CN108872137A - 基于多光谱在线检测鸡肉硫代巴比妥酸的方法 - Google Patents
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Abstract
本发明公开了基于多光谱在线检测鸡肉硫代巴比妥酸的方法，本发明是一种快速、无损、不使用化学试剂且无需预处理的高光谱成像技术来检测鸡肉中的硫代巴比妥酸，以弥补现有技术所存在的不足，从而实现鸡肉硫代巴比妥酸(TBA)的大规模在线检测。本发明具有以下有益效果：本发明只需要对被测样品进行非接触的光谱扫描且不需预处理，对样品没有破坏性；试验过程中不使用任何危险的化学试剂，即绿色快速又节约成本。
Description
技术领域
本发明涉及食品质量与安全检测领域，具体涉及基于多光谱在线检测鸡肉硫代巴比妥酸的方法。
背景技术
当今，鸡肉因可以提供给人们日常生活所需的蛋白质、盐、维生素和矿物质等营养物质，而成为越来越受欢迎的食物。然而鸡肉是极易腐败的食物，一般新鲜鸡肉在4℃冷藏时，其货架期是3-5天，并随着贮藏温度的升高，其货架期越短。当鸡肉腐败时其表面会发粘且有刺鼻的臭味，引起此现象的主要原因是脂质的氧化所致，而硫代巴比妥酸(TBA)作为脂质氧化的产物，则可用来反映脂质的氧化程度。一般检测鸡肉硫代巴比妥酸最常用的方法是分光光度法，此方法通常对样品具有破坏性，费时费力且需要使用一些危险的化学溶剂，不适合肉品企业的大规模在线检测。当今，随着肉品行业的快速发展，此行业亟需一种快速、无损的在线检测技术来满足大规模的生产要求。
发明内容
为了解决现有技术的不足，本发明提供了一种快速、无损、不使用化学试剂且无需预处理的高光谱成像技术来检测鸡肉中的硫代巴比妥酸，以弥补现有技术所存在的不足，从而实现鸡肉硫代巴比妥酸(TBA)的大规模在线检测。
本发明的技术方案是：基于多光谱在线检测鸡肉硫代巴比妥酸的方法，采集样品的反射模式的高光谱图像，对获得的光谱图像进行预处理以及光谱数据的提取，把提取的光谱数据代入到下式，
YTBA＝0.604+1.52X900.55nm-1.023X903.845nm-2.533X908.787nm+3.309X917.022nm+2.379X928.551nm+1.274X935.138nm-1.341X946.664nm-0.574X1022.378nm+0.67X1068.446nm+1.748X1152.341nm+1.252X1183.596nm-0.912X1226.37nm-0.896X1259.28nm-1.367X1384.418nm+0.575X1643.713nm-1.46X1693.439nm,其中YTBA为鸡胸肉中硫代巴比妥酸的含量值，X900.55nm、X903.845nm、X908.787nm、X917.022nm、X928.551nm、X935.138nm、X946.664nm、X1022.378nm、X1068.446nm、X1152.341nm、X1183.596nm、X1226.37nm、X1259.28nm、X1384.418nm、X1643.713nm、X1693.439nm，分别为波长在900.55nm、903.845nm、908.787nm、917.022nm、928.551nm、935.138nm、946.664nm、1022.378nm、1068.446nm、1152.341nm、1183.596nm、1226.37nm、1259.28nm、1384.418nm、1643.713nm、1693.439nm处的光谱反射率值。
本发明的进一步改进包括：
所述的方法中光谱图像的预处理理按照以下公式进行：
其中C为校正后的图像，R为原始光谱图像；B为黑板图像，其反射率为0％，W为白板图像，其反射率为99.9％
本发明具有以下有益效果：本发明只需要对被测样品进行非接触的光谱扫描且不需预处理，对样品没有破坏性；试验过程中不使用任何危险的化学试剂，即绿色快速又节约成本。
附图说明
图1是114个鸡胸肉样品的全波段光谱特征图；
图2是回归系数法对鸡胸肉最优波长的提取；
图3是鸡胸肉TBA含量预测值与实测值之间的相关性。
