Chemometrics and Intelligent Laboratory Systems最新文献

In modern complex industrial operations, timely fault detection is imperative. While statistical process monitoring is widely used in practice, conventional approaches are usually insensitive to incipient faults (IFs) whose magnitudes are not obvious. To this end, an innovative approach is presented for IF detection in dynamic processes. To begin with, canonical variate residuals (CVRs) are generated by using the canonical variate dissimilarity analysis (CVDA) algorithm. The next step involves calculating statistics for the CVRs and arranging a corresponding statistic matrix. Afterward, the Mahalanobis distance index is constructed for fault detection purpose. The main reasons that this developed approach possesses high sensitivity to IFs in dynamic processes lie in the utilization of CVDA and the idea of monitoring extracted statistics rather than original residuals. Finally, its effectiveness and merits are demonstrated via a numerical example and a benchmark process.

In this work, a novel electrochemical sensor was fabricated for simultaneous determination of patulin (PT) and citrinin (CT) in apple and pear fruit samples. A glassy carbon electrode (GCE) was modified with graphene-multiwalled carbon nanotubes-ionic liquid (Gr-MWCNTs-IL) which was used as a platform to electrochemical synthesis of molecularly imprinted polymers (MIPs) by using PT and CT as templates, maleic acid as a functional monomer, and ethylene glycol dimethacrylate as a cross linker with the aim of preconcentration and simultaneous determination of the PT and CT. Experimental variables affecting fabrication of the structure of the sensor and hydrodynamic differential pulse voltammetric (HDPV) response of the sensor were optimized by a small central composite design and desirability function. After optimization, the HDPV responses of the sensor were calibrated by multivariate calibration methods in the ranges of 0.5–13 fM and 1.5–18 fM for PT and CT, respectively, with the help of PLS-1, RBF-PLS, rPLS, LS-SVM, and RBF-ANN with the aim of selecting the best algorithm to assist the sensor. Our results confirmed the best performance was observed from RBF-ANN which was used for the analysis of apple and pear fruit samples. Limit of detections of the sensor assisted by RBF-ANN for determination of PT and CT were 0.08 and 0.61 fM, respectively. Several commercial brands were analyzed by the use of sensor assisted by RBF-ANN and HPLC-UV, and the results confirmed performance of the sensor was admirable and comparable with the reference method with lower cost, faster response, and easier procedure which made it to be a reliable alternative method for simultaneous determination of PT and CT in real matrices.

Extended Multiplicative Signal Correction (EMSC) is a multivariate linear modelling technique for multi-channel measurements that can identify and correct for different types of systematic variation patterns, known or unknown. It is typically used for pre-processing to separate light absorbance spectra, obtained by diffuse reflectance of intact samples, into three main sources of variation: additive variations due to chemical composition (≈Beer's law), mixed multiplicative and additive variations due to physical light scattering (≈Lambert's law) and more or less random measurement noise. The present work evaluates the use of EMSC to pre-process near infrared spectra obtained by hyperspectral imaging of Scots pine sapwood, inoculated with two different basidiomycete fungi and at various degradation stages. The spectral changes due to fungal decay and resulting mass loss are assessed by interpretation of the EMSC parameters and the partial least squares regression (PLSR) results. Including a cellulose (analyte) or bound water (interferent) spectral profile in the EMSC pre-processing model generally improves the predictive performance of the PLS modelling, but it can also make it worse. The inclusion of the additional polynomial baselines does not necessarily lead to a better separation of the physical and chemical effects present in the spectra. The estimated EMSC parameters provide insight into the differences in decay mechanisms. A detailed analysis of the EMSC results highlights advantages and disadvantages of using a complex pre-processing model.

