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The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. The continued efficient use of whey as a fermentable substrate to produce alcoholic beverages will benefit from improving the measurement of key components relevant to the fermentability of whey. One component of fermentation that is not well studied in whey is the concentration of fermentable nitrogen, either as free amino nitrogen (FAN) or yeast-assimilable nitrogen (YAN). Fermentable nitrogen in media is essential for yeast cells to replicate. Insufficient concentrations of FAN or YAN to support the growing yeast population can result in sluggish or "stuck" fermentations. We evaluated 3 common methods of fermentable nitrogen determination and compared them for use in whey fermentation systems, based on ninhydrin, nitrogen by o- phthalaldehyde (NOPA), and formaldehyde pH (Sørensen titration) values. Each measurement method was evaluated independently using standard addition curves and compared for overall accuracy using paired t -tests (α = 0.05). Although the formaldehyde pH method showed high precision, it did not measure nitrogen accurately, as it overestimated YAN in whey by up to 6 times relative to other tests. The ninhydrin and NOPA methods both showed accuracy for fermentable nitrogen determination in whey based on analysis of standardized nitrogen sources. We concluded that either the ninhydrin FAN or NOPA YAN method may be used to determine the fermentable nitrogen content in whey. This is expected to improve the ability of producers to use whey as a fermentation medium. [ABSTRACT FROM AUTHOR]

Minimizing pollution from the dairy sector is paramount; one potential cause of such pollution is excess nitrogen. Nitrogen pollution contributes to a deterioration in water quality as well as an increase in both eutrophication and greenhouse gases. It is therefore essential to minimize the loss of nitrogen from the sector, including excretion from the cow. Breeding programs are one potential strategy to improve the efficiency with which nitrogen is used by dairy cows, but they rely on routine access to individual cow information on how efficiently each cow uses the nitrogen it ingests. A total of 3,497 test-day records for individual-cow nitrogen efficiency metrics along with milk yield and the associated milk spectra were used to investigate the ability of milk infrared spectral data to predict these nitrogen traits; both traditional partial least squares regression and neural networks were used in the prediction process. The data originated from 4 farms across 11 yr. The nitrogen traits investigated were nitrogen intake, nitrogen use efficiency, and nitrogen balance. Both nitrogen use efficiency and nitrogen balance were calculated considering nitrogen intake, nitrogen in milk, nitrogen in the conceptus, nitrogen used for the growth, nitrogen stored in body reserves, and nitrogen mobilized from body reserves. Irrespective of the nitrogen-related trait being investigated, the best predictions from 4-fold cross validation were achieved using neural networks that considered both the morning and evening milk spectra along with milk yield, parity, and DIM in the prediction process. The coefficient of determination in the cross validation was 0.61, 0.74, and 0.58 for nitrogen intake, nitrogen use efficiency, and nitrogen balance, respectively. In a separate series of validation approaches, the calibration and validation was stratified by herd (n = 4) and separately by year. For these scenarios, partial least squares regression generated more accurate predictions compared with neural networks; the coefficient of determination was always lower than 0.29 and 0.60 when validation was stratified by herd and year, respectively. Therefore, if the variability of the data being predicted in the validation datasets is similar to that in the data used to develop the predictions, then nitrogen-related traits can be predicted with reasonable accuracy. In contrast, where the variability of the data that exists in the validation dataset is poorly represented in the calibration dataset, then poor predictions will ensue., (© 2024, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)