Variation of Detailed Protein Composition of Cow Milk Predicted from a Large Database of Mid-Infrared Spectra

Franzoi, Marco; Niero, Giovanni; Visentin, Giulio; Penasa, M.; Cassandro, Martino; Marchi, Massimo De

doi:10.3390/ani9040176

Cited by 51 publications

(40 citation statements)

References 34 publications

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“…A constant decrease of k-casein was observed through the entire lactation. The whey protein, α-lactalbumin and βlactoglobulin decreased until 75 DIM and increased during the remaining part of lactation (Franzoi et al, 2019). In our study the caseins reached a maximum in mid lactation.…”

Section: Resultssupporting

confidence: 55%

“…In our study the content of caseins was higher than in Dutch Holstein-Friesians. Franzoi et al, (2019)observed that the proportion of αcasein and β-lactoglobulin in Italian Holstein-Friesian breed (41.64% and 5,91% respectively) was lower compared with results of Schopen et al, 2009 in first-parity Dutch-Holstein-Friesian cows, whereas β-casein was higher compared with the same study (30.44% and 27.7%, respectively). Franzoi et al, 2019 considered that such difference can be attributed to the different cow parities and lactation stages including the sampling, to diversities in farming system and area.…”

Section: Resultsmentioning

confidence: 60%

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Study on milk composition and milk protein distribution in Romanian Holstein cattle

Pelmuş

Lazãr

Palade

et al. 2020

Archiva Zootechnica

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The aim of this study was to determine milk quality indices as well as the milk protein composition in Romanian Holstein cattle raised under the conditions of experimental farm of INCDBNA-IBNA. The study was carried out on 22 milk samples. The types of different milk proteins were identified by SDS-PAGE technique. Sampling day and milk chemical composition were performed during the milking period of studied cattle. The quality indices were breed-specific for protein (3.38%) and higher for fat (4.39%).Milk proteins analysis of Romanian Holstein cattle separated by SDS-PAGE electrophoresis showed the presence of four major caseins (αs1-, αs2-, β- and k-casein) and two whey proteins (β-lactoglobulin, α-lactalbumin). The caseins accounted 77.28% of the total milk proteins, while the major proteins (β-lactoglobulin, α-lactalbumin) from the whey represented 22.72% of the total proteins. αs1-casein + αs2-casein had a higher expression (36.01%) followed by β-casein (31.45%), β-lactoglobulin (18.16%), k-casein (9.82%) and α-lactalbumin (4.56%). The most of milk samples was characterized by a medium expression level of both caseins and whey proteins

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Section: Resultssupporting

confidence: 55%

Section: Resultsmentioning

confidence: 60%

Study on milk composition and milk protein distribution in Romanian Holstein cattle

Pelmuş

Lazãr

Palade

et al. 2020

Archiva Zootechnica

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“…The prediction quality reported in the validation set for Ca, Mg, Na, and P is presented in Table 5. Model performances were satisfactory to predict Ca and P (R 2 > 0.67) in Soyeurt et al (2009); Toffanin et al (2015), Visentin et al (2016), and Franzoi et al (2019). But the same minerals were poorly predicted (R 2 < 0.55) by Gottardo et al (2015); Bonfatti et al (2016), Malacarne et al (2018), and Fleming et al (2019).…”

Section: Milk Compositionmentioning

confidence: 93%

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

Bresolin

Dórea

2020

Front. Genet.

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High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near-and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid-and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

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“…The case study area was Bolzano Province, which is characterized by small-scale farms with traditional livestock feeding (forage or hay and concentrates). Moreover, approximately 20% of the herds are transhumed to mountain pastures in the summer season [14,15].…”

Section: Data Collectionmentioning

confidence: 99%

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

Benedet

Franzoi

Manuelian

et al. 2020

Animals

Self Cite

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Serum metabolic profile is a common method to monitor health and nutritional status of dairy cows, but blood sampling and analysis are invasive, time-consuming, and expensive. Milk mid-infrared spectra have recently been used to develop prediction models for blood metabolites. The current study aimed to investigate factors affecting blood β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), and urea nitrogen (BUN) predicted from a large milk mid-infrared spectra database. Data consisted of the first test-day record of early-lactation cows in multi-breed herds. Holstein-Friesian cows had the greatest concentration of blood BHB and NEFA, followed by Simmental and Brown Swiss. The greatest and the lowest concentrations of BUN were detected for Brown Swiss and Holstein-Friesian, respectively. The greatest BHB concentration was observed in the first two weeks of lactation for Brown Swiss and Holstein-Friesian. Across the first month of lactation, NEFA decreased and BUN increased for all considered breeds. The greatest concentrations of blood BHB and NEFA were recorded in spring and early summer, whereas BUN peaked in December. Environmental effects identified in the present study can be included as adjusting factors in within-breed estimation of genetic parameters for major blood metabolites.

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Variation of Detailed Protein Composition of Cow Milk Predicted from a Large Database of Mid-Infrared Spectra

Cited by 51 publications

References 34 publications

Study on milk composition and milk protein distribution in Romanian Holstein cattle

Study on milk composition and milk protein distribution in Romanian Holstein cattle

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

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