About

Emmanuel Hatzakis is an Associate Professor at The Ohio State University in the Department of Food Science and Technology. His office is located in the 233 Parker Food Science and Technology Building. His research focuses on food science and technology, contributing to the understanding and development of food processing, safety, and quality. As a faculty member, he is involved in teaching, research, and service activities within the department, supporting advancements in food science through scholarly work and collaboration.

Research topics

• Organic chemistry
• Biology
• Biochemistry
• Chemistry
• Physiology
• Endocrinology
• Microbiology
• Genetics
• Nanotechnology
• Food science
• Materials science
• Chromatography
• Biophysics
• Medicine
• Immunology
• Pharmacology
• Internal medicine

Selected publications

• A robust method for monitoring the growth and metabolism of probiotic bacteria in vitro

  LWT · 2025-03-03 · 1 citations

  articleOpen accessSenior authorCorresponding

  Lactic acid bacteria are involved in many food, nutritional, and biotechnological applications, where carbohydrates are their main source of energy. Although the study of bacterial growth and metabolism in vitro offers several advantages, protocols detailing optimum conditions for a particular bacterium are often not well-established. The objective of this work was to develop a protocol with optimized bacterial growth and sampling conditions for a specific probiotic bacterium ( Lactiplantibacillus plantarum ) used a model system and apply this protocol to study the interaction between a substrate and this bacterium in vitro for downstream microbiological and metabolomic analyses. Here we detail an optimized medium composition (mCFBM 3) for controlled L. plantarum growth. Use of this medium allowed us to assess growth promotion, effects of treatments and select optimal time points during fermentative growth for downstream broad-spectrum (NMR, LC-MS) and targeted (fatty acid; LC-MS/MS) metabolomic analyses. While untargeted LC-MS analyses allowed for the putative identification of a wider range of products, NMR-based metabolomics proved an efficient and rapid tool for the analysis of major bacterial metabolites, allowing putative identification of the metabolic products formed per colony forming unit, and enabling monitoring of the production and degradation of compounds during fermentation. • Developed a protocol to study probiotic bacteria's responses to growth substrates • Optimized conditions for precise metabolomics of bacterial growth • Employed NMR for rapid analysis of major metabolites • Combined targeted LC-MS/MS and broad-spectrum LC-MS for full metabolic profiling • Provided comprehensive metabolic profiles through integrated analyses

  PublisherDOI

• Author response for "Impact of thermal, high‐pressure and ultra‐shear pasteurisation technologies on beetroot juice metabolites using untargeted nuclear magnetic resonance spectroscopy"

  2024-06-13

  peer-reviewSenior author
  PublisherDOI

• Analysis and authentication of avocado oil by low‐field benchtop NMR spectroscopy and chemometrics

  Journal of Food Science · 2024-06-05 · 12 citations

  articleOpen accessSenior authorCorresponding

  Abstract Avocado oil is a nutritious, edible oil produced from avocado fruit. It has high commercial value and is increasing in popularity, thus powerful analytical methods are needed to ensure its quality and authenticity. Recent advancements in low‐field (LF) NMR spectroscopy allow for collection of high‐quality data despite the use of low magnetic fields produced by non‐superconductive magnets. Combined with chemometrics, LF NMR opens new opportunities in food analysis using targeted and untargeted approaches. Here, it was used to determine poly‐, mono‐, and saturated fatty acids in avocado oil. Although direct signal integration of LF NMR spectra was able to determine certain classes of fatty acids, it had several challenges arising from signal overlapping. Thus, we used partial least square regression and developed models with good prediction performance for fatty acid composition, with residual prediction deviation ranging 3.46–5.53 and root mean squared error of prediction CV ranging 0.46–2.48. In addition, LF NMR, combined with unsupervised and supervised methods, enabled the differentiation of avocado oil from other oils, namely, olive oil, soybean oil, canola oil, high oleic (OL) safflower oil, and high OL sunflower oil. This study showed that LF NMR can be used as an efficient alternative for the compositional analysis and authentication of avocado oil. Practical Application Here, we describe the application of LF‐NMR for fatty acid analysis and avocado oil authentication. LF‐NMR can be an efficient tool for targeted and untargeted analysis, thus becoming an attractive option for companies, regulatory agencies, and quality control laboratories. This tool is especially important for organizations and entities seeking economic, user‐friendly, and sustainable analysis solutions.

