基于骨蛋白拉曼光谱特异性的肉骨粉种属鉴别方法

为了快速检测肉骨粉的种属来源,该研究开发了一种简便、可靠、科学、高效的肉骨粉种属鉴别方法。以87个肉骨粉(猪,鸡,牛和羊肉骨粉)为研究对象,利用拉曼光谱技术,结合化学计量学方法,建立了基于骨蛋白拉曼光谱特性的肉骨粉种属鉴别分析方法与模型。研究结果表明:根据偏最小二乘判别分析(PartialLeastSquaresDiscriminant Analysis,PLS-DA)模型,发现鸡和哺乳动物(猪,牛和羊)肉骨粉主要在1 659、2 930、2 852、1 246和1 455 cm-1附近的特征谱带具有差异性;猪和反刍动物(牛和羊)肉骨粉主要是在2 852、1 659和1 246 cm-1附近的特征谱带具有差异性;牛和羊肉骨粉主要是在878、853、2 930、2 852、1 246、1 455和1 659 cm-1附近的特征谱带具有差异性,并且PLS-DA模型鉴别肉骨粉的灵敏度和特异度均大于93.8%。研究结果可以丰富肉骨粉种属鉴别方法体系以及为开发便携式拉曼光谱仪提供参考。

Species-specific identification of meat and bone meal based on Raman spectral analysis of bone protein

Abstract: Meat and bone meal can be widely used as an important protein feed raw material in breeding industry, because of its high content of protein and biological value. However, the feed safety issues occurred frequently around the world, such as mad cow disease and sheep pruritus. There is an urgent need to develop an identification technology with a simple, reliable, reasonable, and efficient way to distinguish the specific species of meat and bone meal. In this study, Raman spectroscopy and chemometrics methods were used to establish a systematic approach for the identification of species in meat and bone meal. The proposed method was based on the difference in Raman spectral characteristics of bone protein. This is due mainly to the composition and structure of bone proteins vary with species, while the bone protein was also the dominated component of meat and bone meal. A total of 87 source-reliable samples of meat and bone meal were analyzed, including 23 porcine, 22 poultry, 20 bovine, and 22 ovine in the measurement. Given the extraction procedures of proteins were complex, the bone protein was enriched via the extracting bone particles, and then ground into powder. All the powdered bone particles were scanned by the Raman spectrometer from 400 to 3 600 cm-1, where the Raman characteristics of bone protein, including 3200-2800, 1800-1200 cm-1, and 900-800 cm-1, were selected for further analysis. A three-step protocol was established for discriminant analysis combined with partial least squares method. The first model was used for the identification of poultry and mammal (porcine, bovine and ovine) meat and bone meal, where six latent variables were selected to establish the partial least square-discriminant analysis (PLS-DA) model. The specific fingerprint characteristic bands were at 1659, 2930, 2852, 1246 cm-1, and 1455 cm-1, respectively. Both the sensitivity and specificity of discriminant models were achieved at 100%, indicating that the PLS-DA discriminant analysis model based on the Raman characteristic band of bone protein can be well suited to distinguish poultry and mammal (porcine, bovine and ovine) meat and bone meal. The second model was used for the identification of non-ruminant (porcine) and ruminant (bovine and ovine) meat and bone meal, where ten latent variables were selected to establish a PLS-DA model according to the Raman characteristic bands of bone protein. The fingerprint characteristic bands were located at 2852, 1659 cm-1, and 1246 cm-1, respectively. Both the sensitivity and specificity of discriminant model can also be 100%, indicating that the PLS-DA discriminant analysis model can be used to well distinguish non-ruminant (porcine) and ruminant (bovine and ovine) meat and bone meal. The third model was used for the identification of bovine and ovine meat and bone meal, where eleven latent variables were selected to establish a PLS-DA model. Their fingerprint characteristic bands were at 878, 853, 2930, 2852, 1246, 1455 cm-1, and 1659 cm-1, respectively. Both the sensitivity and specificity of discriminant model can be obtained over 93.8%, indicating that the PLS-DA discriminant analysis model can be used to well distinguish bovine and ovine meat and bone meal. The findings can be contributed much to the rapid development of a portable Raman spectrometer system for species-specific identification of meat and bone meal in feed industry.