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Short communication: Predictive ability of Fourier-transform mid-infrared spectroscopy to assess CSN genotypes and detailed protein composition of buffalo milk

机译:简短交流:傅里叶变换中红外光谱技术评估水牛牛奶CSN基因型和详细蛋白质组成的预测能力

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摘要

The aim of this work was to test the applicability of Fourier-transform mid-infrared spectroscopy (FT-MIR) for the prediction of the contents of casein (CN) and whey protein fractions in buffalo milk. Buffalo milk samples spectra were collected using a MilkoScan FT2 (Foss, Hillerod, Denmark) over the spectral range from 5,000 to 900 wavenumber × cm~(-1). Contents of protein fractions, as well as CSN1S1 and CSN3 genotypes, were assessed by reversed phase HPLC. The highest coefficients of determination in cross-validation (1 -VR) were obtained for the contents (g/L of milk) of total protein and CN (1 - VR = 0.92), followed by the content of β-CN, total whey protein, and α_(S2)-CN (1 -VR of 0.87, 0.77, and 0.63, respectively). Conversely, contents of α_(S2)-CN, γ-CN, glycosylated-κ-CN, total κ-CN, and whey protein fractions were predicted with poor accuracy (1 - VR <0.51). When protein fractions were expressed as percentages to total protein, 1 - VR values were never greater than 0.61 (β-CN). Only 56 and 70% of the observations were correctly classified by discriminant analysis in each of 2 groups of CSN1S1 and CSN3 genotypes, respectively. Results showed that FT-MIR spectroscopy is not applicable when prediction of detailed milk protein composition with high accuracy is required. Predictions may play a role as indicator traits in selective breeding, if the genetic correlation between FT-MIR predictions and measures of milk protein composition are high enough and predictions of protein fraction contents are sufficiently independent from the predicted total protein content.
机译:这项工作的目的是测试傅里叶变换中红外光谱(FT-MIR)在预测水牛乳中酪蛋白(CN)和乳清蛋白组分含量方面的适用性。使用MilkoScan FT2(Foss,Hillerod,丹麦)在5,000至900波数×cm〜(-1)的光谱范围内收集水牛牛奶样品的光谱。通过反相HPLC评估蛋白质级分以及CSN1S1和CSN3基因型的含量。对于总蛋白和CN(1-VR = 0.92)的含量(牛奶中的g / L),获得交叉验证的最高确定系数(1-VR),然后是总乳清的β-CN含量蛋白和α_(S2)-CN(1-VR分别为0.87、0.77和0.63)。相反,预测的α_(S2)-CN,γ-CN,糖基化的κ-CN,总κ-CN和乳清蛋白组分的含量准确度较差(1-VR <0.51)。当蛋白质分数以占总蛋白质的百分比表示时,1-VR值永远不会大于0.61(β-CN)。通过判别分析,分别将CSN1S1和CSN3基因型的两组分别正确地分类了56%和70%。结果表明,当需要高精度预测详细的乳蛋白成分时,FT-MIR光谱法不适用。如果FT-MIR预测和乳蛋白成分测度之间的遗传相关性足够高,并且蛋白质组分含量的预测与预测的总蛋白含量充分独立,则预测可能会在选择性育种中充当指示性状。

著录项

  • 来源
    《Journal of dairy science》 |2015年第9期|6583-6587|共5页
  • 作者单位

    Department of Comparative Biomedicine and Food Science, and Animals and Environment (DAFNAE) University of Padova, 35020 Legnaro, Padova, Italy;

    Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) University of Padova, 35020 Legnaro, Padova, Italy;

    Department of Comparative Biomedicine and Food Science, and Animals and Environment (DAFNAE) University of Padova, 35020 Legnaro, Padova, Italy;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    buffalo milk; protein composition; casein fractions; spectroscopy;

    机译:水牛牛奶蛋白质组成;酪蛋白级分;光谱学;
  • 入库时间 2022-08-17 23:23:38

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