Progress 10/01/23 to 09/30/24
Outputs
PROGRESS REPORT Objectives (from AD-416): Objective 1: Determine the impact of genetic variation and carbon dioxide concentration on the macronutrient digestibility in carbohydrate-rich foods with increasing matrix complexity. Sub-Objective 1. Rice to wheat to beans starch/fiber profiles Sub-Objective 2. Impact of elevated CO2 and water management strategies on carbohydrate structure. Sub-Objective 3. Dark matter is a valuable source for assessing the impact of genetics and environmental conditions on the nutritional value of beans. Objective 2 Non-targeted metabolomic methods will provide the tools to understand the �dark matter� in food. Sub-objective 1: Impact of in vitro gastrointestinal digestion on stability, and bioaccessibility of �nutritional dark matter� in fruits and vegetables. Sub-Objective 2: Dark matter as dietary biomarkers for foods in controlled feeding studies Objective 3: Develop software programs that use machine language (ML) and artificial intelligence (AI) approaches for rapid analysis of complex chromatographic-mass spectral data in foods and determine sources of variability. Sub-Objective 1: Expert methods for the analysis of chemical families of compounds. Sub-Objective 2: Develop a Food Secondary Metabolite Database (FSMD) Sub-Objective 3: Establish On-Line Methods & Secondary Metabolite Database (FSMD) Approach (from AD-416): Dietary fiber and phytochemicals have been termed nutritional �dark matter�, classes with thousands of possible compounds and chemical structures, not generally measured directly. MAFCl will employ a multimodal approach to identify and quantify biologically relevant species in this dark matter. For carbohydrates, methods will be developed for physical characterization of dietary fiber. The possible impact of genetic and climate variables on the digestibility of carbohydrates and putative interactions between carbohydrate and phytochemical species will be investigated. For phytochemical compounds to have an impact on health they must first survive the oral, gastric, and intestinal phases of digestion. Methods will be developed for this smaller subset of compounds that allow for accurate quantification, including methods to interpolate response factors for compounds with no commercially available pure standard. In collaboration with other units in the BHNRC, MAFCL will validate in vitro results against in vivo digestion results and examine the contribution of microbial metabolism to the processing of phytochemicals. MAFCL will take a leading role in the nutritional dark matter community, establishing tools for quality control in the detection and positive identification of phytochemicals and fiber in foods. MAFCL will develop new tools for identification of dark matter, including proanthocyanidins and fiber and make them available to the metabolomic and nutrition community. Curcuminoid content in turmeric roots from the U.S. commercial market and five countries. The dried root samples were ground and extracted with methanol. The filtered extracts were assayed by high performance liquid chromatography coupled with a diode-array detector. Currently, we are analyzing both the total curcuminoid content and their profile. Furthermore, we collected over ten popular turmeric dietary supplements from different commercial vendors and are investigating both, the profiles and total content variations of curcuminoids. Analysis of foods with INFOGEST, an artificial digestion device. Set up the INFOGEST and did preliminary tests with a few different foods in collaborator's lab at UMD. Purchased all major instruments, ordered chemicals, reagents and enzymes for setting the method in 307C. Currently waiting for lab space in B307C. A lack of staffing for scientific and administrative support has hampered research progress. Scientists time is spent performing administrative tasks, working to procure research materials, finding work- arounds to accommodate facilities problems, and handling the increased administrative burden of new reporting and contracting requirements. Continued facility problems have hampered progress, with long stretches of the year where lab temperature exceeds 80 �F. Vital chemical safety equipment, in particular chemical fume hoods, which frequently alarm for low flow, or cease operation altogether. Delays in processing tickets for new hires has resulted in a scientific staffing shortage. A technician position that was submitted over a year ago, delays due to position classification and understaffing in Human Resources have resulted in this position remaining vacant with no indication of a time frame for recruiting or hiring. Procurement continues to be a problem. Essential lab supplies are a challenge to obtain as we lack a purchase card for our unit. In summary, a considerable amount of an scientist�s time is spent handling infrastructure problems that prevent research from occurring. Pig fecal diet biomarkers