CN112036619A - 电子鼻结合贝叶斯算法判别烤鸭是否超过货架终点的方法 - Google Patents
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Abstract
本发明公开了一种电子鼻结合贝叶斯算法判别烤鸭是否超过货架终点的方法，本发明首先根据不同货架时间下烤鸭样品的电子鼻香气响应信号得到电子鼻雷达指纹图谱；然后基于专家知识和感官评价得到的不同储存温度下烤鸭样品货架期，将各温度下储存的烤鸭样本分为两种货架类别——货架期内(未超过货架终点)与货架期外(超过货架终点)；接着就对不知道是否在货架期内的烤鸭，根据其电子鼻信号x，利用贝叶斯算法分别计算该烤鸭样本i属于各货架类别的概率，并选择概率最大时所对应的货架类别作为该烤鸭是否超过货架终点的判别结果，这种方法对于烤鸭样本量总体偏少的情况，能够较好的预测其是否在货架期内。
Description
技术领域
本发明涉及货架时间预测技术领域，具体涉及一种电子鼻结合贝叶斯算法判别烤鸭是否超过货架终点的方法。
背景技术
烤鸭在储存的过程中受到微生物和酶的影响发生一系列性质改变而产生异味或削弱正常香气，且异味的强度或正常香气的强度与烤鸭的储存时间存在一定的关系。因此消费者通常根据烤鸭的挥发性气味来辨别烤鸭的储存时间和接受程度。然而人工判别往往受到主观因素的影响，如自身身体状况、情绪和外界环境的影响，即使是经过专业培训的专家评价员也难以完全准确判断，并且对烤鸭货架时间预测的消费者测试需要组织一定数量的人群，工作量大、费用昂贵。
与传统的化学检测方法如气相色谱(GC)和气质联用技术(GC-MS)不同，电子鼻通过传感器模拟人体鼻腔的感知细胞，通过信号转换和模式识别等一系列数据分析技术完成对样品的检测。电子鼻检测速度快、精度高、重复性好，检测所需费用低廉且对样品的预处理要求较低。更重要的是，与GC和GC-MS不同，电子鼻是通过模拟人体的嗅觉系统完成样品的分析，更能够体现出样品的整体气味特征。
贝叶斯(Bayes)判别的基本思想是假定对所研究的对象(总体)在抽样前已有一定的认识，常用先验概率分布来描述这种认识。然后基于抽取的样本再对先验认识作修正，得到所谓后验概率分布，而各种统计推断都基于后验概率分布来进行。贝叶斯判别不同于经典的统计方法，它的一个显著特点就是在保证决策风险尽可能小的情况下，尽量应用所有可能的信息。因此贝叶斯判别能较好地解决本研究中样本量总体偏小的情况。
由于烤鸭货架期的鉴定需要耗费大量的人力、物力和时间，因此很难提供大量不同储藏温度中不同储藏时间下的样本作为模型的训练集。其它判别模型的建立，需要通过大量的样本量去可靠估计模型中的参数。而在此情况下，利用贝叶斯模型建立判别方法，依然有很好的表现，可以获得准确且稳定的分类效果。因为贝叶斯模型不需要进行参数估计，它的核心思想在于概率分析(先验概率分析与后验概率分析)。同时，贝叶斯模型结构精简，待定参数少，分析速度快，适用于实际生产应用。
针对电子鼻和贝叶斯判别结合能不能有效的预测烤鸭的货架时间，还没有现有技术报道。
发明内容
本发明的目的在于提供一种电子鼻结合贝叶斯算法判别烤鸭是否超过货架终点的方法，该方法对于烤鸭样本量总体偏少的情况，能够较好的预测其是否在货架期内。
为实现上述目的，本发明的技术方案为：
电子鼻结合贝叶斯算法判别烤鸭是否超过货架终点的方法，包括如下步骤：
(1)首先确定烤鸭样品生产及货架起始时间；为了保证实验用烤鸭样品是在同一时间同一批次同一工艺条件下生产出来的，特在某国家级肉制品加工中心订制本项目所需要的烤鸭样品。采用传统挂炉烤制方法、杀菌模式与包装方式制作，每只烤制后鸭子重量越400g，每半只1袋真空包装。
生产后当天运回实验现场，进入预定储存环境放置并进行相应的货架时间记录，同时将生产后当天作为产品的货架起始时间；
(2)将烤鸭样品分别储存在不同的温度下，然后确定不同储存温度下的品质检测时间点；检测时间点具体设计如下表1所示：
表1烤鸭货架期加速温度与对应温度下品质检测时间点设计
(3)将所获得的不同货架时间烤鸭样品都放置于-18℃环境下冷冻保藏，当所有不同货架时间的样品都放置于此冷冻保藏环境时，采用低温慢速解冻的方式，将不同货架时间的烤鸭从-18℃环境下取出置于室温环境下解冻17h，将解冻后的烤鸭样品从真空包装袋中取出，用无菌刀具切取烤鸭腹部鸭肉，置于研钵中研磨均匀，称重后加入20ml的电子鼻进样瓶中；
