Abstracts of the
2018 American Dairy Science Association
®
Annual Meeting
June 24–27, 2018
Knoxville, Tennessee
Journal of Dairy Science
®
J
OURNAL
OF
D
AIRY
S
CIENCE
®
SINCE
1917
1800 S. Oak St., Ste 100, Champaign, IL 61820Phone 217/356-5146 | Fax 217/378-4083 | adsa@assochq.org | http://www.journalofdairyscience.org JOURNAL MANAGEMENT COMMITTEE
T. Schoenfuss, Chair (18) University of Minnesota H. Dann (19) WH Miner Institute D. M. Barbano (20) Cornell University P. Cardoso (21) University of Illinois Matthew C. Lucy
University of Missouri, Board Liaison S. Pollock (ex offi cio)
American Dairy Science Association
L. Adam (ex offi cio)
American Dairy Science Association P. Studney (ex offi cio)
American Dairy Science Association
EDITORIAL BOARD S. Andrew (20) USA K. Aryana (20) USA A. Bach (19) Spain H. Barkema (19) Canada J. M. Bewley (19) USA R. C. Bicalho (19) USA D. Bickhart (19) USA R. Brandsma (18) USA A. Brito (20) USA C. Burke (18) New Zealand T. Byrne (18) New Zealand V. Cabrera (19) USA M. Calus (20) the Netherlands R. Cerri (18) Canada A. Cruz (20) Brazil S. Davis (18) New Zealand M. de Veth (19) USA T. DeVries (20) Canada S. Drake (18) USA P. Erickson (18) USA P. M. Fricke (20) USA K. Galvao (20) USA J. Giordano (18) USA O. Gonzalez-Recio (20) Australia R. Govindasamy-Lucey (20) USA B. Gredler (18) Switzerland J. Gross (19) Switzerland T. Hackmann (19) USA H. Hammon (18) Germany K. Harvatine (18) USA A. J. Heinrichs (20) USA L. Hernandez (19) USA S. Hiss-Pesch (18) Germany A. Hristov (19) USA R. Jimenez-Flores (20) USA M. Johnson (20) USA I. Kanevsky-Mullarky (18) USA D. Kelton (20) Canada V. Krömker (20) Germany A. F. Kertz (18) USA V. Krömker (20) Germany C. Kuhn (18) Germany R. Laven (18) New Zealand I. Lean (20) Australia E. Lewis (20) Ireland M. L. Marco (20) USA S. McDougall (18) New Zealand B. M. Mehta (18) India S. Meier (18) New Zealand K. Moyes (18) USA R. Narasimmon (18) USA T. Nennich (20) USA D. Nydam (19) USA C. Oberg (20) USA G. Opsomer (19) Belgium T. Overton (20) USA J. Pantoja (20) Brazil M. Rhoads (19) USA C. Risco (20) USA T. Schoenfuss (18) USA L. Shalloo (18) Ireland A. Sipka (18) USA L. Tauer (20) USA S. Tsuruta (18) USA M. E. Van Amburgh (20) USA T. Vasiljevic (19) Australia M. A. G. von Keyserlingk (18) Canada R. Wadhwani (18) USA E. Wall (20) Switzerland J. Wang (19) China L. Ward (18) USA R. Ward (20) USA M. Wattiaux (20) USA P. Weimer (20) USA W. Weiss (18) USA R. White (20) USA N. Widmar (20) USA M. Wiedmann (20) USA Q. Zebeli (18) Austria ADSA OFFICE RS President K. Schmidt
Kansas State University
Vice President
G. Dahl
University of Florida
Treasurer
M. Faust
Data Driven Genetics
Past President L. Armentano University of Wisconsin Directors K. Griswold (18) Kemin Industries S. Clark (18) Iowa State University B. Bradford (19) Kansas State University B. Nelson (19) Daisy Brand J. Quigley (20) Cargill Animal Nutrition
T. Dawson (20) Chr. Hansen Executive Director P. Studney Champaign, IL ADSA FOUNDATION R. F. Roberts (18), Chair Penn State University H. Dann (20), Vice Chair W H Miner Inst.
K. Kalscheur (19), Secretary South Dakota State University
M. Faust (18), Treasurer Data Driven Genetics
Trustees:
V. Mistry (18)
South Dakota State University E. Schwab (18)
Vita Plus Corp.
J. Partridge (19) Michigan State University E. Bastian (20)
United Dairymen of Idaho
FASS PUBLICATIONS STAFF journals@assochq.org
Susan Pollock, Managing Editor Louise Adam Chris Davies Mandy Eastin-Allen Sharon Frick Christine Horger Ron Keller Lisa Krohn Shauna Miller
Journal of Dairy Science (ISSN 0022-0302) is published monthly on behalf of the American Dairy Science Association® by FASS Inc., Champaign, IL, and Elsevier Inc., 360 Park Avenue South, New York, NY 10010-1710. Business and Editorial Offi ce: 1600 John F. Kennedy Blvd., Ste. 1800, Philadelphia, PA 19103-2899. Customer Services Offi ce: 3251 Riverport Lane, Maryland Heights, MO 63043. Periodicals postage paid at New York, NY, and additional mailing offi ces. The electronic edition of the journal (ISSN 1525-3198) is published online at http://www.journalofdairyscience.org.
Editor-in-chief
Matthew C. Lucy (19) University of Missouri
lucym@missouri.edu; 573/882-9897
Dairy Foods (all subsections)
John McKillip, Senior Editor (19) Ball State University
Lisbeth Goddik, Editor (18) Oregon State University Federico Harte, Editor (18) Penn State University Michael Miller, Editor (20) University of Illinois Yves Pouliot, Editor (19) Université Laval
Scott A. Rankin, Editor (20) University of Wisconsin–Madison
Animal Nutrition
Paul Kononoff, Senior Editor (19) University of Nebraska Jeffrey L. Firkins, Editor (20) The Ohio State University Masahito Oba, Editor (19) University of Alberta Zhongtang Yu, Editor (18) The Ohio State University
Genetics and Genomics
Christian Maltecca, Senior Editor (20) North Carolina State University Andrés Legarra, Editor (20) INRA
Nicolo Macciotta, Editor (18) University of Sassari
Health, Behavior, and Well-being
Tanya Gressley, Senior Editor (20) University of aware
Jon Huxley, Editor (20) Massey University Stephen LeBlanc, Editor (18) University of Guelph Pamela Ruegg, Editor (19) Michigan State University Dan Weary, Editor (18) University of British Columbia
Management and Economics
John Roche, Senior Editor (18) Dairy NZ, New Zealand Albert De Vries, Editor (18) University of Florida
Physiology
Stephen Butler, Senior Editor (18) Teagasc, Ireland
David Beede, Editor (18) Michigan State University Gerd Bobe, Editor (20) Oregon State University
Invited Reviews
Abstract range Page no.