for fruit and vegetable intake. Exploring the link between diet and health has obtained significant attention. BHNRC identified potential dietary biomarkers from pig fecal samples with fruit and vegetable interventions using liquid chromatography-high resolution mass spectrometry, multivariate statistical analysis, and metabolic pathway prediction and network exploration. The integration of data- driven and knowledge-based analytical methods enables the exploration of interconnected metabolic pathways and potential interactions between identified biomarkers. Based on this strategy, a significant number of potential biomarkers related to the intake of a diet enriched in flavanols, flavones, flavan-3-ols, and anthocyanins were identified. This work is being used collaboratively with scientists at the Diet, Genomics, and Immunology Lab of USDA-ARS. Identification of dietary biomarkers to identify compounds that can be used to verify food intake. The Methods and Application of Food Composition Laboratory (MAFCL), in collaboration with labs at NIH, USDA, Harvard, and the Fred Hutchinson Center, is participating in the Dietary Biomarker Development Consortium to identify biomarkers that can be used to verify food intake. MAFCL has analyzed 44 food samples which were used for the large scale controlled feeding studies. All the samples were lyophilized and and analysed using a non-targed metabololic approach. The chromatogram and high resolution mass spectrometric data will be shared by NIH-National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and USDA for database use. Impact of light intensity and wavelength on secondary metabolite composition. MAFCL is collaborating with a Beltsville Agricultural Research Center (BARC) lab to evaluate the phytochemical profiles of the broccoli/�ruby streak� mustard/kale grown under different light emitting diode (LED) light intensites, receipes. The findings were used for optimization of growing conditions of leafy greens under controlled environmental agriculture. An improved analytical method for the analysis of glucosinolates. Glucosinolates, sulfur containing compounds foundin brassica vegetables, have purported value in mitigating cancer, but are extremely difficult to analyze due to their chemical variation. MAFCL developed an improved method as an alternative to the ISO 9167-1 method for quantitation of glucosinolates (GLSs). MAFCL developed an efficient extraction and purification procedure using a commercially available weak anion exchange solid-phase extraction (SPE) cartridge. The method demonstrated comparable quantification of total and individual GLSs on certified rapeseeds and other brassica vegetables compared to the ISO method. The developed SPE method is simpler and more efficient, allowing application to a large sample size with reduced analysis time, improved repeatability and accuracy, and possible automation. This work is being used collaboratively with scientists at the Food Quality Laboratory of USDA- ARS. Development of a high resolution mass spectrometry botanical database. MAFCL is continuing to develop the USDA Botanical Data Central (BDC) in collaboration project with Ohio University. More than 50 botanical samples have been purchased from American Herbal Pharmacopoeia (AHP). Data are in the process uploading the LC/MS chromatograms to BDC. Identification of the peaks of the LC/MS chromatograms is ongoing and will take a considerable amount of time. Online versions of FlavonQ and GLS Finder, as well as an analysis of varicance-principal component analysis (ANOVA-PCA tool are being refined to guide the development of other food categorization systems for nutrient diversification. Chemical differences between Wild and cultivated American ginseng. American ginseng is a pharmacologically and agriculturally important crop native to Tennessee. The wild roots have greater market value and contain a higher level of active pharmacological compounds (i.e., ginsenosides) compared to their cultivated counterpart. Samples have been analyzed by high resolution mass spectrometry and the data are currently being processed using chemometric methods. Analysis of pro-anthocyanidins. MAFCL is continuing to develop a strategy for modernization of methods for analysis of proanthocyanidins. This will include determination of total monomer concentration and the fraction of A-type and B-type linkages of the polymers. Determination of fiber content in whole wheat. We are investigating the dietary fiber content of three fractions (germ, bran, and endosperm) and the whole grain from two wheat varieties (white hard wheat and red hard wheat) using a semiautomated Ankom fiber analyzer. Three molecular fractions (insoluble dietary fiber (IDF), soluble dietary fiber precipitate (SDFP), and soluble dietary fiber solubles (SDFS) from the two wheat cultivars will be investigated for detailed carbohydrate analysis with the liquid chromatography-mass spectrometry-based methods to