The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits
Open accessAbstract
Interest in methods that routinely and accurately measure and predict animal characteristics is growing in importance, both for quality characterization of livestock products and for genetic purposes. Mid-infrared spectroscopy (MIRS) is a rapid and cost-effective tool for recording phenotypes at the population level. Mid-infrared spectroscopy is based on crossing matter by electromagnetic radiation and on the subsequent measure of energy absorption, and it is commonly used to determine traditional milk quality traits in official milk laboratories. The aim of this review was to focus on the use of MIRS to predict new milk phenotypes of economic relevance such as fatty acid and protein composition, coagulation properties, acidity, mineral composition, ketone bodies, body energy status, and methane emissions. Analysis of the literature demonstrated the feasibility of MIRS to predict these traits, with different accuracies and with margins of improvement of prediction equations. In general, the reviewed papers underlined the influence of data variability, reference method, and unit of measurement on the development of robust models. A crucial point in favor of the application of MIRS is to stimulate the exchange of data among countries to develop equations that take into account the biological variability of the studied traits under different conditions. Due to the large variability of reference methods used for MIRS calibration, it is essential to standardize the methods used within and across countries.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785
Infrared Spectroscopy: Fundamentals and Applications
Stuart, B. H. (2004) · Analytical Techniques in the Sciences · doi:10.1002/0470011149 · Cited by 4,427
Near-Infrared Technology: In the Agricultural and Food Industries
Williams, P. C., & Norris, K. (2001) · Andalas University Repository (Andalas University) · Cited by 2,749
Haaland, D. M., & Thomas, E. V. (1988) · Analytical Chemistry · doi:10.1021/ac00162a020 · Cited by 2,539
Effect of dietary fatty acids on serum lipids and lipoproteins. A meta-analysis of 27 trials
Mensink, R. P., & Katan, M. B. (1992) · Arteriosclerosis and Thrombosis: A Journal of Vascular Biology · doi:10.1161/01.atv.12.8.911 · Cited by 1,477
A user-friendly guide to Multivariate Calibration and Classification
Næs, T., Isaksson, T., Fearn, T., & Davies, T. (2017) · doi:10.1255/978-1-906715-25-0 · Cited by 1,401
Houle, D., Govindaraju, D. R., & Omholt, S. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2897 · Cited by 1,279
Roche, J. R., Friggens, N. C., Kay, J. K., Fisher, M. W., Stafford, K. J., & Berry, D. P. (2009) · Journal of Dairy Science · doi:10.3168/jds.2009-2431 · Cited by 1,254
Cited by
Newton, J. E., Nettle, R., & Pryce, J. E. (2020) · Agricultural Systems · doi:10.1016/j.agsy.2020.102811 · Cited by 94
Metadata
- Authors
- De Marchi, M., Toffanin, V., Cassandro, M., & Penasa, M.
- Venue
- Journal of Dairy Science
- Publisher
- American Dairy Science Association
- Published
- 2014-03
- Volume
- 97
- Issue
- 3
- Pages
- 1171-1186
- ISSN
- 0022-0302 (ISSN-L)
- OpenAlex
- W2038242845
- Cited by
- 332
- References
- 57
- FWCI
- 26.34
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. https://app.cochat.ai/papers/10.3168/jds.2013-6799
- The paper
- De Marchi, M., Toffanin, V., Cassandro, M., & Penasa, M. (2014). Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits. Journal of Dairy Science. https://doi.org/10.3168/jds.2013-6799
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.