Publications
Featured Publication:
45) "Metabolic signatures of the integrated profile of cardiovascular autonomic modulation and cardiorespiratory fitness in apparently healthy individuals."
Signini, Étore De Favari, Alex Castro, Patrícia Rehder‐Santos, Juliana Cristina Milan‐Mattos, Juliana Magalhães de Oliveira, Alberto Porta, Renato Lajarim Carneiro, Antônio Gilberto Ferreira, Regina Vincenzi Oliveira, and Aparecida Maria Catai.
Cardiovascular autonomic modulation (CAM) and cardiorespiratory fitness (CRF) are well-established predictors of health. Identifying metabolites associated with integrated CAM-CRF profiles may help characterize healthy physiological states. This study aimed to investigate metabolic signatures representing distinct CAM-CRF profiles in apparently healthy individuals. Non-obese individuals (n = 127, 43 ± 14 years) underwent fasting blood collection for serum metabolome (SM) analysis, cardiovascular assessment, and a cardiopulmonary exercise test to access CAM and CRF. CAM-CRF profiles were obtained separately by sex using principal components analysis (PCA) of CAM and CRF. Subjects' scores from the first two principal components of the PCA were used to generate the groups. Groups' SM were compared using one-way ANOVA (controlling for age) and metabolite correlations were analyzed using the subjects' scores (controlling for age and body mass index), considering p < 0.01. In females, low sebacic acid levels were associated with high cardiac parasympathetic modulation (CPM) and greater cardiovascular complexity. In males, low ornithine levels corresponded to a profile with high CPM, baroreflex sensitivity (BRS), and CRF. Choline, betaine, N,N-dimethylglycine levels in females, and glucose and sarcosine in males, were negatively correlated with CPM, BRS, CRF and cardiovascular complexity. These metabolites reflect integrated CAM-CRF conditions, enhancing the understanding of underlying metabolic profiles. Physiological Reports 14, no. 2 (2026): e70739.
44) Laganà, Antonio, Barbara Billè, Angela Di Pietro, Alessio Facciolà, Consuelo Celesti, Daniela Iannazzo, Mariachiara Galati, Maria Maisano, Tiziana Cappello, and Giuseppa Visalli. "Toxicological and metabolomic assessment of the acute and sub-chronic effects of nanoceria (≤ 50 nm) on the human alveolar cells A549." Journal of Hazardous Materials (2026): 141239.
43) de Andrade Príncipe, Leonardo, Pedro Henrique Marchi, Carolina Dantas Micheletti, João Marcos Bovetto de Campos Valim, Nara Regina Brandão Cônsolo, Raquel Silveira Pedreira, Juliana Toloi Jeremias et al. "Serum metabolomics identifies metabolic changes in obese cats fed enzymatically hydrolyzed poultry byproduct meal." Journal of Veterinary Internal Medicine 40, no. 1 (2026): aalaf075.
42) Ko, Haeun, Chan Johng Kim, Seungyeon Choi, Jaegyun Noh, Seung Won Kim, Juhun Lee, Seohyun Byun et al. "Commensal microbe-derived butyrate enhances T follicular helper cell function to boost mucosal vaccine efficacy." Microbiome 14, no. 1 (2026): 37.
41) Kalra, Sanjana, Toheeb O. Ayinde, and Abiola O. Olaitan. "The critical role of hcpR in regulating nitrosative stress defense in Clostridioides difficile." Applied and Environmental Microbiology (2026): e01988-25.
40) Singh, Upendra;Al Ahmadi, Renad Z.;Al#Nemi, Ruba;Dhahri, Manel;Alarawi, Mohammed;Aziz, Abdul;Bushulaybi, Faisal Abdulaziz;Mashtoly, Tamer Abdalla;Emwas, Abdul#Hamid;Jaremko, Lukasz;Jaremko, Mariusz. “Enhancing detection of low abundance metabolites in proton NMR through band selective suppression and presaturation (2026)
39) Cirillo, Arianna, Guillaume Resimont, Justine Massias, Yann Guitton, François Jouret, Emmanuelle Vidal-petiot, Martin Flamant, Pierre Delanaye, and Pascal de Tullio. "Multiplatform urine metabolomics for non-invasive prediction of one-year renal function decline in kidney transplant recipients: a pilot study." (2026).
