Multivariate Analysis Methods Research Articles

Metabolic Response to Small Molecule Therapy in Colorectal Cancer Tracked with Raman Spectroscopy and Metabolomics

AbstractDespite numerous screening tools for colorectal cancer (CRC), 25 % of patients are diagnosed with advanced disease. Novel diagnostic technologies that are early, accurate, and rapid are imperative to assess the therapeutic efficacy of clinical drugs and identify new biomarkers of treatment response. Here Raman spectroscopy (RS) was used to track metabolic reprogramming in KRAS‐mutant HCT116 and SW837 cells, and KRAS wild‐type CC cells. RS combined with multivariate analysis methods distinguished nonresponsive, partially responsive, and responsive cells treated with cetuximab, a monoclonal antibody for EGFR inhibition, sotorasib, a clinically approved KRAS inhibitor, and various doses of trametinib, an inhibitor of the MAPK pathway. Cells treated with a combination of subtoxic doses of trametinib and BKM120, an inhibitor of the PI3K pathway, showed a synergistic response between the two pathways. Using a supervised machine learning regression model, we established a scoring methodology trained to a priori predict therapeutic response to new treatment combinations. RS metabolites were verified with mass spectrometry, and enrichment pathways were identified, including amino acid, purine, and nicotinate and nicotinamide metabolism that differentiated monotherapy from combination therapy. Our approach may ultimately be applicable to patient‐derived primary cells and cultures of patient tumors to predict effective drugs for individualized care.

Statistical applications in the biomedical sciences: A review

This study covers applications areas of statistics in biological science. The integration of statistical methods into the biological sciences has revolutionized data analysis, enhancing the accuracy of data analysis and interpretation. This review aims to provide a comprehensive overview of the key statistical techniques used in biomedical research, highlighting their applications, advantages, and limitations. We conducted a systematic literature search across major databases, we focused on studies that have employed descriptive statistics, inferential statistics, regression analysis, multivariate analysis, survival analysis, Bayesian statistics, and machine learning methods in biomedical sciences. Our findings reveal that statistical methods are indispensable in various areas, including genomics, proteomics, epidemiology, ecology, clinical trials, and pharmacology. Each section of this review details the specific applications of these methods, supported by relevant case studies that illustrate their practical implementation and impact. This review underscores the indispensable role of statistics in biological research, providing actionable insights and recommendations for researchers aiming to leverage statistical tools to drive scientific discovery.

Estimation equation of limb lean soft tissue mass in Asian athletes using bioelectrical impedance analysis.

The lean soft tissue mass (LSTM) of the limbs is approximately 63% of total skeletal muscle mass. For athletes, measurement of limb LSTM is the basis for rapid estimation of skeletal muscle mass. This study aimed to establish the estimation equation of LSTM in Asian athletes using bioelectrical impedance analysis (BIA). A total of 198 athletes (121 males, 77 females; mean age 22.04 ± 5.57 years) from different sports in Taiwan were enrolled. A modeling group (MG) of 2/3 (n = 132) of subjects and a validation group (VG) of 1/3 (n = 68) were randomly assigned. Using the InBody S-10, resistance and reactance were measured at 50 kHz from the right palm to the right sole while the participant was in the supine position. Predictor variables were height (h), weight (W), age, Sex, Xc, resistance index (RI; RI = h2 / R). LSTM of arms and legs measured by dual-energy X-ray absorptiometry (DXA) was the response variable. Multivariate stepwise regression analysis method was used to establish BIA estimation equations as ArmsLSTMBIA-Asian and LegsLSTMBIA-Asian. Estimation equations performance was confirmed by cross-validation. Estimation equation "ArmsLSTMBIA-Asian = 0.096 h2/R- 1.132 Sex + 0.030 Weight + 0.022 Xc- 0.022 h + 0.905, r2 = 0.855, SEE = 0.757 kg, n = 132" and "LegsLSTMBIA Asian = 0.197h2/R" + 0.120 h- 1.242 Sex + 0.055 Weight- 0.052 Age + 0.033 Xc -16.136, r2 = 0.916, SEE = 1.431 kg, n = 132" were obtained from MG. Using DXA measurement results of VG for correlation analysis and Limit of Agreement (LOA) of Bland-Altman Plot, ArmsLST is 0.924, -1.53 to 1.43 kg, and LegsLST is 0.957, -2.68 to 2.90 kg. The established single-frequency BIA hand-to-foot (whole body) estimation equation quickly and accurately estimates LSTM of the arms and legs of Asian athletes.