ABSTRACTS
American Dairy Science Association
®Sunday, June 24, 2018
SYMPOSIA AND ORAL SESSIONS
Late-Breaking Original Research ...LB1–LB8 ...i
Workshop: National Animal Nutrition Program (NANP) Models ... 1–7 ...1
Workshop: Spore Sources and Transmission from Farm to Fork—Detection and Control Strategies ... 8–10 ...3
ADSA 2018 Mini Symposium: Priorities for Fiber Research (DC33 Follow-Up) ... 11 ...4
ADSA Graduate Student Symposium: Manuscript Writing for Graduate Students ... 12–15 ...5
Monday, June 25, 2018
POSTER PRESENTATIONS
ADSA Graduate Student Dairy Foods Poster Competition ...M1–M8 ...7
ADSA Graduate Student (MS) Production Poster Competition ...M9–M16 ...10
ADSA Graduate Student (PhD) Production Poster Competition ...M17–M29 ...13
ADSA-SAD Undergraduate Original Research Poster Competition ...M30–M41 ...18
Animal Behavior and Well-Being I ...M42–M56 ...22
Animal Health I ...M57–M73 ...28
Animal Health II ... M74–M93, M327 ...33
Breeding and Genetics I ...M94–M103 ...40
Dairy Foods I: Cheese ... M104–M110 ...43
Dairy Foods II: Microbiology ... M111–M124 ...46
Dairy Foods III ...M125–M149 ...51
Extension Education I ...M150–M157 ...59
Forages and Pastures I ...M158–M171 ...62
Lactation Biology I ...M172–M182 ...68
Physiology and Endocrinology I ...M183–M200 ...71
Production, Management, and Environment I ...M201–M215 ...77
Reproduction I ...M216–M224 ...83
Ruminant Nutrition I ...M225–M318 ...87
Small Ruminant I ...M319–M324 ...119
Teaching/Undergraduate and Graduate Education ...M325–M326 ...121
SYMPOSIA AND ORAL SESSIONS
ADSA Graduate Student Dairy Foods Oral Competition ... 16–25 ...122
ADSA Graduate Student (PhD) Production Oral Competition ... 26–35 ...126
Animal Behavior and Well-Being Platform Session: Assessment of Affective
States of Dairy Cattle ... 36–42 ...130
Animal Health I ... 43–52 ...133
ARPAS Symposium: Sustainable Dairy Production ... 53–55 ...137
Breeding and Genetics I: Health and Fertility ... 56–65 ...138
Dairy Foods: Joint ADSA-American Society of Nutrition Symposium:
New Views on Milk and Human Health ... 68–71 ...141
Forages and Pastures I ... 72–77 ...143
Abstract range Page no.
Reproduction: Joint ADSA-SSR Symposium: The Immune–Reproduction
Nexus—The Good, the Bad, and the Ugly ... 101–104 ...155
Ruminant Nutrition I: Fat ... 105–116 ...157
Ruminant Nutrition Symposium: Management and Nutrition of Dairy Cattle
in the New Era of Automation ... 117–122 ...162
Small Ruminant Platform Session: Addressing Management Challenges and
Improving Performance in Small Ruminants ... 123–128 ...164
ADSA-SAD Undergraduate Dairy Foods Oral Competition ... 129–132 ...167
ADSA Graduate Student (MS) Production Oral Competition ... 133–137 ...169
Animal Behavior and Well-Being I ... 138–146 ...171
Animal Health II ... 147–154 ...174
Animal Health: Joint ADSA-National Mastitis Council Platform Session: Milk Quality
and the Dairy Industry Today ... 156–164 ...177
Breeding and Genetics II: Methodologies, Inbreeding and Breeding Strategies ... 165–174 ...180
Dairy Foods I: Cheese ... 175–182 ...184
Extension Education I ... 183–185 ...187
Forages and Pastures Symposium: Fiber Digestibility—From Cell Wall Composition
to Forage Utilization ... 186–189 ...189
Joint MILK and Lactation Biology Symposium: Milk Globules, Vesicles, and
Exosomes—Update, Origin, Structure, and Function ... 190–194 ...191
Production, Management, and Environment II ... 195–207 ...193
Ruminant Nutrition II: Methane ... 208–218 ...198
Ruminant Nutrition Platform Session I: Rumen Function and Health ... 219–228 ...202
ADSA-SAD Undergraduate Original Research Oral Competition ... 229–237 ...206
ADSA-SAD Undergraduate Dairy Production Oral Competition ... 238–246 ...209
Small Ruminant I ... 247–254 ...212
Teaching, Undergraduate and Graduate Education Symposium: Active Learning—From
Theory to Practice ... 255–260 ...215
Tuesday, June 26, 2018
POSTER PRESENTATIONS
Animal Behavior and Well-Being II ... T1–T15 ...217
Animal Health III ... T17–T54 ...223
Breeding and Genetics II ...T55–T61, T304, T305 ...235
Dairy Foods IV: Cheese ... T62–T70 ...238
Dairy Foods V: Microbiology ... T71–T83 ...241
Dairy Foods VI ... T84–T107 ...245
Forages and Pastures II ... T108–T126 ...253
Growth and Development I ... T127–T145 ...260
Lactation Biology II ... T146–T154 ...267
Physiology and Endocrinology II ... T155–T174 ...270
Production, Management, and Environment II ... T175–T202 ...277
Reproduction II ... T203–T210 ...287
Ruminant Nutrition II ... T211–T303 ...290
SYMPOSIA AND ORAL SESSIONS
ADSA Southern Branch Graduate Student Oral Competition ... 261–262 ...323
Animal Health III ... 264–272 ...324
Breeding and Genetics Symposium: Fertility—Filling the Gaps ... 273–278 ...327
Abstract range Page no.
Growth and Development I ... 293–302 ...334
Lactation Biology: Joint ADSA/NMC Session: Advances in Mammary Health
and Immunology ... 303–307 ...338
Physiology and Endocrinology II ... 308–319 ...340
Production, Management, and Environment III ... 320–330 ...344
Reproduction I ... 331–335 ...348
Ruminant Nutrition III: Forages, Fiber, and Grains ... 336–345 ...350
Ruminant Nutrition IV: Additives... 346–355 ...354
Ruminant Nutrition Symposium: Interface of Environment and Nutrition—Targeted
Nutrition to Overcome Heat Stress ... 356–360 ...358
ADSA Southern Branch Symposium: Sustaining the Southern Dairy Industry:
University Research, Teaching, and Extension Outlook ... 361–363 ...360
Animal Behavior and Well-Being II ... 364–373 ...361
Animal Health IV... 375–385 ...365
Breeding and Genetics III: Feed Efficiency, Crossbreeding, and Production ... 387–396 ...369
Dairy Foods III: Microbiology and Health ... 397–405 ...373
Dairy Foods Processing Symposium: Emerging Processing Technologies to Improve
Quality and Functionality of Dairy Ingredients ... 287, 406–409 ...376
Growth and Development/Ruminant Nutrition Symposium: Post-Weaning and Beyond ... 410–413 ...378
Lactation Biology I ... 414–419 ...380
Physiology and Endocrinology III ... 420–429 ...383
Reproduction Symposium: Recent Innovations in Reproductive Management ... 430–434 ...387
Ruminant Nutrition V: Calves and Heifers ... 435–444 ...389
Wednesday, June 27, 2018
SYMPOSIA AND ORAL SESSIONS
Animal Health Symposium: Bovine Tuberculosis—An Ongoing Animal Health Challenge ... 445–448 ...393
Breeding and Genetics: Joint ADSA and Interbull Session: Phenotyping and Genetics
in the New Era of Sensor Data from Automation ... 449–454 ...395
CSAS Symposium: Genomic Alterations and Implications on Health—Gut and Beyond ... 455–460 ...397
Dairy Foods IV: Chemistry ... 461–469 ...399
Dairy Foods V: Processing: Utilization of Whey ... 470–475 ...402
Lactation Biology II ... 476–482 ...404
Ruminant Nutrition Platform Session II: Protein and Amino Acid Nutrition ... 483–494 ...407
Ruminant Nutrition VI: Early Lactation and Inflammation ... 495–506 ...412
Ruminant Nutrition VII ... 507–517 ...416
Author Index ...420
Workshop: National Animal Nutrition Program (NANP) Models
1 Introduction and model construction: Part I (lecture). T.J. Hackmann*1, M. D. Hanigan2, and V. L. Daley3, 1University of
Florida, Gainesville, FL, 2Virginia Tech, Blacksburg, VT, 3National
Animal Nutrition Program, University of Kentucky, Lexington, KY.