38) Xie, Zhongping, Hong Zou, Lijing Gong, Minghui Lin, and Caihua Huang. "Establishment of a mouse model of TMAO‐induced cardiac injury and application of MICT intervention." Animal Models and Experimental Medicine (2026).
37) Mäntyselkä, Sakari. "Anabolic glucose metabolism in muscle: Role of the serine synthesis pathway and mechanical loading." PhD diss., University of Jyväskylä, 2026.
Featured Publication:
36) "Effects of pulsed electric fields (PEFs) on fermentation quality characteristics and microbial community of kimchi."
Kim, Si-Yeon, Se-Ho Jeong, Yang-An Kang, Jiyun Jung, Ki-Min Lee, Emmanuel Hitayezu, Intan Rizki Mauliasari, Kwang Hyun Cha, and Dong-Un Lee
Kimchi, a lactic acid bacteria (LAB)-fermented food, is consumed globally at approximately 1.5 million tons annually. This study evaluated, for the first time, the effect of pulsed electric field (PEF) pretreatment on the microbial community of kimchi. Chinese cabbage was pretreated at 1.5 kV/cm, brined with varying NaCl concentrations (5, 10, or 15 g/L), and fermented at 4°C for 25 days. PEF pretreatment effectively maintained cellular integrity and volume. The dominant microbial taxa differed based on pretreatment, with Weissella prevailing in PEF-treated groups and Lactobacillus in untreated samples. PEF pretreatment inhibited Lactobacillus growth, the primary lactic acid producer, resulting in distinct sensory profiles compared with untreated samples. The PEF-treated group exhibited a slower LAB growth rate, with fermentation delayed by approximately 5–7 days, compared with the untreated group. These findings suggest that PEF pretreatment can effectively control the fermentation and microbial composition of kimchi, extending the shelf life. LWT (2026): 118942.
35) Reddin, Carl J., Sandra Götze, Charlotte Eymann, Christian Bock, Gisela Lannig, Magnus Lucassen, and Hans-Otto Pörtner. "Acute warming combined with hypoxia and hypercapnia challenges but does not overwhelm Ostrea edulis passive tolerance mechanisms." Journal of Experimental Biology (2026): jeb-250898.
34) Garla, Venkateswarlu, Bhargavi Suddikattu, Priyanka Ingle, Saurabh Dahiya, M. Suchitra, Sridhar Babu Gummadi, Sumitra Singh, and Rakesh K. Sindhu. "18 NMR spectroscopy: molecular structure decoding." Handbook of Natural Bioactive Compounds: Extraction, Isolation and Identification (2026): 453.
Featured Publication:
33) "Protection of multiple aspects of Alzheimer’s disease pathology using dietary supplementation with taurine."
Tognoni, Christina M., Rajshree Ghosh Biswas, Zeynep Melis Suar, Isabel Carreras, Alpaslan Dedeoglu, and Bruce G. Jenkins.
As Alzheimer’s disease (AD) continues to rise amongst the aging population, preventative measures such as dietary or lifestyle changes represent an attractive option to mitigate the burden. Taurine, known for its antioxidant and anti-inflammatory properties, may also play a neuroprotective role. This study investigates the protective effects of taurine supplementation in 5xFAD mice. Taurine was administered through drinking water at doses of 0, 500, 1000, 2000, 4000 mg/kg/day, with no change in water consumption or body mass was observed. Postmortem markers of neuroinflammation using cytokine profiling demonstrated that 2000 mg/kg/day was effective at invoking a protective response against AD progression. An acute dose of this concentration, in older mice, was also sufficient at protecting the dentate gyrus against gliosis and preventing volume loss. Supplementation of taurine for 1–2 months in older mice also led to a small reduction in the Aβ42 burden. This suggests that in pre-clinical models of AD, both long-term and acute administration of taurine can mitigate pathological AD characteristics. High-resolution magic angle spinning magnetic resonance spectroscopy (HRMAS-MRS) was used to analyze and differentiate the molecular profile of 3 key AD-affected regions: frontal cortex, ventral and dorsal hippocampus. Significant changes in 5 metabolites (GABA, glutamate, NAA, aspartate and scyllo-inositol) were observed in AD at two different ages (3–4 months and 8 months). Taurine supplementation changed the values of a number of metabolites, including NAA and glutamate, to levels closer to that of the wild-type mice, suggesting neuroprotection of these metabolites. Overall, these findings support dietary taurine supplementation as a promising preventative strategy for AD. Scientific Reports (2026).
