摘要
高光谱成像与近红外光谱(near infrared spectroscopy,NIR)技术是现代食品检测领域的重要手段,本研究对2种技术在鸡肉品质无损检测中的预测精度进行研究。选用62份新鲜程度不同的鸡胸肉,提取其高光谱感兴趣区域(region of interest,ROI)的光谱曲线,并测定样品的挥发性盐基氮(total volatile base nitrogen,TVB-N)含量和菌落总数(total viable count,TVC),利用OPUS 6.0光谱处理软件搜寻最佳的光谱预处理和波段组合,分别建立2个指标的偏最小二乘法(partial least square,PLS)定量分析模型。NIR样本选用30份新鲜程度不同的鸡胸肉,测定其TVB-N含量和TVC,建立PLS的交叉验证模型。结果表明:利用高光谱的ROI平均光谱建立的TVB-N含量与TVC模型的相关系数(R^2)分别为0.965和0.919,均方根误差(root mean square error of cross validation,RMSECV)分别为0.121和0.215;利用NIR建立的TVB-N含量与TVC预测模型的R2分别为0.801和0.780,RMSECV分别为0.232和0.312。由此可见,基于高光谱的ROI区域光谱建立的预测模型在鸡肉品质无损检测中具有比NIR更高的预测精度。
Hyperspectral imaging and near infrared spectroscopy （NIR） are two important techniques in modem food detection. This study intended to study the prediction accuracy of the two techniques for non-destructive chicken quality detection. Totally 62 chicken breast samples with different freshness were selected for hyperspectral imaging. Spectral data were extracted from the region of interest （ROI）. Total volatile base nitrogen （TVB-N） content and total viable count （TVC） were measured. The optimal combination of spectral pretreatment and band was searched by OPUS software （version 6.0）. A predictive model to quantify TVB-N and TVC was established by means of partial least squares （PLS） regression, respectively. Moreover, another 30 samples with different freshness were used to develop a PLS model for predicting TVB-N content and TVC by NIR spectroscopy, respectively. The performance of each model was evaluated using cross-validation. The results showed that the correlation coefficients （R2） of the TVB-N content and TVC prediction models developed from the ROI average spectra from hyperspectral images were 0.965 and 0.919 with a root mean square error of cross validation （RMSECV） of 0.121 and 0.215, respectively, while those of the prediction models established from NIR spectra were 0.801 and 0.780 with a RMSECV of 0.232 and 0.312, respectively. It can be concluded that the model based on ROI spectra from hyperspectral images has higher prediction accuracy for chicken quality compared with the NIR model.
出处
《肉类研究》
北大核心
2017年第12期30-35,共6页
Meat Research
基金
国家自然科学基金面上项目(61473009)
北京市自然科学基金青年科学基金项目(4122020)
北京工商大学两科培育基金项目(19008001270)
关键词
鸡肉新鲜度
高光谱成像
偏最小二乘法
近红外光谱
chicken freshness
hyperspectral imaging
partial least squares （PLS）
near infrared spectroscopy （NIR）
