Park B, Whittaker A D, Miller R K, Bray D E
Department of Agricultural Engineering, Texas A&M University, College Station 77843-2117.
J Anim Sci. 1994 Jan;72(1):117-25. doi: 10.2527/1994.721117x.
Frequency analysis of Fourier spectra from ultrasonic signals was used for predicting intramuscular fat content of beef tissue. The most significant parameter in the frequency domain for predicting intramuscular fat concentration in beef was the number of local maxima. It represents the discontinuity of the Fourier spectrum caused by inhomogeneous fat concentrations in the longissimus muscle, which had the correlation coefficient .89 (P < .05) when a 2.25-MHz shear probe was used. The optimum frequency for predicting the amount of intramuscular fat content in the longissimus muscle was found to be 1.92 MHz. A multivariate regression model was developed using parameters in the frequency domain as follows: percentage of fat concentration = 1.790 - 2.373x (lower frequency) + .049x (bandwidth) + 1.178x (local maxima) (R2 = .82). Validation demonstrated that the multivariate model in the frequency domain was capable of predicting intramuscular fat concentration with an average of 1.17 percentage of fat error (P < .05). The multivariate model was most appropriate for predicting intramuscular fat below 4%. The mean accuracy of the model in the frequency domain was approximately 79%.
利用超声信号傅里叶谱的频率分析来预测牛肉组织的肌内脂肪含量。在频域中预测牛肉肌内脂肪浓度的最重要参数是局部最大值的数量。它代表了由背最长肌中脂肪浓度不均匀引起的傅里叶谱的不连续性,当使用2.25兆赫的剪切探头时,其相关系数为0.89(P < 0.05)。发现预测背最长肌肌内脂肪含量的最佳频率为1.92兆赫。使用频域中的参数建立了多元回归模型如下:脂肪浓度百分比 = 1.790 - 2.373x(较低频率)+ 0.049x(带宽)+ 1.178x(局部最大值)(R2 = 0.82)。验证表明,频域中的多元模型能够以平均1.17%的脂肪误差预测肌内脂肪浓度(P < 0.05)。该多元模型最适合预测低于4%的肌内脂肪。频域中模型的平均准确率约为79%。