identify and quantify their total monosaccharide, glycosidic linkage, and the free saccharide compositions of different fractions. Principal component analysis of the acquired data will be used to identify the differences in monosaccharide, glycosidic linkage, and the free saccharide content in the different grain and Ankom fiber analyzer fractions. Variations of secondary metabolites in dry beans. Approximately 2000 dry bean samples of different genotypes and growing locations were provided by collaborators from the Pulse Crop Health Initiative. All samples were initially ground in a coffee grinder and the ground samples were screened by near-infrared spectral analysis. Four hundred samples were selected from different clusters identified with near-infrared (NIR) spectral analysis. These samples were extracted and analyzed by high-resolution mass spectroscopy to identify the difference in secondary metabolites based on cultivar and growing locations. The analysis is currently underway. Artificial Intelligence (AI)/Machine Learning (ML) Machine learning has been used extensively at MAFCL for the last 15 years. � Machine learning methods include common chemometric methods, i.e., principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA), soft independent modeling of class analogy (SIMCA), and similar algorithms. � All computing resources are in-house, using stand-alone platforms purchased from commercial companies. Methods are implemented on commercial chemometric platforms such as MatLab (Mathworks) and Solo (Eigenvector Research). No resources are used from SCINet�s HPC clusters (Ceres and Atlas), commercial cloud services, or computing resources provided by an external collaborator. IT doesn�t have the programs to support our research. � Machine learninghas been instrumental to MAFCLs research for the last 15 years for botanical authentiation, experimental design, detection of instrumental drift, processing of complex mass spectral data, and deconvolution of data to determine the interaction of genetics, environment, and management. � MAFCL has a NACA with Dr. Peter Harrington at Ohio University, a well- known chemometrition who serves as a consultant for machine learning problems. It should be noted that, over the years, USDA has not provided any intellectual support. � USDA has only recently jumped on the AI/machine learning bandwagon and is now stripping 25% of our discretionary funds and providing nothing in return.
Impacts
(N/A)
Publications
- Dong, W., Yang, X., Chen, P., Sun, J., Harnly, J.M., Zhang, M., Zhang, N. 2024. Study of uv-vis molar absorptivity variation and quantitation of anthocyanins using molar relative response factor. Food Chemistry. 444:138653. https://doi.org/10.1016/j.foodchem.2024.138653.
- Geng, P., Harnly, J.M., Sun, J., Chen, P. 2024. Variability and determinants of secondary metabolite profiles in cranberries (Vaccinium macrocarpon) from key cultivation states. Journal of Agriculture and Food Research. 15:100983. https://doi.org/10.1016/j.jafr.2024.100983.
- Li, Y., Zhang, M., Pehrsson, P.R., Harnly, J.M., Chen, P., Sun, J. 2024. Fast and simple solid phase extraction-based method for glucosinolate determination: an alternative to ISO-9167 method. Foods. 13(5):650. https:/ /doi.org/10.3390/foods13050650.
- Teng, Z., Luo, Y., Sun, J., Pearlstein, D.J., Oehler, M., Fitzwater, J.D., Zhou, B., Hussan, M.A., Chang, C.Y., Chen, P., Wang, Q., Fonseca, J.M. 2024. Effect of far-red light on biomass accumulation, plant morphology, and phytonutrient composition of ruby streaks mustard at microgreen, baby leaf, and flowering stages. Journal of Agricultural and Food Chemistry. 72(17):9587�9598. https://doi.org/10.1021/acs.jafc.3c06834.
- Choe, U., Liu, Z., Li, Y., Sun, J., Wu, X., Pehrsson, P.R., Xie, Z., Zhang, Y., Wang, T.T., Yu, L., Gao, B. 2023. Chemical compositions of dill (Anethum graveolens L.) water and ethanol extracts and their potential in reducing the COVID-19 risk and free radical scavenging capacities. Food Chemistry. 3(10):1654�1662. https://doi.org/10.1021/acsfoodscitech.3c00206? urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-as.
- Tareq, F.S., Kotha, R.R., Natarajan, S.S., Sun, J., Luthria, D.L. 2023. An untargeted metabolomics approach to study the variation between wild and cultivated soybeans. Molecules. 28(14):5507. https://doi.org/10.3390/ molecules28145507.
- Tareq, F.S., Singh, J., Ferreira, J.F., Sandhu, D., Suarez, D.L., Luthria, D.L. 2024. A targeted and an untargeted metabolomics approach to study the phytochemicals of tomato cultivars grown under different salinity conditions. Journal of Agricultural and Food Chemistry. 72(14):7694-7706. https://doi.org/10.1021/acs.jafc.3c08498.
- Bukowski, M.R., Goslee, S.C. 2023. Climate-based variability in the essential fatty acid composition of soybean oil. The American Journal of Clinical Nutrition. 119:1. https://doi.org/10.1016/j.ajcnut.2023.08.024.
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