基于拉曼光谱技术的猪瘦肉新鲜度快速无损检测方法研究

Study on Rapid Nondestructive Detection of Pork Lean Freshness Based on Raman Spectroscopy

猪肉是我国主要肉类消费产品,其新鲜度与居民健康息息相关。目前感官检测、理化检测、微生物检测是其新鲜度的通用检测方法,但感官检测存在可靠性、可比性差,理化检测和微生物检测存在耗时长、操作繁琐、破坏样品等问题,因此建立猪肉快速无损检测方法应用意义重大。拉曼光谱作为一种检测技术,具有快速、无损的特点,仅用激光探头照射样品就可获得样本拉曼谱图,便携式拉曼光谱更是为食品现场检测提供了新途径,有望实现加工业快速实时大批量检测。目前未见拉曼光谱技术快速检测猪肉新鲜度理化指标的研究,因此采用便携式拉曼光谱仪对冷藏猪瘦肉新鲜度进行快速检测。对随时间变化的样本进行拉曼光谱采集并同时监测其对应的新鲜度指标,如挥发性盐基氮(TVB-N)、pH、颜色L^(*)值、a^(*)值、b^(*)值,采用标准正态变量变换(SNV)、曲线平滑(SG)、归一化(NL)、多元散射校正(MSC)、基线校正(BL)、去趋势化处理(DFA)等单方法对拉曼光谱进行预处理,采用偏最小二乘回归(PLSR)建立基于全波段光谱的猪瘦肉新鲜度指标定量预测模型。结果表明,各指标全波段PLSR模型预测性能较为理想,TVB-N和pH的全谱最佳模型为SNV-PLSR,预测集相关系数(R_(P))分别为0.948和0.886,颜色L^(*)、颜色a^(*)、颜色b^(*)的全谱最优模型分别为SNV-PLSR、DFA-PLSR、MSC-PLSR,R_(P)分别为0.827,0.858和0.900。采用回归系数法(RC)筛选各指标最优模型光谱波段,建立各指标优选波段PLSR模型,结果表明,TVB-N模型和pH模型可以简化,仅用20%的光谱波段就可达到较好的预测效果,优选波段TVB-N模型和pH模型的R_(P)分别为0.933和0.880。便携式拉曼光谱在快速检测猪瘦肉新鲜度方面尤其是在预测与新鲜度最相关的指标TVB-N含量显示出巨大的潜力,为猪瘦肉新鲜度的现场快速无损检测提供了一种新方法。

Pork is the main meat consumption product in China.Its freshness is closely related to the health of residents.At present,the most common detection methods for meat quality include sensory testing,physical and chemical testing,and microbiological testing,but sensory detection is less reliable and comparable.Physical and chemical testing and microbiological testing have many problems,such as time-consuming,complicated operation and destroying samples,thus establishing a fast and nondestructive detection method has great significance.Raman spectroscopy is fast and nondestructive as a detection technology.Moreover,portable Raman spectroscopy provides a new way for food spot detection and is expected to achieve rapid real-time mass detection in the processing industry.At present,there is no study on the rapid detection of physical and chemical indexes of pork freshness by Raman spectroscopy.Therefore,a portable Raman spectrometer was used in this study to detect the freshness of cold storage lean pork.Collecting the Raman spectroscopy of samples with time and monitoring the corresponding freshness index,including total volatile base nitrogen(TVB-N),pH,L^(*),a^(*),and b^(*).Raman spectra were pre-processed by standard normal variable transformation(SNV),curve smoothing(SG),normalize(NL),multiple scattering correction(MSC),baseline(BL),and Detrending(DFA).Partial least squares regression(PLSR)was used to establish a quantitative prediction model of pork freshness indicators based on full displacements of Raman spectroscopy.The results indicated that the PLSR model based on the Raman spectrum had a good performance predicting pork freshness.The optimal model for TVB-N and pH was SNV-PLSR,and the correlation coefficient was 0.948 and 0.886,respectively.The optimal models for color L^(*),color a^(*),and color b^(*)were SNV-PLSR,DFA-PLSR,and MSC-PLSR,respectively.The correlation coefficients were 0.827,0.858 and 0.900,respectively.The regression coefficient method(RC)was used to screen the optimal spectral bands of each index model,and the PLSR model of the optimal spectral bands of each index was established.The results showed that the TVB-N and pH models could be simplified,and only 20%of the spectral bands can achieve a good prediction effect.TheR_(P)of the TVB-N model and pH model were 0.933 and 0.880,respectively.Raman spectroscopy providing us with a spot detection method shows great potential in rapidly detecting pork freshness,especially in predicting TVB-N content.