拉曼光谱法快速检测猪肉脯中的掺伪鸡肉

目的 建立拉曼光谱法快速、准确、无损地检测猪肉脯样品中掺假鸡肉的方法。方法 制备33份猪肉中掺入不同比例鸡肉的肉脯样品,采集拉曼光谱数据,分别采用标准正态变换、多元散射校正、卷积平滑、归一化、一阶导数等5种不同预处理方法,对原始光谱数据进行预处理,采用连续投影算法、竞争性自适应重加权算法及随机蛙跳算法对光谱数据进行特征波长筛选,建立偏最小二乘法(partial least squares,PLS)模型对猪肉脯进行定性定量判别。结果 拉曼光谱数据经过多元散射校正处理的效果最佳,竞争性自适应重加权算法竞筛选效果更佳,构建猪肉脯中猪肉含量的PLS定量模型,其预测集决定系数和预测均方根误差分别为0.9762、7.2998。建立的PLS判别模型的校正集和预测集总判别正确率分别为100.00%和98.33%。结论 拉曼光谱分析技术可有效用于定性鉴别猪肉脯是否掺伪及定量分析猪肉肉脯中掺入鸡肉的比例,为肉脯掺假的快速无破坏性检测的应用提供支持。

Rapid detection of adulterated chicken meat in dried pork slice by Raman spectroscopy

Objective The counterfeiting and adulteration of dried pork has impacted the rights and well-being of consumers. This method allows for accurate and efficient detection, without compromising the integrity of the sample. To efficiently detect quality and safety issues in adulterated dried meat, Raman spectroscopy has been established to detect chicken meat in pork samples rapidly and non-invasively. Methods In this experiment, 33 dried meat samples consisting of a mixture of pork and chicken were prepared and analysed. Raman spectral data was collected from the samples using preprocessed raw spectral data that had undergone one of five pre-processing methods. These included standard normal transform, multiple scattering correction, convolutional smoothing, normalisation and first-order derivative. Technical abbreviations were explained when first used. The spectral data were screened for characteristic wavelengths using the continuous projection, competitive adaptive reweighting, and random frog hopping algorithms. A partial least squares (PLS) model was then established for the qualitative and quantitative discrimination of preserved pork meat. Results The Raman spectral data were corrected using the multiple scattering correction. The competitive adaptive reweighting algorithm displayed a more effective screening effect. The PLS model showed a correlation coefficient (Rp2) of 0.9762 and a root mean square error (RMSE) of 7.2998 for pork content (%). The PLS model''s calibration set and prediction set had correctness rates of 100% and 98.33%, respectively. This provides valuable technical support for the rapid and accurate non-destructive detection of adulterated dried pork. Conclusion Raman spectroscopy is a valuable tool for identifying and analyzing dried pork samples. Raman spectroscopy can be effectively utilized to qualitatively identify whether dried pork has been adulterated or not, as well as quantitatively analyze the proportion of pork content. This provides technical support for rapid and accurate non-destructive detection of dried pork adulteration.