Journal of Chemometrics最新文献

In several previously published studies, Lavine and coworkers have demonstrated that infrared (IR) spectra from all layers of an intact multilayered automotive paint chip can be collected in a single analysis by scanning across each layer of a cross sectioned paint chip using a Fourier transform IR imaging microscope. Applying alternating least squares to the spectral data, the IR spectrum of each layer of an original equipment manufacturer paint chip can be extracted from a line map of the spectral image. To further develop this imaging technique for automotive paint analysis, the capability to cross section “small” paint chips (1 mm or less) using an ultramicrotome has been incorporated into our current imaging methodology. An ultramicrotome does not require epoxy or other embedding media for the paint chip and will simplify the analysis. However, extracting the IR spectra for each layer of an original equipment manufacturer paint chip by alternating least squares can be problematic for thin peels (less than one micron thickness), necessitating the use of target testing factor analysis to determine whether a specific layer is present in the line map and modified alternating least squares to recover the IR spectrum of the layer. Using a new sample preparation technique and the appropriate multivariate curve resolution methods, high quality IR spectra of the layers of a modern automotive paint system can be obtained from paint fragments that are smaller than what is practical to analyze by conventional Fourier transform IR spectroscopy.

All signals obtained as instrumental response of analytical apparatus are affected by noise, as in Raman spectroscopy. Whereas Raman scattering is an inherently weak process, the noise background may lead to misinterpretations. Although surface amplification of the Raman signal using metallic nanoparticles has been a strategy employed to partially solve the signal-to-noise problem, the preprocessing of Raman spectral data through the use of mathematical filters has become an integral part of Raman spectroscopy analysis. In this paper, a Tikhonov modified method to remove random noise in experimental data is presented. In order to refine and improve the Tikhonov method as a filter, the proposed method includes Euclidean norm of the fractional-order derivative of the solution as an additional criterion in Tikhonov function. In the strategy used here, the solution depends on the regularization parameter, $�\lambda$, and on the fractional derivative order, $�\alpha$. As will be demonstrated, with the algorithm presented here, it is possible to obtain a noise-free spectrum without affecting the fidelity of the molecular signal. In this alternative, the fractional derivative works as a fine control parameter for the usual Tikhonov method. The proposed method was applied to simulated data and to surface-enhanced Raman scattering (SERS) spectra of crystal violet dye in Ag nanoparticles colloidal dispersion.

Today more than ever, space science is a vibrant and exciting field. The Mars 2020 Perseverance Rover took off and landed at the height of the Covid-19 pandemic, and the scientific results of its payload are already paying dividends to the scientific community. Meanwhile on Earth, scientific development, far from having halted, remains as active as ever before, albeit with some hiccups over the last 3 years due to restrictions. Nevertheless, the scientific community, and more specifically, the space science community, has remained steadfast in its pursuit of knowledge. And at the core of this pursuit is the ever-growing field of chemometrics.

All in all, the body of work in this special issue represents a tremendous effort on the part of the authors, and we could not be more pleased. We must admit that the continued submissions and forthcoming work made it hard for us to declare a conclusion to this special issue. Indeed, we could have continued receiving submissions indefinitely. However, all good things must come to an end, if for no other reason than to open the door for future endeavors. Whether that means the continuation of methodological work, the inevitable continuation of instrument development for the search of life or other priorities of the space science communities, or simply reflections on where we are headed, there is much to be done and disseminated. But as long as we continue having fun and pushing the proverbial envelope, special issues such as this one should be executed every few years to ensure that the fields of space science and chemometrics benefit from the synergy of our long-standing interdisciplinarity.

For now, enjoy the ride and shoot for the stars, if only to land on the Moon or Mars!

The purpose of model transfer is to solve the problem that multivariate calibration models cannot be shared among different near-infrared spectrometers. Taking gasoline as the research object, the transfer analysis of its octane number model was carried out. The gasoline samples collected by two near-infrared spectrometers of the same type were used as the research object. The screening wavelengths with consistent and stable signals (SWCSS) combined with competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projections algorithm (SPA) were used to reduce the adverse effects of invalid wavelengths in the SWCSS method; therefore, the analysis ability of the master model to the slave samples was improved. Partial least squares regression (PLSR) models based on SWCSS-UVE, SWCSS-CARS, and SWCSS-SPA algorithms were established, and comparison was made between their analytical capabilities for slave samples and those of SWCSS, direct standardization (DS) algorithm , piecewise direct standardization (PDS) algorithm, and slope/bias (S/B) algorithm. The results shown that the SWCSS-UVE and SWCSS-CARS methods can be used to establish models from the 231 and 6 wavelengths selected from the consistent wavelengths, respectively. The root mean square error of prediction (RMSEP) of the gasoline octane number (RON) content of the direct analysis of the spectrum measured by the slave machine was reduced from 5.7490 to 0.3226 and 0.3250, respectively, which was better than the single SWCSS and DS, PDS, and SWCSS-SPA methods, and was close to the model transfer accuracy of the S/B algorithm. The transfer accuracy of SWCSS-UVE and SWCSS-CARS was not much different, but the wavelength variable involved in the model transfer of the latter was much smaller than that of the former, and the AIC value of SWCSS-CARS was −59.59, which was much smaller than the akaike information criterion (AIC) value of SWCSS-UVE 398.42.