具体实施方式
下面结合实施例对本发明做详细说明。
实施例
一种基于多光谱在线检测鸡肉硫代巴比妥酸的方法的步骤如下：
(1)本实施例中的新鲜鸡胸肉购自于当地的农贸市场，且均为当天现杀的新鲜鸡胸肉。在实验室将整块鸡胸肉分割成小样品(3cm*3cm*1cm)，共获得114个样品，再平均分成7份，放进一次性塑料盒里盖盖放置在4℃的冰箱进行冷藏，在0、1、2、3、4、5、6天各取出一份进行试验；
(2)在试验之前，提前30min打开高光谱成像系统预热，同时鸡肉样本也提前30min从冰箱内取出待其恢复至室温后立即进行反射模式光谱图像的采集，光谱图像采集速度为6.54mm/s，曝光时间为4.65ms；
(3)对采集过光谱图像的样品要立即采用分光光度法检测其硫代巴比妥酸(TBA)的含量，114个样品的硫代巴比妥酸(TBA)的含量按照从小到大的顺序排列，其数据统计如下表1：
表1 114个样品的硫代巴比妥酸法的含量
(4)对获得光谱图像按照以下公式进行预处理即黑白板校正；
其中C为校正后的图像，R为原始光谱图像；B为黑板图像，其反射率为0％，W为白板图像，其反射率为99.9％。
对校正过的光谱图像进行光谱数据的提取，获得的114个样品的光谱特征如图1：
(5)使用偏最小二乘(PLSR)算法来建立步骤(3)所获得的鸡肉样品的硫代巴比妥酸的含量和步骤(4)所获得的全波段内光谱数据之间的预测模型，即建模集模型，当所建模型的相关系数R越接近于1，均方根误差RMSE越小且交叉验证集的相关系数和均方根误差越接近于建模集时说明建模集模型的精度和稳定性就越好。结果如表2：
表2全波段的鸡胸肉TBA含量的PLSR模型
从表2中可以得出所建立的建模集PLSR模型的相关系数R高达0.968，均方根误差低至0.016，其中交叉验证集的模型相关系数也接近于建模集，表明建模集的模型精度高且较稳定。
(6)在900-1700nm全波段内，共有486个波长，而并不是所有的波长都对所建模型具有贡献，其中存在大量的冗余信息，为了剔除冗余信息保留有用信息，通过回归系数法(RC)来提取最优波长，以降低数据的计算量，从而提高计算机的运行速度。结果如图2：
从图2中可以得出使用回归系数法从全波段内提取了16个最优波长，分别为900.55nm、903.845nm、908.787nm、917.022nm、928.551nm、935.138nm、946.664nm、1022.378nm、1068.446nm、1152.341nm、1183.596nm、1226.37nm、1259.28nm、1384.418nm、1643.713nm、1693.439nm。
(7)再次采用偏最小二乘(PLSR)法来关联步骤(3)所获得的114个鸡肉硫代巴比妥酸的含量与步骤(6)所提取的16个最优波长，获得优化后的偏最小二乘(PLSR)预测模型，其结果如表3：
表3最优波长所建立的鸡胸肉TBA含量的PLSR预测模型
(8)从表中可得出使用最优波长数所建立的PLSR模型相关R为0.964，均方根误差RMSEC为0.017与交叉验证集的即接近，且与全波段建模集的相关系数和均方根误差差距极小，故使用最优波长所建立的PLSR模型精度即高又稳定，所建立的此模型相当理想。
(9)获得的最优波长的PLSR模型公式如下：
YTBA＝0.604+1.52X900.55nm-1.023X903.845nm-2.533X908.787nm+3.309X917.022nm+2.379X928.551nm+1.274X935.138nm-1.341X946.664nm-0.574X1022.378nm+0.67X1068.446nm+1.748X1152.341nm+1.252X1183.596nm-0.912X1226.37nm-0.896X1259.28nm-1.367X1384.418nm+0.575X1643.713nm-1.46X1693.439nm,其中YTBA为鸡胸肉中硫代巴比妥酸的含量值，X900.55nm、X903.845nm、X908.787nm、X917.022nm、X928.551nm、X935.138nm、X946.664nm、X