基于高光谱成像技术的滩羊肉新鲜度快速检测研究
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摘要
滩羊肉的新鲜度是其品质安全的一个重要衡量指标,也是肉品品质安全控制的关键环节。挥发性盐基氮(TVB-N)是表征肉品腐败过程主要的化学信息,能有效地评价出滩羊肉的新鲜度。然而, TVB-N的传统检测过程繁琐且人为影响因素大,检测结果缺乏客观性和一致性,不能满足当今肉品检测过程无损、快速、高效的需求。高光谱成像技术符合现代检测技术向多源信息融合方向发展的需求,已在食品安全领域得到广泛应用。利用可见/近红外高光谱成像技术(400～1 000 nm)结合动力学和化学计量学方法以及计算机编程技术,将同时实现滩羊肉贮存期内(15℃环境)TVB-N浓度的快速检测和贮藏期的预测。研究中提取每个样品感兴趣区域的平均光谱数据,选用蒙特卡洛算法剔除异常样本。采用X-Y共生距离(SPXY)法划分为校正集和预测集,分别选用多元散射校正(multiplicative scatter correction,MSC)、卷积平滑(savitzky-golay,SG)、标准变量变换(standard normalized variate,SNV)、归一化(normalization)、基线校准(baseline)五种方法对原始光谱数据进行预处理,优选出最佳预处理方法。采用竞争性自适应重加权法(campetitive adaptive reweighted sampling,CARS)和连续投影算法(successive projections algorithm,SPA)分别提取了21个和6个特征波长。为优化模型并提高其模型精度,采用SPA算法对CARS所选特征波长进行二次提取,优选出14个特征波长。基于所提取的特征波长建立TVB-N浓度的PLSR模型,优选出SNV-CARS-SPA-PLSR模型具有较高的预测能力(R_c~2=0.88, RMSEC=2.51,R_p~2=0.65, RMSEP=2.11)。同时,建立了滩羊肉TVB-N变化与贮藏时间的动力学模型,并将优化后的光谱模型和动力学反应模型相结合建立了滩羊肉光谱吸光度值与贮藏时间的高光谱动力学模型,实现对贮藏时间的预测,并通过PLS-DA判别模型对滩羊肉贮藏时间进行判别分析(校正集判别准确率为100%,预测集为97%)。研究表明,利用可见/近红外高光谱成像技术结合动力学和化学计量学方法以及计算机编程技术,可以有效地实现滩羊肉品质智能监控与质量安全快速无损分析,为开发实时在线检测装备提供理论参考。
The freshness of Tan mutton is an important index of its quality and safety, and it is also a key link in the quality control of meat products. Total Volatile Basic Nitrogen(TVB-N) is the main chemical information which can effectively reflect the loss of freshness of Tan mutton. However, the traditional detection method of TVB-N must destroy the samples, the detection process is tedious, the man-made influencing factors are large, and the test result is lack of objectivity and consistency. Hyperspectral imaging technology which is a non-destructive method meets the needs of modern detection technologies for multi-source information fusion that has been widely used in the field of food safety. This paper used visible/near-infrared spectroscopic imaging technology(400～1 000 nm) combined with dynamics and chemometrics methods and computer programming to achieve the rapid detection of TVB-N concentration and prediction of safe storage period during the storage period of Tan mutton(15 ℃). The research contents were as follows: The average spectral data for each sample area of interest were extracted and the monte carlo algorithm was selected to eliminate the abnormal samples. The X-Y symbiotic distance(Sample set partitioning based on joint X-Y distances, SPXY) was used to divide the mutton set into the correction set and the prediction set. Multiplicative Scatter Correction(MSC), Savitzky-Golay(SG), Standard Normalized Variate(SNV), normalization(Normalization) and baseline calibration(Baseline) were used to preprocess the original spectral data. 21 and 6 feature wavelengths were extracted by the Campetitive Adaptive Reweighted Sampling(CARS) and Successive Projections Algorithm(SPA). In order to simplify the model and improve the accuracy of prediction of the model, the SPA algorithm was used to perform secondary extraction of selected feature wavelengths of CARS and 14 feature wavelengths were selected. A PLSR model with TVB-N concentration was established based on the extracted characteristic wavelengths, and the SNV-CARS-SPA-PLSR model was preferred to have a higher prediction ability(R_c~2=0.88, RMSEC=2.51, R_p~2=0.65, RMSEP=2.11) Meanwhile, a dynamic model of mutton TVB-N change and storage time could be established. Finally, the dynamic model of spectral absorbance value and storage time of mutton were established by combining the optimized spectral model with the dynamic first order reaction model, and predicte the storage time, and the PLS-DA model was realized to discriminate the storage time of mutton(the correction set discriminant accuracy rate was 100%, and the prediction set is 97%). The result showed that visible/near-infrared hyperspectral imaging technology in combination with dynamics and chemometrics methods and computer programming could effectively detect TVB-N index of mutton rapidly and non-destructively, and be realized to monitor the quality and safety of mutton and provide a theoretical reference for developing on line defection equipment.
