peer-reviewedThe data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300–2800 cm−1

Allen, Paul

Downey, Gerard

Kerry, Joseph P.

Nian, Yingqun

O’Donnell, Colm P.

Zhao, Ming

Data in Brief

English

The data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300–2800 cm−1. Keywords: Selected Raman shift ranges, Sensory attributes, Bull beef, Partial least squares regression model

Ming Zhao

Yingqun Nian

Paul Allen

Gerard Downey

Joseph P. Kerry

Colm P. O’Donnell

Directory of Open Access Journals

Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes

T-Stór

Q1Q2Q3123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354Contents lists available at ScienceDirectData in BriefData in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎https://d2352-34(http://cDOIn CorrE-mPleasregreattribjournal homepage: www.elsevier.com/locate/dibData ArticlePerformances of full cross-validation partial leastsquares regression models developed usingRaman spectral data for the prediction of bullbeef sensory attributesMing Zhao a,n, Yingqun Nian b,c, Paul Allen b, Gerard Downey a,Joseph P. Kerry c, Colm P. O’Donnell aa School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Irelandb Department of Food Quality and Sensory Science, Teagasc Food Research Centre, Ashtown, Dublin 15,Irelandc School of Food and Nutritional Sciences, University College Cork, Cork, Irelanda r t i c l e i n f oArticle history:Received 10 February 2018Received in revised form13 February 2018Accepted 17 April 2018Keywords:Selected Raman shift rangesSensory attributesBull beefPartial least squares regression modelsoi.org/10.1016/j.dib.2018.04.05609/& 2018 Published by Elsevier Inc. Thisreativecommons.org/licenses/by/4.0/).of original article: https://doi.org/10.1016/jesponding author.ail address: ming.zhao@ucd.ie (M. Zhao).e cite this article as: M. Zhao, et alssion models developed using Rautes, Data in Brief (2018), https://a b s t r a c tThe data presented in this article are related to the research articleentitled “Application of Raman spectroscopy and chemometrictechniques to assess sensory characteristics of young dairy bullbeef” (Zhao et al., 2018) [1]. Partial least squares regression (PLSR)models were developed on Raman spectral data pre-treated usingSavitzky Golay (S.G.) derivation (with 2nd or 5th order polynomialbaseline correction) and results of sensory analysis on bull beefsamples (n ¼ 72). Models developed using selected Raman shiftranges (i.e. 250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1)were explored. The best model performance for each sensoryattributes prediction was obtained using models developed onRaman spectral data of 1300–2800 cm−1.& 2018 Published by Elsevier Inc. This is an open access articleunder the CC BY license(http://creativecommons.org/licenses/by/4.0/).is an open access article under the CC BY license.foodres.2018.02.007., Performances of full cross-validation partial least squaresman spectral data for the prediction of bull beef sensorydoi.org/10.1016/j.dib.2018.04.056i555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108M. Zhao et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎2Specifications TableSMTHDEEDPlease cite this articregression models dattributes, Data in Bubject area Spectroscopy, Chemometricsore specificsubject areaPerformance of PLSR models developed using selected Raman shift ranges (i.e.250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1)ype of data Tableow data wasacquiredRaman spectroscopy, Results of sensory analysis, Chemometricsata format .docxperimentalfactorsRaman spectral data were pre-treated using Savitzky Golay (S.G.) derivation with2nd or 5th order polynomial baseline correction.xperimentalfeatures–ata sourcelocationSchool of Biosystems and Food Engineering, University College Dublin, Belfield,Dublin 4, Irelandata accessibility Data is with this articleDValue of the data To demonstrate PLSR models developed using Raman spectra in the 1300–2800 cm−1 range cangive best prediction performance on sensory attributes of bull beef. Results of this work are in agreement with a previous study by [2] that the Raman frequency rangeof 1300–2800 cm−1 is the most suitable range for prediction of bull beef eating quality parameters. This data suggested other researchers to select an optimal Raman shift range for further meatscience studies.1. DataPLSR models were developed on Raman data pre-treated using Savitzky Golay (S.G.) derivationwith 2nd and 5th order polynomial baseline correction. Prediction performance of models developedusing selected Raman shift ranges (i.e. 250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1) weresummarized in Table 1. PLS models developed using S.G. derivation pre-treated Raman spectra in the1300–2800 cm−1 range performed best (R2CV values of 0.36–0.84) while spectra in the range 900–1800 cm−1 performed worst (R2CV values of 0.03–0.66).2. Experimental design, materials