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Feasibility of the direct application of near-infrared reflectance spectroscopy on intact chicken breasts to predict meat color and physical traits

机译:在完整的鸡胸肉上直接应用近红外反射光谱法来预测肉的颜色和物理特性的可行性

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摘要

Physical and color characteristics of chicken meat were investigated on 193 animals by directly applying a fiberoptic probe to the breast muscle and using the visible-near-infrared (NIR) spectral range from 350 to 1,800 nm. Data on pH was recorded 48 h postmortem (pH); lightness (L*), redness (a*), and yellowness (b*) 48 h postmortem; thawing and cooking losses and shear force after freezing. Partial least squares regressions were performed using untreated data, raw absorbance data (log(1/R)), and multiplicative scatter correction plus first or second derivative spectra. Models were validated using full cross-validation, and their predictive ability was determined by root mean square error of cross-validation (RMSE(CV)) and correlation coefficient of cross-validation (r(cv)). Means (+/- SD) of pH, L*, a*, b*, thawing loss, cooking loss, and shear force were 5.83 +/- 0.13, 44.54 +/- 2.42, -1.90 +/- 0.62, 3.21 +/- 3.28, 4.84 +/- 2.44%, 19.39 +/- 2.95%, and 16.08 +/- 3.83 N, respectively. The best prediction models were developed using log(1/R) spectra for b* (r(cv) = 0.93; RMSE(CV) = 1.16) and a* (r(cv) = 0.88; RMSE(CV) = 0.29), while a medium predictive ability of NIR was obtained for pH, L*, and thawing and cooking losses (r(cv) from 0.69 to 0.76; RMSE(CV) from 0.01 to 1.73). Finally, predicted model for shear force (r(cv) = 0.41; RMSE(CV) = 3.18) was unsatisfactory. Results suggest that NIR is a feasible technique for the assessment of several quality traits of intact breast muscle.
机译:通过将光纤探针直接应用于胸肌并使用350至1,800 nm的可见-近红外(NIR)光谱,对193只动物的鸡肉的物理和颜色特征进行了研究。死后48小时记录pH值。死后48小时的亮度(L *),红色(a *)和黄色(b *);冷冻后解冻和蒸煮损失以及剪切力。使用未处理的数据,原始吸光度数据(log(1 / R))和乘法散射校正加上一阶或二阶导数光谱进行偏最小二乘回归。使用完全交叉验证对模型进行验证,并通过交叉验证的均方根误差(RMSE(CV))和交叉验证的相关系数(r(cv))确定模型的预测能力。 pH,L *,a *,b *,解冻损耗,蒸煮损耗和剪切力的平均值(+/- SD)为5.83 +/- 0.13、44.54 +/- 2.42,-1.90 +/- 0.62、3.21 + /-3.28、4.84 +/- 2.44%,19.39 +/- 2.95%和16.08 +/- 3.83N。最佳预测模型是使用log(1 / R)谱图开发的,b *(r(cv)= 0.93; RMSE(CV)= 1.16)和a *(r(cv)= 0.88; RMSE(CV)= 0.29) ,而获得的NIR对pH,L *以及融化和蒸煮损失的预测能力中等(r(cv)从0.69至0.76; RMSE(CV)从0.01至1.73)。最后,剪切力的预测模型(r(cv)= 0.41; RMSE(CV)= 3.18)并不令人满意。结果表明,近红外光谱技术是评估完整乳房肌肉多个品质特征的可行技术。

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