Deep learning algorithms represented by convolutional neural networks bring new opportunities for spectral analysis technology. Convolutional neural networks are more straightforward than traditional chemometric algorithms for detecting the quality of agricultural products, reducing the procedures of spectral preprocessing and band selection, and with higher prediction accuracy. However, there are few research papers on the relevance of the explanation of the convolutional neural networks model mechanism, and the reader cannot fully understand convolutional neural networks feature learning. In this study, convolutional neural networks combined with the subregional voting method were used to predict the moisture content of semen ziziphi spinosae. Firstly, 10 regions of interest were divided using the subregional voting method, and the results of network models were compared. It was found that the average spectrum of the fifth region of interest had the best prediction of moisture content because it was closest to the central region of semen ziziphi spinosae. Based on this, a convolutional neural network containing three convolutional layers, three pooling layers, and one fully connected layer is proposed. Partial least squares regression, backpropagation neural network, and convolutional neural networks were established to predict the moisture content of semen ziziphi spinosae. The correlation coefficient of the prediction set of the partial least squares regression is 0.98 after the multiplicative scatter correction preprocessed the spectra, and correlation coefficient of the prediction set of the backpropagation neural network is 0.83 after the standard normal variate preprocessed the spectra. The correlation coefficient of the prediction set of the convolutional neural networks established by using the raw spectra reached 0.99. The spectral preprocessing method can improve the prediction set correlation coefficient of partial least squares regression and backpropagation neural network. Still, it will reduce the prediction ability of convolutional neural networks. This study also analyzed the effect of different learning rates on the performance of convolutional neural networks, and it was found that the training loss and training accuracy performed most consistently when the learning rate was 0.01. Secondly, this study also visualized the output feature maps of the three convolutional layers of convolutional neural networks and verified the effectiveness of convolutional neural networks feature band extraction. This study provides new ideas for deep learning in the online detection of seed moisture content.

Multi-way data analysis is popular in chemometrics for the decomposition of, for example, spectroscopic or chromatographic higher-order tensor datasets. Parallel factor analysis (PARAFAC) and its extension, PARAFAC2, are extensively employed methods in chemometrics. Applications of PARAFAC2 for untargeted data analysis of hyphenated gas chromatography coupled with mass spectrometric detection (GC-MS) have proven to be very successful. This is attributable to the ability of PARAFAC2 to account for retention time shifts and shape changes in chromatographic elution profiles. Despite its usefulness, the most common implementations of PARAFAC2 are considered quite slow. Furthermore, it is difficult to apply constraints (e.g., non-negativity) to the shifted mode in PARAFAC2 models. Both aspects are addressed by a new shift-invariant tri-linearity (SIT) algorithm proposed in this paper. It is shown on simulated and real GC-MS data that the SIT algorithm is 20–60 times faster than the latest PARAFAC2-alternating least squares (ALS) implementation and the PARAFAC2-flexible coupling algorithm. Further, the SIT method allows the implementation of constraints in all modes. Trials on real-world data indicate that the SIT algorithm compares well with alternatives. The new SIT method achieves better factor resolution than the benchmark in some cases and tends to need fewer latent variables to extract the same chemical information. Although SIT is not capable of modeling shape changes in elution profiles, trials on real-world data indicate the great robustness of the method even in those cases.

Chemists have been pursuing general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure-activity relationship (QSAR) models have limited application domains; for example, they tend to have poor generalization performance when applied to molecules with parent structures different from those of the trained molecules. This paper attempts to develop a new QSAR method that is theoretically possible to predict various properties of molecules with diverse structures. The proposed deep electron cloud-activity relationships (DECAR) and deep field-activity relationships (DFAR) methods consist of three essentials: (1) a large number of molecule entities with activity data as training objects and responses; (2) three-dimensional electron cloud density (ECD) or related field data by the accurate density functional theory methods as input descriptors; and (3) a deep learning model that is sufficiently flexible and powerful to learn the large data described above. DECAR and DFAR are used to distinguish 977 sweet and 1965 non-sweet molecules (with 6-fold data augmentation), and the classification performance is demonstrated to be significantly better than the traditional least squares support vector machine (LS-SVM) models using traditional descriptors. DECAR and DFAR would provide a possible way to establish a widely applicable, cumulative, and shareable artificial intelligence-driven QSAR system. They are likely to promote the development of an interactive platform to collect and share the accurate ECD and field data of millions of molecules with annotated activities. With enough input data, we envision the appearance of several deep networks trained for various molecular activities. Finally, we could anticipate a single DECAR or DFAR network to learn and infer various properties of interest for chemical molecules, which will become an open and shared learning and inference tool for chemists.

