近红外特征光谱的羊肉TVB-N浓度预测模型

为提高生鲜羊肉储存期内（4，8和20 ℃环境）挥发性盐基氮（TVB-N）的近红外光谱（NIR）检测的稳定性和准确性，选取特征光谱和预测模型是关键步骤。以121个羊肉样品为实验对象，采集生鲜羊肉680~2 600 nm波段的近红外光谱。以多元散射校正（MSC）、标准正态变换（SNV）等散射校正方法，Savitzky-Golay卷积平滑（SGS）、移动平均平滑（MAS）等平滑处理方法，以及归一化（Normalization）、中心化（Centering）、标准化（Autoscaling）等尺度缩放方法分别预处理光谱数据后建立偏最小二乘法（PLS）预测模型。比较发现SGS处理的光谱建模效果最好。利用蒙特卡洛采样（MCS）法及马氏距离法（MD）消除了羊肉光谱的5个异常数据。运用光谱-理化值共生距离（SPXY）算法划分总样本的75%（87个）为校正集样本，剩余29个为验证集样本，利用竞争性自适应重加权法（CARS）、无信息变量消除法（UVE）、改进的无信息变量消除法（IUVE）和连续投影算法（SPA）提取特征光谱得到的波长个数分别为14，713，144和15。将全光谱和4种方法提取的特征波长作为输入变量建立预测模型，CARS提取的波长所建立模型的性能优于UVE、IUVE和SPA提取的波长所建立模型的性能，表明CARS方法可以有效简化输入变量并提高预测模型的性能。改进后得到的IUVE法相比于UVE法，筛选出的波长数更少且模型性能有所提升。以提取的特征波长建立PLS，支持向量机（SVM）和最小二乘支持向量机（LS-SVM）预测模型，SVM模型得到最优的校正集预测结果，其中CARS-SVM预测模型的校正决定系数（R2_C）和校正均方根误差（RMSEC）分别为0.939 1和1.426 7，最优的验证集预测效果为LS-SVM预测模型得到，其中IUVE-LS-SVM预测模型的验证决定系数（R2_V）和验证均方根误差（RMSEV）分别为0.856 8和1.886 2。基于近红外特征光谱建立简化、优化的生鲜羊肉储存期TVB-N预测模型，为实现快速无损检测生鲜羊肉中的TVB-N浓度提供技术支持。

Prediction Model of TVB-N Concentration in Mutton Based on Near Infrared Characteristic Spectra

In order to improve the stability and accuracy of near-infrared spectroscopy (NIR) detection of total volatile basic nitrogen (TVB-N) in fresh mutton during storage (at 4 ℃, 8 ℃, 20 ℃), the selection of characteristic spectra and prediction models is the key step of NIR spectroscopy research. The 121 mutton samples were taken as experimental objects, the NIR spectra between 680 and 2 600 nm of fresh mutton samples were collected. The scattering correction methods, including multi scattering correction (MSC), standard normal transformation (SNV), and smoothing methods including Savitzky Golay convolution smoothing (SGS), moving average smoothing (MAS), and scaling methods including normalization, centring and auto scaling, were adopted to pretreat NIR spectra, and then PLS prediction models were built, by comparison, it is found that the spectra treated with SGS got the best modeling effect. Monte Carlo sampling (MCS) method and Mahalanobis distance method (MD) were used to eliminate 5 abnormal data of mutton spectra. The sample-set partitioning based on joint x-y distance (SPXY) algorithm was used to split 75% (87 samples) of the total samples as calibration set samples and the remaining 29 were validation set samples. The competitive adaptive reweighted sampling (CARS) algorithm, uninformative variable elimination (UVE) algorithm, improved uninformative variable elimination (IUVE) algorithm, successive projections algorithm (SPA) were employed to select characteristic wavelengths, and wavelength numbers were 14, 703, 144 and 15, respectively. The full spectra and the characteristic wavelengths selected by the four methods were taken as input variables to build prediction models, the results show that the performance of the model built with the wavelengths selected by CARS is better than the model built with the wavelengths selected by UVE, IUVE and SPA, and it shows that CARS method can effectively simplify the input variables and improve the performance of the prediction model. Compared with the UVE algorithm, the IUVE algorithm can select fewer wavelengths and improve the model’s performance. The PLS models, support vector machine (SVM) models and least squares support vector machine (LS-SVM) models were established with the selected characteristic wavelengths. The optimal prediction results of the calibration set are obtained by SVM models, in which the calibration determination coefficient (R2_C) and root mean square error of calibration (RMSEC) of the CARS-SVM prediction model were 0.939 1 and 1.426 7, respectively. LS-SVM prediction model achieves the optimal prediction results of validation set, and the validation determination coefficient (R2_V) and the root mean square error of validation (RMSEV) of IUVE-LS-SVM prediction model were 0.856 8 and 1.886 2, respectively. The simplified and optimized TVB-N prediction models for fresh mutton during the storage period are established based on NIR characteristic spectra, which provides reference and technical support for rapid and non-destructive detection of TVB-N concentration in fresh mutton.