This lecture will provide an overview of mathematical models, their types, and their construction. The general objective of mathematical modeling is to take a hypothesis, convert it a system of equations, and determine how well the equations describe reality. The specific objec-tives depend on the application, but could include predicting nutrient digestibility or intake. In this way, modeling is no different from any other scientific exercise—the first step is for the investigator to identify the hypothesis and objective. There are different types of models, and the investigator should choose a type suited to the specific objectives. In defining its type, a model can be categorized as static or dynamic, empirical or mechanistic, and deterministic or stochastic. Historically, nutrient requirement models have been static, empirical, and determin-istic; they provided snapshots in time, did not describe mechanisms underlying responses, and did not consider inherent biological variance. These models were easy to derive, and have served the community well for more than a century. The Molly cow model is dynamic, mechanistic, and deterministic predicting responses through time based on underly-ing elements of digestion and metabolism without consideration of biological variation. After the investigator identifies the hypothesis, objective, and model type, the next step of constructing a model is to draw a block diagram. This diagram organizes the model conceptually. Rectangles in the diagram represent state variables, and arrows con-necting the rectangles show the relationship of the variables. In a model of carbohydrate digestion in the rumen, for example, rectangles would represent pools of fiber, starch, and sugars, and an arrow connecting fiber and sugars would represent hydrolysis of fiber. This approach of representing pools or compartments within a system is referred to as compartmental modeling. In the remaining steps of constructing a model, the investigator translates the block diagram into system of equations, defines values of equation parameters, and solves the model so it can generate predictions. If evaluation of the model shows predictions are inadequate, earlier steps are repeated to refine the model.
Key Words: mathematical model, dynamic, rumen
2 Introduction and model construction: Part II (exercises).
M. D. Hanigan*1, V. L. Daley2, and T. J. Hackmann3, 1Virginia Tech,
Blacksburg, VA, 2National Animal Nutrition Program, University of
Kentucky, Lexington, KY, 3University of Florida, Gainesville, FL.
The principles of mathematical modeling in agricultural sciences are well described by France and Thornley (1984). They categorized models as static or dynamic, empirical or mechanistic, and deterministic or stochastic, although, in practice, these categories are a continuum. This talk and exercise will focus on the mechanics of building and solving a compartmental model of intestinal N metabolism. A simple regression equation is often used to represent static processes; for example, dCP = CP_In × 0.65. This approach only considers fractional digestion of CP in the gut, and ignores any effects of other factors such as passage rate or microbial activity. In this simple model, a fast rate of passage would have the same digestibility as a slow rate. If one wants to represent residence time effects on CP digestion, then consideration of the pool size is needed. Mechanisms controlling CP digestion in the rumen can be incorporated into the model to yield better estimates. A dynamic
model with a rumen pool of CP and a representation of rates of passage and degradation driven by microbial activity can be constructed and fitted to data to derive information on those mechanisms An intestinal model can be linked to the rumen model to further predict intestinal digestions and amino acid absorption. If rates of passage and degrada-tion are known, it can also be used to predict outcomes when system inputs are manipulated. A representation of this system will be built by participants using R and fit to example data. The model can easily be extended, as there is no mathematical limit to the complexity that can be incorporated. Pool size, and thus the fluxes driven by pool size, can be solved numerically using a computer and numerical integration algorithms. As demonstrated with the example problem, compartmental modeling is very useful for modeling nutrient metabolism and animal performance as nutrient flow through a series of compartments and into product or excreta can be represented.
Key Words: mathematical model, type, review
3 Model evaluation: Part I (lecture). E. Kebreab*, University of California, Davis, Davis, CA.
Model evaluation indicates the level of accuracy and precision of model performance by assessing the credibility and reliability of a model in comparison to measured observations. Quantitative statistical model evaluation methods can be classified into 3 types including (1) standard regression statistics, which determines strength of linear relationship, (2) error index, which quantifies deviation in observed units, and (3) rela-tive model evaluation that are dimensionless. Within the first category, analysis of residuals involves regressing residuals against predicted or other model variables. In this method, the model is unbiased if residu-als are not correlated with predictions and the slope is not significantly different from zero. Predicted values can also be centered making the slope and intercept estimates in the regression orthogonal and thus, independent. This allows for mean biases to be assessed using the intercepts of the regression equations, and the slopes to determine the presence of linear biases. Mean square error of prediction (MSEP) and its square root (RMSEP) are commonly used methods of error index type of evaluation. In general, RMSEP values less than half of observed SD may be considered having a good performance. The MSEP can be decomposed into (1) error due to overall bias of prediction, (2) error due to deviation of the regression slope from unity, and (3) error due to the disturbance. Examples of the third category include the concordance correlation coefficient (CCC). The CCC can be represented as a product of 2 components: a correlation coefficient estimate that measures preci-sion (range 0 to 1, where 1 = perfect fit) and a bias correction factor that indicates how far the regression line deviates from the line of unity (range from 0 to 1 and 1 indicates that no deviation from the line of unity has occurred). During model evaluation, a combination of the methods described above should be used to gain insight on model performance.
Key Words: evaluation, model
4 Model evaluation: Part II (exercises). E. Kebreab*, University of California, Davis, Davis, CA.
and predicted data will be made available to the participants. Based on principles covered, the participants will be asked to calculate the mean square error of prediction (MSEP) and its square root (RMSEP), which are one of the most commonly used methods of model evaluation. Furthermore, the exercise includes calculated the MSEP decomposition into (1) error due to overall bias of prediction, (2) error due to deviation of the regression slope from unity, and (3) error due to the disturbance. The participants will be asked to calculate another model evaluation category, which is the concordance correlation coefficient (CCC). The participants are expected to express CCC as a product of 2 components: a correlation coefficient estimate that measures precision (range 0 to 1, where 1 = perfect fit) and a bias correction factor that indicates how far the regression line deviates from the line of unity (range from 0 to 1 and 1 indicates that no deviation from the line of unity has occurred). Finally, participants will be asked to compare results from the 2 different categories of model evaluation.
Key Words: model performance, modeling, prediction accuracy 5 Meta-analysis: Part I (lecture). R. R. White*, Virginia Poly-technic Institute and State University, Blacksburg, VA.
Meta-analysis of literature is used in animal nutrition research to gain a more comprehensive understanding of the response being studied. Using weighted, mixed-effects, regression, most meta-analytical data sets can be evaluated. In these analyses, data are first gathered using clearly defined search criteria. Collected data should include the response vari-ables of interested, standard errors reported, and explanatory varivari-ables under consideration. Once data are compiled, they should be checked for outliers and possible errors when transferring data. To remove individual study statistical analysis effects, data should be partitioned into mixed- and fixed-effect analyses and standardized. When data are clean and errors are standardized, backward, stepwise regression with fixed-effects for variables of interest and random-effects for things like study and location can be conducted. Variables should be removed from the model according to a predetermined cutoff, usually a P-value of 0.05. When all variables in the model are significant, removed variables can be iteratively re-tested to ensure that factors were not removed due to model instability. Correlation between factors can then be assessed using variance inflation factors (VIF). Parameters with a VIF above 10 should only be kept when parameters are correlated by calculation. When variables are correlated, the variable with highest VIF can be removed. Backward, stepwise regression, elimination at a significance cutoff, and elimination based on correlation should be iterated until model has only significant parameters and is not highly correlated. This procedure provides a framework for most animal nutrition meta-analyses, but may require some adjustments based on available data.
