Many data analysis methods actually combine optimization of several criteria. In this paper, a framework is offered for categorizing such multi-criteria methods. In particular, it categorizes multiset and three-way analysis methods as well as penalized methods and combinations thereof. The framework aims to stimulate critical evaluation of methods and reflection on the purpose of methods and, by signaling gaps, to help the development of new data analysis methods.
Diabetes is a common and serious global disease that damages blood vessels in the eye, leading to vision loss. Early and accurate diagnosis of this issue is crucial to reduce the risk of visual impairment. The typical deep learning (DL) methods for diabetic retinopathy (DR) grading are often time-consuming, resulting in unsatisfactory detection performance due to inadequate representation of lesion features. To overcome these challenges, this research proposes a new automated mechanism for detecting and classifying DR, aiming to identify DR severities and different stages. To figure out and capture feature characteristics from DR samples, a conjugated attention mechanism and vision transformer are utilized within a collective net model, which automatically generates feature maps for diagnosing DR. These extracted feature maps are then fused through the feature fusion function in a fused attention net model, calculating attention weights to produce the most powerful feature map. Finally, the DR cases are identified and discriminated using the kernel extreme learning machine (KELM) model. For evaluating DR severity, our work utilizes four different benchmark datasets: APTOS 2019, MESSIDOR-2 dataset, DiaRetDB1 V2.1, and DIARETDB0 datasets. To illuminate data noise and unwanted variations, two preprocessing steps are carried out, which include contrast enhancement and illumination correction. The experimental results evaluated using well-known indicators demonstrate that the suggested method achieves a higher accuracy of 99.63% compared to other baseline methods. This research contributes to the development of powerful DR screening techniques that are less time-consuming and capable of automatically identifying DR severity levels at a premature level.
In MCR analyses, the similarity of pairs of spectra or concentration profiles can be measured in terms of the acute angle that is enclosed by the representing vectors. Acute angles between vectors can be generalized to pairs of subspaces. So-called canonical angles, also called principal angles, measure the mutual orientation of a pair of subspaces. This work discusses how angles and canonical angles can support multivariate curve resolution analyses. A canonical angle analysis (CAA) can help to detect changes of the chemical composition during a chemical reaction in a way comparable, but different to the evolving factor analysis (EFA).
In this work, two alternative ways of analyzing three-way data with multivariate curve resolution alternating least squares (MCR-ALS) using the trilinearity constraint are described and compared. Different synthetic datasets and experimental three-way datasets covering different scenarios are analyzed, and the results obtained are compared. The two new different ways of applying the trilinearity constraint are named flexible trilinearity alignment (FTA) and shift invariant transformation (SIT). The effects of noise in the application of both types of constraints are investigated in detail. Results show that both approaches are particularly adequate for those cases like in gas chromatography and especially in liquid chromatography where the elution profiles of the same chemical component in different chromatographic runs are not totally reproducible because they are time shifted, although they preserve their shape. When strong time shifts and co-elution occur, then the “standard” trilinear model does not work, and alternative approaches should be used, such as the MCR extended bilinear model to multiset (multirun) data, or the proposed relaxation of the trilinearity constraint in the FTA and SIT methods to capture the time drift changes produced in the elution profiles of the resolved components.
In this work, a novel one-class classification algorithm one-class convolutional autoencoder (OC-CAE) was proposed for the detection of abnormal samples in the excitation–emission matrix (EEM) fluorescence spectra dataset. The OC-CAE used Boxplot to analyze the reconstruction errors and used the LOF algorithm to handle features extracted by the hidden layer in the convolutional autoencoder (CAE). The fused information provides the basis for more accurate pattern recognition, ensures flexibility in model training, and can obtain higher model specificity, which is important in the field of food quality control. To demonstrate the reliability and advantages of OC-CAE, two EEM cases related to the authentication of food including the Zhenjiang aromatic vinegar (ZAV) case and the camellia oil (CAO) case were studied. The results showed that OC-CAE identified all abnormal samples in the two cases, reflecting excellent performance in the detection of abnormal samples, and that it, coupled with EEM, would be an effective tool for the authenticity identification of food.
A robust method for multiplicative scatter correction (MSC) in infrared spectroscopy is presented. Using quantile regression, the outlier wavelengths (concentration-dependent wavelengths) that are irrelevant to the regression are identified and therefore excluded from the regression model. This new MCS method, which could be implemented in its simple or extended form, is much simpler than the recently proposed methods and has only one hyperparameter (the quantile value) to be adjusted. To achieve this, a scoring function based on residual analysis can automatically determine the correct quantile value. The method is first explained using simulation data sets and then its validation is explained by analysing some experimental data sets. It was found that our new method can perform well in the presence of strong outlying variables. On the other hand, when the data sets are not associated outlying wavelengths, this method behaves similarly to the conventional MSC method.
