Variation of iodine content in bovine milk and predictive ability of mid-infrared spectroscopy

Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle

Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a detailed panel of 17 macro, trace, and environmental elements in bovine milk, using partial least squares regression (PLS) and machine learning approaches. The automatic machine learning significantly outperformed the PLS regression in terms of prediction performances of the mineral elements. For macrominerals, the R2 ranged from 0.59 to 0.78. Promising predictability was achieved for Cu and B (R2 = 0.66 and 0.74, respectively) and more moderate ones for Fe, Mn, Zn, and Al (R2 from 0.48 to 0.58). These results provide a reliable basis for a rapid and cost-effective quantification of these traits, serving as a resource for dairy farmers seeking to enhance the quality of milk production and optimize cheese properties.

The effects of dietary iodine content, milking system, and farming practices on milk iodine concentration and quality traits

Various management practices can influence milk quality traits in dairy cattle. As an example, an increasing investment in automatic milking system to substitute milking parlors has been observed in the last 2 decades in dairy farms which could have affected certain bulk milk quality traits. What is more, milking practices can also affect certain milk parameters; as an example, teat disinfectants containing I are used in commercial farms where pre- or postdipping is performed, leading to presence of some I in the bulk milk. However, this trace mineral is also supplied in cows' diet to fulfill their nutritional requirements, partly contributing to the milk I final concentration. Therefore, the aim of this study was to evaluate the sources of variation of milk I along with other traditional milk quality traits. A total of 91 dairy farms in northeastern Italy were enrolled in the study. In each farm, diet and bulk milk samples were collected on the same day for chemical analysis. Concentration of I, in particular, was determined in both milk and feed with gold standard. Pearson correlations were calculated among the traits available for milk and diet, and a general linear model was used to test significance of fixed effects (feeding system, milking system, farming system, herd size, herd stage of lactation, and sampling month) on milk quality traits including the I concentration. In the case of milk I, diet I and presence of I-based predipping and postdipping teat disinfect application were also tested as fixed effects. Results showed a positive linear correlation between milk and diet I content (correlation coefficient [r] = 0.78). Although milk I was also positively correlated with lactose content (r = 0.25), dietary I was not correlated with other milk traits. Milk I content was significantly affected by dietary I, I-based predipping teat disinfectant application, and herd composition. Compared with conventional farms, organic farms showed lower protein content and greater somatic cell score (SCS) but similar milk I. Milking system significantly affected only lactose content and SCS of milk. Sampling month was only significant for milk urea nitrogen and herd composition, feeding system, herd size, and herd average days in milk did not modify milk gross composition and SCS. In conclusion, dietary supply of I is the main factor affecting milk I concentration and findings suggest that I level in milk can be naturally improved in dairy cows by modulating the I content in the diet administered. However, further research is needed to evaluate the effect of I-based sanitizers on milk I.

Assessment of energy dispersive X-ray fluorescence (ED-XRF) for quantification of iodine in non-lyophilized milk

Iodine represents a fundamental element for human health, with particular regard to thyroid function. Dietary intake of milk naturally rich in iodine becomes of primary importance in the prevention of syndromes related to iodine deficiency. The concentration of iodine in milk is characterized by wide variability, mainly related to animal feed and level of mineral supplementation. Therefore, there is interest in the development of fast analytical techniques which are able to predict milk iodine concentration. The aim of the present study was to investigate the effectiveness of energy-dispersive X-ray fluorescence (ED-XRF) for the prediction of iodine in cow milk. Results showed moderate accuracy of the ED-XRF technique, with a coefficient of determination in cross validation of 0.60. This study represents a first contribution towards the possibility to discriminate milk with high or low iodine concentration, as an essential preliminary step for the introduction into the market of naturally fortified milk.