6 Meta-analysis: Part II (exercises). D. M. Liebe* and R. R.
White, Virginia Polytechnic Institute and State University,
Blacks-burg, VA.
This meta-analysis workshop will work through an example data set using a common analysis procedure to illustrate the usefulness of meta-analysis as a tool in ruminant nutrition research. The workshop will focus on use of R and R Studio for conducting meta-analysis. The example data set includes literature reporting how microbial N outflows from the rumen are influenced by dietary nutrient intakes, marker type, sampling location, rumen pH and rumen volatile fatty
acid or ammonia concentrations. The workshop will walk through a multi-step process used to evaluate data for common errors, correct standard errors, and derive models using a backward, stepwise regres-sion procedure. Packages reviewed will include those required to read in data (xlsx, XLConnect, googlesheets), those required to handle data (dplyr, reshape2), those required to visualize data (ggplot2), and those required to fit linear mixed effect models (nlme, lme4, lmerTest). The workshop will walk participants through how data should be structured in text files or spreadsheets; how packages can be used to read data in from these external formats; how data can be handled and queried once read into R or R Studio; how data can be visualized using the ggplot2 package; and how models can be derived using linear mixed-effects models weighted for the inverse of study standard errors. At the end of the workshop, participants should be able to (1) organize data for use in a meta-analysis; (2) read data into R from a variety of formats; (3) visualize data distributions for assessment of common data entry errors; (4) calculate weights for use in meta-analysis; (5) derive a model using a multi-step backward elimination approach; and (6) evaluate the fit of a model using common fit statistics.
Key Words: meta-analysis, nutrition, regression
7 Opportunities for federal funding of modeling research. S.
I. Smith* and M. A. Mirando, USDA-National Institute of Food and
Agriculture, Institute of Food Production and Sustainability, Wash-ington, DC.
The Food, Conservation, and Energy Act of 2008 (Public Law 110–246; i.e., the 2008 Farm Bill) established the National Institute of Food and Agriculture (NIFA) within the US Department of Agriculture (USDA). NIFA directs federal funding to advance agricultural research, educa-tion, and extension to solve agricultural challenges. In FY2017, NIFA invested a total of approximately $1.5 billion: $854 million in Research and Education Activities, $478 million in Extension Activities, $159.6 million in Mandatory and Endowment Funding, and $36.0 million in Integrated Activities, as designated in the 2017 Congressional Appro-priations Act (Public Law No: 115–31). This investment is broadly split between Capacity Funding and Competitive Grant Funding. NIFA administers more than 30 Competitive Programs with broad eligibil-ity. NIFA’s flagship competitive program is the Agriculture and Food Research Initiative (AFRI). Mathematical modeling is recognized as a powerful tool that can be effectively applied to organize and intercon-nect what is known, to highlight knowledge gaps and areas needing research, and to prompt investigators to ask better questions. As a result, there are currently 80 active AFRI livestock projects with a modeling component, 8 of which directly involve the dairy enterprise. These dairy projects address a broad array of topics including ruminal metabolism, genomic prediction, whole genome selection, host-pathogen interac-tion, metabolic diseases, antimicrobial resistance and nutrition. NIFA has published 3 AFRI Request for Applications (RFAs) in FY 2018; (1) the Foundational and Applied Science Program RFA, (2) the Education and Workforce Development RFA and (3) the Sustainable Agricultural Systems RFA. It should be noted that modeling is specifically invited in 9 of the Foundational program area priorities, as well as the program area priority for Sustainable Agricultural Systems. A list of NIFA RFAs can be found at https://nifa.usda.gov/rfa-list
Workshop: Spore Sources and Transmission from Farm
to Fork—Detection and Control Strategies
8 Introduction to dairy-relevant sporeformers and detection methodologies. M. Wiedmann*, College of Food Science, Cornell University, Ithaca, NY.
Spore-forming bacteria are a diverse group of bacteria that can grow at various temperatures, survive extreme conditions, and persist in farm and processing environments for years. These organisms are capable of growing in and affect the quality of fluid milk, cheese, and dairy powders. The dairy industry must adopt a systems approach to reducing the impact of spore-forming bacteria. Key to this goal is the development and implementation of appropriate testing methods for spore-forming bacteria, from spoilage organisms to pathogens. This session will introduce participants to the most common spore-forming bacteria encountered in the dairy industry, and how methodologies have emerged to detect, enumerate, and track these organisms.
9 On-farm sources and control strategies. N. Martin*, Cornell University, Ithaca, NY.
Spore-forming bacteria are found ubiquitously in natural environments, including on dairy farms. Manure, soil, water and other materials in cow environments can harbor millions of spores, exposing the cow and, ultimately, raw milk to these organisms. Research indicates that various
on-farm management practices are associated with the presence and levels of spore-forming bacteria in bulk tank raw milk. This session will explore the types of management practices in various locations across the United States that are associated with psychrotolerant, mesophilic and thermophilic spores in bulk tank raw milk. Additionally, attendees will learn about approaches to reducing spore levels in raw milk through simple and inexpensive intervention strategies.
10 Introduction to dairy relevant sporeformers and detection methodologies. T. Erickson*, Ecolab, St. Paul, MN.
ADSA 2018 Mini Symposium:
Priorities for Fiber Research (DC33 Follow-Up)
11 Priorities for future research to improve fiber utilizationby animals. D. R. Mertens*, Mertens Innovation & Research LLC, Belleville, WI.
The objective is to summarize the results of the 33rd ADSA Discover Conference, Integrated Solutions to Fiber Challenges, held Septem-ber 2017; convey future research priorities that were identified; and promote discussion and networking to implement needed research for fiber utilization. Conference sessions included plants and climate; fiber analysis for animals; impact of plants on animals; animal by fiber interactions: getting the most out of fiber; and modeling fiber utiliza-tion by animals. It provided a unique opportunity for professional interactions that fostered discussion and identified areas of research that would improve fiber utilization. The final session of the conference summarized recommendations and challenges facing future research. Participants concluded that advances in chemical analyses have been significant, and the use of crude fiber for feed tags and regulation should be abandoned. Six broad categories of nutritional fiber research were identified: (1) chemical analysis, (2) biological (digestion) analysis, (3) physical analysis, (4) fiber fermentation, (5) fiber modeling, and (6) next
transformational change in utilization. Survey participants ranked these categories (highest priority first): 2, 4, 3, 1, 5, and 6. These categories were divided into 35 specific items and ranked 0 to 100. Three topics averaged >75: (1) continue research on fiber utilization of forage and by-products, (2) additional graduate training using regional/national research teams, and (3) investigating fiber physical characteristics. Five topics, which averaged 70–74, were combined: improve biological meth-ods of measuring microbial fiber digestion and their interactions, and create a consortium of animal producers, allied seed/feed industries, and governments to prioritize and fund fiber research and improve analytical and mathematical skills of future researchers. Remaining topics will be discussed. Attendees at the conference identified important priorities and discussed how to meet these priorities for the dairy industry. This mini-symposium is intended to broaden the discussion about the future of fiber utilization research with scientists at the annual ADSA meeting.
