Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Predicting bovine milk protein composition based on Fourier transform infrared spectra.

Journal of dairy science - 1 Nov 2011

Rutten M J M, Bovenhuis H, Heck J M L, van Arendonk J A M

Abstract excerpt

Phenotypic information on individual protein composition of cows is important for many aspects of dairy processing with cheese production as the center of gravity. However, measuring individual protein composition is expensive and time consuming. In this study, we investigated whether protein composition can be predicted based on inexpensive and routinely measured milk Fourier transform infrared (FTIR) spectra....

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cattle
Share to Milk
Share to Milk Proteins

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Biological

Share to Phenotype

Share to Predictive Value of Tests

Share to Spectroscopy, Fourier Transform Infrared

Source

Identifiers

PMID: 22032392
DOI: 10.3168/jds.2011-4520

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Predicting bovine milk protein composition based on Fourier transform infrared spectra.PMID 22032392Read ↗

Genetic components of milk Fourier-transform infrared spectra used to predict breeding values for milk composition and quality traits in dairy goats.Semantic proximity Read ↗

Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows.Semantic proximity Read ↗

Short communication: predictive ability of Fourier-transform mid-infrared spectroscopy to assess CSN genotypes and detailed protein composition of buffalo milk.Semantic proximity Read ↗

Prediction of β-lactoglobulin genotypes based on milk Fourier transform infrared spectra.Semantic proximity Read ↗

Predicting milk protein fractions using infrared spectroscopy and a gradient boosting machine for breeding purposes in Holstein cattle.Semantic proximity Read ↗

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods.Semantic proximity Read ↗

Genetic analysis of the Fourier-transform infrared spectra of bovine milk with emphasis on individual wavelengths related to specific chemical bonds.Semantic proximity Read ↗

Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy.Semantic proximity Read ↗

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data.Semantic proximity Read ↗

Capitalizing on fine milk composition for breeding and management of dairy cows.Semantic proximity Read ↗

Short communication: Genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbéliarde, Normande, and Holstein dairy cattle breeds.Semantic proximity Read ↗

Quantitative and qualitative detailed milk protein profiles of 6 cattle breeds: Sources of variation and contribution of protein genetic variants.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.