A disease caused by cellular alterations that is unrestrained cell growth and division is cancer. Many anticancer medications, including those used to treat blood, breast, and skin cancer, may have their physical, chemical, and biological features predicted. This paper presents novel distance-based topological indices (TIs) computed using the suggested KP-polynomial with blood cancer drugs. The objective of the QSPR investigation is to determine the mathematical correlation between the analyzed properties (such as Molar Volume, Refractive Index, etc.) and different descriptors associated with the molecular structure of the medications. A polynomial regression model is employed to assess the predictive capability of TIs. The results are represented using a correlation coefficient to establish the connection between the predicted and observed values of blood cancer drugs. This theoretical method could potentially enable chemists and health care professionals to anticipate the characteristics of blood cancer drugs without the need for actual experimental tests. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient multicriteria decision making technique TOPSIS for ranking of said disease treatment drugs and physicochemical characteristics.

With the advancement of data science and technology, the complexity and diversity of data have increased. Challenges arise when dealing with a larger number of variables than the sample size or the presence of multicollinearity due to strong correlations among variables. In this paper, we propose a moving window sparse partial least squares method that combines the sliding interval technique with sparse partial least squares. By utilizing sliding interval partial least squares regression to identify the optimal interval and incorporating sparse partial least squares for variable selection, the proposed method offers innovations compared to traditional partial least squares (PLS) approaches. Monte Carlo simulations demonstrate its performance in variable selection and model prediction. We apply the method to seawater spectral data, predicting the concentration of chemical Oxygen demand. The results show that the method not only selects reasonable spectral wavelength intervals but also enhances predictive performance.

Diabetes and dyslipidemia are well-established risk factors for cardiovascular disease, which is the primary cause of death both in Brazil and globally. Fourier-transform mid-infrared spectroscopy (FTIR-MIR) generates spectral fingerprints of biomolecules, allowing for correlation with metabolic changes, while remaining a rapid, non-invasive, and non-destructive method. The study provided a proof of concept for the effectiveness of FTIR-MIR in screening diabetes, pre-diabetes, hypercholesterolemia, hypertriglyceridemia, and mixed dyslipidemia in blood serum. After acquiring mid-infrared spectra of 60 human serum samples, both unsupervised and supervised analysis models were developed. Principal component analysis (PCA) was used for pattern recognition and to determine how closely related the samples were based on their spectral profiles. The results obtained by the supervised models showed a clear discriminative ability to distinguish both diabetic and dyslipidemic samples from healthy subjects by multivariate analysis performed on FTIR-MIR spectra. High accuracy rates of more than 90 % were achieved for diabetes and dyslipidemia diagnosis with PLS-DA. Dyslipidemia type discrimination could be attributed mainly to the amide I region [1720-1600 cm⁻¹, (ν(CO)] and altered lipid concentration in the 3000-2800 cm⁻¹ region, whereas the discrimination of diabetes and prediabetes was primarily due to the altered conformational protein in the Amides I [1720-1600 cm⁻¹, ν(CO)] and Amide II [1570-1480 cm⁻¹, δ(NH) + ν(CH)] range.

The rapid and non-destructive measurement of coal moisture content is essential in the coal industry for production, transportation and utilization purposes. Existing measurement methods have still drawbacks, such as being time-consuming, producing destructive samples and yielding unstable outcomes. To address these issues, this paper explored the utilization of broadband microwave spectrum for intelligent coal moisture measurement. A multi-type outliers detection method based on the Monte-Carlo cross-validation (MCCV) strategy was used to prevent masking effect of microwave spectra. In order to effectively extract microwave spectral features and establish correlations with coal moisture, a novel neural network model, UC-PLSR, is proposed by combining U-Net, Convolutional Block Attention Module (CBAM) and Partial Least Squares Regression (PLSR) algorithm. Furthermore, a design scheme/case of a microwave measurement device for coal moisture was presented, offering guidance for the development of rapid coal moisture measurement instruments or on-site measurement systems. Experimental results demonstrated that the proposed model outperformed traditional chemometrics methods, achieving superior prediction accuracy and generalization capability with $R^2$ = 0.8756, MAE = 1.2523 and RMSE=1.6560.

