinkage parameter and compare it with the MLE. The simulation results indicate that in the presence of multicollinearity, the Liu estimator outperforms the MLE uniformly. Finally, using an empirical application on chemical data, we show the benefit of the new approach to applied researchers. Beta regression is popular when the dependent variable is restricted to the interval [0, 1], such as rates and proportions. This paper suggests Liu estimators that may be used in the presence of multicollinearity. We show that the Liu estimator outperforms the MLE in a simulation study. Finally, using an empirical application on chemical data, we show the benefit of the new approach. [ABSTRACT FROM AUTHOR]

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]

Transfer learning (TL), the sub‐discipline of machine learning devoted to learning from different domains, has gained increasing attention over the past decade. With the current contribution, we aim at giving a concise overview on theory, concepts, and applications of TL from a chemometrician's perspective and draw some connections to previous work on calibration model updating/adaptation and calibration transfer. Furthermore, we provide a demonstration of the application of TL in analytical chemistry and discuss the benefits and challenges associate with its use for real‐world problems. We conclude the paper by discussing some open problems and by contemplating on future research directions. [ABSTRACT FROM AUTHOR]

The steady advances in technology employed in smartphones, coupled with the high availability and the ease access to these devices, increased the interest in applying it to analytical purposes. The main goal is to develop simple, fast, and low‐cost analytical methods, generating immediate results. Different analytical strategies using smartphones have been proposed to identify or determine different analytes in a wide range of matrices. Some of them use the smartphone to capture the images, to process them, and to provide the analytical results. Others use this device just for image acquisition. In this context, this review aimed to relate previous works that used smartphones to acquire images for analytical purposes, with a special focus on PhotoMetrix application, which is a tool for chemical analysis. This application allows the decomposition of digital images acquired by smartphone cameras and to process them within the same device, allowing in situ chemical analysis. [ABSTRACT FROM AUTHOR]

Orthogonal projections to latent structures (OPLS) and target projection (TP) are two alternative methods for separation of predicting and non-predicting parts of the predictor matrix in partial least squares regression (PLSR), which can also be applied on principal component regression (PCR). An additional new method called PLSO is developed in the paper. In all three methods, the predicting score vector is a scaled version of the fitted response vector. Otherwise, the resulting score and loading vectors are different, although OPLS and TP are identical within similarity transformations. All these relations are here found by simple projections of the fitted predictor matrix from PLSR or PCR, and the similarities and differences are illustrated in discriminant analysis examples. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Edmund R. Malinowski is well known for his factor analysis‐based work; he is clearly less well known for his chemical model‐based analyses of chemical data. In this contribution, we discuss the, at the time innovative, idea of subjecting the primary model‐free analysis results to a secondary quantitative model‐based evaluation. Two examples from his research serve as illustrations: the complexation of Cu2+ with cyclodextrin and the dimerisation and trimerisation of methylene blue. In both examples, the spectrophotometric titration data are first analysed by window factor analysis, WFA, resulting in the concentration profiles. These primary, model‐free results allowed Malinowski to gain qualitative insight in the chemical processes. Additional quantitative analyses of the concentration profiles, based on the previously obtained reaction model, resulted in the numerical values of the underlying equilibrium constants. This contribution relates and compares this methodology with alternative ways of analysing the same data. Model‐free analyses result in the shapes of concentration profiles and spectra. Secondary analysis of the concentration profiles is possible and can result in quantitative information, for example, rate or equilibrium constants. It is advantageous to avoid this 2‐step process by executing a straight model‐based analysis. [ABSTRACT FROM AUTHOR]

The origins of chemometrics in quantitative analytical chemistry is contrasted to the emerging growth points of statistically based hypothesis testing and generation such as in applications to metabolomics and heritage science. [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]

This review systematizes for the first time the here called "Chemometrics‐assisted color histogram‐based analytical systems" under the acronym CACHAS. A comprehensive discussion of the fundamentals, general characteristics, and applications are presented in order to direct practical aspects for developing methods that use color histograms as input data for the construction of chemometric models. For this, analytical information is rapidly acquired using a simple image acquisition device (such as digital camera, webcam, scanner, or smartphone), without need for any image processing, other than extraction of the histograms, which are constructed by counting the frequency distribution of the different color tones (conveniently specified through a color space) of all pixels delimited by a region of interest in a digital image. As a consequence, CACHAS have been increasingly used for qualitative and quantitative purposes in food, agricultural, forensic, biomedical, and microbiological analysis, reinforcing the principles of green analytical chemistry. [ABSTRACT FROM AUTHOR]

Paul Gemperline has been active in many areas of data analysis in chemistry. In this contribution, we build on his developments in advanced process analysis. The most prominent aspect is data fusion, the combined analysis of measurements taken on different instruments, using different measurement techniques, and different chemical conditions. We have successfully applied these principles to the investigation of the chemistry of CO2 in aqueous amine solutions. There are two fields of chemometrics analyses, those which attempt at analysing the data without prior knowledge and those which based the analysis on as much chemical knowledge as possible. Paul Gemperline has been active in both. In this contribution, we concentrate on the chemistry‐based analysis ideas that have been developed by Paul: hard modelling and the globalisation of the analysis of several data sets. This contribution applies both above ideas for the investigation of the aqueous chemistry of CO2. [ABSTRACT FROM AUTHOR]

