Journal of Chemometrics最新文献

With the development of analytical instrument towards more and more high-way and complex, it is very important and meaningful work to obtain ultra-high-way chemical data and explore its analytical methods. In this paper, a novel and excellent six-way algorithm combination method (six-way ACM) was proposed. In addition, a real chemically meaningful ultra-high-way sexalinear data array was obtained and constructed for the first time. The proposed six-way data array has highly collinearity, which puts forward higher requirements for parsing this data array to a certain extent. To verify the feasibility of the proposed algorithm, it was used to analyze the above real sexalinear six-way data array and a series of simulated six-way data arrays with different noise levels. The results of real data and simulated data demonstrate that the proposed method can be well used in the analysis of six-way data arrays and shows fascinating performance, including insensitive to excessive number of components, fast convergence speed, and suitable for high collinearity and high noise data. Compared with three-way, four-way, and five-way calibration methods, the six-way ACM provides higher sensitivity, a lower limit of detection, a lower limit of quantification, and more stable and accurate results, showing an outstanding “higher-order advantages” and better ability to handle collinearity problems. This work provides not only data analysis method for high-order instruments that may emerge in the future but also real data support and methodological reference for theoretical research on high-order tensor algebra.

Tea color is a part of tea quality, and illegal addition of lead chrome green (LCG) to improve tea quality cannot be identified by human eyes. This paper is based on near-infrared (NIR) reflectance spectroscopy to detect LCG stained tea and to investigate the feasibility of qualitative and quantitative methods. Firstly, the LCG in tea was qualitatively analyzed by partial least squares discriminant analysis (PLS-DA), random forest (RF), and least squares support vector machine (LSSVM) classification models, and the results showed that the classification accuracy of LSSVM reached 100%. For quantitative analysis, Savitzky–Golay convolutional smoothing (S-G) preprocessing combined with three feature extraction algorithms, namely, joint competitive adaptive weighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were used to build partial least squares (PLS), RF, and LSSVM regression models sequentially on the preprocessed data. The S-G-UVE-LSSVM showed the best regression prediction ability in detecting LCG in tea, with a tested R² of 0.96. These results show the feasibility of NIR spectroscopy for the detection of added LCG in tea.

Halitosis is a condition associated with bad breath. Although halitosis is a disease in its own right, it is often a symptom of more serious diseases (diabetes mellitus, renal failure, azotemia, etc.). The currently used method for diagnosing halitosis is the organoleptic method, which relies on a trained specialist evaluating the patient's breath odor. This approach to diagnosing halitosis is subjective, uncomfortable for both patient and doctor, and necessitates the involvement of a specially trained professional. As an alternative, instrumental diagnostics employing metal oxide semiconductor (MOS) sensor arrays offer a promising avenue by enabling patient classification through predeveloped models. This paper considers the application of seven MOS sensors of different compositions at three different temperatures. Different methods of chemometric data analysis were applied: k-nearest neighbors (kNN), decision trees (DT), support vector machine (SVM), logistic regression (LR), and projection on latent structures discrimination analysis (PLSDA). All applied methods demonstrated their effectiveness and achieved selectivity, sensitivity, and accuracy values exceeding 85%. Additionally, a combined classifier leveraging responses from all previously studied classifiers was explored, achieving near-perfect classification accuracy.

In this paper, an innovative method for the simultaneous determination of nitrite, nitrate, and COD in water in the presence of turbidity as a source of noise in spectroscopic data has been investigated. UV–Vis absorption spectrometry and advanced machine learning are proposed to develop a stacking model, a sophisticated modeling approach that combines several basic models (PLS, Lasso, and Ridge regression) and a meta-regressor (Random Forest regressor) to improve prediction accuracy by incorporating baseline correction and principal component analysis (PCA) to mitigate the effects of turbidity on spectroscopic data. After applying these corrections, a significant improvement was observed: The root mean square error (RMSE) and the mean absolute error (MAE) were significantly reduced, and the correlation coefficient (R²) between predicted and actual values of nitrite, nitrate, COD, and turbidity was greater than 0.96, for all compounds in the test data set, that demonstrate the ability of the proposed stacking model to accurately predict nutrient concentrations simultaneously, even in complex environments; the proposed model may provide a valuable alternative to wet chemical methods. Due to its high accuracy and fast response, the proposed model can be used as an algorithm for the construction of nutrient sensors. This paper highlights the importance of integrating advanced modeling and data correction techniques to improve the robustness and accuracy of predictive models in environmental chemistry, thus providing valuable information for environmental monitoring and management.

In recent years, the role of analytical chemistry has undergone a gradual transformation, evolving from a mere participant to a pivotal decision-maker in process optimisation. This shift can be attributed to the advent of sophisticated analytical instrumentation, which has ushered in a new era of analytical capabilities. This article presents a review of the developments in the application of intelligent analysis techniques, including infrared (IR) spectroscopy, Raman spectroscopy, and laser-induced breakdown spectroscopy (LIBS), in the processing of complex systems over the past decade. The review provides an introduction to the fundamental principles of these analytical techniques and examines the evolution of their instrumentation to accommodate online process monitoring. The analysis of spectral data in complex system processes represents a fundamental aspect of the attainment of on-site quality monitoring, process optimisation and control. Accordingly, the review provides a comprehensive overview of the methodologies employed in process chemometrics, encompassing spectral preprocessing, feature selection, modelling techniques, and optimisation strategies for model performance. Furthermore, this article presents a summary of three intelligent spectral analysis tools, namely infrared spectroscopy, Raman spectroscopy, and LIBS, which are widely employed in process simulation, monitoring, optimisation, and control across multiple disciplines, including the environment, energy, biology, and food. The objective of this review is to provide a valuable reference point and guidance for the further promotion and utilisation of spectral intelligent analysis instruments, with the aim of promoting their in-depth application and development in a greater number of fields.