Department of Animal Science, Iowa State University, Ames, IA 50010, United States.
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50010, United States; School of Engineering, Jiangxi Agricultural University, Nanchang, China.
Meat Sci. 2018 Nov;145:79-85. doi: 10.1016/j.meatsci.2018.05.021. Epub 2018 May 29.
The utility of Raman spectroscopic signatures of fresh pork loin (1 d & 15 d postmortem) in predicting fresh pork tenderness and slice shear force (SSF) was determined. Partial least square models showed that sensory tenderness and SSF are weakly correlated (R = 0.2). Raman spectral data were collected in 6 s using a portable Raman spectrometer (RS). A PLS regression model was developed to predict quantitatively the tenderness scores and SSF values from Raman spectral data, with very limited success. It was discovered that the prediction accuracies for day 15 post mortem samples are significantly greater than that for day 1 postmortem samples. Classification models were developed to predict tenderness at two ends of sensory quality as "poor" vs. "good". The accuracies of classification into different quality categories (1st to 4th percentile) are also greater for the day 15 postmortem samples for sensory tenderness (93.5% vs 76.3%) and SSF (92.8% vs 76.1%). RS has the potential to become a rapid on-line screening tool for the pork producers to quickly select meats with superior quality and/or cull poor quality to meet market demand/expectations.
本研究旨在探究新鲜猪里脊肉（宰后 1d 和 15d）的拉曼光谱特征在预测新鲜猪肉嫩度和切片剪切力（SSF）方面的应用。偏最小二乘模型表明，感官嫩度和 SSF 呈弱相关（R=0.2）。采用便携式拉曼光谱仪（RS）在 6s 内采集拉曼光谱数据。建立了 PLS 回归模型，以从拉曼光谱数据定量预测嫩度评分和 SSF 值，但预测效果非常有限。研究发现，对于宰后 15d 的样本，预测精度明显高于宰后 1d 的样本。还建立了分类模型，以预测感官质量两端的嫩度为“差”与“好”。对于宰后 15d 的样本，用于感官嫩度（93.5%比 76.3%）和 SSF（92.8%比 76.1%）的分类到不同质量等级（第 1 百分位到第 4 百分位）的准确性也更高。RS 有望成为猪肉生产者的快速在线筛选工具，以便快速选择优质肉类，或剔除劣质肉类，以满足市场需求/期望。