ADSA Graduate Student Symposium: Manuscript Writing
for Graduate Students
12 Tips and tricks for turning your ideas into peer-reviewed publications. M. A. G. von Keyserlingk* and D. M. Weary, Univer-sity of British Columbia, Vancouver, BC, Canada.
A major goal for researchers is to tell our colleagues (and others) about our work. This is formally done through the publishing of our findings in the peer-reviewed literature, but for many of us this is a daunting task. How do we take our data and tell the story that will result in publication? In this talk, we provide some simple steps to help ease the process. The work starts with the formulation of an interesting and important research question. This question needs to be situated within the current literature, building upon existing ideas, and helping to fill a recognized gap. A clear research question may also help in identifying predictions that inform experimental design and what is measured, allowing you to focus on a few well-reasoned ideas, and avoid including measures simply because they are easy to collect. Before starting data collection, try to write a first draft of the Introduction and Methods sections of your paper, using the format of your target journal. This will force you to clearly describe your research question and to link your proposed methods with the main ideas. The Methods section should also describe your proposed statistical analysis and the power analysis you used to calculate sample size, and follow one of the reporting guidelines specified by the journal (such as ARRIVE). As you begin data collection, you can revise the Methods to reflect any changes you have made. Before you begin statistical analysis, carefully scrutinize the raw data, using plots to check for outliers. Take special effort to develop the figures and tables describing your main findings—these will be the stars of your paper. Before you start, try to develop an eye for what types of graphical reporting you find most helpful, and use these ideas when presenting your results. The discus-sion should carefully integrate your results into the literature, identify new ideas and gaps for future research, and end with a clear and specific conclusion. A high-quality paper requires many drafts, so be prepared to take the time needed to polish your efforts, including seeking out critical comments from readers whose work you admire.
Key Words: authorship, publishing, scientific writing
13 Collaborating with co-authors: Writing, presenting, and publishing. D. M. Barbano*, Cornell University, Ithaca, NY.
Organization, planning, and inclusive communication among co-authors are the keys to success. Collaboration in writing, presenting, and publish-ing will flow a bit differently for review papers versus research papers, but the basic principles are the same. In both cases, the co-authors need to agree on the target journal and who will be the corresponding author. For review papers, each co-author is normally responsible for a section(s) and the lead author will do the integration. For original research papers, the sequence is a bit different. The manuscript development and planning has to start with clear and measurable objectives, an experimental design, and a plan for statistical analysis of the data. This should be done before you do the research. In writing, start with a title page, an introduction section with only the last sentence(s) written (i.e., the objectives of the research), and descriptive first-level and second-level section titles for the remainder of the paper. Step 1: Write the materials and methods in complete detail (best if this is done while doing the research). Step 2: Analyze data and make final form data tables and figures with all statisti-cal analysis included. Have all co-authors agree on the main messages from each table and figure. You are not ready to start writing the results
and discussion section until step 2 is complete. This is the step where people waste too much time writing before the data (and co-authors) are ready for them to write. Step 3: Write your story about your data (don’t worry about the literature yet). Have all co-authors review and provide input before going to step 4. Step 4: Next, bring in appropriate discussion of literature citations to compare with your story. Some previous work may agree and some may differ. Provide a balanced perspective. Step 5: Write a short conclusion about your results, not the literature. Stick to facts that are statistically significant. Have all co-authors review and revise. Step 6: Write the introduction including only background refer-ences that are necessary to understand the topic and to logically lead the reader to your objective statement that was written earlier. Step 7: Write the abstract with the objective(s), a brief experimental approach, and then add the conclusions that match step 5.
Key Words: writing, publishing, presenting
14 Manuscript preparation, navigating journal submission, and the peer-review process. L. E. Armentano*, University of Wis-consin, Madison, WI.
A good paper starts with a good experiment, which in turn starts with a clearly stated hypothesis. The introduction must end with this hypothesis, and the final paragraph of your paper must say what you conclude about that hypothesis. Write a draft introduction before you start your experiment. You will update this before submission, but your hypothesis should not change. As you do your research, start writing sentences for your methods. Make sure they describe exactly what you are doing. This means you have read the original methodology, even if citing it through another paper. If you are also keeping track of the references you will cite, that makes 3 big parts of your paper that you have drafted before finishing your study. I prefer combining results and discussion text in one section and put most of my results in either a table or figure. Some reviewers want discussion of every result, but I submit papers that contain “minor” results in some table rows that I do not discuss. If a reviewer asks, add text to the discussion about these results. The less you say in a discussion, the more you focus on the core objective. I write for my reviewers, so I would rather have to add distracting discussion text at their request after they have read my focused discussion. Certainly, anything else interesting that comes up is worth mentioning but don’t elaborate or speculate on ideas beyond the scope of your experiment. Do know how your results compare with the existing literature to make sure something is not way off—you do have to discuss anything that seems unusual. A good paper comprises unambiguous, direct, short, minimally complicated sentences that each convey a single idea. Integrate related ideas using paragraphs of simple sentences. Have others read your paper, especially people outside your immediate research group. Writing is an iterative process, so expect to modify your precious writing in response to reviewers. Respect the input of all your reviewers, and if you failed to make them understand something, rewrite your paper so that anyone reading the final version is not similarly misled. Know the journal guidelines.
15 Will your research impact dairy farmers? C. Geiger*, Hoard’s Dairyman, Fort Atkinson, WI.
Scientists do a marvelous job conducting research and sharing those findings in scientific journals. However, will that work ultimately change how dairy producers care for cows and produce nutritious dairy products for consumers? Writing for a lay audience, including dairy farmers and their consultants, is a far different proposition than authoring material for scientific journals. To be successful, authors need to convert detailed research into an easy-to-read article while still maintaining the integrity of the technical work. When writing, remember your reader is a busy person who puts in more work hours than the average American. When you write, outline your article, marshal your facts, and tell your story with personal candor. Express yourself simply and concisely. Keep your sentences short and uncomplicated. Short paragraphs add to readership comprehension. Present just one idea per paragraph. Highly technical acronyms are readership busters. To improve comprehension, consider commonly used vernacular. If the sentence cannot be comprehended the first time, it needs further editing. Anticipate practical reader questions, and answer them in the article; if you cannot, say so and why. Get to
the meat of your message immediately. Tell the reader something in the first paragraph. Unless historical background is essential, omit it. Too many readers will flip the page if the first few paragraphs don’t have anything to offer. Word counts should not go over 1,100 to 1,200 words. Put action in your title. Avoid label-type titles such as, “Breed-ing cows.” Compell“Breed-ing titles, 48 characters or less, and strong subtitles add to readership. That review-type subtitle will stimulate curiosity in your material and can help the reader know what you think is impor-tant about the material. Keep titles to one line and move all details to footnotes. Also, inserting subheads in the article can call out important points between paragraphs. A theme setter-type photograph can add tremendously to the presentation of the article. That photo should be related to the material presented and clarify points made in the article. Graphics also aid readership. Good charts, graphs, and tables can help the author break up the text and make the page more pleasing to the eye.
ADSA Graduate Student Dairy Foods Poster Competition
M1 Development and validation of a rapid method formea-surement of casein in raw milk using front-face fluorescence spectroscopy and chemometrics. Y. B. Ma* and J. K. Amamcharla, Food Science Institute, Animal Sciences and Industry, Kansas State University, Manhattan, KS.