(4)采用电子鼻采集不同货架(储存)时间下烤鸭的香气响应信号，得出不同储存时间下烤鸭样品的电子鼻雷达指纹图谱，结合专家知识和感官评价得到的不同储存温度下烤鸭样品的货架期，将各温度下储存的烤鸭样本分为两种货架类别——货架期内(未超过货架终点)与货架期外(超过货架终点)，然后将各储存温度下的烤鸭样品按2:1的比例通过分层抽样分成训练集和验证集，根据训练集的样本计算各温度下各货架类别样本的均值与方差，
式中，
为样本属于k类货架类别时训练样本第n根传感器的响应均值，当k＝0时，样本为货架期内；当k＝1时，样本为货架期外，
为样本属于k类时训练样本第n根传感器的响应值的方差；
分别计算在烤鸭的电子鼻信号为x时，样本i属于各货架类别的概率，并选择概率最大时对应的货架类别作为该烤鸭是否超过货架终点的预测结果。
arg maxk＝0,1P(xi|yk) (2)
本发明的优点：
本发明能够根据电子鼻采集的不同货架(储存)时间下烤鸭的香气响应信号，得出不同储存时间下烤鸭样品的电子鼻雷达指纹图谱，结合专家知识和感官评价得到的不同储存温度时间下烤鸭样品的货架期，将各温度下储存的烤鸭样本分为两种货架类别——货架期内(未超过货架终点)与货架期外(超过货架终点)，然后利用贝叶斯判别再分别计算在烤鸭的电子鼻信号为x时，样本i属于各货架类别的概率，并选择概率最大时对应的货架类别作为该烤鸭是否超过货架终点的预测结果，这种方法对于烤鸭样本量总体偏少的情况，能够较好的预测其是否在货架期内。
对于任何一款可食用产品，不管是消费者、生产商还是质检部门都非常关心，该产品是不是在货架期内、有没有超过货架终点、有没有变质、食用后是不是安全；若是在货架期内的话，具体是储存了多少天不是很重要，毕竟都是在安全期内。本方法抓住了食用产品销售中的关注点，因此没有去预测产品具体的储存时间，也没有去预测新创制产品的具体货架终点，而是根据事先专家知识和感官评价得到的货架终点参考值，对未知的、来路不明的产品通过贝叶斯判别模型可以预测该产品是否超过货架终点，从而确定该产品是否在安全食用期内。由此可见，该方法特别适合于市场质量检测，也合适于工厂内对未标签或标签不易识别的产品是否超过货架终点的快速检测。
附图说明
图1是不同储存温度下电子鼻指纹图谱；
图中，(a)t＝75℃；(b)t＝65℃；(c)t＝55℃；(d)t＝45℃；(e)t＝25℃。
具体实施方式
下面将通过具体实施例来对本发明进行进一步的详细的解释和说明，然而应当理解，可以以各种形式实现本发明而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本发明，并且能够将本发明的范围完整的传达给本领域的技术人员。
如在通篇说明书及权利要求当中所提及的“包含”或“包括”为一开放式用语，故应解释成“包含但不限定于”。说明书后续描述为实施本发明的较佳实施方式，然所述描述乃以说明书的一般原则为目的，并非用以限定本发明的范围。本发明的保护范围当视所附权利要求所界定者为准。
如无特殊说明，本发明采用各种方法均为常规的方法，各种材料和试剂均能通过商业的途径获得。
一、仪器与设备
实验选择的电子鼻检测器为法国Alpha M.O.S公司生产的FOX 4000电子鼻检测系统，它是由18个金属氧化物传感器(MOS)组成的传感器阵列。实验选择的进样方法为静态顶空进样，进样设备为AlphaM.O.S公司的HS100自动进样器。
二、试验方法
1、烤鸭样品生产及货架起始时间确定
为了保证实验用烤鸭样品是在同一时间同一批次同一工艺条件下生产出来的，特在某国家级肉制品加工中心订制本项目所需要的烤鸭样品。采用传统挂炉烤制方法、杀菌模式与包装方式制作，每只烤制后鸭子重量越400g，每半只1袋真空包装。
生产后当天运回实验现场，进入预定储存环境放置并进行相应的货架时间记录。同时将生产后当天作为产品的货架起始时间。
2、加速实验温度设计
设置5个储存温度：25℃、45℃、55℃、65℃、75℃。其中25℃代表室温储存；45℃和55℃为微生物生长的适宜温度，由电热恒温箱控制；65℃和75℃能起到加速作用，并与烤鸭在常温下品质衰变的现象相似，也由电热恒温箱控制。
3、各储存温度下品质检测时间点设置
