Chemometric authentication of the organic status of milk on the basis of trace element content

Rodríguez‐Bermúdez, Ruth; López‐Alonso, Marta; Miranda, Marta; Fouz, Ramiro; Galdo, Inmaculada Orjales; Herrero-Latorre, Carlos

doi:10.1016/j.foodchem.2017.08.011

Cited by 50 publications

(28 citation statements)

References 26 publications

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“…In addition to the aforementioned factors influencing milk mineral content, diet and farming system could also be included; however, the impact is not clear especially with the presence of studies referring to a greater impact of the geographical conditions (i.e., soil origin and seasonal variations) and management, i.e., the inclusion of mineral supplements in the diet [176]. Gabryszuk, et al [177] found that while Ca, Mg, and P concentrations in milk were highest in intensive production systems with no grazing (TMR) as opposed to pasture feeding farms, it is likely that grazing cows may ingest higher levels of dietary Al, increasing its content in milk.…”

Section: Impact Of Feeding System On the Micronutrients And Lactosmentioning

confidence: 99%

The “Grass-Fed” Milk Story: Understanding the Impact of Pasture Feeding on the Composition and Quality of Bovine Milk

Alothman

Hogan

Hennessy

et al. 2019

Foods

108

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Milk is a highly nutritious food that contains an array of macro and micro components, scientifically proven to be beneficial to human health. While the composition of milk is influenced by a variety of factors, such as genetics, health, lactation stage etc., the animal’s diet remains a key mechanism by which its nutrition and processing characteristics can be altered. Pasture feeding has been demonstrated to have a positive impact on the nutrient profile of milk, increasing the content of some beneficial nutrients such as Omega-3 polyunsaturated fatty acids, vaccenic acid, and conjugated linoleic acid (CLA), while reducing the levels of Omega-6 fatty acids and palmitic acid. These resultant alterations to the nutritional profile of “Grass-Fed” milk resonate with consumers that desire healthy, “natural”, and sustainable dairy products. This review provides a comprehensive comparison of the impact that pasture and non-pasture feeding systems have on bovine milk composition from a nutritional and functional (processability) perspective, highlighting factors that will be of interest to dairy farmers, processors, and consumers.

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Section: Impact Of Feeding System On the Micronutrients And Lactosmentioning

confidence: 99%

The “Grass-Fed” Milk Story: Understanding the Impact of Pasture Feeding on the Composition and Quality of Bovine Milk

Alothman

Hogan

Hennessy

et al. 2019

Foods

108

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“…Trace mineral composition can be used for determining authenticity of organic milk. Concentrations of 14 mineral elements in 98 samples of milk from northern Spain were measured by ICP-MS. Making use of an optimized multilayer feed-forward artificial neural network (MLF-ANN), a classification model was developed to discriminate between organic and non-organic milk within a 5% margin of error [123]. However, using trace elements for authentication of milk is highly dependent on geographical origin of the samples, indicating that different prediction models need to be developed for different geographical locations.…”

Section: Vegetablesmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

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Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

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“…[22,38] Therefore, based on matrices of both the volatile and alkylamide compositions presented here, two unsupervised pattern recognition methods, PCA and HCA, were applied to quickly obtain by a high-quality evaluation of the volatile and alkylamides differentiation, improving the knowledge of the solvent effects. [22,38] Therefore, based on matrices of both the volatile and alkylamide compositions presented here, two unsupervised pattern recognition methods, PCA and HCA, were applied to quickly obtain by a high-quality evaluation of the volatile and alkylamides differentiation, improving the knowledge of the solvent effects.…”

Section: Chemometric Analysismentioning

confidence: 99%

“…Traditionally, multivariate analysis is applied to explore a complex matrix of data to determine variations and relationships among samples and their compositions for a number of variables. [22,38] Therefore, based on matrices of both the volatile and alkylamide compositions presented here, two unsupervised pattern recognition methods, PCA and HCA, were applied to quickly obtain by a high-quality evaluation of the volatile and alkylamides differentiation, improving the knowledge of the solvent effects.…”

Section: Chemometric Analysismentioning

confidence: 99%

Sensory Characteristics and Antioxidant Activity of Zanthoxylum bungeanumMaxim. Pericarps

Wang

et al. 2019

Chemistry & Biodiversity

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In this study, Zanthoxylum bungeanum extracts were prepared using seven solvents: de-ionized water, methanol, ethanol, acetic acid, ethyl acetate, chloroform, and benzene. The volatile compositions in the extracts were qualitatively analyzed using headspace solid-phase microextraction coupled with gas chromatography mass spectrometry detection, and the alkylamide composition was determined using high-performance liquid chromatography. The extract compositions differed with respect to the solvents. A total of 49 volatile components belonging to four groups - terpenoids, alcohols, esters, and ketones -were identified in the extracts. The Z. bungeanum extracts were either ester or terpenoid type, dominated by linalyl acetate. Multivariate analysis divided the extracts into three distinct groups based on principal component analysis and hierarchical clustering analysis. Water, methanol, and ethanol with strongly polarity, could be applied in the food and pharmaceutical industries. This article is protected by copyright. All rights reserved.

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Chemometric authentication of the organic status of milk on the basis of trace element content

Cited by 50 publications

References 26 publications

The “Grass-Fed” Milk Story: Understanding the Impact of Pasture Feeding on the Composition and Quality of Bovine Milk

The “Grass-Fed” Milk Story: Understanding the Impact of Pasture Feeding on the Composition and Quality of Bovine Milk

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

Sensory Characteristics and Antioxidant Activity of Zanthoxylum bungeanumMaxim. Pericarps

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