近红外光谱法快速评定牛肉品质

Rapid Evaluation of Beef Quality by NIRS Technology

应用近红外反射光谱技术（NIRS）,采用偏最小二乘法（PLS）,建立了牛肉理化特性的近红外预测模型。从屠宰加工厂选取经48 h排酸后的里脊、眼肉、腿肉、臀肉、外脊等部位的牛肉样品114份,采用多元散射校正（MSC）、一阶导数、标准正态变量（SNV）预处理方法,谱区为950~1 650 nm,建立牛肉水分、脂肪、蛋白质3个化学参数以及pH、肉色（CEI L＊,a＊,b＊）和剪切力（WBSF）3个物理参数的校正模型。其校正相关系数（R2）分别为0.947 2（水分）,0.924 5（脂肪）,0.934 6（蛋白质）,0.620 2（pH）,0.820 3（L＊）,0.864 6（a＊）,0.753 0（b＊）,0.475 9（WBSF）。校正标准差（RMSEC）分别为0.313 3（水分）,0.221 0（脂肪）,1.243 2（蛋白）,0.744 6（pH）,1.778 3（L＊）,1.394 2（a＊）,1.763 9（b＊）,1.074 3（WBSF）。应用所建立的模型对30个实际牛肉样品的理化参数进行预测,并对预测值与实测值进行t检验,检验结果显示预测值与实测值差异不显著,说明模型适合于快速评价牛肉的品质。从预测的准确度看,化学指标预测的精确度明显高于物理指标。

The aim of the present study was to develop a near-infrared reflectance （NIR） spectroscopy rapid method for evaluation of beef quality. Partial least squares （PLS） prediction model for the physic-chemical characteristics such as moisture, fat, protein, pH, color and WBSF in beef was established with good veracity. One hundred fourteen samples from five different parts of beef carcass （tenderloin, ribeye, topside, shin, striploin） were collected from meat packer after 48 h aging. Spectra were obtained by scanning sample from 950 to 1 650 nm and pretreated the model by MSC, SNV and first derivative. Predictive correlation coefficients of physic-chemical parameters in beef were 0. 947 2（moisture）, 0. 924 5（fat）, 0. 934 6（protein）, 0. 620 2（pH）, 0. 820 3（L）, 0. 864 6（a~ ）, 0. 753 0（b＊ ） and 0. 475 9（WBSF） respectively. Root mean square errors of calibration （RMSEC） were 0. 313 3（moisture）, 0. 221 0（fat）, 1. 243 2（protein）, 0. 744 6（pH）, 1. 778 3（L^＊ ）, 1. 394 2（a^＊ ）, 1. 763 9（b^＊ ） and 1. 0743（WBSF）. They were externally validated with additional 30 beef samples. Statistics showed that there was no significant difference between predicted value and those obtained with conventional laboratory methods. The results showed that NIRS is a rapid, effective technique for evaluating beef quality. The predictions for chemical characteristics gave higher accuracy than prediction for physical characteristics.