32) Teras¹, Roland Martin, Jyri Teras, Igor Kuprijanov, Caroline Khaddaj, Adriana Kalmõkova, Liisi Karlep, Ago Samoson, Lauri Toom, Airi Rump, and Sirje Rüütel Boudinot. "Systemic purinergic dysregulation in melanoma revealed by soluble P2X4 receptor fragments." (2026).
31) Pereira, Ramona Ramalho de Souza, Caíque Olegário Diniz E. Magalhães, Elizabeth Luciana Marinho Miguel, Larissa Vieira Toledo, Débora Ribeiro Orlando, Alan Rodrigues Teixeira Machado, Bruno Del Bianco Borges, Luciano José Pereira, Marco Fabrício Dias‐Peixoto, and Eric Francelino Andrade. "Alterations in brain metabolites in rats with experimental periodontitis: A metabolomic approach." Journal of Periodontology (2026).
30) Suthar, Nisha J., Eoin Wims, Michael Dineen, Raghunath Pariyani, Denis Lynch, Lorraine M. Bateman, David T. Mannion et al. "Effects of including white clover and plantain species in perennial ryegrass pastures on qualitative indicators and metabolome of milk." International Journal of Dairy Technology 79, no. 1 (2026): e70089.
29) Shrestha, Manju, Yun‐Seo Kil, Yunju Jo, Simmyung Yook, Ki Hyun Kim, Dongryeol Ryu, Joo‐Won Nam, and Jee‐Heon Jeong. "Comparative metabolomic and transcriptomic analysis of 2D and 3D mesenchymal stem cell cultures for improved therapeutic applications." British Journal of Pharmacology 183, no. 2 (2026): 379-392.
28) Kim, Yujin, Se Hee Lee, Mi-Ja Jung, Jisu Lee, Yeon Bee Kim, Young-Shick Hong, and Tae Woong Whon. "Strain-specific metabolic endpoints and predictive phase classification in gnotobiotic kimchi fermentation." Food Chemistry (2026): 147886.
27) Pradhan, Libun, Chinmay Kumar Sahoo, Blessymol Varghese, Prasanta Purohit, Manoj Kumar Patro, Samira Kumar Behera, and Sulakshana P. Mukherjee. "Exploring Dysregulated Plasma Metabolites in Sickle‐Cell Disease Patients Using Comparative NMR‐Based Metabolomics." Magnetic Resonance in Chemistry 64, no. 1 (2026): 5-11.
26) Medina-Mendoza, Gustavo G., Elvia Becerra-Martínez, Yair Cruz-Narváez, Gerardo Noriega-Altamirano, José Javier Castro-Arellano, Oscar Camacho-Nieto, and Diego Hidalgo-Martínez. "Effect of agroecological and conventional farming systems on the metabolomic profile of yellow and red maize assessed by 1H NMR." Food Chemistry: X (2026): 103508.
25) Ursulino, Jeferson S., Edmilson RR Junior, Larissa S. Pinto, Paula Sandrin-Garcia, Denise Q. Nascimento, Thiago S. Fragoso, and Thiago M. Aquino. "NMR-based metabolomics reveals distinct metabolic profiles in juvenile systemic lupus erythematosus and juvenile idiopathic arthritis." Clinica Chimica Acta (2026): 120836.
24) De Rosa, Michele, Silvia Canepari, Giovanna Tranfo, Ottavia Giampaoli, Adriano Patriarca, Agnieszka Smolinska, Federico Marini, Lorenzo Massimi, Fabio Sciubba, and Mariangela Spagnoli. "Unveiling the Metabolic Fingerprint of Occupational Exposure in Ceramic Manufactory Workers." Toxics 14, no. 1 (2026): 56.
23) Babaee, Saeedeh, Moses Mayonu, Nora E. Demers, Gerardo Toro-Farmer, Lisa A. Waidner, and Bo Wang. "Metabolomics analysis of eastern oysters (Crassostrea virginica) exposed to Vibrio cholerae toxin." International Journal of Environmental Health Research (2026): 1-14.
22) Toledo-Gil, Rosa, Pasquale Crupi, Jose Enrique Yuste-Jiménez, and Fernando Vallejo. "Multiplatform Metabolomics for the Design and Characterization of a Mediterranean Plant-Based Lyophilized Extract from Agro-Industrial By-Products." (2026).