Ion mobility spectrometry (IMS) is a promising analytical technique for mass spectrometry (MS)-based compound identification by providing collision cross-section (CCS) value as an additional dimension with structural information. Here, GraphCCS was proposed to accurately predict the CCS value and expand the coverage of CCS libraries. A new adduct encoding method was proposed to encode SMILES strings and adduct types of compounds into adduct graphs. GraphCCS extended its predictive capability to ten different adduct types. A very deep graph convolutional network with up to 40 GCN layers was built to predict CCS values from adduct graphs. A curated dataset with 12,775 experimental CCS values was used to train, validate, and test the GraphCCS model. The resulting CCS predictions achieved a median relative error (MedRE) of 0.94 % and a coefficient of determination (R²) of 0.994 on the test set. Results on external test sets showed that GraphCCS outperformed AllCCS2, CCSbase, SigmaCCS, and DeepCCS. Based on the developed GraphCCS method, a large-scale in-silico database was built, including 2,394,468 CCS values. Those CCS values can be used to filter false positives complementary to retention times and tandem mass spectra. Finally, the effectiveness of GraphCCS in assisting compound identification was tested on a mouse adrenal gland lipid dataset with 1,960 lipids. The results demonstrated that the in-silico CCS values combined with MS spectra and retention times can efficiently filter the false positive candidates.

In this work, a novel biosensing platform was fabricated based on modification of a rotating glassy carbon electrode (GCE) with chitosan-ionic liquid (Ch-IL) composite film, electrochemical synthesis of gold palladium platinum trimetallic three metallic alloy nanoparticles (AuPtPd NPs) onto its surface, and electrosynthesis of dual templates molecularly imprinted polymers (MIPs) where morphine (MO) and codeine (COD) used as template molecules. The AuPtPd NPs were synthesized under different electrochemical conditions, and surfaces of electrodes were investigated by digital image processing, and the best electrode was chosen. Effects of experimental variables on response of the biosensor to MO and COD were optimized by a central composite design (CCD), and under optimized conditions (concentration of the phosphate buffered solution (PBS): 0.09 M, pH of the PBS: 3.21–3.2, time of immersion: 204.8–205 s, and rotation rate: 2993.51–3000 rpm) the biosensor responses to MO and COD were individually calibrated (1–20 pM for MO and 0.5–12 pM for COD), three-way calibrated by PARASIAS, PARAFAC2, and MCR-ALS, and validated in the presence of ascorbic acid and uric acid as uncalibrated interference. Finally, performance of the biosensor in simultaneous determination of MO and COD in the presence of ascorbic acid and uric acid as uncalibrated interference in human serum samples were verified and compared with the results of HPLC-UV as the reference method which guaranteed it as a reliable method.

Characterizing sample composition and visualizing the distribution of its chemical compounds is a prominent topic in various research and applied fields. Integrating spatial and spectral information, hyperspectral imaging (HSI) plays a pivotal role in this pursuit. While self-modelling curve resolution techniques, like multivariate curve resolution - alternating least squares (MCR-ALS), and clustering methods, such as K-means, are widely used for HSI data analysis, their effectiveness in complex scenarios, where the structure of the data deviates from the models’ assumptions, deserves further investigation. The choice of a data analysis method is most often driven by research question at hand and prior knowledge of the sample. However, overlooking the structure of the investigated data, i.e. linearity, geometry, homogeneity, might lead to erroneous or biased results. Here, we propose an exploratory data analysis approach, based on the geometry of the data points cloud, to investigate the structure of HSI datasets and extract their main characteristics, providing insight into the results obtained by the above-mentioned methods. We employ the principle of essential information to extract archetype (most linearly dissimilar) spectra and archetype single-wavelength images. These spectra and images are then discussed and contrasted with MCR-ALS and K-means clustering results. Two datasets with varying characteristics and complexities were investigated: a powder mixture analyzed with Raman spectroscopy and a mineral sample analyzed with Laser Induced Breakdown Spectroscopy (LIBS). We show that the proposed approach enables to summarize the main characteristics of hyperspectral imaging data and provides a more accurate understanding of the results obtained by traditional data modelling methods, driving the choice of the most suitable one.