and methodsFor the prediction of beef sensory attributes, partial least squares regression (PLSR) models weredeveloped using pre-processed Raman spectroscopic data (X data) collected on the 21st day post-mortem using pre-selected frequency ranges (i.e. 250–3380 cm−1, 900–1800 cm−1, 1300–2800 cm−1);these were selected on the basis of spectral signal intensities. Measured values of sixteen sensoryattributes were used as individual Y variable for PLS regression. Leave-one-out cross-validation wasperformed to evaluate the performance of PLSR models using parameters such as root mean squareerror of calibration (RMSEC) and cross-validation (RMSECV), the coefficient of determination oncalibration (R2C) and cross-validation (R2CV) and the bias which is calculated as the differencebetween the average of actual and predicted values for each data set [3]. For a satisfactory predictionperformance, the value of R2 is expected to be close to 1 while values of RMSECV and bias areexpected to be close to 0.le as: M. Zhao, et al., Performances of full cross-validation partial least squareseveloped using Raman spectral data for the prediction of bull beef sensoryrief (2018), https://doi.org/10.1016/j.dib.2018.04.056i109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162Table 1Summary of full cross-validation PLSR model performances for sensory attributes prediction using all beef samples (n ¼ 72).Please cite this article as: M. Zhao, et al., Performances of full cross-validation partial least squaresregression models developed using Raman spectral data for the prediction of bull beef sensoryattributes, Data in Brief (2018), https://doi.org/10.1016/j.dib.2018.04.056iM. Zhao et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 3163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216Table 1 (continued )Please cite this article as: M. Zhao, et al., Performances of full cross-validation partial least squaresregression models developed using Raman spectral data for the prediction of bull beef sensoryattributes, Data in Brief (2018), https://doi.org/10.1016/j.dib.2018.04.056iM. Zhao et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎4217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270Table 1 (continued )Please cite this article as: M. Zhao, et al., Performances of full cross-validation partial least squaresregression models developed using Raman spectral data for the prediction of bull beef sensoryattributes, Data in Brief (2018), https://doi.org/10.1016/j.dib.2018.04.056iM. Zhao et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 5Q4Q5Q6271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305PLSR, partial least squares regression models; S.G., Savitzky Golay; der., derivatives; nor.u.v., normalisation on unit vectors; #PLS loadings, number of PLS loadings; R2C, coefficient determination of calibration; RMSEC, root mean square error of cali-bration; R2CV, correlation coefficient of determination in cross-validation; RMSECV, root mean square error of cross-validation;IT, Initial Tenderness; ED, Ease of Disintegration; Res-, Residual (after effects); n, numbers of samples. (Note: The best per-formed PLS models developed in the Raman shifts of 1300–2800 cm−1 were highlighted in yellow).M. Zhao et al. / Data in Brief ∎ (∎∎∎∎) ∎∎∎–∎∎∎6AcknowledgmentsThe authors wish to thank Carol Griffin and Carmel Farrell (Teagasc Ashtown) for training of thebeef sensory panel.Funding sourcesThis work was funded by the Teagasc Walsh Fellowship Scheme. We wish to acknowledge TeagascJohnstown Castle Research Centre for rearing the dairy bulls and Dawn Meats for slaughtering thedairy bulls.Transparency document. Supplementary materialTransparency data associated with this article can be found in the online version at https://doi.org/10.1016/j.dib.2018.04.056.References[1] M. Zhao, Y. Nian, P. Allen, G. Downey, J.P. Kerry, C.P. O’Donnell, Application of Raman spectroscopy and chemometrictechniques to assess sensory characteristics of young dairy bull beef (In press), Food Res. Int. (2018), http://dx.doi.org/10.1016/j.foodres.2018.02.007.[2] Y. Nian, M. Zhao, C.P. O’Donnell, G. Downey, J.P. Kerry, P. Allen, Assessment of physico-chemical traits related to eatingquality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometrics, Food Res. Int. 99(2017) 778–789.[3] T. Næs, T. Isaksson, T. Fearn, T. Davis, A User-friendly Guide to Multivariate Calibration and Classification. Chapter 13.Validation, NIR Publications, UK (2002) 163–167.Please cite this article as: M. Zhao, et al., Performances of full cross-validation partial least squaresregression models developed using Raman spectral data for the prediction of bull beef sensoryattributes, Data in Brief (2018), https://doi.org/10.1016/j.dib.2018.04.056i

The data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” (Zhao et al., 2018) [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300–2800 cm−1

O'Donnell, Colm P.