The casein content in raw milk is important for the industry as it influ-ences the cheese yield. The casein content is determined by the dif-ference between true protein and non-casein protein in raw milk. The objective of this study was to develop a rapid quantification method for casein in raw milk using front-face fluorescence spectroscopy (FFFS). To prepare milk samples for calibration, raw skim milk was obtained from Kansas State University’s dairy farm and ultrafiltered to increase the protein concentration. The casein content of retentate and permeate were measured by a reference method. The retentate and permeate were combined at different ratios to make 10 calibration samples with casein content ranging from 0.37 to 3.7%. Sample preparation for the FFFS involved thoroughly mixing 7 mL calibration sample with 0.6 mL acetic acid (10% wt/wt) to precipitate the casein. Sample mixture was vortexed and transferred immediately to a quartz cuvette. Tryptophan emission spectrum of the mixture was immediately measured by a spectrofluo-rometer with a 1% attenuator (excitation wavelength at 280 nm; emis-sion wavelength range from 300 to 440 nm) at 25°C. The process was repeated twice to obtain a sample size of 20 for the calibration model. Prediction models were developed using principal component regression and partial least square regression (PLSR) and validated with the leave-one-out cross-validation (LOOCV). The principal component regression and PLSR models showed LOOCV correlation coefficients of 0.970 and 0.988, root mean square error (RMSE) of 0.39% and 0.24%, and ratio of prediction to deviation of 4.5 and 4.7, respectively. The developed models were independently validated by 5 raw milk samples collected on different days. Principal component regression and PLSR predictions had a mean difference of 0.12% and 0.11% casein compared with the reference method and RMSE of 0.19% and 0.19%, respectively. The mean bias of 2 prediction models is not significantly different from 0 (P > 0.05). The FFFS method showed potential quantification of casein in raw milk, but validation on a large sample set is further required.
Key Words: partial least square regression (PLSR), principal
compo-nent regression, front-face fluorescence spectroscopy (FFFS)
M2 Hunter versus CIE color measurement systems for analy-sis of milk-based beverages. N. Cheng*1, D. Barbano2, and M. A.
Drake1, 1North Carolina State University, Raleigh, NC, 2Cornell
University, Ithaca, NY.
Both Hunter (L, a, b) and International Commission on Illumination (CIE; L*, a*, b*) color measurement systems are used for instrumental measurement of food color but which system is best for fluid milk is not known. The objective of our work was to determine the differences in sensitivity of Hunter and CIE methods at 2 different viewer angles for measurement for whiteness, red/green, and blue/yellow color of milk based beverages. Sixty combinations of milk-based beverages were formulated (2 replicates) with a range of fat level from 0.2 to 2%, true protein level from 3 to 5%, and casein as a percent of true protein from 5 to 80% to provide a wide range of milk-based beverage color. In addition, commercial skim, 1% and 2% fat HTST pasteurized fluid milks were analyzed. All beverage formulations were HTST pasteur-ized and cooled to 4°C before analysis. Measurement viewer angle (2
versus 10°) had very little impact on objective color measures of milk-based beverages with a wide range of composition for either the Hunter or CIE color measurement system. Temperature (4, 20, and 50°C) of color measurement had a large impact (P < 0.05) on the results of color measurement in both the Hunter and CIE measurement systems. The effect of milk beverage temperature on color measurement results was the largest for skim milk and the least for 2% fat milk (P < 0.05). This highlights the need for proper control of beverage serving temperature for sensory panel analysis of milk-based beverages with very low fat content and for control of milk temperature when doing objective color analysis for quality control in manufacture of milk-based beverages. The Hunter system of color measurement was more sensitive to differences in whiteness among milk based beverages than the CIE system (P < 0.05), while the CIE system was much more sensitive to differences in yellowness among milk based beverages (P < 0.05). There was little difference between the Hunter and CIE system in sensitivity to green/ red color of milk based beverages. In defining milk based beverage product specifications for objective color measures for dairy product manufacturers, the viewer angle, color measurement system (CIE versus Hunter) and sample measurement temperature should be specified along with type of illuminant.
Key Words: color, milk, whiteness
M3 Optimizing the emulsification properties of heated whey protein isolate (WPI)-pectin complexes for emulsions containing 20% oil at pH 5.0. A. Kotchabhakdi* and B. Vardhanabhuti, Univer-sity of Missouri, Columbia, MO.
There has been increasing interest in developing food ingredients for clean-label applications. We have previously shown that heated whey protein and pectin complexes (HCPX) formed at pH above pI have improved emulsification properties and stability when emulsions con-tained 5% oil. However, it is not fully understood whether these HCPX could stabilize emulsions containing higher oil content as in sauces and salad dressings. The objective of this study was to optimize the emulsification properties of HCPX in emulsions containing 20% oil at pH 5.0. The HCPX were formed by heating mixed 3 wt% whey protein isolate (WPI) and pectin (0, 0.3, 0.45 wt%) at pH 5.5, 5.8, and 6.2 at 85°C for 15 min. Emulsions were made, followed by pH adjustment to 5.0. Final emulsions contained 20 wt% oil, 2 wt% protein and 0 to 0.3 wt% pectin. Emulsification properties were assessed by measuring droplet size, ζ-potential, rheological properties and creaming stability. Emulsions stabilized by heated WPI without pectin had the average droplets sizes >36 μm and ζ-potentials ranging from −27.2 to −19.8 mV. They were not stable and separated into 2 layers within a few hours. The HCPX-stabilized emulsions showed significant improvement in emulsification properties and stability. Mean droplet sizes significantly decreased (P < 0.05) and ranged from 1.6 to 21 μm while droplets became more negatively charged with ζ-potential ranging from −37 to −40.9 mV. Both heating pH and pectin concentration during HCPX formation played important roles on the emulsification properties of the HCPX. The most stable emulsions (>30 d) were those stabilized by HCPX formed with 0.45% pectin at heating pH of 5.5 and 5.8. Formation pH also influenced the rheological behavior of the emulsions with those stabilized by HCPX formed at pH 6.2 being more viscous.
emulsions containing higher oil content. They can be utilized as clean-label ingredients in applications such as sauces and dressings.
M4 Evaluation of the drying kinetics of micellar casein concentrate and reduced-mineral micellar casein concentrate at different solids concentrations. H. N. Vora* and L. E. Metzger, Dairy and Food Science Department, South Dakota State University, Brookings, SD.
Micellar casein concentrate (MCC) was prepared by microfiltration and diafiltration of skim milk to produce a retentate with approximately 22% total solids (95% casein as a percentage of true protein). Modified (reduced mineral) micellar casein concentrate (MMCC) was prepared by diluting the MCC retentate to 7% solids and injecting CO2 to pH 5.9
and ultrafiltered to produce a retentate with approx. 22% total solids. Three sets of trials were performed on separate skim milk lots for both MCC and MMCC. The drying kinetics of the MCC and MMCC from each trial were then studied using single droplet drying (SDD). The SDD approach involves a single droplet suspended on the tip of a glass filament, where changes in droplet diameter, mass, and temperature can be measured during drying. The aim of this study was to develop and compare a predictive model generated using SDD for MCC and MMCC which can be used as a tool to optimize the drying conditions and reduce costly plant trials when developing new ingredients with unique functional properties. In this study, 2 ± 0.05 µL droplets of MCC and MMCC were dried using SDD at 2 different levels of total solids: 10% and 20% at 90°C with hot air at a velocity of 0.8 m/s. Droplet diameter and mass change data were collected and processed using Adobe After Effects 7.0 to enable the extraction of images. Although the pattern of change in average diameter data obtained from SDD was same for both MCC and MMCC, there was a significant difference observed during the average diameter change between MCC and MMCC (P < 0.05) at both the solids level MCC showed a rapid change in average diameter compared with MMCC. The curves of average mass change obtained from SDD were plotted against time. It was observed that as the total solids level increases the drying time increases, which is mainly due to the formation of crust on the particle and subsequent slower moisture migration to the surface of the particle with higher total solids level in both MCC and MMCC.