Multivariate statistical analysis and bespoke deviation network modeling for geochemical anomaly detection of rare earth elements

Rare earth elements (REEs), a significant subset of critical minerals, play an indispensable role in modern society and are regarded as “industrial vitamins,” making them crucial for global sustainability. Geochemical survey data proves highly effective in delineating metallic mineral prospects. Separating geochemical anomalies associated with specific types of mineralization from the background reflecting geological processes has long been a significant subject in exploration geochemistry. The processing of high-dimensional, non-linear geochemical survey data necessitates a systematic framework to address common issues, including missing values, the closure effect, the selection of appropriate multivariate analysis methods, and anomaly detection techniques in order to detect geochemical anomalies associated with mineral occurrences. The Curnamona Province in South Australia is considered an emerging REE province with significant REE mineralization potential. In this study, we use data from this region to evaluate the performance of a novel machine learning-based framework that incorporates data pre-processing, multivariate statistical analysis, and anomaly recognition to address challenges such as missing data, noise interference, data imbalance and high non-linearity. We utilize lithogeochemical data to map potential greenfield regions of REE mineralization. The primary advantages of our framework lie in its provision of an effective random forest-based data imputation method, utilization of isometric log-ratio transformation to eliminate the closure effect, and reduction of the impact of outliers on data interpretation through robust principal component analysis. Additionally, the framework utilizes a deviation network to learn anomaly scores from complex, non-linear data under imbalanced data conditions, identifying geochemical anomalies associated with REE occurrences by leveraging prior knowledge rather than those caused by data noise or anthropogenic factors. The anomalous areas identified by this framework delineate all known REE deposits and extend to the surrounding regions. Furthermore, a close spatial coupling relationship exists between these strongly anomalous areas and the felsic granite intrusions. The comprehensive workflow for processing geochemical data proposed in this study can effectively address common challenges in the geochemical exploration of critical minerals. The identified geochemical anomalies can provide important clues for subsequent exploration.

Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning

Myasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully elucidated, and early diagnosis is still a question mark. Diagnostic paraclinical tests are also time-consuming, burden patients financially, and sometimes all test results can be negative. Therefore, rapid, cost-effective novel methods are essential for the early accurate diagnosis of MG. Here, we aimed to determine MG-induced spectral biomarkers from blood serum using infrared spectroscopy. Furthermore, infrared spectroscopy coupled with multivariate analysis methods e.g., principal component analysis (PCA), support vector machine (SVM), discriminant analysis and Neural Network Classifier were used for rapid MG diagnosis. The detailed spectral characterization studies revealed significant increases in lipid peroxidation; saturated lipid, protein, and DNA concentrations; protein phosphorylation; PO2-asym + sym /protein and PO2-sym/lipid ratios; as well as structural changes in protein with a significant decrease in lipid dynamics. All these spectral parameters can be used as biomarkers for MG diagnosis and also in MG therapy. Furthermore, MG was diagnosed with 100% accuracy, sensitivity and specificity values by infrared spectroscopy coupled with multivariate analysis methods. In conclusion, FTIR spectroscopy coupled with machine learning technology is advancing towards clinical translation as a rapid, low-cost, sensitive novel approach for MG diagnosis.

Folic acid supplement prescribing practice during the protective period and its associated factors among health professionals in selected governmental health centers in Addis Ababa, Ethiopia, 2023: A cross-sectional study.

Background: Supplementing folic acid (FA) before and in the first month of conception is an essential preventive factor, especially for neural tube defects (NTDs) and other congenital anomalies. Aim: The research aimed to assess FA supplement prescribing practice during the protective period and its associated factors among health professionals in selected governmental health centers in Addis Ababa, Ethiopia, in 2023. Methods: An institutionalized cross-sectional survey was carried out in Addis Ababa public health centers with a total sample size of 396 in February 2023. Systematic random sampling methods were used, and after each respondent's signed consent, a row of data was gathered using pretested self-administered questionnaires. The data was coded, interred to Epi Data 4.6.0.6 and transferred to Statistical Package for Social Sciences 27 software. Then a binary and multivariable logistic regression analysis method was used to show the associated variables with FA prescribing practice using a confidence interval (CI) of 95% and a significance value < 0.05. Results: The total prevalence of FA prescribing practice during the periconceptional period was 64.4% [95% CI (59.68-69.12)]. But those prescribed during the protective period were 26.7%. Ever since the birth of a neonate with NTDs, not prescribing a dose of 4 mg of FA for women with NTD, the type of women for whom FA was prescribed were associated with FA prescribing practice during the protective period. Conclusion: The healthcare professional's prescribing practice during the protective period was still low and needs more attention to strengthen FA supplements.

