为实现对多样本新鲜羊肉营养含量的快速检测,该研究利用近红外光谱(near-infrared reflectance spectroscopy,NIRS)技术构建了新鲜羊肉中6种营养成分的定量分析模型。于武威市民勤县采集203份新鲜羊肉,并测定其水分(moisture,MT)、粗脂肪(ether extract,EE)、粗蛋白(crude protein,CP)、葡萄糖(glucose,Glu)、粗灰分(crude ash,Ash)及总磷(phosphorus,P)的含量。使用WINISI III与Foss Calibrator定标软件分别建立羊肉6种营养成分的NIRS模型并对其结果进行比较。WINISI III软件定标结果显示,羊肉MT、EE、CP预测模型的预测决定系数(coefficient of determination for validation,RSQ)和外部验证相对分析误差(ratio of performance to deviation for vali- dation,RPD)分别为0.83与2.47、0.90与3.60、0.81与2.79;Glu、Ash预测模型的RSQ和RPD分别为0.54与3.05、0.54与1.91;P预测模型的RSQ和RPD为0.45与1.80 。Foss Calibrator软件定标结果显示,MT、EE、CP的交互验证均方根误差[root mean square error of cross-verification,RMSEP(cross)]和决定系数(coefficient of determination,R2)分别为0.631与0.84、0.326与0.87、0.468与0.83 ;Glu、Ash的RMSEP(cross)和R2分别为0.127 与0.53、0.179与0.51;P的RMSEP(cross)和R2为0.086与0.33。2种定标软件得到的结论基本一致,均表明MT、EE、CP的预测模型可在实际生产中精确预测;Glu、Ash的预测模型可在大量样品的粗略分析与筛选时应用,但还需继续优化;P的预测模型相关性较差,不能在实际生产中应用。
In order to rapidly determine the nutrient content of a large number of fresh lamb samples, this study used near-infrared reflectance spectroscopy (NIRS) to construct a quantitative analysis model of six nutrients in fresh lamb, Totally 203 fresh mutton samples were collected in Minqin county, Wuwei city, and moisture content (moisture, MT), crude fat (ether extract, EE), crude protein (crude protein, CP), glucose (glucose, Glu), crude ash (crude ash, Ash) and total phosphorus (total phosphorus, P) content of samples were measured, WINISI III and Foss Calibrator calibration software were used to establish NIRS models of 6 kinds of mutton nutrients and the results were compared, WINISI III software calibration results showed that the coefficient of determination for validation (RSQ) and the ratio of performance to deviation for validation (RPD) of the MT, EE, and CP prediction models were 0.83 and 2.47, 0.90 and 3.60, 0.81 and 2.79 respectively;the RSQ and RPD of the Glu and Ash prediction models were 0.54 and 3.05, 0.54 and 1.91, respectively;the RSQ and RPD of the P predictive model were 0.45 and1.80, respectively, The calibration results of Foss Calibrator software showed that the root means a square error of cross-verification [RMSEP(cross)] and coefficient of determination (R2)of MT, EE, and CP were 0.631 and 0.84, 0.326 and 0.87, 0.468 and 0.83, respectively;RMSEP (cross) and R2 of Glu and Ash were 0.127 and 0.53, 0.179 and 0.51, respectively; RMSEP (cross) and R2 of P were 0.086 and 0.33, respectively, The conclusions obtained by the two-calibration software were similar, indicating that the prediction models of MT, EE, and CP can be accurately predicted in actual production, The prediction models of Glu and Ash can be used to roughly analyze and screen a large number of samples, and need to be further optimized. The prediction model of P had a poor correlation and cannot be applied in actual production.
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