Key Words: single droplet drying, micellar casein concentrate,
reduced-mineral micellar casein concentrate
M5 Whey proteins enhance color and stability of anthocyanin pigments. G. Miyagusuku-Cruzado*, R. Jimenez-Flores, and M. M.
Giusti, The Ohio State University, Columbus, OH.
Current trends show that the food industry is moving away from synthetic colorants and looking for natural alternatives. Anthocyanins (ACN) are plant flavonoids with vivid colors that range from red to blue, but their application as food colorants is restricted by their limited stability, particularly at pH close to neutral. Nevertheless, some studies have shown that ACN have higher stability in dairy systems than in buffers at the same pH. We hypothesized that milk components such as proteins can interact with ACN, and that certain ACN may have a higher affinity for proteins due to specific structural conformations. Our objective was to evaluate whether whey proteins can interact with ACN leading to enhanced color and stability. Model solutions were prepared by diluting ACN from different sources with pH 3 citric acid – Na2HPO4
buffer until a λvis-max absorbance of 0.7 was reached, followed by
addi-tion of whey protein isolate (WPI) at different concentraaddi-tions (0, 0.01, 0.05, 0.1, 0.5 and 1.0 mg/mL model solution). Absorption spectra was
measured after 15 min and color parameters (CIELab) were calculated using ColorBySpectra software. Model solutions were heated to 90°C for up to 500 min to test heat stability, with samples taken every 50 min. ACN content was measured using the pH differential method. Addition of WPI resulted in a significant absorption increase (P < 0.05) at the λvis-max up to 17%. Color of the model solutions was enhanced (ΔE >5),
becoming noticeably darker with WPI concentrations as low as 0.05 mg/mL for Berberis boliviana, 0.5 mg/mL for purple corn and grape skin, and 1.00 mg/mL for black carrot and red cabbage. The absorp-tion increase at the λvis-max was dependent on WPI concentration, some
fitting a linear model while others an exponential one, suggesting that some ACN may have higher affinity for WPI than others. Also, WPI addition significantly increased thermal resistance of ACN (P < 0.05). Further studies will focus on different anthocyanin chemical structures and the possible utilization of acid whey to stabilize ACN pigments. This could facilitate the transition from synthetic colorants to natural and healthier alternatives.
Key Words: whey protein isolate (WPI), acid whey, natural colorant M6 Production and storage stability of liquid micellar casein concentrate. A. R. A. Hammam* and L. E. Metzger, South Dakota State University, Brookings, SD.
Micellar casein is a high protein ingredient that can be used as a valuable source of intact casein in process cheese formulations. The objective of this study was to produce a highly concentrated micellar casein (HC-MC) and evaluate its storage stability. Skim milk was pasteurized at 72°C for 16 s and kept at ≤ 4°C until the following day when it was heated in a plate heat exchanger to 50°C and microfiltered with a ceramic GP MF system (0.1μm) in a feed and bleed mode to produce a 3 × MF retentate (1 kg of retentate:2 kg of permeate). Subsequently, the retentate was diluted 2× with soft-water (2 kg of water:1 kg of retentate) and again microfiltered at 50°C to a 3× concentration as described previously. The retentate was then cooled to 4°C, and stored overnight. The following day, the retentate was heated to 65°C and microfiltered in a recirculation-mode until the total solid reached approximately 22%. Subsequently, the temperature was increased to 74°C and microfiltration was contin-ued until the permeate flow rate reached less than 5 L/h. The HC-MC retentate was transferred at 74°C to sterilized vials and stored at 4°C. This trial was repeated 3 times using 3 separate batches of raw milk. During microfiltration, the mean cumulative SP removal in the first, second, and third stages was 46, 77, and 83%, respectively. The mean HC-MC at time zero contained 25.42% total solids (TS), 20.20% true protein (TP), 0.09% NPN, 0.55% NCN, 19.80% CN, 2.0% ash, 97.70% CN%TP, and 0.45% SP. The NCN content increased significantly (P < 0.05) from 0.55 to 0.76% during 2 mo of storage. The NPN also increased over time from 0.095% at time zero to 0.12% after 2 mo of storage. The mean aerobic bacterial count in HC-MC at time zero was 2.6 ± 0.16 log cfu/mL and increased to 3.5 ± 0.89 and 4.3 ± 0.97 log cfu/mL after 1 and 2 mo of storage, respectively. Coliform, yeasts, and mold were not detected at any time point. This study determined that HC-MC could be manufactured using ceramic MF membranes with over 25% TS and greater than 95% CN%TP. The impact of the small increase in NCN and NPN during 2 mo of storage on process cheese characteristics will be evaluated in subsequent studies.
Key Words: microfiltration, micellar casein, shelf life
M7 Use of micro- and nano-bubbles for improving the func-tional properties of Greek-style yogurt. K. S. Babu*, D. Z. Liu, and
Increased awareness of health benefits has driven the popularity of Greek-style yogurt (GSY) in recent years. However, increased protein content in the GSY leads to increased graininess, higher viscosity, and chalky mouthfeel. The objective of this study was to investigate the efficiency of micro- and nano-bubbles for improving the physical, rheological, and functional properties of GSY. In this study, a custom-built system was used to incorporate micro- and nano-bubbles (MNB) in GSY. The base for GSY was formulated to a protein content of 10% (wt/wt) using nonfat dry milk, micellar casein concentrates, and water. Control GSY (C-GSY; GSY pumped through the positive displacement pump without attaching the MNB generator) and MNB-treated GSY (MNB-GSY) were compared and evaluated for physical, rheological characteristics such as apparent viscosity, % loss-of-structure (measure of the rate of thixotropic breakdown), syneresis, water-holding capacity (WHC) before and after storage at 5°C for 1, 2, 3, and 4 weeks. Two replicates of C-GSY and MNB-GSY were manufactured and the data were analyzed as repeated measures (SAS Institute Inc.). The density of freshly prepared MNB-GSY and C-GSY was 0.97 and 1.04 g/cm3,
respectively. When compared with C-GSY, the syneresis, WHC, and grain counts were significantly different (P < 0.05) after the MNB treatment and subsequent storage time. After the wk 2, 3, and 4, the MNB-GSY samples showed ~58.4%, ~43.1%, and ~50.1% lesser appar-ent viscosity compared with the corresponding weeks C-GSY samples. The syneresis of the MNB-GSY was significantly lower (P < 0.05), ~19% than C-GSY after storage for 4 wk. After storage for 4 wk, the % loss-of-structure for the C-GSY and MNB-GSY was 32% and 20.1%, respectively. Before storage, the grain counts of C-GSY and MNB-GSY were ~143 and ~37 grain counts/g of yogurt, respectively. After storage for 4 wk, the grain counts of C-GSY and MNB-GSY were ~178 and ~4 grain counts/g of yogurt, respectively. Overall, the incorporation of MNB into GSY showed significant improvements in the rheological and functional properties of GSY.
Key Words: micellar casein concentrate, rheology, micro- and
nano-bubbles
M8 Ratiometric fluorescence spectroscopy—A novel technique for rapid detection of bacterial endospores. N. Awasti* and S.
Anand, Midwest Dairy Food Research Center, Dairy and Food
Sci-ence Department, South Dakota State University, Brookings, SD.