  PublisherOA PDFDOI

• A Robust Method for Monitoring the Growth and Metabolism of Probiotic Bacteria in Vitro

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Impact of thermal, high-pressure and ultra-shear pasteurisation technologies on beetroot juice metabolites using untargeted nuclear magnetic resonance spectroscopy

  International Journal of Food Science & Technology · 2024-07-08 · 2 citations

  articleOpen accessSenior author

  Abstract The impact of three food pasteurisation technologies, namely thermal, high-pressure and ultra-shear processing, on the metabolites of beetroot juice was evaluated using a processomics approach with nuclear mass resonance (NMR) as an analytical technique. Two batches of beetroots acquired from different local grocery stores were used for this study. Beetroot juice obtained from these batches was subjected to high-pressure processing (HPP) at 600 MPa and 25 °C for 5 min, ultra-shear technology processing (UST) at 400 MPa and 30 °C and thermal processing (TP) at 96 °C for 12 min. Principal component analysis (PCA) for the two batches indicated that both extrinsic factors such as processing parameters (temperature, pressure, shear and holding time) and intrinsic factors such as the origin of the beetroot influenced the PCA plot. When the influence of intrinsic parameters was minimised by studying a single batch processed by TP, HPP and UST, distinct clusters for different processing methods were formed, indicating that processing influenced the metabolites. While processing is not the main factor determining the final composition, as indicated by PCA with different batches, supervised techniques like orthogonal partial least-squares discriminant analysis (OPLS-DA) and random forest (RF) demonstrated that processing does impact the beetroot juice metabolome. Seven metabolites (leucine, alanine, valine, glutamine, gamma-aminobutyric acid, fructose and glucose) were identified as potential process-induced biomarkers.

  PublisherOA PDFDOI

• A Robust Method for Monitoring the Growth and Metabolism of Probiotic Bacteria in Vitro

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Identification of Compounds That Impact Consumer Flavor Liking of American–European Hazelnut Hybrids Using Nontargeted LC/MS Analysis

  Journal of Agricultural and Food Chemistry · 2024-03-27 · 7 citations

  article

  American–European (Corylus americana × Corylus avellana) hazelnut hybrids are being developed for the Midwest-growing region of the United States. However, an inadequate understanding of the compounds that impact the consumer acceptance of hazelnuts limits breeding programs. Nontargeted liquid chromatography/mass spectrometry (LC/MS) chemical profiles of 12 roasted hybrid hazelnut samples and the corresponding consumer flavor liking scores were modeled by orthogonal partial least squares with good fit and predictive ability (R2Y > 0.9, Q2 > 0.9) to identify compounds that impact nut liking. The five most predictive compounds (1–5) were negatively correlated to flavor liking, selected as putative markers, purified by multidimensional preparative LC/MS, structurally elucidated (nuclear magnetic resonance, MS), quantified, and validated for sensory relevance. Compound 1 was identified as 1″-O-3′-b-glucofuranosyl-1′-O-1-b-glucofuranosyl-(2,6-dihydroxyphenyl)-ethan-4-one. Compounds 2 and 4 were identified as rotamers of 2-(3-hydroxy-2-oxoindolin-3-yl) acetic acid 3-O-6′-galactopyranosyl-2″-(2″oxoindolin-3″yl) acetate, whereas compounds 3 and 5 were identified as rotamers of 1″-O-1′-b-glucofuranosyl-9-O-6′-b-glucopyranosyl-2″-(2″-oxoindolin-3″yl) acetate. Sensory evaluation determined that all compounds were characterized by bitterness and/or astringency. The sensory threshold values of compounds 1–5 were determined to be below the concentrations reported in 91, 83, 41, 25, and 41% of all 12 hybrid hazelnut samples, respectively, indicating they contributed to aversive flavor attributes.

  PublisherDOI

• Data from Noninvasive Urinary Metabolomic Profiling Identifies Diagnostic and Prognostic Markers in Lung Cancer

  2023-03-30

  preprintOpen access

  <div>Abstract<p>Lung cancer remains the most common cause of cancer deaths worldwide, yet there is currently a lack of diagnostic noninvasive biomarkers that could guide treatment decisions. Small molecules (<1,500 Da) were measured in urine collected from 469 patients with lung cancer and 536 population controls using unbiased liquid chromatography/mass spectrometry. Clinical putative diagnostic and prognostic biomarkers were validated by quantitation and normalized to creatinine levels at two different time points and further confirmed in an independent sample set, which comprises 80 cases and 78 population controls, with similar demographic and clinical characteristics when compared with the training set. Creatine riboside (IUPAC name: 2-{2-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-oxolan-2-yl]-1-methylcarbamimidamido}acetic acid), a novel molecule identified in this study, and <i>N</i>-acetylneuraminic acid (NANA) were each significantly (<i>P</i> < 0.00001) elevated in non–small cell lung cancer and associated with worse prognosis [HR = 1.81 (<i>P</i> = 0.0002), and 1.54 (<i>P</i> = 0.025), respectively]. Creatine riboside was the strongest classifier of lung cancer status in all and stage I-II cases, important for early detection, and also associated with worse prognosis in stage I-II lung cancer (HR = 1.71, <i>P</i> = 0.048). All measurements were highly reproducible with intraclass correlation coefficients ranging from 0.82 to 0.99. Both metabolites were significantly (<i>P</i> < 0.03) enriched in tumor tissue compared with adjacent nontumor tissue (<i>N</i> = 48), thus revealing their direct association with tumor metabolism. Creatine riboside and NANA may be robust urinary clinical metabolomic markers that are elevated in tumor tissue and associated with early lung cancer diagnosis and worse prognosis. <i>Cancer Res; 74(12); 3259–70. ©2014 AACR</i>.</p></div>

