On-Line Noninvasive Prediction of Cholesterol Level of Fresh Pork within NIR Medium Wavelength Region with Portable Near-Infrared Spectrometer
WANG Hui1, TIAN Han-you1, ZHANG Shun-liang1, ZHANG Hao2, ZHAO Bing1, LI Jia-peng1, QIAO Xiao-ling1*
1. Beijing Key Laboratory of Meat Processing Technology, China Meat Research Center, Beijing 100068, China
2. Beijing Key Laboratory of Functional Dairy, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
摘要： 应用便携式近红外光谱仪采集320份生鲜猪肉在近红外光谱中波区的光谱信息，采用不同优化方法建立猪肉胆固醇预测模型，并对异常样品的剔除及组合预处理方法对模型性能的改善进行了分析。研究表明：通过对异常值的二次剔除，并使用SG一阶导数(savitzky-golay first derivative, SG 1stD)、SG平滑(savitzky-golay smoothing, SGS)和正交信号校正(OSC)的组合预处理方法，可获得最佳生鲜猪肉胆固醇预测模型，其参数如下：校正集相关系数(Rc)=0.9137，校正标准差(standard error of calibration, SEC)=2.5607，验证集相关系数(Rp)=0.656 7，预测标准差(standard error of prediction, SEP)=4.985 5，主因子数(principal factor, PF)=4，范围误差比(ratio of performance to standard deviation, RPD)=2.5032，相对预测标准差(relative standard error of prediction, RSEP)=8.625 4%，SEP/SEC=1.946 8，说明模型在近红外光谱中波区对猪肉胆固醇的分辨能力和预测准确度较好，通过向校正集中补充代表性样品可使模型稳健性进一步改善。对检验集样品预测值(prediction value, PV)与参比值(reference value, RV)的t检验显示二者之间无显著性差异(p＞0.05)，检验集样品总体预测准确率为62.5%，其中50～70 mg·(100 g)-1区段的局部预测准确率达到91.7%，可以用于生鲜猪肉胆固醇浓度的在线快速初步定量分析。该研究将便携式近红外光谱用于在近红外中波区对生鲜猪肉及肉制品中胆固醇浓度的分析和检测，通过进一步的研究和改进，可将其应用于产品的原料分级、品质和过程控制及市售产品的抽检等。
关键词：便携式近红外光谱仪；中波近红外光谱；在线无损检测；生鲜猪肉；胆固醇浓度
Abstract：Portable near infrared spectrometer was applied to collect 320 pieces of fresh pork spectral information in NIR medium wavelength region. Prediction models of fresh pork cholesterol level with NIR spectroscopy were established through partial least squares method combined with different spectroscopy preprocessing methods. The effects of outlier samples elimination and combination of different preprocessing methods on the prediction model performance were discussed. The result showed that the optimum prediction model of fresh pork cholesterol level was achieved with the application of two optimization procedures, eliminating outliers twice and combination of SG first order derivative, SG smoothing and orthogonal signal correction, and the relevant parameters as follows: Rc=0.913 7, SEC=2.560 7, Rp=0.656 7, SEP=4.985 5, MF=4, RPD=2.503 2, RSEP=8.625 4%, SEP/SEC=1.946 8, which indicated the reliability, resolution capacity and prediction accuracy of this model in NIR medium wavelength region were acceptable. The robustness of optimal prediction model could be further improved by adding more representative and typical sample of different cholesterol level range into the calibration set. Paired-samples t-test showed non-significance between the predicted value and reference value (p＞0.05), and the total prediction accuracy of testing samples was 62.5%, and partial prediction accuracy was 91.7% in cholesterol range of 50～70 mg·(100 g)-1, which showed that this model could be applied into on-line rapid preliminary quantitative analysis of cholesterol level of fresh pork. In this research it was the first time that portable near-infrared spectrometer was applied into the analysis and detection of cholesterol level of fresh pork products within NIR medium wavelength region, and with further study and improvement, the prediction model could also be applied to raw material classification, quality and process control, random inspection of commercially available meat and meat products.