Cork Open Research Archive

Title Performances of full cross-validation partial least squares regressionmodels developed using Raman spectral data for the prediction of bullbeef sensory attributesAuthor(s) Zhao, Ming; Nian, Yingqun; Allen, Paul; Downey, Gerard; Kerry,Joseph P.; O'Donnell, Colm P.Original citation Zhao, M., Nian, Y., Allen, P., Downey, G., Kerry, J. P. and O’Donnell,C. P. (2018) 'Performances of full cross-validation partial least squaresregression models developed using Raman spectral data for theprediction of bull beef sensory attributes', Data in Brief. doi:10.1016/j.dib.2018.04.056Type of publication Article (peer-reviewed)Link to publisher'sversionhttps://www.sciencedirect.com/science/article/pii/S2352340918304098http://dx.doi.org/10.1016/j.dib.2018.04.056Access to the full text of the published version may require asubscription.Rights © 2018, the Authors. Published by Elsevier Inc. Open Access. Thisarticle is made available under the CC-BY 4.0 license.https://creativecommons.org/licenses/by/4.0/Item downloadedfromhttp://hdl.handle.net/10468/6225Downloaded on 2019-01-07T05:41:33ZData ArticlePerformances of full cross-validation partial leastsquares regression models developed usingRaman spectral data for the prediction of bullbeef sensory attributesMing Zhao a,n, Yingqun Nian b,c, Paul Allen b, Gerard Downey a,Joseph P. Kerry c, Colm P. O’Donnell aa School of Biosystems and Food Engineering, University College Dublin, Belﬁeld, Dublin 4, Irelandb Department of Food Quality and Sensory Science, Teagasc Food Research Centre, Ashtown, Dublin 15,Irelandc School of Food and Nutritional Sciences, University College Cork, Cork, Irelanda r t i c l e i n f oArticle history:Received 10 February 2018Received in revised form13 February 2018Accepted 17 April 2018Available online 23 April 2018Keywords:Selected Raman shift rangesSensory attributesBull beefPartial least squares regression modelsa b s t r a c tThe data presented in this article are related to the research articleentitled “Application of Raman spectroscopy and chemometrictechniques to assess sensory characteristics of young dairy bullbeef” [1]. Partial least squares regression (PLSR) models weredeveloped on Raman spectral data pre-treated using SavitzkyGolay (S.G.) derivation (with 2nd or 5th order polynomial baselinecorrection) and results of sensory analysis on bull beef samples(n ¼ 72). Models developed using selected Raman shift ranges (i.e.250–3380 cm1, 900–1800 cm1 and 1300–2800 cm1) wereexplored. The best model performance for each sensory attributesprediction was obtained using models developed on Ramanspectral data of 1300–2800 cm1.& 2018 Published by Elsevier Inc. This is an open access articleunder the CC BY license(http://creativecommons.org/licenses/by/4.0/).Contents lists available at ScienceDirectjournal homepage: www.elsevier.com/locate/dibData in Briefhttps://doi.org/10.1016/j.dib.2018.04.0562352-3409/& 2018 Published by Elsevier Inc. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).DOI of original article: https://doi.org/10.1016/j.foodres.2018.02.007n Corresponding author.E-mail address: ming.zhao@ucd.ie (M. Zhao).Data in Brief 19 (2018) 1355–1360Speciﬁcations TableSubject area Spectroscopy, ChemometricsMore speciﬁc subject area Performance of PLSR models developed using selected Raman shiftranges (i.e. 250–3380 cm1, 900–1800 cm1 and 1300–2800 cm1)Type of data TableHow data was acquired Raman spectroscopy, Results of sensory analysis, ChemometricsData format .docExperimental factors Raman spectral data were pre-treated using Savitzky Golay (S.G.)derivation with 2nd or 5th order polynomial baseline correction.Experimental features –Data source location School of Biosystems and Food Engineering, University College Dublin,Belﬁeld, Dublin 4, IrelandData accessibility Data is with this articleValue of the data To demonstrate PLSR models developed using Raman spectra in the 1300–2800 cm1 range cangive best prediction performance on sensory attributes of bull beef. Results of this work are in agreement with a previous study by Nian et al. [2] that the Ramanfrequency range of 1300–2800 cm1 is the most suitable range for prediction of bull beef eatingquality parameters. This data suggested other