CN110376159A - 基于近红外漫透射光谱的鸭梨黑心病快速鉴别方法 - Google Patents
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Abstract
本发明提供了一种基于近红外漫透射光谱的鸭梨黑心病快速鉴别方法,该方法的主要步骤:挑选包含有黑心病鸭梨的鸭梨样品,动态在线装置采集鸭梨样品光谱数据并保存;光谱用SNV和MSC算法处理后,在MATLAB软件中观察鸭梨样品光谱能量谱的分布情况;在经过预处理方法处理的光谱里面找出能量峰出现的波段范围,采用Correlation analysis method方法来确定最佳的波长组合,建立鸭梨健康梨和黑心病病梨的判别模型,以实现鸭梨黑心病的快速无损判别。本发明的方法是具有检测速度快,病害识别正确率高,无损检测等优点的判别方法,应用本发明在优选波长的条件下进行黑心病判别,可以降低黑心病导致的推柜率,为鸭梨的出口贸易提供技术支持。
Description
技术领域
本发明涉及鸭梨黑心病的判别方法,基于近红外漫透射光谱的鸭梨黑心病快速鉴别方法。
背景技术
鸭梨树适合种植在pH值7.5至8.0、土层厚度较深、土壤有机质1.0%以上的沙壤土中。鸭梨不仅富含蛋白质、脂肪、多种矿物质、果酸、维生素和碳水化合物,而且具有良好的药用功效,中国药典《本草从新》中对其“清心肺、利肠、止咳消痰、清喉降火、醒酒解毒”的功效加以记载。近些年,随着水果出口贸易的发展和国内消费市场的日渐繁荣,梨的质量逐年提升,产量稳步提高,收获面积高居世界之首,已经成为仅次于苹果、柑橘产业之后的第三大水果产业。
鸭梨黑心病又称空心病,是一种常见的非传染性生理病害。鸭梨黑心病成因大致有两种:一种是种植过程和采摘后的鸭梨在储藏过程中,出现了冷害等自然灾害及突然降温造成了低温伤果,形成黑心病;另一种是鸭梨缺素和果实衰老,鸭梨晚熟且贮藏时间长,引起细胞组织中酚类物质增加,造成酚类物质的酶触氧化,形成黑心病。鸭梨黑心病仅凭肉眼从外观上是很难识别的,有多年从业经验的农艺师也无法准确判别出鸭梨的黑心。鸭梨黑心病影响鸭梨内部品质,降低鸭梨的品质,造成出口鸭梨成批退货,影响鸭梨出口贸易。
目前,检测黑心鸭梨的主要判别方法是人工切开目视识别的抽样方法,该方法虽然准确,但属于有损检测,速度慢,成本高,难以满足鸭梨出口贸易的需求。因此,为了减少果农在对外出口中的损失,提高鸭梨产业附加值水平,研究快速、准确、无损检测鸭梨黑心病的在线判别技术是迫切需要的。
发明内容
针对背景技术提出的问题,本发明提供一种基于近红外漫透射光谱的鸭梨黑心病快速鉴别方法。
由于健康鸭梨和黑心鸭梨在化学成分上存在差异性,C-H、N-H、O-H等基团在可见/近红外谱区合频、倍频吸收强度不同,反映在近红外光谱能量谱的峰数、峰强、峰位的不同,化学成分差异越明显,光谱特征区别越明显。因此,本发明以检测到的鸭梨的近红外光谱为研究基础,首先确定判别鸭梨黑心病的特征波段,然后建立鸭梨黑心病的定性判别模型,最终实现鸭梨黑心病的快速鉴别。该方法的主要步骤:挑选包含有黑心病鸭梨样品,动态在线装置采集鸭梨样品光谱数据并保存;光谱用SNV和MSC算法处理后,在MATLAB软件中观察鸭梨样品光谱能量谱的分布情况;在经过预处理方法处理的光谱里面找出能量峰出现的波段范围,采用Correlation analysis method方法来确定最佳的波长组合,建立鸭梨健康梨和黑心病梨的判别模型,以实现鸭梨黑心病的快速无损判别,最终实现黑心病梨的快速鉴别。
具体地,一种基于近红外漫透射光谱的鸭梨黑心病快速鉴别方法,包括以下步骤:
(1)分别采集含有黑心病鸭梨的鸭梨样品的近红外漫透射光谱,并保存光谱数据;
(2)将采集了光谱的鸭梨样品剖开,区分黑心病鸭梨和健康鸭梨,并对黑心鸭梨进行标记;
(3)分别对光谱数据进行多元散射校正(MSC)、标准正态变量变换(SNV)预处理,在MATLAB软件中观察鸭梨样品光谱能量谱的分布情况;
(4)对经过预处理的光谱数据采用correlation analysis method方法来确定最佳的波长组合,找出健康鸭梨和黑心病鸭梨在能量谱上不同波峰处的差异,确定出用于黑心病判别的特征波段;
(5)从最佳的波长组合中选出四个波长,按波长长度依次标记为第一波长、第二波长、第三波长和第四波长;