以温度为基准的Q10模型，就是指食品的温度每上升10度后，食品的反应速率较原来的反应速率大约增加1～4倍。同时储存时间设计的经验公示为：
其中fl为低温试验组取样间隔，fh为高温试验组取样间隔，Q10为温度系数，Δ为温差。
在本项目的预实验中，初设常温保存烤鸭货架期为3个月，Q10初设值为1.82，计算75℃下的货架期为4.5天，通过实验发现很多消费者还接受此时的样品，并且理化检测都合格，进一步实验发现货架期出现的时间推迟3～4天左右。同样在65℃下，理论计算时间在8天左右，但实验发现货架期出现时间也同样推后。
通常同一储存温度至少设置5个不同货架时间点，并且必须包含如下几个时间点：占货架期时间分别为0％(基准点)、50％(中点)、100％(失效点)和1个超出失效点的百分比时间点，如125％。为了品质衰变模型建立更加可靠，以及考虑烤鸭在品质货架期后期容易发生变化的实际情况，将品质检测重点放在货架期后期的评估上，由此增加了其对应的评估点(65％、80％、90％)。结合经验公式与预实验结果，各储存温度下样品的货架时间设计如表1所示。
表1烤鸭货架期加速温度与对应温度下品质检测时间点设计
4、电子鼻检测方式及实验材料制备
为了利于不同货架时间烤鸭品质的对比分析，在此采用单点检测方式。也就是在同一检测实验中，电子鼻所检测的不同样品来源于不同的货架时间，这样就避免了电子鼻因检测时间不同所带来的误差，便于样品间的品质比较。
为了达到单点检测方式，首先将烤鸭样品同时放置在设定的货架条件中，然后对于达到货架时间的样品脱离此货架条件并放置于特定的储存环境，当所有的不同货架时间样品都置于此储存环境后，就可以进行单点评价实验。本项目单点检测方式中所获得的不同货架时间烤鸭样品都放置于-18℃环境下冷冻保藏，因为此环境能保证最小程度的样品品质变化，使得在这种环境下烤鸭能保持预测货架时间下的品质，从而达到经过单点检测方式后的不同样品仍然都能代表不同货架时间的信息。
试验设置了5种解冻方式，并根据解冻后烤鸭的品质保持程度进行判断。这5种解冻方式为：(1)置于4℃冷藏冰箱17h后，微波解冻；(2)置于室温环境17h后解冻；(3)喷淋解冻1h后，微波解冻；(4)喷淋解冻0.5h后，微波解冻；(5)微波处理后，置于室温环境解冻。通过实验发现第2种方式对于真空包装烤鸭的解冻后品质保持最完整。这也完全符合其他研究所说的对于较厚的畜胴体多采用低温慢速解冻。从另一个角度也印证了其他研究所说的：微波解冻的温度比缓慢解冻温度高，所以对品质的影响可能比较大，微波处理还存在边角效应，并且对微波条件控制要求严格。
所以，电子鼻检测实验样品前，采用低温慢速解冻，将不同货架时间的烤鸭从-18℃环境下取出置于室温环境解冻17h，这对于真空包装烤鸭解冻后的品质保持最为完整。将解冻后的烤鸭样品从真空包装袋中取出，用无菌刀具切取烤鸭腹部鸭肉，置于研钵中研磨均匀，称重后加入20ml的电子鼻进样瓶中。一共5个不同储存温度，每个储存温度7个不同货架时间的烤鸭样品，共计35组，每组3个平行样品，共105份待测样品。
实验选择的电子鼻检测器为法国Alpha M.O.S公司生产的FOX 4000电子鼻检测系统，它是由18个金属氧化物传感器(MOS)组成的传感器阵列。实验选择的进样方法为静态顶空进样，进样设备为Alpha M.O.S公司的HS100自动进样器。实验参数见表2。烤鸭样品经电子鼻检测后得到18根气味响应曲线，每条响应曲线代表传感器在120s内的检测结果。实验选择每条响应曲线最大绝对值作为该传感器的输出值。为减小噪音干扰增强信号强度，实验以空气作为空白对照，减小空白校正。
表2电子鼻检测参数
6、烤鸭电子鼻图谱分析
图1为5种储存温度下，不同储存时间下烤鸭样品的电子鼻雷达指纹图谱。从图中可以看出，除t＝25℃外，其它4种温度下，不同储存天数的烤鸭样品间存在一定差异，尤其是各储存温度的前三阶段时间。相比而言各储存温度的后四阶段时间之间差异较小。其中样品间的差异主要体现在P30/2、PA/2、T70/2以及T30/1这4根传感器中。从五幅图中可以看出，储存温度为45和55℃时，不同样品间的差异和同种样品间的稳定性最佳。而储存温度为65和75℃时，不同样品间区分度较好但同种样品间的稳定性较差，这是由于温度较高时，样品品质变化较快，同种样品内成分差异也较大。当储存温度为25℃时，由于货架期较长，变化缓慢，样品间差异较小，仅P30/2和T70/2两根传感器能对样品有一定的区分能力。
