Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk

Bonfatti, V.; Vicario, D.; Degano, L.; Lugo, Alejandro; Carnier, Paolo

doi:10.3168/jds.2016-11951

Cited by 9 publications

(29 citation statements)

References 11 publications

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“…The PLS factors are thus expected to give better prediction of the trait than the one with latent traits from PCA. However, with PLS, information about other milk composition traits not included in the calibration may not be retained in the factor scores, as also indicated by Dagnachew et al (2013b) and Bonfatti et al (2017). The expected better prediction of traits with PLS model was confirmed.…”

Section: Evaluation Of the Ip And Dp Approachessupporting

confidence: 59%

“…It is also in contrast to the work of Dagnachew et al (2013b), who reported better prediction in accuracy of EBV for milk contents in goat by DP than IP approaches. Bonfatti et al (2017) also reported results that are mostly in contrast with the work of Dagnachew et al (2013b), who found high rank correlations (>0.94) between IP and DP predicted EBV of all traits investigated. Bonfatti et al (2017) reported <0.5 rank correlations between EBV predicted by IP and DP for most traits included in their study.…”

Section: Evaluation Of the Ip And Dp Approachescontrasting

confidence: 48%

“…Bonfatti et al (2017) also reported results that are mostly in contrast with the work of Dagnachew et al (2013b), who found high rank correlations (>0.94) between IP and DP predicted EBV of all traits investigated. Bonfatti et al (2017) reported <0.5 rank correlations between EBV predicted by IP and DP for most traits included in their study. Reasons why the DP approach resulted in better prediction for EBV (e.g., Dagnachew et al, 2013b), but not for phenotypic are not clear, but could be due to difference in methods of comparison (correlation vs prediction error variance) and type of parameters compared (phenotype vs EBV).…”

Section: Evaluation Of the Ip And Dp Approachescontrasting

confidence: 48%

“…Dagnachew et al (2013b) also used 8 latent traits to extract genetic component of the FT-MIR spectra and indicated the possible existence of relevant information in the remaining 1% of the total spectral variation. Interestingly, Bonfatti et al (2017) also showed that a considerable amount of information needed to predict phenotypes is lost when using 99% of original spectral variability, and loss of such information could affect prediction of EBV from spectral information. Those authors further showed that information left in 0.01% of original spectral variability is fundamental for prediction of some of the traits included.…”

Section: Evaluation Of the Ip And Dp Approachesmentioning

confidence: 99%

“…However, independent chemical analyses (reference values) for the milk content were not available in that study (i.e., the study relied on phenotypes predicted based on the same spectra for both model calibration and evaluation). Recently, Bonfatti et al (2017) compared the IP and DP approaches to estimate EBV for several traits related to fine composition and technological properties of milk and reported rank correlations ranging from 0.07 to 0.96, but <0.5 for most traits. In the present study, we adopted the IP and DP approaches to predict phenotype (not EBV) for BHB having an independent reference value for this trait.…”

Section: Introductionmentioning

confidence: 99%

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An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

Belay

Dagnachew

Kowalski

et al. 2017

Journal of Dairy Science

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Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables.

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Section: Evaluation Of the Ip And Dp Approachessupporting

confidence: 59%

Section: Evaluation Of the Ip And Dp Approachescontrasting

confidence: 48%

Section: Evaluation Of the Ip And Dp Approachescontrasting

confidence: 48%

Section: Evaluation Of the Ip And Dp Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

Belay

Dagnachew

Kowalski

et al. 2017

Journal of Dairy Science

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Computer Network Intrusion Anomaly Detection Based on Rough Fourier Fast Algorithm

Duan

2022

Mathematical Problems in Engineering

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Since there is a close relationship between network information security attack events and time complexity, it is necessary to count the degree of correlation between the current connection record and the connection record within a certain period of time before. Only in this way can the relationship between network connection data and network information security attack events be better reflected. In this paper, a rough Fourier fast algorithm based on rough set theory is proposed. Based on the characteristic attributes of the intrusion detection data set with the most value of character attributes as the data division basis, the computer network intrusion anomaly detection data set is intelligently divided into small data sets, so as to carry out attribute reduction. The network intrusion detection rule update experiment adopts the misuse detection method, extracts some samples from the KDD99 data set for training, obtains the computer network intrusion detection rules in the hierarchical decision table, and uses the incremental learning algorithm to update the rules, compared with the intrusion detection rules expressed by the decision table to test the feasibility and effectiveness of the rule update, and compared with the improved RSDB, RE-RFE algorithm, and KNN algorithm to evaluate the effect of the rough Fourier fast detection model applied to the problem of network intrusion anomaly detection. Using the attribute subset reduced by the rough Fourier algorithm to perform classification and modeling of computer network intrusion anomaly detection is significantly reduced, and the average time is reduced by 0.09 seconds, and this paper lays a good foundation for the application of network security intrusion detection algorithm.

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The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle

Tiplady

Lopdell

Littlejohn

et al. 2020

J Animal Sci Biotechnol

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Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform midinfrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.

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Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk

Cited by 9 publications

References 11 publications

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

Computer Network Intrusion Anomaly Detection Based on Rough Fourier Fast Algorithm

The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle

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