(5.1)若第二波长与第一波长的比值小于设定的阈值,则判断对应的鸭梨样品为黑心病鸭梨;若第二波长与第一波长的比值不小于设定的阈值,则判断对应的鸭梨样品为健康鸭梨;
(5.2)若第四波长与第三波长的比值小于1,则判断对应的鸭梨样品为健康鸭梨;若第四波长与第三波长的比值不小于1,则判断对应的鸭梨样品为黑心病鸭梨;
若根据步骤(5.1)判断的结果和根据步骤(5.2)判断的结果均为黑心病鸭梨,则确定对应的鸭梨样品为黑心病鸭梨;否则,确定对应的鸭梨样品为健康鸭梨;
(6)将特征波段范围内的近红外光谱能量谱作为输入变量,构建PLS-DA判别模型,采集待测鸭梨的近红外漫透射光谱并将其输入到PLS-DA判别模型内进行判别,从而实现黑心病的判别。
优选的,在所述的最佳的波长组合中,第一波长为634nm,第二波长为674nm,第三波长为720nm,第四波长为810nm。
优选的,所述的特征波段为634nm-810nm。
优选的,所述的设定的阈值为2.5。
优选的,所述的近红外漫透射光谱采用可见近红外漫透射光谱在线检测装置进行采集,可见近红外漫透射光谱在线检测装置能够检测到的波长范围为300nm-1100nm。
本发明的优点在于:以检测到的鸭梨样品近红外漫透射光谱能量谱为基础,首先确定出判别鸭梨黑心病的特征波段;然后建立鸭梨黑心病的定性判别模型,最终实现鸭梨黑心病的快速鉴别。
附图说明
图1为实施例中黑心病鸭梨的近红外漫透射光谱能量谱对比图。
图2为实施例中前两个主成分的主成分散点图。
图3为实施例中峰值判别法的判别结果图。
图4为实施例中优选波段的最佳波段组合图。
图5为实施例中PLS-DA的判别图。
具体实施方式
下面结合附图和具体实施例对本发明做进一步说明。
实施例1
(a)处理实验样品:挑选含有黑心病鸭梨的鸭梨样品,剔除其中的擦伤、霉变等样品,擦拭掉鸭梨表面水分,用记号笔在鸭梨赤道部位依次标记4个点,在实验室条件下保存12个小时;
(b)采集光谱数据:每次开机预热30分钟后,用白色特氟纶球作参比,校正可见近红外漫透射光谱在线检测装置,调节到检测鸭梨时稳压电源对应的电流值,在光谱查看界面观察能量谱强度标准差变化,当变化范围在1%范围内,电压稳定,可以开始采集光谱;
(c)光谱预处理:采用Unscrambler化学计量学软件对原始光谱进行多元散射校正(MSC)、标准正态变量变换(SNV)等预处理;
(d)将经过预处理的光谱在MATLAB软件中打开,观察含有黑心病鸭梨样品光谱能量谱的分布情况,并用origin作图观察变化规律;
(e)在波长范围488-904nm范围内应用Unscrambler对经过预处理的光谱进行主成分分析;主成分分析法可以将光谱信息压缩成若干个主成分的得分,主成分的载荷向量对应着该变量在主成分中的贡献,第一主成分累计贡献率占69%,第二主成分累计贡献率占29%,取前两个主成分进行分析;
(f)以光谱能量谱特征波长为变量,结合主成分分析法的判别方法如下所示:
F(λ)为能量光谱曲线方程,f(λ)为基线方程,F(λi)为波长λi处的光谱能量值,f(λi)为波长λi处的基值,1为光谱间的间距。
实施例2
采用correlation analysis method优选波段的方法用来选择区别黑心鸭梨的最佳的波长点组合。在波长范围350-1000nm之间,对450个光谱波长变量进行了如下的操作:每一个梨的分类标签γ被赋予了1和4,其中标签γ赋值为1时表示黑心鸭梨,标签γ赋值为4时表示健康鸭梨。组合波长的相关系数R2的计算公式如下,出现组合波长的相关系数R2的最大值的波长组合即为要优选的最佳波长组合。
r1是分类标签γ和x1的相关系数,r2是分类标签γ和x2的相关系数,rx是x1和x2间的相关系数,相关系数R2代表着不同波长组合的相关系数。通过correlation analysismethod方法优选的波段,波长组合634nm和674nm以及720nm和810nm处出现明显的波峰、波谷。
实施例3