The determination of nitrite concentration is crucial due to its toxicity. A novel model has been developed to accurately determine nitrite concentration within the non-linear range, utilizing the Zambelli method. Previously, techniques for measure nitrite concentration were primarily restricted to the linear range. This new method employs UV-Visible absorption spectra and correlated component regression (CCR) to determine nitrite concentration within the range of 0.27–11.34 ppm. A wavelength selection strategy in conjunction with partial least squares (PLS) was implemented prior to applying CCR. The spectral data underwent pre-processing using standard normal variant (SNV) and Savitzky Golay (SG) techniques, and a backward selection (BS) strategy with PLS was applied to select wavelengths. The 15 most sensitive wavelengths, determined through the RMSE_CV criterion, were utilized to create a PLS model within the range 377–497 nm, resulting in a model with R²_C = 0.9999 and R²_CV = 0.9999, RMSE_C = 0.006, RMSE_CV = 0.027. A CCR model was then established using the 15selected wavelengths and nitrite concentration. The results yielded strong correlation between predicted and measured nitrite values with R²_C = 0.9996, RMSE_C = 4.7491 E-15, RMSE_CV = 0.0004, and MAPE = 0.68%. The method has been validated through an accuracy profile, which demonstrates that 80% of future results will fall within the 10% acceptability limit within the validation range of 1.30–8.83 mg/L.

The Journal of Chemometrics is pleased to announce a special issue focused on multivariate analysis of data from designed experiments. ANOVA (Analysis of Variance) is the standard method for analyzing data from experimental designs. The classical ANOVA methods are however univariate and do not handle multiple collinear response variables. Designed experiments with multivariate outputs are prevalent across various scientific disciplines, necessitating methods that appropriately consider both the experimental design and the multivariate nature of the data.

Several multivariate ANOVA techniques have been presented already. The most prevalent approaches involve combining ANOVA with PCA (principal component analysis) or other exploratory component-based techniques in different ways. Some commonly used methods in this context include ASCA, ANOVA-PCA, AComDim, and fifty-fifty MANOVA. These methods integrate ANOVA and PCA in different ways to extract meaningful information from multivariate data. Additionally, there are alternative methods that replace PCA with partial least squares (PLS) regression, which allows for the utilization of PLS-specific validation and variable importance routines. One major advantage of all these methods is that they not only offer interpretation and variable importance metrics from latent variable-based methods but also provide estimates of multivariate effect sizes accompanied by corresponding significance testing.

Despite the progress made in recent years, the field of multivariate analysis of data from designed experiments is still young. Several open questions remain unanswered, and there is a need to make the methodology available to a broader audience. The aim of this special issue was therefore to stimulate and explore advances in methods, applications, and software for multivariate ANOVA.

The collection of papers includes methodical improvements, practical applications, a tutorial, and a software demonstration. Application areas range from spectroscopic control of fermentation processes to metabolomics and gene expressions. Overall, this issue showcases the power and applicability of multivariate ANOVA methods in a wide range of domains.

I am greatly honored to have been selected as the new Editor-in-Chief of Journal of Chemometrics. I am pleased and enthusiastic to contribute to the history of a journal that has been around since the very early days of chemometrics, and I will do my best to drive it into new developments.

I would like to express my warm and sincere thanks to the outgoing Editor-in-Chief, Age Smilde, for his contribution, leadership and confidence, and my deepest gratitude to Ru Qin Yu who has for many years very actively and served with dedication the Journal as Editor. Building upon the legacy and tradition of my renowned predecessors, and on the significant progress made over the past few years under Age's editorship, I will strive to maintain the excellence of Journal of Chemometrics, attracting impactful papers and disseminating new information in our field.

With Anna de Juan from the University of Barcelona and Hailong Wu from the University of Hunan, the Journal welcomes two new Editors who will complete an engaged team of worldwide international experts serving as members of the editorial and advisory boards. Without losing our identity, we will work to address new trends in data science in chemistry and identify challenging applications of chemometrics. We will continue to expand the space for topical publications, to solicit the best scientific findings through tutorials, perspective papers and feature issues from authors investigating new topics towards novel frontiers, attracting young researches and recognizing their early work. We will also increase our efforts to establish and implement reproducible research and data accessibility. With the help of a dedicated Wiley managing team, the publication cycle will be improved. Authors will continue benefiting from helpful feedback provided by a broad pool of highly competent and dedicated reviewers, who are the pillars of the journal's high quality and reputation. We greatly value their commitment to the journal.

With the aim of enduring the success of Journal of Chemometrics, we will always welcome your comments, suggestions and feedback.

I look forward to interacting with you on a Chemometric occasion.