The performance of multivariate calibration models ŷ = f(x) for the prediction of a numerical property y from a set of x-variables depends on the type of scaling of the x-variables. Common scaling methods are autoscaling (dividing the centered x by its standard deviation s) and Pareto scaling (dividing the centered x by sP with P = 0.5). The adjusted Pareto scaling presented here varies the exponent P between 0 (no scaling) and 1 (autoscaling) with the aim of obtaining an optimum prediction performance for ŷ. Related scaling methods based on the variable spread are range scaling and vast scaling; while level scaling is based on the location (central value) of the variable. These scaling methods and robust versions are compared for models created by partial least-squares (PLS) regression. The applied strategy repeated double cross validation (rdCV) evaluates the model performance for test set objects and considers its variability. Results with three data sets from chemistry show: (a) the efficacy of the different scaling methods depends on the data structure; (b) optimization of the Pareto exponent P is recommended; (c) range scaling or vast scaling may be better than adjusted Pareto scaling; (d) in general a heuristic search for the best scaling method is advisable. Overall, the consideration of different variants of scaling allow for a flexible adjustment of the variable contributions to the calibration model.
This work aims to characterize the serum profile of athletes after the administration of ecdysteroids, natural steroid hormones recently reported to enhance athletic performance. The combination of mass spectrometry and chemometric tools may allow to differentiate physiological effects from post-training and intake-driven effects. Serum samples were collected from 46 healthy male volunteers and divided into four groups: control (two capsules/day of Peak Ecdysone without training), placebo (two capsules without ecdysteroids with training), Ec1 (two capsules/day Peak Ecdysone with training), and Ec2 (eight capsules/day Peak Ecdysone with training). Metabolic profiling was measured using a SCIEX Triple Quadrupole LC-MS/MS system coupled with the Biocrates AbsoluteIDQ p180 kit, which allows quantitation of a large panel of metabolites that were subjected to multivariate analysis. Unsupervised analysis of the data found no significant differences between the placebo and the ecdysteroid supplementation groups. By merging Ec1 and Ec2 into a single group, coded as treated, a clear discrimination between the control and placebo groups was observed. Phosphatidylcholines were among the most significant features of ecdysteroids administration, showing a dose-dependent effect in Ec1 and Ec2 groups. As specific metabolic phenotypes can result from years of training, the discrimination of physiological effects from those caused by the administration of banned substances can be a valuable analytical strategy for the interpretation of adverse analytical findings in the anti-doping field.
Trilinearity is a property of some chemical data that leads to unique decompositions when curve resolution or multiway decomposition methods are used. Curve resolution algorithms, such as Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS), can provide trilinear models by implementing the trilinearity condition as a constraint. However, some trilinear analytical measurements, such as excitation–emission matrix (EEM) measurements, usually exhibit systematic patterns of missing data due to the nature of the technique, which imply a challenge to the classical implementation of the trilinearity constraint. In this instance, extrapolation or imputation methodologies may not provide optimal results. Recently, a novel algorithmic strategy to constrain trilinearity in MCR-ALS in the presence of missing data was developed. This strategy relies on the sequential imposition of a classical trilinearity restriction on different submatrices of the original investigated dataset, but, although effective, was found to be particularly slow and requires a proper submatrix selection criterion. In this paper, a much simpler implementation of the trilinearity constraint in MCR-ALS capable of handling systematic patterns of missing data and based on the principles of the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is proposed. This novel approach preserves the trilinearity of the retrieved component profiles without requiring data imputation or subset selection steps and, as with all other constraints designed for MCR-ALS, offers the flexibility to be applied component-wise or data block-wise, providing hybrid bilinear/trilinear models. Furthermore, it can be easily extended to cope with any trilinear or higher-order dataset with whatever pattern of missing values.
This study presents a case of processing X-ray computed tomography (CT) data for pork scans using chemometric latent space modeling. The distribution of voxel intensities is shown to exemplify a multivariate, multi-collinear signal mixture. While this concept is not novel, it is revisited here from a chemometric perspective. To extract meaningful information from such multivariate signals, latent space modeling based on partial least squares (PLS) is an ideal solution. Furthermore, a robust PLS approach is even more effective for latent space modeling, as it can extract latent spaces unaffected by outliers, thereby enhancing predictive modeling. As an example, lean meat percentage is predicted using X-ray CT data and robust PLS regression. This method is applicable to X-ray CT quantification analysis, particularly in cases where unclear, erroneous, and outlying observations are suspected in the data.
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