1022.378nm、X1068.446nm、X1152.341nm、X1183.596nm、X1226.37nm、X1259.28nm、X1384.418nm、X1643.713nm、X1693.439nm，分别为波长在900.55nm、903.845nm、908.787nm、917.022nm、928.551nm、935.138nm、946.664nm、1022.378nm、1068.446nm、1152.341nm、1183.596nm、1226.37nm、1259.28nm、1384.418nm、1643.713nm、1693.439nm处的光谱反射率值。
(10)测试
①获取38个待测鸡胸肉样品的近红外高光谱图像；
②对获取的光谱图像进行预处理并对提取光谱数据；
③把提取的光谱数据代入到步骤(9)所获得的最优波长的建模集的模型中，最后便可获得38个待测鸡胸肉硫代巴比妥酸的预测值；
④将鸡胸肉硫代巴比妥酸的预测值与传统方法所测得的值进行线性拟合，其相关系数高达0.958，均方根误差为0.019，其真实值与预测值之间的相关很好，结果如图3。表明本发明的结果与实际测得的鸡胸肉TBA含量之间的差异较小，其高光谱成像技术在鸡胸肉硫代巴比妥酸方面法的检测具有很大的潜力，故此发明具有很大的可行性。
以上显示和描述了本发明的基本原理和主要特征和本发明的优点。本行业的技术人员应该了解，本发明不受上述实施例的限制，上述实施例和说明书中描述的只是说明本发明的原理，在不脱离本发明精神和范围的前提下，本发明还会有各种变化和改进，这些变化和改进都落入要求保护的本发明范围内。本发明要求保护范围由所附的权利要求书及其等效物界定。
Claims (2)
1.基于多光谱在线检测鸡肉硫代巴比妥酸的方法，其特征在于，采集样品的反射模式的高光谱图像，对获得的光谱图像进行预处理以及光谱数据的提取，把提取的光谱数据代入到下式，
YTBA＝0.604+1.52X900.55nm-1.023X903.845nm-2.533X908.787nm+3.309X917.022nm+2.379X928.551nm+1.274X935.138nm-1.341X946.664nm-0.574X1022.378nm+0.67X1068.446nm+1.748X1152.341nm+1.252X1183.596nm-0.912X1226.37nm-0.896X1259.28nm-1.367X1384.418nm+0.575X1643.713nm-1.46X1693.439nm,其中YTBA为鸡胸肉中硫代巴比妥酸的含量值，X900.55nm、X903.845nm、X908.787nm、X917.022nm、X928.551nm、X935.138nm、X946.664nm、X1022.378nm、X1068.446nm、X1152.341nm、X1183.596nm、X1226.37nm、X1259.28nm、X1384.418nm、X1643.713nm、X1693.439nm，分别为波长在900.55nm、903.845nm、908.787nm、917.022nm、928.551nm、935.138nm、946.664nm、1022.378nm、1068.446nm、1152.341nm、1183.596nm、1226.37nm、1259.28nm、1384.418nm、1643.713nm、1693.439nm处的光谱反射率值。
2.根据权利要求1所述的方法，其特征在于，所述光谱图像的预处理理按照以下公式进行：
其中C为校正后的图像，R为原始光谱图像；B为黑板图像，其反射率为0％，W为白板图像，其反射率为99.9％。
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| CN111272697A (zh) * | 2020-04-27 | 2020-06-12 | 江苏益客食品集团股份有限公司 | 一种基于近红外高光谱的硫代巴比妥酸含量检测方法 |
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