7、基于电子鼻的烤鸭货架终点超过与否(超期)判别贝叶斯模型建立
贝叶斯(Bayes)判别的基本思想是假定对所研究的对象(总体)在抽样前已有一定的认识，常用先验概率分布来描述这种认识。然后基于抽取的样本再对先验认识作修正，得到所谓后验概率分布，而各种统计推断都基于后验概率分布来进行。贝叶斯判别不同于经典的统计方法，它的一个显著特点就是在保证决策风险尽可能小的情况下，尽量应用所有可能的信息。因此贝叶斯判别能较好地解决本研究中样本量总体偏小的情况。模型建立的具体步骤如下：
1)根据之前专家知识和感官评价，确定75°、65°、55°、45°和25°的货架期分别为8天、8天、8天、21天和81天。根据各温度下的货架期，将各温度下的烤鸭样本分为货架期内(I)与货架期外(O)。各温度下不同储存时间的样本及对应的货架类别标签如表3所示：
表3烤鸭样品的储存温度、储存时间以及货架类别
2)将各储存温度下的21个烤鸭样品按2:1的比例分成训练集和验证集。为保证建模集与预测集样本分布的一致性，训练集和验证集的划分采用分层抽样的原则。
3)根据训练集的样本计算各温度下各货架类别样本的均值与方差。该Bayes模型选取高斯函数为最大似然函数，并计算样本属于对应货架类别的概率：
其中
为样本属于k类时训练样本第n根传感器的响应均值(当k＝0时，样本为货架期内；当k＝1时，样本为货架期外)，
为样本属于k类时训练样本第n根传感器的响应值的方差。
4)分别计算在烤鸭电子鼻信号为x时，样本i属于各货架类别的概率。并选择概率最大时对应的货架类别作为该烤鸭是否超过货架终点的预测结果:
arg maxk＝0,1P(xi|yk) (2)
综合以上各步，样品在各储存温度下的货架类别预测模型分别为：
·当T＝75°时：
其中μn0＝[0.05,0.01,0.01,0.01,0.01,0.02,0.32,0.54,0.37,0.52,0.16,0.35,0.35,0.2,0.16,0.16,0.41,0.43],μn1＝[0.06,0.01,0.01,0.01,0.01,0.02,0.32,0.54,0.37,0.52,0.17,0.35,0.34,0.2,0.15,0.17,0.41,0.44]，σn0＝[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],σn1＝[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01].在此条件下验证集的最终预测准确率为85.7％。
·当T＝65°时：
其中μn0＝[0.04,0.01,0.00,0.01,0.00,0.02,0.31,0.43,0.27,0.40,0.16,0.34,0.28,0.15,0.10,0.14,0.29,0.32],μn1＝[0.04,0.01,0.00,0.01,0.00,0.02,0.30,0.42,0.26,0.39,0.16,0.32,0.27,0.15,0.11,0.14,0.29,0.31],σn0＝[0.04,0.01,0.01,0.01,0.01,0.02,0.31,0.27,0.40,0.40,0.16,0.34,0.28,0.15,0.10,0.14,0.29,0.32],σn1＝[0.04,0.01,0.01,0.01,0.01,0.02,0.30,0.42,0.26,0.39,0.16,0.32,0.27,0.15,0.11,0.14,0.29,0.31].在此条件下验证集的预测准确率为85.7％。
·当T＝55°时：
其中μn0＝[0.04,0.01,0.00,0.01,0.01,0.02,0.37,0.46,0.28,0.42,0.19,0.38,0.29,0.19,0.11,0.17,0.3,0.33],μn1＝[0.04,0.01,0.00,0.00,0.01,0.02,0.3,0.43,0.27,0.4,0.15,0.32,0.28,0.15,0.11,0.14,0.3,0.32]，σn0＝[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],σn1＝[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01].在此条件下验证集的预测准确率为71.4％。