The current spore detection methods rely on cultural techniques, having limitations of time, efficiency, and sensitivity. Spore coat contains
cal-cium dipicolinic acid (CaDPA) as a major constituent, which can serve as a biomarker for bacterial endospores. We report a rapid and sensitive technique for detection of bacterial endospores by using ratiometric fluorescence-based sensors. This method is based on the detection of CaDPA that enhances luminescence of lanthanide ion, when complexed with a semiconducting polymer. A CaDPA standard curve was gener-ated at excitation-emission wavelength of λ284-λ528 by using Synergy 2
fluorescence spectrophotometer. Intensity was recorded after chelating semiconducting fluorescent polyfluorene (PFO) dots with terbium ions, sensitized by different volumes of CaDPA (0.1 μM). All trials were conducted in the replicates of 3 and mean ± SE were calculated. The standard curve so generated showed a linear relationship (R2 = 0.98) in
experimental concentration range of 2.5 to 25 nM of CaDPA, with corre-sponding intensity (a.u.) of 545 to 2130. Endospores of an aerobic spore former, Bacillus licheniformis ATCC 14580, were produced at 37°C for 15 d, on Brain Heart Infusion agar. The efficiency of sporulation was evaluated by spore staining and plating techniques. Total CaDPA content in spores was estimated after suspending reducing concentrations of spores (logs 9.0 through 1.0 cfu/mL, at 1-log intervals) in HPLC-grade water. For higher spore spiking levels such as 9.2 ± 0.03, 8.4 ± 0.05, 7.1 ± 0.13 and 6.3 ± 0.02 logs, the mean CaDPA content values, observed from the standard curve, were 9.4, 7.2, 6.2 and 5.3 nM, whereas, for lower levels of 4.2 ± 0.05, 3.1 ± 0.04, 2.0 ± 0.11, and 1.36 ± 0.09 logs, we observed 3.8, 3.3, 2.2 and 1.3 nM mean CaDPA content. Our results indicated a linear relationship of the CaDPA content of endospores with that of the endospore counts, and the standard curve of CaDPA concentration. This study provides a proof of concept for a potential application of this technique to rapidly detect bacterial endospores in dairy and food industry. Further studies are in progress in our laboratory to standardize this technique for dairy product matrices such as cheese, whey proteins, and powders.
ADSA Graduate Student (MS) Production Poster Competition
M9 Response of Holstein dairy cattle to a sodium propionatesupplement fed postpartum. M. Wukadinovich* and H. A. Rossow, University of California, Davis, Davis, CA.
Subclinical (SCK) and clinical (CK) ketosis is a metabolic disease common in dairy cattle and can decrease milk production, reproductive efficiency, and increase risk of being culled from the herd. Tradition-ally, cows have been supplemented with glucogenic precursors either by drenching or inclusion in the TMR to decrease ketone formation and increase blood glucose levels. The objective of this experiment was to examine the incidence of SCK and CK, levels of ketones and glucose in blood, and milk yield in Holstein dairy cattle fed a molasses-based sodium propionate supplement (Innovative Liquids LLC) for the first 14 DIM. On a commercial dairy in California, a total of 226 cows and 102 heifers were systematically enrolled in C with a subset of 74 cows and 39 heifers bled, and 200 cows and 106 heifers were enrolled in GP with a subset of 81cows and 36 heifers bled in a switchback design. Blood glucose and β-hydroxybutyric acid (BHB) concentrations were measured on 3, 7, and 14 DIM using NovaMax meters (Nova Diabetes Care Inc., Billerica, MA). Ketosis was defined as BHB levels of 1.0–1.4 mmol/L for SCK or BHB levels of > 1.4 mmol/L for CK. Glucose concentrations were defined as low (≤40 mg/dL) or adequate (>40 mg/dL). Data were analyzed using the Mixed Procedure of SAS (v. 9.4, SAS Institute, 2015). Results are presented as least squares means ± standard error. Average blood BHB and glucose concentrations did not differ between treatments for primiparous or multiparous cows (C 0.53 ± 0. 02, GP 0.55 ± 0.02 mmol/L BHB, P = 0.5; C 44 ± 0.77, GP 43 ± 0.78 mg/dL glucose, P = 0.6). Concentration of blood glucose was inversely related to BHB (P < 0.01). Treatment GP did not affect milk yield compared with C for primiparous or multiparous cows during the first 21 DIM (C 29.5 kg milk /d ± 0.94, GP 30.0 ± 0.96 kg milk/d; P = 0.5). The incidence of SCK and CK was low during this study; 9 C cows and 7 GP cows were subclinical, and 6 C cows and 9 GP cows were clinical. Therefore, in this study there was little improvement in the incidence of CK and SCK or increase in milk yield with supplementation.
Key Words: β-hydroxybutyric acid (BHB), subclinical ketosis,
clini-cal ketosis
M10 Effects of timing of local anesthesia on cortisol and adrenocorticotropic hormone levels in calves after dehorning. A.
J. Mathias*1, C. C. Williams1, C. Scully2, and S. J. Blair1, 1Louisiana
State University AgCenter, Baton Rouge, LA, 2Louisiana State
Uni-versity School of Veterinary Medicine, Baton Rouge, LA.
Dehorning is a painful animal management procedure that is commonly performed in dairy calves. The use of local anesthesia lessens the physiological and behavioral effects of dehorning in calves. Twenty-four intact Holstein heifer calves (6 to 8 wk of age) were assigned to 1 of 4 treatments (n = 6 calves/treatment) to evaluate effects of timing of local anesthesia on physiological indicators of stress associated with pain of dehorning. Treatments included anesthesia without dehorning (CON); dehorning without anesthesia (NO_ANET); anesthesia followed by immediate dehorning (ANET_0); and anesthesia with a 10-min delay before dehorning (ANET_10). Approximately 2 h before dehorning, jugular catheters (14 gauge, 3.5 inch; MILA International, Inc., Erlanger, KY) were inserted. Blood samples were collected 10 min before and immediately before (0-min sample) the initiation of the experiment. For the groups that received local anesthesia, a cornual nerve block was
performed with 5 mL of 2% lidocaine hydrochloride on both horns. In NO_ANET, calves received 5 mL of 0.9% saline in place of lidocaine. Blood samples were collected at 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 40, 50, 60, 90, and 120 after dehorning. Plasma concentrations were measured by radioimmunoassay for cortisol at every time point and adrenocorti-cotropic hormone (ACTH) through 30 min. At 25 min after dehorning, cortisol concentrations were lower (P < 0.05) in CON than ANET_0, whereas ANET_10 and NO_ANET were not significantly different from either CON or ANET_0. At 1 and 2 min post-dehorning, ACTH concen-trations were lower (P < 0.05) in ANET_10 and CON than ANET_0 and NO_ANET. At 3, 4, 5, and 10 min post-dehorning, ACTH concentrations were lower in CON than ANET_0 and NO_ANET, and ANET_10 did not differ significantly from any treatment groups. Because the observed differences in plasma ACTH concentrations dissipated within 5 min and plasma cortisol concentrations returned to pretreatment levels within 1 h of dehorning for all calves, it is inconclusive as to whether or not there is a benefit to waiting 10 min after the administration of lidocaine to dehorn calves.
Key Words: dehorning, calves, cortisol
M11 Feeding a low-starch fresh cow diet may increase NDF digestibility. C. E. Knoblock*1, W. Shi1, I. Yoon2, and M. Oba1, 1Department of Agricultural, Food and Nutritional Science,
Univer-sity of Alberta, Edmonton, AB, Canada, 2Diamond V, Cedar Rapids,
IA.