researchers to select an optimal Raman shift range for further meatscience studies.1. DataPLSR models were developed on Raman data pre-treated using Savitzky Golay (S.G.) derivationwith 2nd and 5th order polynomial baseline correction. Prediction performance of models developedusing selected Raman shift ranges (i.e. 250–3380 cm1, 900–1800 cm1 and 1300–2800 cm1) weresummarized in Table 1. PLS models developed using S.G. derivation pre-treated Raman spectra in the1300–2800 cm1 range performed best (R2CV values of 0.36–0.84) while spectra in the range 900–1800 cm1 performed worst (R2CV values of 0.03–0.66). Results shown in this work are the sup-plementary materials of the research article 'Application of Raman spectroscopy and chemometrictechniques to assess sensory characteristics of young dairy bull beef' [1].2. Experimental design, materials and methodsFor the prediction of beef sensory attributes, partial least squares regression (PLSR) models weredeveloped using pre-processed Raman spectroscopic data (X data) collected on the 21st day post-mortem using pre-selected frequency ranges (i.e. 250–3380 cm1, 900–1800 cm1, 1300–2800cm1); these were selected on the basis of spectral signal intensities. Measured values of sixteensensory attributes were used as individual Y variable for PLS regression. Leave-one-out cross-validation was performed to evaluate the performance of PLSR models using parameters such asroot mean square error of calibration (RMSEC) and cross-validation (RMSECV), the coefﬁcient ofdetermination on calibration (R2C) and cross-validation (R2CV) and the bias which is calculated as thedifference between the average of actual and predicted values for each data set [3]. For a satisfactoryprediction performance, the value of R2 is expected to be close to 1 while values of RMSECV and biasare expected to be close to 0.M. Zhao et al. / Data in Brief 19 (2018) 1355–13601356Table 1Summary of full cross-validation PLSR model performances for sensory attributes prediction using all beef samples (n ¼ 72).M. Zhao et al. / Data in Brief 19 (2018) 1355–1360 1357Table 1 (continued )M. Zhao et al. / Data in Brief 19 (2018) 1355–13601358Table 1 (continued )PLSR, partial least squares regression models; S.G., Savitzky Golay; der., derivatives; nor.u.v., normalisation on unit vectors; #PLS loadings, number of PLS loadings; R2C, coefﬁcient determination of calibration; RMSEC, root mean square error of cali-bration; R2CV, correlation coefﬁcient of determination in cross-validation; RMSECV, root mean square error of cross-validation;IT, Initial Tenderness; ED, Ease of Disintegration; Res-, Residual (after effects); n, numbers of samples. (Note: The best per-formed PLS models developed in the Raman shifts of 1300–2800 cm1 were highlighted in yellow).M. Zhao et al. / Data in Brief 19 (2018) 1355–1360 1359AcknowledgmentsThe authors wish to thank Carol Grifﬁn and Carmel Farrell (Teagasc Ashtown) for training of thebeef sensory panel.Funding sourcesThis work was funded by the Teagasc Walsh Fellowship Scheme. We wish to acknowledge TeagascJohnstown Castle Research Centre for rearing the dairy bulls and Dawn Meats for slaughtering thedairy bulls.Transparency document. Supplementary materialSupplementary data associated with this article can be found in the online version at https://doi.org/10.1016/j.dib.2018.04.056.References[1] M. Zhao, Y. Nian, P. Allen, G. Downey, J.P. Kerry, C.P. O’Donnell, Application of Raman spectroscopy and chemometrictechniques to assess sensory characteristics of young dairy bull beef, Food Res. Int. 107 (2018) 27–40. http://dx.doi.org/10.1016/j.foodres.2018.02.007.[2] Y. Nian, M. Zhao, C.P. O’Donnell, G. Downey, J.P. Kerry, P. Allen, Assessment of physico-chemical traits related to eatingquality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometrics, Food Res. Int. 99(2017) 778–789.[3] T. Næs, T. Isaksson, T. Fearn, T. Davis, A User-friendly Guide to Multivariate Calibration and Classiﬁcation. Chapter 13.Validation, NIR Publications, UK (2002) 163–167.M. Zhao et al. / Data in Brief 19 (2018) 1355–13601360

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Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes

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