Metabolomic biomarkers identify differences in milk produced by Holstein cows and other minor dairy animals

Yang, Yongxin; Zheng, Nan; Zhao, Xiaowei; Zhang, Yangdong; Han, Rongwei; Yang, Jinhui; Zhao, Shengguo; Li, Songli; Guo, Tongjun; Zang, Changjiang; Wang, Jiaqi

doi:10.1016/j.jprot.2015.12.031

Cited by 80 publications

(61 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, several nontargeted MS applications have been developed for the analysis of metabolites, such as for the geographical origin of cocoa [133], almonds [134], olive oil [135], oranges [136], for the detection of fish [137,138] and meat [139,140] fraud, for the tracing of agricultural practices [141,142] or for the QC of milk [143,144].…”

Section: Metabolomics-based Methodsmentioning

confidence: 99%

Food authentication in real life: How to link nontargeted approaches with routine analytics?

Creydt¹,

Fischer²

2020

Electrophoresis

View full text Add to dashboard Cite

In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding highresolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.

show abstract

Section: Metabolomics-based Methodsmentioning

confidence: 99%

Food authentication in real life: How to link nontargeted approaches with routine analytics?

Creydt¹,

Fischer²

2020

Electrophoresis

View full text Add to dashboard Cite

show abstract

“…The X-axis represents PLS-DA component 1 while the Y-axis represents PLS-DA component 2.The ellipse in the plots defines the Hotelling's T2 confidence region corresponding to a 95% confidence interval for the observations. [45] Furthermore, the quality of the models was evaluated based on the basis of cumulative parameters R 2 X (variation in X explained by the model), R 2 Y (amount of Y explained by the model), and Q 2 (the cumulated crossvalidation for R 2 Y). [46] The model's goodness-of-fit and predictive ability are regarded as a good when the cumulative values of Q 2 and R 2 Y were greater than 0.5.…”

Section: Discussionmentioning

confidence: 99%

Fourier transform infrared spectroscopy and multivariate analysis of milk from different goat breeds

Salleh

Selamat

Meng

et al. 2019

International Journal of Food Properties

View full text Add to dashboard Cite

A deeper understanding of milk composition from different dairy goat breeds would address the potential variance in quality control, as well as to optimize the production process of dairy products. Understanding these parameters will assist in evaluating goat milk quality. This study was designed to identify potential differentiating features the milk from different goat breeds based on fourier transform infrared spectroscopy (FTIR) coupled with multivariate analysis to detect the compositional differences. Eighteen freeze-dried goat milk samples of different breeds (Jamnapari, Saanen and Toggenburg) were analyzed. The results showed a clear discrimination between three different breeds using Partial least square discriminant analysis. The value Q 2 and R 2 Y values are 0.981 and 0.958, which means the model exhibited goodness-of-fit and predictive features that would allow milk samples to be segregated by breed reliably. Result of chemical composition show Jamnapari milk was superior in terms of protein, fat and lactose content with values of 3.7%, 4.20%, and 5.30%, respectively, compared to other breeds. These analyses showed that only Jamnapari goat milk is different compared to the other two breeds.

show abstract

“…871 Li, et al, 2018) Abbreviations: Gas chromatography-mass spectrometry detection (GC-MS). (Yang, Zheng, Zhao, Zhang, Han, et al, 2016) Bovine milk Addition of water and adulterants 1 H TD-NMR, PCA, PLRS, SIMCA, and kNN…”

Section: A N U S C R I P Tmentioning

confidence: 99%

Analytical methods combined with multivariate analysis for authentication of animal and vegetable food products with high fat content

Valdés

Beltrán

Mellinas

et al. 2018

Trends in Food Science & Technology

View full text Add to dashboard Cite

Background: Food fraud is described as a violation of food law, which is intentionally committed to get an economic or financial gain through the consumer's swindle resulting in multi-million business and posing a public health threat. The main fraudulent practices are mislabelling of composition, certificates of origin, health claims, and artificial increases in weight or volume caused by replacement, dilution, addition or removal of some ingredients. Hardly 68% of the food fraud violations are produced in animal and vegetable products with high fat content (27% meat, 13% fish, 11% oils, 10% dairy products, 4% nuts and seeds and 3% animal by-products) becoming a crucial issue for food processing industries. Scope and approach: The present review focuses on the main authentication techniques and methods employed to clarify the authenticity of both animal and vegetable fat food products emphasizing the importance of the use of robust and reliable analytical techniques combined with multivariate analyses. Key findings and conclusions: Targeted approaches, such as as chromatography and DNA-based methods, combined with multivariate analysis have shown high accuracy, sensitivity and selectivity, allowing the simultaneous evaluation of multiple analytes. In addition, non-target methods, such as those based on spectroscopic techniques, have been used to establish the geographic origin of food products with quick response, low cost, non-destructive character and also offering the possibility to be miniaturized.

show abstract

Metabolomic biomarkers identify differences in milk produced by Holstein cows and other minor dairy animals

Cited by 80 publications

References 52 publications

Food authentication in real life: How to link nontargeted approaches with routine analytics?

Food authentication in real life: How to link nontargeted approaches with routine analytics?

Fourier transform infrared spectroscopy and multivariate analysis of milk from different goat breeds

Analytical methods combined with multivariate analysis for authentication of animal and vegetable food products with high fat content

Contact Info

Product

Resources

About