Associations between the detailed milk mineral profile, milk composition, and metabolic status in Holstein cows

The causes of variation in the milk mineral profile of dairy cattle during the first phase of lactation were studied under the hypothesis that the milk mineral profile partially reflects the animals' metabolic status. Correlations between the minerals and the main milk constituents (i.e., protein, fat, and lactose percentages), and their associations with the cows' metabolic status indicators were explored. The metabolic status indicators (MET) that we used were blood energy-protein metabolites [nonesterified fatty acids, β-hydroxybutyrate (BHB), glucose, cholesterol, creatinine, and urea], and liver ultrasound measurements (predicted triacylglycerol liver content, portal vein area, portal vein diameter and liver depth). Milk and blood samples, and ultrasound measurements were taken from 295 Holstein cows belonging to 2 herds and in the first 120 d in milk (DIM). Milk mineral contents were determined by ICP-OES; these were considered the response variable and analyzed through a mixed model which included DIM, parity, milk yield, and MET as fixed effects, and the herd/date as a random effect. The MET traits were divided in tertiles. The results showed that milk protein was positively associated with body condition score (BCS) and glucose, and negatively associated with BHB blood content; milk fat was positively associated with BHB content; milk lactose was positively associated with BCS; and Ca, P, K and S were the minerals with the greatest number of associations with the cows' energy indicators, particularly BCS, predicted triacylglycerol liver content, glucose, BHB and urea. We conclude that the protein, fat, lactose, and mineral contents of milk partially reflect the metabolic adaptation of cows during lactation and within 120 DIM. Variations in the milk mineral profile were consistent with changes in the major milk constituents and the metabolic status of cows.

Invited review: Iodine level in dairy products-A feed-to-fork overview

The theme of iodine in the dairy sector is of particular interest due to the involvement and the interconnection of several stakeholders along the dairy food chain. Iodine plays a fundamental role in animal nutrition and physiology, and in cattle it is an essential micronutrient during lactation and for fetal development and the calf's growth. Its correct use in food supplementation is crucial to guarantee the animal's recommended daily requirement to avoid excess intake and long-term toxicity. Milk iodine is fundamental for public health, being one of the major sources of iodine in Mediterranean and Western diets. Public authorities and the scientific community have made great efforts to address how and to what extent different drivers may affect milk iodine concentration. The scientific literature concurs that the amount of iodine administered through animal feed and mineral supplements is the most important factor affecting its concentration in milk of most common dairy species. Additionally, farming practices related to milking (e.g., use of iodized teat sanitizers), herd management (e.g., pasture vs. confinement), and other environmental factors (e.g., seasonality) have been identified as sources of variation of milk iodine concentration. Overall, the aim of this review is to provide a multilevel overview on the mechanisms that contribute to the iodine concentration of milk and dairy products.

Short communication: Iodine content in bovine milk is lowly heritable and shows limited genetic variation

Milk and dairy products are considered important sources of iodine in several countries. Despite this, there is a paucity of studies that have investigated sources of variation of milk iodine, especially on a large scale. So far, it is not clear if milk iodine content could be increased through breeding in dairy cattle. Recently, a mid-infrared spectroscopy prediction model has been developed for an indirect quantification of iodine content in cow milk, as it is a faster and less expensive method that allows the prediction at population level. The model has coefficient of determination and ratio of performance to deviation in external validation of 0.57 and 1.44, respectively, and it was used in the present study to predict the iodine content from historical milk spectral data to investigate phenotypic and genetic aspects in the Italian Holstein cattle. Based on the accuracy of the model, the prediction was interpreted as proxy for the real milk iodine concentration (IOD_P). The data set comprised 33,776 test-day records with IOD_P from 4,072 cows. Data of IOD_P were transformed through natural logarithm to achieve a normal distribution. The effect of parity, lactation stage, and month of sampling were investigated, and genetic parameters were estimated using a test-day repeatability animal model. Milk IOD_P decreased with parities and was the lowest in early lactation. Heritability of IOD_P was low (0.025) and it was positively genetically correlated with milk yield and negatively with fat content. Results suggested that it would be challenging to directly improve this trait through breeding strategies in dairy cattle, because IOD_P is mainly affected by temporary environmental factors and thus, cannot be easily improved through genetics. Although preliminary, findings of this study suggest that it would be more convenient to develop feeding and management strategies to drive milk iodine level than to put efforts and resources into breeding strategies. Further studies should validate IOD_P as an indicator trait of milk iodine content by improving reference data and estimating genetic correlation between predicted and measured values.