·当T＝45°时：
其中μn0＝[0.04,0.01,0.00,0.00,0.00,0.02,0.27,0.44,0.29,0.41,0.14,0.30,0.27,0.14,0.11,0.12,0.33,0.35],μn1＝[0.04,0.01,0.00,0.01,0.00,0.02,0.34,0.45,0.29,0.42,0.17,0.35,0.26,0.17,0.11,0.16,0.32,0.34]，σn0＝[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],σn1＝[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01].在此条件下验证集的预测准确率为92.9％。
·当T＝25°时：
其中μn0＝[0.04,0.01,0.00,0.01,0.00,0.02,0.30,0.45,0.29,0.42,0.15,0.32,0.27,0.15,0.11,0.14,0.32,0.35],μn1＝[0.04,0.01,0.00,0.01,0.00,0.02,0.26,0.43,0.28,0.41,0.14,0.29,0.26,0.13,0.1,0.12,0.32,0.34]，σn0＝[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],σn1＝[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01].在此条件下验证集的预测准确率为92.9％。
从以上结果可以看出，利用贝叶斯判别能够对烤鸭样本是否在货架期内进行有效的判断，其预测准确率高。
虽然，上文中已经用一般性说明及具体实施例对本发明作了详尽的描述，但在本发明基础上，可以对之作一些修改或改进，这对本领域技术人员而言是显而易见的。因此，在不偏离本发明精神的基础上所做的这些修改或改进，均属于本发明要求保护的范围。
Claims (6)
1.电子鼻结合贝叶斯算法判别烤鸭是否超过货架终点的方法，其特征在于，包括如下步骤：
(1)首先确定烤鸭样品生产及货架起始时间；
(2)将烤鸭样品分别储存在不同的温度下，然后确定不同储存温度下的品质检测时间点；
(3)将所获得的不同货架时间下的烤鸭样品都放置于-18℃环境下冷冻保藏，当所有不同货架时间的样品都放置于此冷冻保藏环境时，然后采用低温慢速解冻的方式，将不同货架时间的烤鸭从-18℃环境下取出置于室温环境下解冻17h，将解冻后的烤鸭样品从真空包装袋中取出，用无菌刀具切取烤鸭腹部鸭肉，置于研钵中研磨均匀，称重后加入20ml的电子鼻进样瓶中；
(4)采用电子鼻采集不同货架时间下烤鸭的香气响应信号，得出不同储存时间下烤鸭样品的电子鼻雷达指纹图谱，结合专家知识和感官评价得到的不同储存温度下烤鸭样品的货架期，将各温度下储存的烤鸭样本分为两种货架类别——货架期内与货架期外，然后将各储存温度下的烤鸭样品按2:1的比例通过分层抽样分成训练集和验证集，根据训练集的样本计算各温度下各货架类别样本的均值与方差，
式中，
为样本属于k类货架类别时训练样本第n根传感器的响应均值，当k＝0时，样本为货架期内；当k＝1时，样本为货架期外，
为样本属于k类时训练样本第n根传感器的响应值的方差；
分别计算在烤鸭的电子鼻信号为x时，样本i属于各货架类别的概率，并选择概率最大时对应的货架类别作为该烤鸭是否超过货架终点的预测结果。
2.根据权利要求1所述的方法，其特征在于，检测时间点具体设计如下表所示：
3.根据权利要求1所述的方法，其特征在于，不同货架时间烤鸭的电子鼻检测方式为单点检测，即在同一储存温度下的电子鼻检测实验中，电子鼻所检测的不同样品来源于不同的货架时间。
4.根据权利要求1所述的方法，其特征在于，所述电子鼻的检测参数为：样品量4g、顶空温度60℃、顶空时间120s。
5.根据权利要求1所述的方法，其特征在于，所述烤鸭的重量为400±10g,每半只1袋真空包装。
6.根据权利要求1所述的方法，其特征在于，货架时间的起始时间为生产后的当天。
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