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

Bresolin, Tiago; Dórea, João Ricardo Rebouças

doi:10.3389/fgene.2020.00923

Cited by 34 publications

(20 citation statements)

References 188 publications

(231 reference statements)

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“…Apart from management perspectives, these prediction models can also be used to generate phenotypes on a large-scale for genetic improvement of metabolic health, a trait that is difficult and expensive to measure (Pryce et al, 2016;Gengler et al, 2018;Bresolin and Dórea, 2020). Using the data of 1,393 Holstein-Friesian cows, Luke et al (2019a) indicated that despite being lowly heritable, with genomic heritability estimates of 0.09, 0.18, and 0.18, for BHB, fatty acids, and urea, respectively, genomic selection of these traits is possible with accuracies ranging between 0.31 and 0.51.…”

Section: Resultsmentioning

confidence: 99%

Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Luke

Pryce

2021

Journal of Dairy Science

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Increased concentrations of some serum biomarkers are known to be associated with impaired health of dairy cows. Therefore, being able to predict these biomarkers, especially in the early stage of lactation, would enable preventive management decision. Some health biomarkers may also be used as phenotypes for genetic improvement for improved animal health. In this study, we validated the accuracy and robustness of models for predicting serum concentrations of β-hydroxybutyrate (BHB), fatty acids, and urea nitrogen, using milk midinfrared (MIR) spectroscopy. The data included 3,262 blood samples of 3,027 lactating Holstein-Friesian cows from 19 dairy herds in Southeastern Australia, collected in the period from July 2017 to April 2020. The models were developed using partial least squares regression and were validated using 10-fold random cross-validation, herd-year by herd-year external validation, and year by year validation. The coefficients of determination (R 2 ) for prediction of serum BHB, fatty acids, and urea obtained through random cross-validation were 0.60, 0.42, and 0.87, respectively. For the herd-year by herd-year external validation, the prediction accuracies held up comparatively well, with R 2 values of 0.49, 0.33, and 0.67 for of serum BHB, fatty acids, and urea, respectively. When the models were developed using data from a single year to predict data collected in future years, the R 2 remained comparable, however, the root mean squared errors increased substantially (4-10 times larger than compared with that of herd-year by herd-year external validation) which could be due to machine differences in spectral response, the change in spectral response of individual machines over time, or other differences associated with farm management between seasons. In conclusion, the mid-infrared equations for predicting serum BHB, fatty acids, and urea have been validated. The prediction equations could be used to help farmers detect cows with metabolic disorders in early lactation in addition to generating novel phenotypes for genetic improvement purposes.

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Section: Resultsmentioning

confidence: 99%

Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Luke

Pryce

2021

Journal of Dairy Science

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“…Many different spectrometers have been used for measuring the absorbance of electro-magnetic radiation in meat 1 , 23 , 32 . As well as near-infrared (NIR), ultra-violet 33 , 34 , visible 35 , and mid-infrared 33 , 36 radiations have also been used.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the practical obstacles to establishing a selection scheme for the genetic improvement of meat quality traits have recently been partially offset by the availability of small, portable and robust spectrometers 22 that can collect spectra directly from the meat surface in the working environment (abattoirs, meat processing plants, retail outlets, etc.) without the need to take, transport, preserve and process samples 23 . Portable spectrometers differ greatly in size, spectrum extension and definition, technical features, and cost.…”

Section: Introductionmentioning

confidence: 99%

Phenotypic and genetic variation of ultraviolet–visible-infrared spectral wavelengths of bovine meat

Bittante

Savoia

Cecchinato

et al. 2021

Sci Rep

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Spectroscopic predictions can be used for the genetic improvement of meat quality traits in cattle. No information is however available on the genetics of meat absorbance spectra. This research investigated the phenotypic variation and the heritability of meat absorbance spectra at individual wavelengths in the ultraviolet–visible and near-infrared region (UV–Vis-NIR) obtained with portable spectrometers. Five spectra per instrument were taken on the ribeye surface of 1185 Piemontese young bulls from 93 farms (13,182 Herd-Book pedigree relatives). Linear animal model analyses of 1481 single-wavelengths from UV–Vis-NIRS and 125 from Micro-NIRS were carried out separately. In the overlapping regions, the proportions of phenotypic variance explained by batch/date of slaughter (14 ± 6% and 17 ± 7%,), rearing farm (6 ± 2% and 5 ± 3%), and the residual variances (72 ± 10% and 72 ± 5%) were similar for the UV–Vis-NIRS and Micro-NIRS, but additive genetics (7 ± 2% and 4 ± 2%) and heritability (8.3 ± 2.3% vs 5.1 ± 0.6%) were greater with the Micro-NIRS. Heritability was much greater for the visible fraction (25.2 ± 11.4%), especially the violet, blue and green colors, than for the NIR fraction (5.0 ± 8.0%). These results allow a better understanding of the possibility of using the absorbance of visible and infrared wavelengths correlated with meat quality traits for the genetic improvement in beef cattle.

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“…However, FTIR spectroscopy potentially provides better means for exploring subtle chemical distinctions and minor chemical components than NIR. Today, FTIR is widely used in dairy industries for routine analysis for determination of chemical features such as fat, protein, lactose and urea contents [ 12 ]. Information on groups of fatty acids and even main single fatty acids can also be available from the spectra [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Information on groups of fatty acids and even main single fatty acids can also be available from the spectra [ 13 , 14 ]. Studies also indicate use of FTIR in analysis of features such as protein composition, milk coagulation properties, acidity, and milk minerals [ 12 , 15 ]. Considerable efforts have also been put into the development of FTIR spectroscopy for herd management applications such as EB predictions and cow health monitoring.…”

Section: Introductionmentioning

confidence: 99%

Exploring Dry-Film FTIR Spectroscopy to Characterize Milk Composition and Subclinical Ketosis throughout a Cow’s Lactation

Rachah

Reksen

Tafintseva

et al. 2021

Foods

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The use of technologies for measurements of health parameters of individual cows may ensure early detection of diseases and maximization of individual cow and herd potential. In the present study, dry-film Fourier transform infrared spectroscopy (FTIR) was evaluated for the purpose of detecting and quantifying milk components during cows’ lactation. This was done in order to investigate if these systematic changes can be used to identify cows experiencing subclinical ketosis. The data included 2329 milk samples from 61 Norwegian Red dairy cows collected during the first 100 days in milk (DIM). The resulting FTIR spectra were used for explorative analyses of the milk composition. Principal component analysis (PCA) was used to search for systematic changes in the milk during the lactation. Partial least squares regression (PLSR) was used to predict the fatty acid (FA) composition of all milk samples and the models obtained were used to evaluate systematic changes in the predicted FA composition during the lactation. The results reveal that systematic changes related to both gross milk composition and fatty acid features can be seen throughout lactation. Differences in the predicted FA composition between cows with subclinical ketosis and normal cows, in particular C14:0 and C18:1cis9, showed that dietary energy deficits may be detected by deviations in distinct fatty acid features.

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

Cited by 34 publications

References 188 publications

Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Phenotypic and genetic variation of ultraviolet–visible-infrared spectral wavelengths of bovine meat

Exploring Dry-Film FTIR Spectroscopy to Characterize Milk Composition and Subclinical Ketosis throughout a Cow’s Lactation

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