Volatilomics and Macro-Composition Analyses of Primary Wuyi Rock Teas of Rougui and Shuixian Cultivars from Different Production Areas.

Wuyi Rock Tea (WRT) is cherished for its exceptional "rock flavor" and its quality shows obvious regional differences. However, the flavor characteristics of Primary Wuyi Rock Teas (PWRTs) from different production areas remain unclear. Here, the Camellia sinensis var. sinensis cv. 'Rougui' and 'Shuixian', two quintessential cultivars for making WRT, planted in Zhengyan, Banyan, at high elevations, and Waishan production areas were used to make PWRTs. We conducted a comprehensive comparison of the sensory attributes, volatile organic compounds (VOCs), and macro-compositions of PWRTs of 'Rougui' and 'Shuixian' cultivars from different producing areas. Sensory evaluation indicated that both 'Rougui' and 'Shuixian' PWRTs from Zhengyan exhibited the best flavor qualities, followed by those from Banyan, at high altitudes, and Waishan production areas. The results of the determination and analysis of VOCs showed 680 VOCs in 'Rougui' and 'Shuixian' PWRTs, and that the different production areas mainly influenced the quantitative pattern of VOCs and rarely the qualitative composition. Integrated multivariate statistical analysis methods revealed that benzyl alcohol, hotrienol, butanoic acid, 2-methyl-, hexyl ester, benzene, (2-nitroethyl)-, and geranyl isobutyrate may be the key VOCs affecting the aroma differences in PWRTs from different production areas. In addition, water-extractable substances, tea polyphenols, caffeine, and free amino acids may be the important macro-compositions that distinguish PWRTs from different production areas. The metabolite basis for differences in the flavor qualities of PWRTs across production areas was elucidated, which may be helpful for the production of high-quality WRT.

Digital footprint approach for the study of competitiveness in wineries

Advances in computation and web-scraping techniques allow us to use real-time information from company websites and social media platforms, namely digital footprint indicators. Additionally to their real-time availability, these indicators are easily accessible, making them a potentially practical tool for monitoring a company’s level of competitiveness. Therefore, this article aims to obtain a multivariate analysis method to accurately predict the wineries’ competitiveness group, applying the methodology to a sample of Valencian wineries to explore the association between digital footprint indicators and competitiveness.Unsupervised learning techniques were implemented to detect outliers and clusters in observations using financial variables obtained from the Sistema de Análisis de Balances Ibéricos (SABI). Thus, clustering was used to identify groups of wine companies differentiating the sample of companies according to their competitiveness characteristics. Subsequently, footprint indicators were used to create multivariate models to predict the above classification of companies. Also, this methodology permits the study of which digital indicators are essential in this prediction, specifically the presence of words on the web and others regarding online company activities on social networks associated with competitiveness. This research provides practical guidance for developing and incorporating the essential digital indicators, which could be applied in wineries and any study of companies’ competitiveness.

Assessment and phenotypic identification of millet germplasm (Setaria italica L.) in Liaoning, China.

This study evaluated millet germplasms in Liaoning Province to support the collection, preservation and innovation of millet germplasm resources. The study was conducted from 2018 to 2020, involved the selection of 105 millet germplasm resources from the Germplasm Bank of the Liaoning Academy of Agricultural Sciences (LAAS), the observation and recording of 31 traits, and the application of multivariate analysis methods to assess phenotypic diversity. From the diversity analysis and correlation analysis, it was found that the tested traits had abundant diversity and complex correlations among them. Principal component analysis (PCA) comprehensively analyzed all quantitative traits and extracted seven principal components. Grey relational analysis (GRA) highlighted the varied contributions of different traits to yield. Through systematic cluster analysis (SCA), the resources were categorized into six groups at Euclidean distance of 17.09. K-mean cluster analysis determined the distribution interval and central value of each trait, then identified resources with desirable traits. The results revealed resources that possess characteristics such as upthrow seedling leaves, more tillers and branches, larger and well-formed ears, and lodging resistance prefer to higher grain yield. It was also discovered that the subear internode length (SIL) could be an indicator for maturity selection. Four specific resources, namely, Dungu No. 1, Xiao-li-xiang, Basen Shengu, and Yuhuanggu No. 1, were identified for further breeding and practical applications.

