挥发性盐基氮（TVB-N）通常被作为评价羊肉新鲜度的理化参考指标。为了揭示高光谱图像技术（HSI）快速检测羊肉新鲜度的可行性, 采集了71个新鲜度具有代表性的羊肉样品的漫反射高光谱图像（400～1　000 nm）, 并利用半微量定氮法测定了其挥发性盐基氮（TVB-N）的化学值。 选择感兴趣区域（ROIs）提取样品的代表性光谱, 采用含量梯度法划分校正集和预测集, 比较不同的光谱预处理方法, 比较逐步多元线性回归（SMLR）、 偏最小二乘（PLSR）和主成分分析（PCR）建模方法, 建立并验证了TVB-N的校正模型。结果表明, 利用多元散射校正（MSC）、 一阶导数、 Savitzky-Golay（S-G）平滑及中心化处理结合的预处理方法, PLSR和PCR模型都可以实现对羊肉TVB-N的定量检测。对于建立的PLSR模型, 采用的预处理方法为MSC、 15点2次S-G平滑、 1阶导数和中心化相结合的方法, 选择的潜变量因子数为11, 获得的校正集的相关系数（R）和校正均方根误差（RMSEC）分别为0.92和3.00 mg·(100 g)-1, 预测集的相关系数（r）、 预测均方根误差（RMSEP）和相对分析误差（RPD）分别为0.92, 3.46 mg·(100 g)-1和2.35。研究表明, 高光谱图像技术可用于准确快速地检测分析羊肉中新鲜度关键指标TVB-N的含量。该研究为采用高光谱图像技术进一步分析羊肉新鲜度其他指标、 改善TVB-N的建模效果及在实际生产中应用该技术提供了基础。

Total Volatile Basic Nitrogen (TVB-N) was usually taken as the physicochemical reference value to evaluate the mutton freshness. In order to explore the feasibility of hyperspectral (HSI) imaging technique to detect mutton freshness, 71 representative mutton samples were collected and scanned using a diffuse reflectance hyperspectral imaging (HSI) system in the Visible-Near infrared (NIR) spectral region (400～1 000 nm), and the chemical values of TVB-N content were determined using the semimicro Kjeldahl method according to the modified Chinese national standard. The representative spectra of mutton samples were extracted and obtained after selection of the region of interests (ROIs). The samples of calibration set and prediction set were divided at the ratio of 3∶1 according to the content gradient method. Optimum HSI calibration models of the mutton (TVB-N) were established and evaluated by comparing different spectral preprocessing methods and modeling methods, which included Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) methods. The results are that through the utilization of Multiplicative Scatter Correction (MSC), first derivative, Savitzky-Golay (S-G) smoothing and mean-centering together, both PLSR and PCR were able to achieve quantitative detection of mutton TVB-N. As for the PLSR model of mutton TVB-N established, the spectral pretreatment methods chosen included MSC, first derivative, S-G (15,2) smoothing and mean-centering, and the latent variables (LVs) number used was 11. As for the calibration set of PLSR model of mutton TVB-N, the correlation coefficient (r) and root mean square error of calibration (RMSEC) were 0.92 and 3.00 mg·(100 g)-1, respectively. As for the prediction set of PLSR model of mutton TVB-N, the correlation coefficient (r), Root Mean Square Error of Prediction (RMSEP), and ratio of standard deviation to standard error of prediction (RPD) were 0.92, 3.46 mg·(100 g)-1 and 2.35, respectively. The study demonstrated that the rapid and accurate analysis of TVB-N, the key freshness attribute, could be implemented by using the hyperspectral imaging (HSI) technique. The study provides the basis for further rapid and non-destructive detection of other mutton freshness attributes by using the hyperspectral imaging (HSI) technique, the improvement of current modeling effect of TVB-N content and the application involved of the technique in the practical production.