光谱采集前将参数设置如下:积分时间100ms,运动速度5个/s,波长范围372-1154nm。之后,对30个健康鸭梨和20个黑心鸭梨赋予不同的分类变量值,由于健康梨和黑心梨具有不同的光谱能量,按照样本特征及正相关规律赋予每个样品分类变量值,经过多次对分类变量进行赋值判别,比较不同赋值分类变量下的判别结果。经过多次比较,其中健康梨的标定值是4,由于微黑心的梨在运输过程会较快地变成黑心,将微黑心和黑心样品共同标定为1。采用Unscrambler对经过多元散射校正(MSC)和标准正态变量变换(SNV)的光谱赋予分类变量值并建立PLS-DA回归模型。选择健康梨和黑心梨的分类阈值,参与建模的所有样品的预测值都在标定值的加减1的范围内,设定预测输出值在某类标定样品的加减1以内,认为样品属于该类型。图5给出了判别结果,当模型的输出值在4加减1的范围内时,认为该样品是健康梨,在1加减1内时,认为该样品是黑心病梨。
该分类判别模型的PLS-DA判别结果如图5所示。
图1表示在波长500-900nm范围内的鸭梨近红外漫透射能量谱,由于在波长范围300-500nm内,存在着基线漂移,杂散光和噪声,对定性和定量判别模型影响较大,因此取500-900nm波长范围研究能量谱的差异。其中,正常梨的能量谱高于严重黑心病梨,轻微黑心病梨的光谱能量最低。正常梨的细胞间充满空气,能量损失主要是散射导致的,而黑心病发病部位在果核周围,严重的果肉呈现棕色或褐色,由于多酚氧化酶的活性增高,促使果心及果肉组织发生氧化,细胞代谢加快,果肉呈棕色或褐色,果核变黑,对可见光光的吸收变强,透过的光的能量减少,探测器接收的能量光谱值低。
图2表示健康梨和黑心梨的欧氏距离主成分散点图。从散点图上可以看出,健康梨距离几何中心的距离与黑心病梨的差距较大,健康梨和黑心梨只有很小的重叠区,用此方法可以较好的对黑心鸭梨和健康鸭梨进行区分。
图3表示峰值判别法的判别结果图。判别模型中,取阈值为1.3,从图中可以看出,少量健康果误判为黑心果,而将黑心梨误判为健康梨的数量比较多,需要进一步探讨降低黑心梨误判率的方法。
图4表示优选波段的最佳波段组合图。波长点656nm在有明显吸收峰的波长点634nm和674nm之间,将波长比403nm/656nm的比值作为阈值,此时组合波长的相关系数R2取得了最佳值,为0.6613。因此,将波长点403nm和656nm的比值作为鸭梨黑心病判别的阈值来进行鸭梨黑心病的判别。
图5表示应用PLS-DA判别鸭梨黑心病的散点图。判别模型中,取健康梨的标定值为4,黑心梨的标定值为1,阈值为3,选取最佳主成分因子数12,建立鸭梨的PLS-DA判别模型,模型的相关性是0.969。从图中可以看出,黑心梨的分布值没有超过3,即没有黑心鸭梨被判别为健康梨,满足判别的要求。
综上所述,本发明着眼于研究和探讨我国鸭梨黑心病的在线分选技术,提出可见/近红外漫透射光谱技术在鸭梨黑心病检测中的应用,寻找出检测迅速、分选正确率高的鸭梨黑心病分选方法,能够降低黑心病的发生率,提高鸭梨内部质量,确保出口鸭梨品质,为提高我国的鸭梨出口创汇的总额做出贡献。
Claims (5)
1.一种基于近红外漫透射光谱的鸭梨黑心病快速鉴别方法,包括以下步骤:
(1)分别采集含有黑心病鸭梨的鸭梨样品的近红外漫透射光谱,并保存光谱数据;
(2)将采集了光谱的鸭梨样品剖开,区分黑心病鸭梨和健康鸭梨,并对黑心鸭梨进行标记;
(3)分别对光谱数据进行多元散射校正(MSC)、标准正态变量变换(SNV)预处理,在MATLAB软件中观察鸭梨样品光谱能量谱的分布情况;
(4)对经过预处理的光谱数据采用correlation analysis method方法来确定最佳的波长组合,找出健康鸭梨和黑心病鸭梨在能量谱上不同波峰处的差异,确定出用于黑心病判别的特征波段;
(5)从最佳的波长组合中选出四个波长,按波长长度依次标记为第一波长、第二波长、第三波长和第四波长;
(5.1)若第二波长与第一波长的比值小于设定的阈值,则判断对应的鸭梨样品为黑心病鸭梨;若第二波长与第一波长的比值不小于设定的阈值,则判断对应的鸭梨样品为健康鸭梨;
(5.2)若第四波长与第三波长的比值小于1,则判断对应的鸭梨样品为健康鸭梨;若第四波长与第三波长的比值不小于1,则判断对应的鸭梨样品为黑心病鸭梨;
若根据步骤(5.1)判断的结果和根据步骤(5.2)判断的结果均为黑心病鸭梨,则确定对应的鸭梨样品为黑心病鸭梨;否则,确定对应的鸭梨样品为健康鸭梨;
(6)将特征波段范围内的近红外光谱能量谱作为输入变量,构建PLS-DA判别模型,采集待测鸭梨的近红外漫透射光谱并将其输入到PLS-DA判别模型内进行判别,从而实现黑心病的判别。