Volatile organic compounds in exhaled breath: a promising approach for accurate differentiation of lung adenocarcinoma and squamous cell carcinoma

Lung cancer subtyping, particularly differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC), is paramount for clinicians to develop effective treatment strategies. In this study, we aimed: (i) to discover volatile organic compound (VOC) biomarkers for precise diagnosis of ADC and SCC, (ii) to investigated the impact of risk factors on ADC and SCC prediction, and (iii) to explore the metabolic pathways of VOC biomarkers. Exhaled breath samples from patients with ADC (n= 149) and SCC (n= 94) were analyzed by gas chromatography-mass spectrometry. Both multivariate and univariate statistical analysis method were employed to identify VOC biomarkers. Support vector machine (SVM) prediction models were developed and validated based on these VOC biomarkers. The impact of risk factors on ADC and SCC prediction was investigated. A panel of 13 VOCs was found to differ significantly between ADC and SCC. Utilizing the SVM algorithm, the VOC biomarkers achieved a specificity of 90.48%, a sensitivity of 83.50%, and an area under the curve (AUC) value of 0.958 on the training set. On the validation set, these VOC biomarkers attained a predictive power of 85.71% for sensitivity and 73.08% for specificity, along with an AUC value of 0.875. Clinical risk factors exhibit certain predictive power on ADC and SCC prediction. Integrating these risk factors into the prediction model based on VOC biomarkers can enhance its predictive accuracy. This work indicates that exhaled breath holds the potential to precisely detect ADCs and SCCs. Considering clinical risk factors is essential when differentiating between these two subtypes.

Identification of high-performing soil groups in grazing lands using a multivariate analysis method

Understanding and quantifying the complex relationships between soil properties and vegetation health is important for sustainable land management and optimising agricultural productivity. This study tested a spatial data-driven framework to identify the soil groups associated with pasture health using publicly available gridded soil attribute layers from Soil and Landscape Grid of Australia (SLGA) over two adjacent southeast Australian river catchments. Principal component analysis (PCA) followed by isocluster unsupervised classification was applied to seventeen SLGA soil attribute layers to identify dominant soil patterns, which showed good spatial agreement with the Enhanced Vegetation Index (EVI) data derived from Landsat-8 imagery. The soil class demonstrating the highest EVI values (HVR class) and the lowest EVI values (LVR class) were determined. A comparison of these classes with soil types defined in the New South Wales Soil Landscape maps confirmed that the HVR class is predominated by agriculturally productive, basalt-derived 'Ant Hill' soils. The cation exchange capacity (CEC) ranging from ∼10 to 20 meq/100 g, clay content ranging from 20 % to 30 %, pHc (pH in calcium chloride solution) between 5 and 6, pHw (pH in water) between 5.5 and 6.5 and SOC between 2.5 % to 4 % were associated with higher EVI values in the HVR soil class within the study area. This study demonstrated an effective framework for identifying key soil attributes that affect agricultural productivity and pinpointing critical locations for grazing land productivity.

Surface quality evaluation through new optical imaging system-based objective speckle for additive manufactured titanium samples

