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Beef Tenderness Prediction by a Combination of Statistical Methods: Chemometrics and Supervised Learning to Manage Integrative Farm-To-Meat Continuum Data

机译:结合统计方法对牛肉的嫩度进行预测:化学计量学和有监督的学习方法以管理从农场到肉场的连续数据

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摘要

This trial aimed to integrate metadata that spread over farm-to-fork continuum of 110 Protected Designation of Origin (PDO)Maine-Anjou cows and combine two statistical approaches that are chemometrics and supervised learning; to identify the potential predictors of beef tenderness analyzed using the instrumental Warner-Bratzler Shear force (WBSF). Accordingly, 60 variables including WBSF and belonging to 4 levels of the continuum that are farm-slaughterhouse-muscle-meat were analyzed by Partial Least Squares (PLS) and three decision tree methods (C&RT: classification and regression tree; QUEST: quick, unbiased, efficient regression tree and CHAID: Chi-squared Automatic Interaction Detection) to select the driving factors of beef tenderness and propose predictive decision tools. The former method retained 24 variables from 59 to explain 75% of WBSF. Among the 24 variables, six were from farm level, four from slaughterhouse level, 11 were from muscle level which are mostly protein biomarkers, and three were from meat level. The decision trees applied on the variables retained by the PLS model, allowed identifying three WBSF classes (Tender (WBSF ≤ 40 N/cm2), Medium (40 N/cm2 < WBSF < 45 N/cm2), and Tough (WBSF ≥ 45 N/cm2)) using CHAID as the best decision tree method. The resultant model yielded an overall predictive accuracy of 69.4% by five splitting variables (total collagen, µ-calpain, fiber area, age of weaning and ultimate pH). Therefore, two decision model rules allow achieving tender meat on PDO Maine-Anjou cows: (i) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain ≥ 169 arbitrary units (AU)) AND (ultimate pH < 5.55) THEN meat was very tender (mean WBSF values = 36.2 N/cm2, n = 12); or (ii) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain < 169 AU) AND (age of weaning < 7.75 months) AND (fiber area < 3100 µm2) THEN meat was tender (mean WBSF values = 39.4 N/cm2, n = 30).
机译:这项试验的目的是整合元数据,这些元数据分布在110种受保护的原产地名称(PDO)缅因州-安茹牛的农场到餐桌上,并结合了化学计量学和监督学习两种统计方法。以确定使用仪器的Warner-Bratzler剪切力(WBSF)分析的牛肉嫩度的潜在预测因子。因此,通过偏最小二乘(PLS)和三种决策树方法(C&RT:分类和回归树; QUEST:快速,无偏见)分析了包括WBSF在内的60个变量,这些变量属于农场屠宰场肌肉肉的4个连续体,高效的回归树和CHAID:卡方自动交互检测)来选择牛肉嫩度的驱动因素并提出预测性决策工具。前一种方法保留了59个中的24个变量,以解释WBSF的75%。在这24个变量中,六个来自农场水平,四个来自屠宰场水平,11个来自肌肉水平,主要是蛋白质生物标志物,三个来自肉类水平。将决策树应用于PLS模型保留的变量,可以识别三种WBSF类(投标(WBSF≤40 N / cm 2 ),中(40 N / cm 2 ) > 2 )和强韧性(WBSF≥45 N / cm 2 )),使用CHAID作为最佳决策树方法。通过五个分裂变量(总胶原蛋白,μ-钙蛋白酶,纤维面积,断奶年龄和最终pH),所得模型的整体预测准确性为69.4%。因此,有两个决策模型规则可以使PDO Maine-Anjou奶牛获得嫩肉:(i)IF(总胶原蛋白<3.6μgOH-脯氨酸/ mg)和(μ-钙蛋白酶≥169任意单位(AU))AND(最终pH <5.55)则肉非常嫩(平均WBSF值= 36.2 N / cm 2 ,n = 12);或(ii)IF(总胶原蛋白<3.6μgOH-脯氨酸/ mg)和(μ-钙蛋白酶<169 AU)且(断奶年龄<7.75个月)且(纤维面积<3100 µm 2 ) )然后将肉变软(平均WBSF值= 39.4 N / cm 2 ,n = 30)。

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