2.根据权利要求1所述的方法,其特征在于:在所述的最佳的波长组合中,第一波长为634nm,第二波长为674nm,第三波长为720nm,第四波长为810nm。
3.根据权利要求1所述的方法,其特征在于:所述的特征波段为634nm-810nm。
4.根据权利要求1所述的方法,其特征在于:所述的设定的阈值为2.5。
5.根据权利要求1-4任一权利要求所述的方法,其特征在于:所述的近红外漫透射光谱采用可见近红外漫透射光谱在线检测装置进行采集,可见近红外漫透射光谱在线检测装置能够检测到的波长范围为300nm-1100nm。
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111141836A (zh) * | 2020-01-13 | 2020-05-12 | 石河子大学 | 一种基于声振多域谱与近红外光谱信息融合的梨果早期内部病害无损检测方法及装置 |
CN111521583A (zh) * | 2020-05-09 | 2020-08-11 | 天津市林业果树研究所 | 一种苹果霉心病检测模型建立的方法 |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1789979A (zh) * | 2004-12-14 | 2006-06-21 | 中国农业大学 | 一种快速无损检测鸭梨内部质量的方法 |
CN107064054A (zh) * | 2017-02-28 | 2017-08-18 | 浙江大学 | 一种基于cc‑pls‑rbfnn优化模型的近红外光谱分析方法 |
CN109632650A (zh) * | 2018-12-14 | 2019-04-16 | 北京农业智能装备技术研究中心 | 可溶性固形物含量的在线检测速度补偿方法及装置 |
-
2019
- 2019-08-22 CN CN201910778197.4A patent/CN110376159A/zh active Pending
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1789979A (zh) * | 2004-12-14 | 2006-06-21 | 中国农业大学 | 一种快速无损检测鸭梨内部质量的方法 |
CN107064054A (zh) * | 2017-02-28 | 2017-08-18 | 浙江大学 | 一种基于cc‑pls‑rbfnn优化模型的近红外光谱分析方法 |
CN109632650A (zh) * | 2018-12-14 | 2019-04-16 | 北京农业智能装备技术研究中心 | 可溶性固形物含量的在线检测速度补偿方法及装置 |
Non-Patent Citations (3)
Title |
---|
SUN XUDONG ET AL.: "Simultaneous measurement of brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy", 《POSTHARVEST BIOLOGY AND TECHNOLOGY》 * |
刘燕德等: "鸭梨黑心病可见/近红外漫透射光谱在线检测", 《光谱学与光谱分析》 * |
孙旭东等: "鸭梨黑心病和可溶性固形物含量同时在线检测研究", 《农业机械学报》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111141836A (zh) * | 2020-01-13 | 2020-05-12 | 石河子大学 | 一种基于声振多域谱与近红外光谱信息融合的梨果早期内部病害无损检测方法及装置 |
CN111521583A (zh) * | 2020-05-09 | 2020-08-11 | 天津市林业果树研究所 | 一种苹果霉心病检测模型建立的方法 |
CN111521583B (zh) * | 2020-05-09 | 2023-08-25 | 天津市农业科学院 | 一种苹果霉心病检测模型建立的方法 |
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