Laser cladding is an effective additive manufacturing technology used for materials’ surface modification to enhance their surface and mechanical properties. Surface roughness is a crucial feature that affects the materials’ quality and lifespan. Additionally, it is a real indicator of hardness and wear resistance. Although the existing methods may precisely measure surface roughness, they require delicate adjustment, can damage surfaces, and have limited working distances. This study presents a novel optical imaging system to quantitatively estimate the quality modifications of additive manufacturing samples by measuring their surface roughness based on objective speckle and advanced multivariate analysis methods. The raw speckle patterns are generated from deposited layers on Ti6Al4V titanium alloys obtained at different laser processing parameters. The proposed analysis approach produces collections of local statistical matrices from which histogram features are extracted. Canonical correlation analysis (CCA) is then proposed to distinguish the most significant features. The correlation between the features (with and without applying CCA) and surface roughness is established by using two machine learning regression algorithms, nonlinear support vector regression (SVR), random forest regression (RF), and k-nearest neighbor regression (kNN). The results confirmed that combining RF and CCA provided a feasible regression model to estimate the surface roughness, with 0.998 for R2 in training samples and maximum values of 0.258, 0.484, and 4.593 %, respectively, for MAE, RMSE, and MAPE in test samples. This demonstrates that the proposed objective speckle imaging system is effective in estimating the quality modification of additive manufacturing samples by measuring their surface roughness.

Methods of multivariate analysis in estimation of the new prospect strawberry selections

Methods of multivariate analysis in estimation of the new prospect strawberry selections

Exploring the causal role of immune cells in vascular dementia: A bidirectional mendelian randomization study

BackgroundThe aim of this study was to explore the causal association between immune cells and VaD based on a two-sample bidirectional Mendelian randomization study. MethodsBidirectional two-sample MR analyses based on pooled datasets from publicly available genome-wide association studies were performed using inverse variance weighted (IVW), weighted median (WE), and MR–Egger regressions to evaluate the causal relationships between immune cells and vascular dementia. Heterogeneity was assessed using Cochran's Q statistic. The reliability of the MR analysis results was verified by using the MR-PRESSO method for outlier detection, the MR–Egger method for horizontal multivariate analysis, and the leave-one-out method for sensitivity analysis. ResultsSpecifically, 27 immunophenotypes were associated with VaD pathogenesis, including Sw mem %lymphocyte (P = 0.043), CD38 on CD20- (P = 0.039), CD11c+ monocyte AC (P = 0.024), DC AC (P = 0.002), CCR2 on CD62L+ myeloid DC (P = 0.039), Resting Treg %CD4 (P = 0.042), Activated & resting Treg %CD4+ (P = 0.038), CD28+ CD45RA− CD8br %CD8br (P = 0.047), NK %CD3− lymphocyte (P = 0.042), CD45 on B cell (P = 0.029), FSC-A on NKT (P = 0.033), CD45 on CD33br HLA DR+ CD14− (P = 0.039) were significantly correlated with increased VaD risk. Additionally, four immune phenotypes, namely, CD19 on CD20−, Resting Treg %CD4, Activated & resting Treg %CD4+, and CD11c+ monocyte AC, showed bidirectional effects on VaD. ConclusionsMR analysis revealed potential causal relationships between certain immune cells and VaD. Our preliminary exploration through immune cell infiltration analysis highlights the significant value of immune cells in VaD. Therefore, this study may provide a new perspective for the prevention and treatment of VaD.

Ground-truth validation of uni- and multivariate lesion inference approaches.

Lesion analysis aims to reveal the causal contributions of brain regions to brain functions. Various strategies have been used for such lesion inferences. These approaches can be broadly categorized as univariate or multivariate methods. Here we analysed data from 581 patients with acute ischaemic injury, parcellated into 41 Brodmann areas, and systematically investigated the inferences made by two univariate and two multivariate lesion analysis methods via ground-truth simulations, in which we defined a priori contributions of brain areas to assumed brain function. Particularly, we analysed single-region models, with only single areas presumed to contribute functionally, and multiple-region models, with two contributing regions that interacted in a synergistic, redundant or mutually inhibitory mode. The functional contributions could vary in proportion to the lesion damage or in a binary way. The analyses showed a considerably better performance of the tested multivariate than univariate methods in terms of accuracy and mis-inference error. Specifically, the univariate approaches of Lesion Symptom Mapping as well as Lesion Symptom Correlation mis-inferred substantial contributions from several areas even in the single-region models, and also after accounting for lesion size. By contrast, the multivariate approaches of Multi-Area Pattern Prediction, which is based on machine learning, and Multi-perturbation Shapley value Analysis, based on coalitional game theory, delivered consistently higher accuracy and specificity. Our findings suggest that the tested multivariate approaches produce largely reliable lesion inferences, without requiring lesion size consideration, while the application of the univariate methods may yield substantial mis-localizations that limit the reliability of functional attributions.