  PublisherDOI

• Data from Noninvasive Urinary Metabolomic Profiling Identifies Diagnostic and Prognostic Markers in Lung Cancer

  2023-03-30

  preprintOpen access

  <div>Abstract<p>Lung cancer remains the most common cause of cancer deaths worldwide, yet there is currently a lack of diagnostic noninvasive biomarkers that could guide treatment decisions. Small molecules (<1,500 Da) were measured in urine collected from 469 patients with lung cancer and 536 population controls using unbiased liquid chromatography/mass spectrometry. Clinical putative diagnostic and prognostic biomarkers were validated by quantitation and normalized to creatinine levels at two different time points and further confirmed in an independent sample set, which comprises 80 cases and 78 population controls, with similar demographic and clinical characteristics when compared with the training set. Creatine riboside (IUPAC name: 2-{2-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-oxolan-2-yl]-1-methylcarbamimidamido}acetic acid), a novel molecule identified in this study, and <i>N</i>-acetylneuraminic acid (NANA) were each significantly (<i>P</i> < 0.00001) elevated in non–small cell lung cancer and associated with worse prognosis [HR = 1.81 (<i>P</i> = 0.0002), and 1.54 (<i>P</i> = 0.025), respectively]. Creatine riboside was the strongest classifier of lung cancer status in all and stage I-II cases, important for early detection, and also associated with worse prognosis in stage I-II lung cancer (HR = 1.71, <i>P</i> = 0.048). All measurements were highly reproducible with intraclass correlation coefficients ranging from 0.82 to 0.99. Both metabolites were significantly (<i>P</i> < 0.03) enriched in tumor tissue compared with adjacent nontumor tissue (<i>N</i> = 48), thus revealing their direct association with tumor metabolism. Creatine riboside and NANA may be robust urinary clinical metabolomic markers that are elevated in tumor tissue and associated with early lung cancer diagnosis and worse prognosis. <i>Cancer Res; 74(12); 3259–70. ©2014 AACR</i>.</p></div>

  PublisherDOI

• Supplementary Materials and Methods, Figures 1 - 10, Tables 1 - 4 from Noninvasive Urinary Metabolomic Profiling Identifies Diagnostic and Prognostic Markers in Lung Cancer

  2023-03-30

  preprintOpen access

  <p>PDF file - 1526KB, Supplementary Table 1 shows random forest analysis results for predictions of lung cancer status in the training set. Supplementary Table 2 shows associations with survival in the training set when the top four predictive metabolites are combined in all cases. Supplementary Table 3 shows associations with survival in the training set, stratified by self-reported race. Supplementary Table 4 shows intraclass correlation coefficients in the quantitated subset. Supplementary Figure 1 depicts workflow of the classification analysis. Supplementary Figure 2 depicts quality control assessment in the training set. Supplementary Figure 3 shows predictions of smoking status in the training set determined by random forest analysis and abundances of tobacco-related metabolites. Supplementary Figure 4 shows overlap of metabolites predictive of lung cancer status in the training set based on random forest analysis, stratified by gender, race and smoking status. Supplementary Figure 5 shows fragmentation patterns of top four predictive metabolites determined by tandem mass spectrometry. Supplementary Figure 6 depicts identification of creatine riboside by NMR. Supplementary Figure 7 shows diurnal effects on top four predictive metabolites. Supplementary Figure 8 shows top four predictive metabolite abundances stratified by smoking status. Supplementary Figure 9 shows Kaplan-Meier survival estimates in the training set depicted for the top four predictive metabolites in stages I-II and their combination. Supplementary Figure 10 shows metabolite abundances stratified by chemotherapy/radiation status and surgery status.</p>

  PublisherDOI

Frequent coauthors

• Andrew D. Patterson

  Pennsylvania State University

  25 shared

• Frank J. Gonzalez

  Colciencias

  23 shared

• Philip B. Smith

  23 shared

• Ewy A. Mathé

  National Center for Advancing Translational Sciences

  23 shared

• Photis Dais

  University of Crete

  21 shared

• Kristopher W. Krausz

  21 shared

• Jeffrey R. Idle

  Western New England University

  20 shared

• Curtis C. Harris

  20 shared

Labs

• Food Science and Technology at The Ohio State UniversityPI

Education

• Ph.D., Food Science and Technology

  The Ohio State University

  2000

• M.S., Food Science and Technology

  The Ohio State University

  1996

• B.S., Food Science and Technology

  The Ohio State University

  1994

Awards & honors

• 2017 IUFoST Young Scientist Award