21) Singh, Upendra, Renad Z. Al Ahmadi, Ruba Al Nemi, Manel Dhahri, Mohammed S. Alarawi, Abdul Aziz, Faisal Abdulaziz Bushulaybi et al. "Enhancing detection of low abundance metabolites in proton NMR through band selective suppression and presaturation." Natural Products and Bioprospecting 16, no. 1 (2026): 16.
20) Dall'Asta, Margherita, Francesca Danesi, and Luca Laghi. "Dietary habits and vaginal." Advances in Vaginal Microbiome and Metabolite Research: Genetics, Evolution, and Clinical Perspectives (2026).
19) Laloë, Denis, Julie Gatien, Camille Dupuy, Catherine Archilla, Ludivine Laffont, Sylvie Ruffini, Eugénie Canon et al. "In vitro production significantly reduces metabolic differences among bovine embryos." Metabolomics 22, no. 1 (2026): 12.
18) Dardmeh, Nazila, Ali A. Moazzami, Masoud Yavarmanesh, Maryam M. Matin, and Hamid Noorbakhsh. "Effect of probiotic administration of Bifidobacterium animalis subsp. lactis BB‐12 and Lactiplantibacillus plantarum ATCC 14917 on metabolic profiles in an IBS‐D rat model: a metabolomic analysis approach." Journal of the Science of Food and Agriculture 106, no. 1 (2026): 620-631.
17) Rivas-Garcia, Lorenzo, Tamara Y. Forbes-Hernández, Pablo Cristóbal-Cueto, David Tébar-García, Alfonso Salinas-Castillo, Ana Cristina Abreu, Ignacio Fernández et al. "Rosa x hybrida: A New Tool for Functional Food Development with Triple-Negative Breast Antitumoral Implications." International Journal of Molecular Sciences 27, no. 2 (2026): 907.
16) Reindl, Alexander, Claudia Samol, Silke Haerteis, Helena U. Zacharias, Katja Dettmer, Peter J. Oefner, and Wolfram Gronwald. "Simultaneous determination of free and total metabolite concentrations in proteinaceous specimens by 1D 1H CPMG NMR." Cell Reports Methods (2026).
15) Park, Eunyoung, Scott A. Gabel, Geoffrey A. Mueller, Gary J. Larson, Caroll A. Co, Qing Shen, Linda S. Birnbaum, Gaylia J. Harry, and Ameer Y. Taha. "Comparison of oxylipins and polar metabolites in postmortem brain of Alzheimer’s Disease and nondemented elderly controls-possible associations for neuroinflammatory regulation and environmental exposures." Available at SSRN 6048145.
14) Patoine, Cole, Julia Sheffler, Trinity Sims, Viviana Gutierrez, Gwoncheol Park, Moses Mayonu, Bo Wang, and Ravinder Nagpal. "Obesity-associated gut microbiome influences diet-induced metabolic and cognitive outcomes in older adults." Gut Microbes Reports 3, no. 1 (2026): 2605879.
13) Kumar, Nitish, and Vikas Jaitak. "Recent advancement in NMR based plant metabolomics: techniques, tools, and analytical approaches." Critical Reviews in Analytical Chemistry 56, no. 1 (2026): 1-25.
12) Hongxin, Cai, Qi Hu, Zhiying Song, Mengyuan Nie, Yufeng Deng, Wenxiang Huang, Fu Xin et al. "Liuwei Dihuang Pills Suppress Hepatocellular Carcinoma via PI3K/AKT/TP53 Pathway: Integrating Network Pharmacology, Metabolomics, and Experimental Validation." Phytomedicine (2026): 157780.
11) Jabłoński, Sławomir Jan, Karolina Anna Mielko-Niziałek, Koji Mori, Piotr Młynarz, and Marcin Ziemowit Łukaszewicz. "1H-NMR metabolomic profiling of Methanobacterium and Methanosarcina reveals unusual intracellular trimethylamine and acetamide production." Available at SSRN 6055895.
10) De Rosa, Michele, Emanuele Vaccarella, Flavia Cerasti, Valentina Lucchesi, Federico Marini, Lorenzo Massimi, Valentina Mastrantonio et al. "Drosophila melanogaster as Model Organism to Assess Brake Dust Toxicity by a Multi-platform Approach." Environmental Pollution (2026): 127661.