Assessment of comprehensive PAH index in roasted Tan lamb by two-dimensional correlation spectroscopy combined with hyperspectral imaging.

Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near-infrared (Vis-NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two-dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579-737-631-449nm. The results indicated that competitive adaptive reweighted sampling-multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426µg/kg, R-squared prediction of 0.8469, and root mean square error of prediction of 2.4119µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis-NIR HSI coupled with multivariate analysis methods.

Hierarchical and mixed uncertainty quantification for simulation-based structural seismic fragility analysis

Seismic fragility assessments (SFA) encompass significant uncertainties, including ground motions, materials, geometry, boundaries, and modeling, which impart inherent uncertainties to the fragility curve. Quantifying these uncertainties is essential for informed decision-making in earthquake disaster mitigation. This paper presents a hierarchical uncertainty quantification (UQ) framework for simulation-based SFA, which can address mixed aleatory and epistemic uncertainties. The framework comprises two levels: At Level 1, ground motion uncertainties are accounted for through record-to-record variation, while structural parameter uncertainties are captured using well-defined probability distributions. The quantification of uncertainty in seismic fragility is accomplished using mean and quantile fragility curves, representing the average outcome and the dispersion of structural fragility, respectively. Fuzziness is also introduced to enhance the objectivity of failure determination. Level 2 addresses uncertainties in the distribution hyper-parameters of structural variables through evidence theory, facilitating an understanding of their influence on the seismic fragility curve and quantifying uncertainty in mean and quantile fragility curves. This UQ framework builds upon the multivariate seismic fragility analysis method, integrating Kriging regression for seismic demand prediction and logistic regression for generating multivariate seismic fragility functions. The proposed framework is validated numerically on an existing three-span reinforced concrete continuous girder bridge, affirming its practical utility and reliability.

Research on the measurement of general risk preference based on multivariate statistical analysis methods

Research on the measurement of general risk preference based on multivariate statistical analysis methods

Towards the establishment of a green and sustainable analytical methodology for hyperspectral imaging-based authentication of wholemeal bread

The increased interest in whole-grain products, along non unified European regulations on the composition of wholemeal bread could lead to its misleading labelling. Therefore, to ensure the safety of consumers, both health-wise and from possible fraud, a novel hyperspectral imaging-based authentication method is being proposed, given that no previous study has combined imaging techniques with authentication of wholemeal flour content. Quantification based on pixel counting by classification (QPC) utilizes multivariate analysis methods such as PLS-DA and SVM to classify pixels within a bread sample (as Wholemeal and White flour), based on their visible-near-infrared spectra, to later estimate its proportion of wholemeal flour. This is in accordance with the heterogeneous nature of bread samples, in which individual pixels belonging to both wholemeal and white flour can be accounted for, which was proved by implementing unsupervised training techniques such as hierarchical cluster analysis (HCA). Results show that the quantification model was able to successfully predict wholemeal flour content with a maximum deviation of 8 g wholemeal flour/100 g flour from the estimated value.

Monitoring of the Homogeneity of Primer Layers for Ink Jet Printing on Polyester Fabrics by Hyperspectral Imaging.

Untreated polyester films and fibers can be hardly printed or coated, in particular if aqueous inks or lacquers have to be applied. Therefore, an adequate primer layer has to be applied first. A cationic polymer formulation based on poly(dimethylamine-co-epichlorohydrin-co-ethylenediamine) (PDEHED) was used as primer layer for digital printing on polyester fabrics. Because of the exceedingly high requirements on the homogeneity of such layers, hyperspectral imaging was used for qualitative and quantitative monitoring of the distribution of the primer layer on the textiles. Multivariate data analysis methods based on the PLS algorithm were applied for quantification of the NIR reflection spectra using gravimetry as a reference method. Optimization of the calibration method resulted in various models with prediction errors of about 1.2 g/m2. The prediction performance of the models was proven in external validations using independent samples. Moreover, a special ink jet printing technology was tested for application of the aqueous primer formulation itself. Since possible clogging of jet nozzles in the print head might lead to inhomogeneity in the coatings such as missing tracks, the potential of hyperspectral imaging to detect such defects was investigated. It was demonstrated that simulated missing tracks can be clearly detected. Consequently, hyperspectral imaging has been proven to be a powerful analytical tool for in-line monitoring of the quality of printability improvement layers and similar systems.