When chemometrics began as a field, what is considered chemometrics and how chemometrics relates to neighboring fields all are issues that continue to impact the field today. This review discusses early efforts to create the new field of chemometrics and considers how these efforts continue to shape the field today. The review points out the need for change in some long-established practices in chemometrics to keep the field strong in the face of rapid changes in data analysis and in chemical instrumentation. [ABSTRACT FROM AUTHOR]

Multivariate calibration is an important procedure for analytical chemistry. Automated or self-configuring methods can be used by scientists who lack expertise, may be embedded into data processing pipelines, and are less prone to user bias; however, the development of such algorithms is often neglected by the chemometrics community. Support vector regression (SVR) is a powerful method for accommodating megavariate data. SVR offers the advantage of fast calibration and flexibility in a variety of loss functions (ie, minimization of the residual error). By embedding bootstrapped Latin partitions (BLPs) into the calibration, the key parameter, the cost C, can be optimized to furnish an automated method. The methods are termed super SVR (sSVR). The BLP predictions of the calibration set accurately model the external prediction error of the entire calibration set. Prediction rates for super partial least squares (sPLS) are compared with sSVRs using three loss functions, Gauss, Laplace, and Huber. For linear data with uniformly distributed noise, sPLS is faster and gave better predictions. However, for data with outliers or a real data set of single-beam near infrared spectra of bovine plasma, gasoline, and wheat, the sSVRs performed better than sPLS, and generally, the Huber loss function gave the best results. [ABSTRACT FROM AUTHOR]

Self-modeling curve resolution methods have continuously been improved during recent years. Many efforts have been made on curve resolution methods to reduce the rotational ambiguity by means of different types of constraints. Choosing proper constraints and cost functions is critically important for the reduction of the rotational ambiguity because the constraints have a direct influence on the accuracy of the area of feasible solution (AFS). In this work, we introduce a new improved cost function, which serves to apply nonnegativity, unimodality, equality, and closure constraints. We also investigate the reduction of the AFS under hard and soft constraints. Another point of this work is to evaluate the accuracy and precision of the reduced AFS in the presence of noise and perturbations, under hard and soft implementation of nonnegativity, unimodality, equality, and closure constraints. A comparison is given between the reduced AFS with soft constraints (small deviations from constraints are accepted) and the reduced AFS under hard constraints (restrictedly forced constraints). A graphical visualization of this comparison is presented for various model problems. The results show that an AFS computation with soft constraints provides more reliable results, especially in the presence of noise. The test problems substantiate significant advantages of soft constraints over hard constraints because the obtained profiles are closer to the potentially true noisy profiles, which contain small deviations from ideal responses. Using tunable parameters ϵ, γ, ω, δ is one of the advantages of soft constrained cost function that allows the small deviations from ideal responses. Ultimately, soft constraints can help to reduce the lack-of-fit, and they are a proper instrument to handle the effect of noise on the AFS. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this contribution, a technique is proposed to create a data-driven interpretation of a given chemometric analysis of a Raman dataset. In real-world applications, the chemometric analysis is fixed by some external measurement, for example, a legal standard, or a set of fixed goals. Thus, the exact chemometric work flow is fixed because of those goals. However, a further optimization, for example, of the measurement itself relies on an interpretation of the resulting chemometric analysis. For this purpose, a data-driven analysis of the chemometric analysis itself has to be carried out. This contribution tries to achieve that goal by combining two methods. The first proposed technique is the calculation of the so-called importance map, which allows the computation of the importance of every channel for a given model and a given dataset. This importance map is constructed after the complete result of an out-of-bag (OOB) validation and the decrease of accuracy by randomized channels. The second technique is the growing of the optimal decision tree based on the action of the model used for chemometric analysis. By this way, a clustering is achieved on which by binary classifiers, the optimal decision tree is grown. This tree can be interpreted as dividing the whole dataset into meta clusters. Combining these techniques, a new way of interpreting datasets based on the chosen model is proposed. This combination closes the gap between chemometric analysis and the need for interpretation. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A true pioneer in the fields of chemometrics and analytical chemistry was lost with the passing of Yi‐Zeng Liang in October 2016. This is an enormous loss to both the local and global community of chemometrics. Yi‐Zeng Liang is recognized as one of the major players in China in the field of chemometrics. As a professor of chemometrics and analytical chemistry in Hunan University and Central South University, he made significant advances within instrumental analysis of complex systems. Liang inspired many students and visiting scholars. As Liang's former students, we have attempted to summarize Liang's contributions to the field over the past 30 years. This review covers different chemometric areas, signal preprocessing, multivariate curve resolution, and multivariate calibration and discusses the application of these methods in traditional Chinese medicine, metabolomics, proteomics, and quantitative structure‐activity relationship research. A true pioneer in the fields of chemometrics and analytical chemistry was lost with the passing of Yi‐Zeng Liang in October 2016. He made significant advances within instrumental analysis of complex systems. This review attempted to summarize Liang's contributions to the field over the past 30 years. It covers different chemometric areas, signal preprocessing, multivariate curve resolution, and multivariate calibration and discusses the application of these methods in traditional Chinese medicine, metabolomics, proteomics, and quantitative structure‐activity relationship research. [ABSTRACT FROM AUTHOR]