Nuclear Magnetic Resonance-Based Metabolomic Comparison of Breast Milk and Organic and Traditional Formula Milk Brands for Infants and Toddlers

A metabolic profiling approach to characterize and discriminate plant-based beverages and milk

The rising demand for nondairy and nonanimal protein sources has increased plant-based beverages (PBB) consumption. However, research on their functional properties, metabolic profile, and discrimination potential is limited. This study evaluated the potential of proton nuclear magnetic resonance (1H NMR) spectroscopy as an authentication method to discriminate milk (cow and goat) and PBB macro-groups, including soy-based, fruit-based (almond and coconut), and cereal-based (rice and oat) beverages, based on their metabolic profile. A total of 22 PBB (soy-, almond-, coconut-, rice-, and oat-based beverages), 4 cow milk, and 4 goat milk cartons were analyzed with 1H NMR spectroscopy to obtain their metabolic profile. Relevant metabolites to discriminate PBB macro-groups and cow and goat milk were identified through the Mann-Whitney U test and partial least squares-discriminant analysis. Results revealed that uridine diphosphate, glucose, and adenosine were key metabolites for the identification of goat and cow milk. At the same time, choline and guanosine emerged as important markers for different PBB macro-group detection. In addition, lactose played a significant role in differentiating milk from PBB. In conclusion, these findings represent an initial step toward applying 1H NMR spectroscopy for authentication and nutritional analysis of PBB, opening the door for further research into their authenticity and metabolic profiling.

NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil

This study investigates the potential of 1H NMR spectroscopy for predicting key chemical and sensory attributes in olive oil. By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. We applied Random Forest regression models to correlate NMR spectra with these parameters, achieving promising results, particularly for predicting major fatty acids, total polyphenols, and tocopherols. We have also found the collected data to be highly effective in classifying olive cultivars and the years of harvest. Our findings highlight the potential of NMR spectroscopy as a rapid, non-destructive, and environmentally friendly tool for olive oil quality assessment. This study introduces a novel approach that combines machine learning with 1H NMR spectral analysis to correlate analytical data for predicting essential qualitative parameters in olive oil. By leveraging 1H NMR spectra as predictive proxies, this methodology offers a promising alternative to traditional assessment techniques, enabling rapid determination of several parameters related to chemical composition, sensory attributes, and geographical origin of olive oil samples.

Effectiveness of mid-infrared spectroscopy for the prediction of cow milk metabolites

Proton nuclear magnetic resonance (¹H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of ¹H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through ¹H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional ¹H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk.

Goat Milk Foodomics. Dietary Supplementation of Sunflower Oil and Rapeseed Oil Modify Milk Amino Acid and Organic Acid Profiles in Dairy Goats

The dietary supplementation of vegetable oils is known to improve the dietary energy density as well as milk fatty acid profile; however, the impacts on the milk foodome is largely unknown. This study investigated the effect of two different sources of unsaturated fatty acids, rapeseed oil and sunflower oil, as a feeding supplement on the milk foodome from dairy goats. Nine Danish Landrace goats at 42 ± 5 days in milk were allocated to three treatment groups for 42 days with three animals per group. A control group received a basal diet made of forage and concentrate at an 85:15 ratio. On top of the basal diet, the second and third groups received rapeseed oil or sunflower oil supplements at 4% of dry matter, respectively. Goat milk was sampled on days 14, 21, and 42. The milk foodome was measured using gas chromatography–mass spectrometry and proton nuclear magnetic resonance spectroscopy. The milk levels of 2-hydroxyisovaleric acid, oxaloacetic acid, and taurine were higher in the milk from goats fed with sunflower oil compared to the control group. More glucose-1-phosphate was found in the milk from goats fed with rapeseed oil compared to the control group. Amino acids, valine and tyrosine, and 2-hydroxyisovaleric acid and oxaloacetic acid were higher in the sunflower group compared to the rapeseed group, while the milk from the rapeseed-fed goats had greater levels of ethanol and 2-oxoglutaric acid compared to the sunflower group. Thus, results show that foodomics is suitable for studying how milk chemistry changes as a function of feeding regime.

The Metabolomic Analysis of Human Milk Offers Unique Insights into Potential Child Health Benefits

PURPOSE OF REVIEW

Human milk is the gold standard of infant nutrition. The milk changes throughout lactation and is tailored for the infant providing the nutrients, minerals and vitamins necessary for supporting healthy infant growth. Human milk also contains low molecular weight compounds (metabolites) possibly eliciting important bioactivity. Metabolomics is the study of these metabolites. The purpose of this review was to examine recent metabolomics studies and cohort studies on human milk to assess the impact of human milk metabolomic analyses combined with investigations of infant growth and development.

RECENT FINDINGS

The metabolite profile of human milk varies among other factors according to lactation stage, gestation at birth, and maternal genes, diet and disease state. Few studies investigate how these variations impact infant growth and development. Several time-related factors affecting human milk metabolome are potentially ubiquitous among mothers, although maternal-related factors are heavily confounded, which complicates studies of metabolite abundancies and variabilities and further possibilities of observing cause and effect in infants.