The role of packaging on the flavor of fluid milk

Cadwallader, D.C.; Gerard, Patrick D.; Drake, M.A.

doi:10.3168/jds.2022-22060

Cited by 14 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2-ethyl-1-hexanol has a "chemical/cleaning agent" aroma, which was detected in raw milk over refrigerated storage time and might be due to the packaging and refrigeration environment [41]. 1-Octen-3-ol was the common flavor active substance in milk and dairy products.…”

Section: Discussionmentioning

confidence: 99%

Effect of a Dairy Cow’s Feeding System on the Flavor of Raw Milk: Indoor Feeding or Grazing

Chi

Yuan

Zhang

et al. 2023

Foods

View full text Add to dashboard Cite

The flavor of fresh, raw milk is considered to be the key to maintaining the quality of dairy products, and is very crucial in affecting a consumer's choice. To better understand the differences in flavor of fresh milk between feeding patterns, we conducted the following study. Twelve Holstein cows reared in pure grazing mode and twelve reared intensively in medium to large farms were selected from the Xinjiang Uygur Autonomous Regions at the same time, and the flavor of their raw milk was analyzed. Aroma profiles and taste attributes were assessed by electronic nose and electronic tongue, respectively, and volatile flavor compounds were characterized and quantified by Headspace-Solid Phase Microextraction/Gas Chromatography-Mass Spectrometry. Thirteen volatile compounds were identified in the indoor feeding pattern and 12 in the grazing; most of them overlapped. W1S, W2S and W5S were the main contributing sensors of the electronic nose for the overall assessment of the aroma profile. Raw milk from grazing had more intense astringency, bitterness, sourness and richness in taste compared to indoor feeding. Different dietary conditions may contribute to a variety of aroma profiles. Oxime-, methoxy-phenyl-, octadecanoic acid, furfural and dodecanoic acid were the key volatile flavor compounds of grazing. Meanwhile, raw milk from indoor feeding patterns was unique in 2-nonanone, heptanoic acid and n-decanoic acid. All three detection techniques were valid and feasible for differentiating raw milk in both feeding patterns, and the compounds were significantly correlated with the key sensors by correlation analysis. This study is promising for the future use of metabolic sources of volatile organic compounds to track and monitor animal feeding systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Effect of a Dairy Cow’s Feeding System on the Flavor of Raw Milk: Indoor Feeding or Grazing

Chi

Yuan

Zhang

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…See Rasmussen and Williams 37 and the appendix of Gasser et al 59 for a more detailed description of Gaussian processes.…”

Section: Author Contributionsmentioning

confidence: 99%

Machine learning-based prediction of fish acute mortality: Implementation, interpretation, and regulatory relevance

Gasser,

Schür,

Perez-Cruz

et al. 2024

Preprint

View full text Add to dashboard Cite

Regulation of chemicals requires knowledge of their toxicological effects on a large number of target species. Traditionally, this knowledge has been acquired throughin vivotesting. The recent effort to find alternatives based on machine learning, however, has not focused on guaranteeing transparency, comparability and reproducibility, which makes it difficult to assess advantages and disadvantages of these methods. Also, comparable baseline performances are needed. In this study, we trained regression models on the ADORE “t-F2F” challenge proposed in [Schüret al.,Nature Scientific data, 2023] to predict acute mortality, measured as LC50 (lethal concentration 50), of organic compounds on fishes. We trained LASSO, random forest (RF), XGBoost, Gaussian process (GP) regression models, and found a series of aspects that are stable across models: (i) using mass or molar concentrations does not affect performances; (ii) the performances are only weakly dependent on how a chemical is represented, but (iii) strongly on how the data is split. Overall, the tree-based models RF and XGBoost performed best and we were able to predict the log10-transformed LC50 with a root mean square error of 0.90, which corresponds to an order of magnitude on the original LC50 scale. On a local level, on the other hand, the models are not able to predict the toxicity of single chemicals accurately enough. Predictions for single chemicals are dominated by a few chemical properties and taxonomic traits are not captured sufficiently by the models. As a consequence, the models are not yet applicable in the regulatory process. Nevertheless, this work contributes to the ongoing discussions on how machine learning can be integrated in the regulatory process.Environmental significanceConventional environmental hazard assessment in its current form will not be able to adapt to the growing need for toxicity testing. Alternative methods, such as toxicity prediction through machine learning, could fulfill that need in an economically and ethically sound manner. Proper implementation, documentation, and the integration into the regulatory process are prerequisites for the usability and acceptance of these models.

show abstract

“…These sensitive nutritional products are highly susceptible to chemical and microbial spoilage due to the rich nutritional elements and high water content they contain (Balthazar et al, 2022;Rejeesh & Anto, 2022). Therefore, it is packaged with packaging materials in order to facilitate the storage, transportation process and increase the shelf life (Cadwallader, Gerard, & Drake, 2023;Rejeesh & Anto, 2022). These packaging materials are polymer-based products and are produced by polymerization of many simple units called monomers, as well as additives such as heat and light stabilizers, colorants, lubricants, antioxidants (Arvanitoyannis & Kotsanopoulos, 2013).…”

Section: Introductionmentioning

confidence: 99%

Investigation and Quantification of Phthalate Esters in Packaged Milk: A Study in Türkiye

İşçi

2024

Turkish JAF Sci.Tech.

View full text Add to dashboard Cite

The aim of this study was to explore the concentrations of PAEs (Phthalate esters) in milk in Türkiye. For this purpose, a methodology was developed to quantify eight different PAEs in milk samples using a combination of dispersive solid-phase extraction (dSPE) and Liquid Chromatography coupled with Tandem Mass Spectrometry (LC-MS/MS). Employing this methodology, the concentrations of PAEs were evaluated in 34 milk samples. Results indicated the presence of PAEs in the milk samples; however, all tested compounds remained within the specific migration limits established by the EU. Among the analyzed PAEs, BBP (Benzyl butyl phthalate) was not detected in any samples, while DMP (di-methyl phthalate) (ND-5.51 µg/L) and DBP (di-butyl phthalate) (ND-7.91 µg/L) exhibited the lowest concentrations. DEHP (bis(2-ethylhexyl)) was identified as the most prevalent plasticizer with a maximum concentration of 41.31 µg/L. In conclusion, this study successfully investigated PAE concentrations in Turkish milk samples using a developed methodology. The results indicated the presence of PAEs within EU-established limits, with DEHP being the predominant plasticizer. Further research and monitoring efforts are crucial to ensure ongoing safety in packaged milk products.

show abstract

The role of packaging on the flavor of fluid milk

Cited by 14 publications

References 44 publications

Effect of a Dairy Cow’s Feeding System on the Flavor of Raw Milk: Indoor Feeding or Grazing

Effect of a Dairy Cow’s Feeding System on the Flavor of Raw Milk: Indoor Feeding or Grazing

Machine learning-based prediction of fish acute mortality: Implementation, interpretation, and regulatory relevance

Investigation and Quantification of Phthalate Esters in Packaged Milk: A Study in Türkiye

Contact Info

Product

Resources

About