挥发性盐基氮(TVB-N)含量是评价猪肉新鲜度的重要理化指标。为了实现快速、无损检测猪肉的新鲜度,优选出与猪肉中TVB-N含量相关的特征波长,将包含特征波长的发光二极管(LED)光源用于多光谱检测系统,测定了猪肉中TVB-N的含量。首先利用可见-近红外(VIS-NIR)高光谱系统对猪肉进行检测,获取高光谱反射率数据,并采用一阶导数(FD)法、标准正态变量变换(SNV)以及其他预处理方法建立猪肉中TVB-N含量的偏最小二乘回归(PLSR)模型;然后分别利用逐步回归算法(SWA)、连续投影算法(SPA)、基因遗传算法(GA)筛选出与TVB-N含量相关的特征波长,利用筛选出的特征波长分别建立PLSR模型与多元线性回归(MLR)模型,比较模型结果后进一步优选特征波长;最后,将含有特征波长的LED光源用于多光谱检测系统,并建立PLSR模型与MLR模型,从而完成对猪肉中TVB-N含量的测定。实验结果表明:利用SWA、SPA、GA这3种算法筛选出的特征波长能够很好地反映全光谱的信息,建立的模型效果较好,变量数大大减少;包含优选特征波长的LED光源在多光谱检测系统中能很好地检测猪肉中的TVB-N含量; MLR模型结果好于PLSR模型结果,MLR模型的校正集相关系数和校正集均方根误差分别为0.9050和3.63×10 ^-5,预测集相关系数和预测集均方根误差分别为0.9040和3.81×10 ^-5。

The content of total volatile basic nitrogen (TVB-N) is an important index for evaluation of pork freshness. To achieve rapid and nondestructive detection of pork freshness, we use a light-emitting diode (LED) light source containing optimal characteristic wavelengths to set up a multispectral detection system by selecting optimal characteristic wavelengths related to the content of TVB-N in pork, and the content of TVB-N in pork is detected by the detection system. Firstly, a visible near infrared (VIS-NIR) hyperspectral system is applied to detect pork to acquire hyperspectral reflectance data, and then different preprocessing algorithms including the first derivative (FD), standard normal variable transformation (SNV), and other methods are utilized to build partial least squares regression (PLSR) model of the TVB-N content in pork. Secondly, some variable selection methods including the step wise algorithm (SWA), successive projections algorithm (SPA), and genetic algorithm (GA) are used to screen the characteristic wavelengths related to TVB-N content. PLSR model and multiple linear regression (MLR) model are established by these characteristic wavelengths. The model results are compared to choose optimal characteristic wavelengths. Lastly, the LED light source containing characteristic wavelengths is used in the multispectral detection system to establish PLSR and MLR models, so that the detection of TVB-N content in pork is completed. The results indicate that the screening characteristic wavelengths by using the SWA, SPA, and GA can reflect full spectral information well. The effect of the established model is good, and the number of variable decreases greatly. The LED light source containing characteristic wavelengths can detect the TVB-N content in pork well in the multispectral detection system. The results of the established MLR model are better than those of the PLSR model. The correlation coefficient and square error of calibration (SEC) set of the MLR model are 0.9050 and 3.63×10 -5, respectively, and the correlation coefficient and square error of prediction (SEP) set are 0.9040 and 3.81×10 -5, respectively.