The freshness of Tan mutton is an important index of its quality and safety, and it is also a key link in the quality control of meat products. Total Volatile Basic Nitrogen(TVB-N) is the main chemical information which can effectively reflect the loss of freshness of Tan mutton. However, the traditional detection method of TVB-N must destroy the samples, the detection process is tedious, the man-made influencing factors are large, and the test result is lack of objectivity and consistency. Hyperspectral imaging technology which is a non-destructive method meets the needs of modern detection technologies for multi-source information fusion that has been widely used in the field of food safety. This paper used visible/near-infrared spectroscopic imaging technology(400～1 000 nm) combined with dynamics and chemometrics methods and computer programming to achieve the rapid detection of TVB-N concentration and prediction of safe storage period during the storage period of Tan mutton(15 ℃). The research contents were as follows: The average spectral data for each sample area of interest were extracted and the monte carlo algorithm was selected to eliminate the abnormal samples. The X-Y symbiotic distance(Sample set partitioning based on joint X-Y distances, SPXY) was used to divide the mutton set into the correction set and the prediction set. Multiplicative Scatter Correction(MSC), Savitzky-Golay(SG), Standard Normalized Variate(SNV), normalization(Normalization) and baseline calibration(Baseline) were used to preprocess the original spectral data. 21 and 6 feature wavelengths were extracted by the Campetitive Adaptive Reweighted Sampling(CARS) and Successive Projections Algorithm(SPA). In order to simplify the model and improve the accuracy of prediction of the model, the SPA algorithm was used to perform secondary extraction of selected feature wavelengths of CARS and 14 feature wavelengths were selected. A PLSR model with TVB-N concentration was established based on the extracted characteristic wavelengths, and the SNV-CARS-SPA-PLSR model was preferred to have a higher prediction ability(R_c~2=0.88, RMSEC=2.51, R_p~2=0.65, RMSEP=2.11) Meanwhile, a dynamic model of mutton TVB-N change and storage time could be established. Finally, the dynamic model of spectral absorbance value and storage time of mutton were established by combining the optimized spectral model with the dynamic first order reaction model, and predicte the storage time, and the PLS-DA model was realized to discriminate the storage time of mutton(the correction set discriminant accuracy rate was 100%, and the prediction set is 97%). The result showed that visible/near-infrared hyperspectral imaging technology in combination with dynamics and chemometrics methods and computer programming could effectively detect TVB-N index of mutton rapidly and non-destructively, and be realized to monitor the quality and safety of mutton and provide a theoretical reference for developing on line defection equipment.
引文
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[3] Qin J,Huang M,Zhu Q,et al.Journal of Food Engineering,2018,218:61.
[4] YANG Wen-ting,BO Shuang,LUO Rui-ming,et al(杨文婷,柏霜,罗瑞明,等).Food Industry Technology(食品工业科技),2017,(19):40.
[5] TIAN Wei-xin,HE Dan-dan,YANG Dong,et al(田卫新,何丹丹,杨东,等).Food and Machinery(食品与机械),2016,(12):70.
[6] WANG Jia-yun,WANG Song-lei,HE Xiao-guang,et al(王家云,王松磊,贺晓光,等).Modern Food Technology(现代食品科技),2014,(6):257.
[7] Ma J,Sun D,Pu H.Journal of Food Engineering,2017,196:65.
[8] Atuonwu J C,Ray J,Stapley A G F.International Dairy Journal,2017,75:41.
[9] FANG Meng-meng,DING Jia-xing,CUI Teng-fei,et al(房盟盟,丁佳兴,崔腾飞,等).Food Industry Technology(食品工业科技),2018,(2):245.
[10] Dai Q,Cheng J,Sun D,et al.Food Chemistry,2016,197:257.
[11] Cheng W,Sun D,Pu H,et al.Food Chemistry,2017,221:1989.
[12] Wibowo S,Grauwet T,Gedefa G B,et al.Food Research International,2015,78:410.
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