ABSTRACT Gridded climate reanalysis datasets can be viewed as image cubes, yet chemometric decomposition methods remain underutilized in climate data analysis. This study applied pixel‐based Multivariate Curve Resolution–Alternating Least Squares (MCR‐ALS) with a trilinearity constraint to ERA5 precipitation data over the Iberian Peninsula (1940–2024). The 33 × 57 × 85 (latitude × longitude × year) data cube was unfolded pixel‐wise into an augmented two‐dimensional matrix, and the trilinearity constraint factorized the spatial profiles into separate latitude and longitude components, yielding an essentially unique decomposition and coherent two‐dimensional reconstructed precipitation maps. Grid‐scale artifacts in the resolved maps were subsequently mitigated by a discrete scalar‐field spectral filtering framework. Five precipitation zones were resolved, jointly explaining 95.6% of the total data variance: a Southwestern France–Western Languedoc Atlantic transition, the Central Pyrenees orographic zone, the Galician Atlantic façade, southern Portugal/Extremadura/Andalusia, and the central‐eastern Mediterranean façade. These zones closely matched those previously identified from ground‐based station data by principal component analysis, supporting the robustness of the pixel‐based gridded decomposition. The proposed framework, combining chemometric image‐based decomposition with a scalar‐field spectral treatment, provides an effective approach to extract interpretable spatiotemporal patterns from long‐term climate reanalysis datasets and is directly extensible to other climate variables.
ABSTRACT In the presence of additive measurement noise, the Pearson moment‐product correlation coefficient between two measured quantities (variables) and is distorted and can be biased upwards or downwards depending on the interplay of the correlated and uncorrelated noise components. This bias can be removed with appropriate mathematical manipulations once the noise variance components have been estimated. In this paper, we derive expressions for the variance and standard error of the corrected sample correlation as a function of the noise variance components, which can be of utility in hypothesis testing and experimental design.
ABSTRACT To address the strong subjectivity of manual evaluation, the low efficiency of traditional physicochemical detection, as well as the issues of high variable dimensionality, redundant information, and subtle inter‐class differences in near‐infrared (NIR) spectral data during the grade identification of Baijiu base liquor, this paper proposes a grade identification method for Baijiu base liquor based on multistrategy fusion zebra optimization algorithm (MFZOA) feature selection and Kolmogorov–Arnold network (KAN). First, a Fourier transform near‐infrared (FT‐NIR) spectrometer was employed to acquire the spectral information of strong‐flavor Baijiu base liquor samples, and the GA‐SPXY method was used to partition the samples into training and validation sets, thus improving the representativeness of sample division. Subsequently, a binary MFZOA feature selection model oriented to classification tasks was constructed, in which candidate feature subsets were encoded as zebra individuals, and the classification performance was used as the fitness evaluation criterion. MFZOA integrates good point set initialization to improve the uniformity of the initial population distribution, a nonlinear dynamic convergence factor to balance global exploration and local exploitation, and adaptive Gaussian mutation to enhance population diversity, thereby improving the search for discriminative wave number subsets. Finally, the selected feature variables were fed into the KAN model, whose learnable univariate functions on network edges enable flexible nonlinear mapping between spectral features and grade labels. Experimental results demonstrate that MFZOA can effectively screen out the characteristic wave numbers relevant to base liquor grade discrimination, and the obtained feature subset exhibits strong class discriminability. Under the same feature input conditions, the KAN model outperforms the compared models including random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), and one dimensional convolutional neural network (1D‐CNN), achieving an accuracy (ACC) of 95.86%, a recall of 96.94%, and a macro‐F1 of 95.52%. The findings indicate that the proposed MFZOA–KAN method can realize rapid and nondestructive grade identification of Baijiu base liquor, and can provide a methodological reference for intelligent grading and quality control in the Baijiu production process.
Raman hyperspectral imaging is an indispensable analytical technique that can provide simultaneous chemical and spatial information of a sample, enabling a higher comprehension and sampling of the analyzed material. However, a known challenge in processing large hyperspectral Raman datasets is the effective removal of spurious, high-intensity peaks caused by cosmic rays (cosmic spikes). Correcting cosmic spikes in complex hyperspectral images is particularly difficult due to the sheer volume of data, the inherent chemical heterogeneity of pixels, variations in background intensity, and the shape of authentic Raman bands. To address this, we propose a novel cosmic spike correction algorithm specifically engineered for Raman hyperspectral imaging. The L-probe algorithm employs the concept of local prominence for the reliable identification of suspicious peaks and spike removal based on the assumption of chemical composition homogeneity across adjacent pixels, allowing targeted and localized correction. The algorithm was rigorously evaluated using a factorial design of experiments (DoE) applied to an artificial hyperspectral image to establish its parameters. Final validation was successfully performed using real hyperspectral images acquired from complex, phase-separated samples, such as cosmetic emulsions, demonstrating the robustness of the algorithm.
ABSTRACT Calibration models can improve agreement between calculated and experimental 13 C NMR shifts without necessarily transferring to a new compound. We reanalyzed a literature‐derived finite‐cluster GIAO‐DFT benchmark comprising carbamazepine (CBZ), theophylline (THP), and ibuprofen (IBP) using compound‐blocked validation and added an independent protocol‐heterogeneous external challenge using lamivudine Form II. Uncalibrated predictions, a one‐parameter offset, two‐parameter affine scaling, and a three‐parameter quadratic model were compared. Across the 20 primary sites, raw RMSE was 1.311 ppm. Leave‐one‐compound‐out aggregate RMSE was 1.065 ppm for offset correction, 1.113 ppm for affine scaling, and 1.052 ppm for quadratic calibration; macro‐averaged RMSE was 0.915, 0.986, and 0.916 ppm, respectively. Offset correction was lower than affine scaling in all three internal folds. In the locked lamivudine challenge (eight 13 C sites), however, raw RMSE was 4.174 ppm, and all three transferred calibrations worsened error: 4.643 ppm offset, 4.623 ppm affine, and 4.592 ppm quadratic. Thus the external API does not support generalization of the internally favorable calibration behavior. Because the external calculation used a published Gaussian09 GIAO workflow rather than the historical finite‐cluster protocol, it is an out‐of‐domain robustness challenge rather than like‐for‐like validation. Fbias is interpreted only retrospectively. The results support a bounded conclusion: Calibration rankings in this small benchmark are fragile to API and protocol domain shift and require prospective validation under a harmonized computational workflow.