9) Garcia Garcia, Carolina, Max Brabender, and William F. Martin. "Prebiotic aqueous reactions catalyzed by native nickel without hydrogen." bioRxiv (2026): 2026-01.
8) Hill, Alexander D., Gil Travish, Marie Phelan, Morgan Hayward, and Carsten P. Welsch. "Detection Limits of Blood Metabolites at Physiological Concentrations Using Benchtop 1^ 1 H NMR." NMR in Biomedicine 39, no. 2 (2026): e70215.
7) Regan, Matthew D., Edna Chiang, Michael Grahn, Marco Tonelli, Fariba M. Assadi-Porter, Garret Suen, and Hannah V. Carey. "Host–microbiome mutualism drives urea carbon salvage and acetogenesis during hibernation." Proceedings of the National Academy of Sciences 123, no. 1 (2026): e2518978123.
6) Basile, Federico, Ana Cristina Abreu, Claudio Cannata, Rosario Paolo Mauro, María Del Carmen Cerón-García, Cherubino Leonardi, and Ignacio Fernández. "Preharvest silicon and triacontanol improve postharvest quality in orange and purple carrots." Postharvest Biology and Technology 232 (2026): 114007.
5) Marino, Carmen, Federica Carrillo, Tommaso Nuzzo, Marcello Serra, Sara Pietracupa, Nicola Modugno, Manuela Grimaldi et al. "Mutations in the GBA1, LRRK2, TMEM175, PARK2, PINK1, and PARK7 genes lead to sex-and genotype-specific serum metabolic changes in patients with Parkinson's disease." medRxiv (2026): 2026-01.
4) Coca, Mario, Cristian Perez‐Fernandez, Ana C. Abreu, Ana M. Salmerón, Miguel Morales‐Navas, Diego Ruiz‐Sobremazas, Teresa Colomina, Ignacio Fernández, and Fernando Sanchez‐Santed. "Modulating Neurotoxic Effects of Prenatal Chlorpyrifos Exposure Through Probiotic and Vitamin D Gestational Supplementation: Unexpected Effects on Neurodevelopment and Sociability." Food Frontiers 7, no. 1 (2026): e70165.
3) Bonazzi, Erica, Fuhua Hao, Andrew D. Patterson, Laurent Peyrin-Biroulet, Benjamin AH Jensen, and Benoit Chassaing. "Microbiota modulation by a human Paneth cell α-defensin fragment protects against DSS-induced colitis." iScience 29, no. 1 (2026).
2) Bisht, Akshay, Jennifer Ahn-Jarvis, Kendall Corbin, Suzanne Harris, Perla Troncoso-Rey, Peter Olupot-Olupot, Nuala Calder et al. "Gut microbial diversity impacts carbohydrate fermentation by children with severe acute malnutrition." iScience (2026).
1) Naika, Mahantesha BN, Y. S. Mamatha, Dwijesh Chandra Mishra, and Belaghihalli N. Gnanesh. "Utilizing Bioinformatics Tools and Machine Learning Techniques to Advance Sustainable Crop Improvement." In Next Generation Crop Improvement for Agricultural Sustainability and Food Security, pp. 259-280. CRC Press, 2026.
Case Studies
The case studies below showcase various studies that were performed using Chenomx technology and services.
Analyzing the Metabolic Effects of Acetaminophen
Developing Biomarkers for Adverse Drug Responses
Pathways From Serological Metabolite Profiles
Acetaminophen is the most common identifiable cause of acute liver failure, and is an excellent model compound for studying liver toxicity. Acetaminophen toxicity has thus become a very active area of research. Identifying and quantifying metabolites associated with acetaminophen toxicity is an integral part of this research, and Chenomx is uniquely equipped to extract this information from biofluid samples.
Drug-induced liver injury is the leading reason that drug candidates fail. Identifying a suite of toxicity biomarkers for use in preclinical screening of drug candidates will allow drugs with harmful side effects to be identified earlier. Biomarkers that can be used to monitor or stage diseases will also offer insight into the net metabolic effect of a drug and can provide information for developing new drugs or adjusting treatment. Chenomx software offers powerful tools to aid in identifying these biomarkers.
Metabolomic data sets obtained using Chenomx NMR Suite often require additional analysis to place them in proper context. The GeneGo pathway analysis platform can help establish this context. In this note, we present an example analysis using serological metabolic profiles from Chenomx NMR Suite, known inflammatory markers from prior studies of the K/BxN mouse model and the GeneGo platform to generate a mechanistic hypothesis for the K/BxN mouse model.
