Non-Destructive Near-Infrared Spectroscopy of Physical and Chemical Indicator of Pork Meat
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维普
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我国是世界上最大的猪肉生产国,同时也是最大的消费国.猪肉的品质影响着人们摄取蛋白质的质量,目前缺乏有效的快速无损检测方法应对巨大的检测需要.为快速测定冷藏猪肉挥发性盐基氮(TVB-N)、pH值和含水率,提出一种新的猪肉品质检测方法,利用近红外光谱技术结合化学计量学方法建立冷藏猪肉TVB-N、pH值和含水率的数学模型.以不同种类的白条猪肉为研究对象,选择排酸完成后的猪最长背部肌肉的切块,采集相应猪肉近红外光谱数据并结合化学计量学试验获取TVB-N、pH和含水率的测量值,对比分析采集到的光谱信息和理化指标,发现在8 600～8 450、7 160～6 950、5 300～5 000 cm-1区域出现显著的吸收峰,而8 600～8 450 cm-1附近吸收峰的峰值却明显小于其他吸收峰.使用SPXY(sample set par-titioning based on joint X-Y distances)算法进行数据集划分,将数据集按照3∶1的比例划分成训练集和测试集.使用蒙特卡罗交叉验证法(MCCV)剔除异常数据,通过偏最小二乘法回归(PLSR)建立TVB-N、pH值、含水率和全波段光谱信息的回归关系.利用数据中心化、Savitzky-Golay(S-G)一阶求导、S-G二阶求导、直接差分二阶求导和多元散射校正等方法对原始光谱进行预处理,探寻合适的预处理方式.结果显示:数据中心化、直接差分二阶求导、二阶求导取得较优的试验效果;故结合竞争性自适应重加权算法(CARS)、非信息变量剔除(UVE)、连续投影算法(SPA)等特征波长提取算法建立PLSR特征波段模型并进行分析.结果显示,TVB-N、pH值、含水率的预测模型结构分别为数据中心化-CARS-PLSR、直接差分二阶求导-CARS-PLSR、二阶求导-CARS-PLSR时具有优异的性能,其中训练集相关系数RC分别为0.947 1、0.998 8、0.997 1,均方根误差(RMSE)分别为1.208 8、0.008 7、0.001 5;测试集相关系数RP分别为0.927 5、0.963 0、0.945 9,RMSE分别为1.683 6、0.051 7、0.005 6.综上可知,近红外波段区域与猪肉TVB-N、pH值、含水率存在显著相关性,利用近红外光谱可以准确预测猪肉中TVB-N、pH值和含水率.
China is the world's largest pork producer and consumer.The quality of pork affects the quality of protein intake for people.There needs to be more effective rapid non-destructive testing methods to cope with the huge testing needs.In order to rapidly determine volatile salt nitrogen(TVB-N),pH and moisture content of frozen pork and to propose a new method for pork quality testing,this paper uses near-infrared spectroscopy combined with chemometric methods to establish mathematical models for TVB-N,pH and moisture content of frozen pork.The NIR spectral data were collected and combined with chemometric tests to obtain the measured values of TVB-N,pH and moisture content.～5 000 cm-1 region,while the absorption peaks around 8 600～8 450 cm-1 were significantly smaller than the other absorption peaks.The SPXY(sample set partitioning based on joint X-Y distances)algorithm was used to partition the data set into a training set and a test set in the ratio of 3∶1.The abnormal data were removed using Monte Carlo cross-validation(MCCV)and a partial least squares regression(PLSR)was used to establish The regression relationships of TVB-N,pH,water content and full-band spectral information were established by partial least squares regression(PLSR),and the raw spectra were pre-processed using data centering,Savitzky-Golay(S-G)first-order derivatization,S-G second-order derivatization,direct difference second-order derivatization and multiple scattering corrections to explore the appropriate pre-processing methods.The results show that the data centering,direct differential second-order derivation and second-order derivation achieve good experimental results,so the combination of competitive adaptive reweighted sampling(CARS),uninformative variables elimination(UVE),and multiplicative scatter correction(MSC)has been applied.The PLSR feature band model was developed and analysed by combining the competitive adaptive reweighted sampling(CARS),uninformative variables elimination(UVE)and successive projections algorithm(SPA).The results showed that the prediction models for TVB-N,pH and water content had excellent performance when the structures were data centered-CARS-PLSR,direct difference second-order derivative-CARS-PLSR and second-order derivative-CARS-PLSR,respectively,where the training set correlation coefficients RC were 0.947 1,0.998 8 and 0.997 1,respectively,and the root mean square errors(RMSE)were 1.208 8,0.008 7 and 0.001 5,respectively;the test set correlation coefficients Rp were 0.927 5,0.963 0 and 0.945 9,respectively,and the RMSE were 1.683 6,0.051 7 and 0.005 6,respectively.In summary,it can be seen that the NIR band region is significantly correlated with pork TVB-N,pH and moisture content,and the use of NIR spectra can The NIR spectra can be used to predict TVB-N,pH and moisture content in pork accurately.
维普
万方数据