Application Notes
Methods
Accurate CSI Quantification for Targeted Profiling
Accurately quantifying metabolites using targeted profiling in Chenomx NMR Suite requires using a chemical shape and shift indicator (CSI) of known concentration. As a result, the accuracy of the CSI concentration has a significant influence on quantification accuracy using this technique. Measuring ratios of the CSI peak area with that of a second, commercially available standard under optimal acquisition conditions offers a simple method of accurately measuring the concentration of the CSI.
Optimizing Spectra for Metabolomics
Chenomx Processor allows you to work with NMR spectra from a variety of sources, helping you apply phasing and baseline correction through manual and semi-automated tools, and offering advanced lineshape correction through reference deconvolution. Processor also allows you to define chemical shape indicator (CSI) parameters and determine sample pH via indicator compounds. These are necessary prerequisites to analyzing your spectra with Chenomx Profiler.
Sample Types
Targeted Profiling of Common Metabolites in Saliva
Saliva is an excellent biofluid for analysis by targeted profiling. It is easily collected by individuals with minimal training, using noninvasive techniques. Targeted profiling of saliva samples can provide identification and quantification of many of the small molecule metabolites commonly found in saliva, and may allow the development of simple screening procedures for a variety of diseases. In this note we present a list of common metabolites found in human saliva, and techniques for targeted profiling of saliva spectra with Chenomx NMR Suite.
Metabolites in Blood Serum and Plasma
Blood serum and plasma are biofluids that are increasingly important in NMR-based metabolomics analysis. In this note we discuss several approaches to the analysis of serum using Chenomx NMR Suite, focusing on relaxation-edited NMR (CPMG) and physical separation of protein and metabolites using ultrafiltration. The CPMG method is simpler to apply, but the spectra are easier to analyze when protein is removed from a sample.
Targeted Profiling of Common Metabolites in Urine
Urine is a readily-collected, information-rich biofluid that can provide insight into the metabolic state of an organism. As a result, urine is often a focus in metabolomics investigations using NMR and MRI spectroscopy, in both diagnostic and monitoring applications. Targeted profiling is a powerful tool that can drive such studies, providing direct identification and quantification of a variety of metabolites. In this note we present a list of common metabolites found in many NMR spectra of human urine and several strategies for approaching targeted profiling of such spectra with Chenomx NMR Suite.
Identifying Metabolites in Biofluids
In this note we present a rapid, efficient method for identifying metabolites in biofluid NMR spectra using targeted profiling. Conventional techniques for identifying and quantifying metabolites in such spectra are labor-intensive and error-prone, as positions and linewidths of peaks can vary widely with changes in pH and other solution matrix effects. The length of time and level of operator skill needed to analyze large numbers of these complex spectra are significant barriers to the widespread application of NMR in metabolomics.
Statistical Analysis
Statistical Analysis of Targeted Profiling Data
Analysis of 1H NMR spectra in metabolomics studies often requires multivariate pattern recognition techniques to extract meaningful results. Targeted profiling offers the ability to analyze spectra based directly on the identity and quantities of individual compounds. Profiles created using targeted profiling in Chenomx NMR Suite can be used as input in statistical software packages such as Umetrics SIMCA-P. Performing PCA on Chenomx targeted profiles yields information-rich results that allow rapid biological interpretation, since group separation can be directly correlated to variations in specific metabolite concentrations.
Correcting Lineshapes in NMR Spectra
In this note we present a method for removing lineshape distortions from nuclear magnetic resonance (NMR) spectra prior to more detailed analysis. Reference deconvolution is a method of reconstructing an ideal spectrum by removing lineshape distortions caused by field inhomogeneity. Applying reference deconvolution has been found to improve the quality of fit of a spectrum using targeted profiling in Chenomx NMR Suite. However, successful application of reference deconvolution to a spectrum requires some preprocessing and the presence of an appropriate reference peak.
Statistical Analysis of Spectral Binning Data
Analysis of 1H NMR spectra in metabolomics studies often requires multivariate pattern recognition techniques to extract meaningful results. Spectral binning is an effective data reduction technique commonly used to prepare spectra for multivariate analysis. Spectral binning output from the Profiler module of Chenomx NMR Suite can readily be analyzed with multivariate analysis software packages like Umetrics SIMCA-P.