电子鼻传感器技术判断芒果成熟度和成熟期质量分类应用
improved maturity and ripeness classifications of magnifera indica cv. harumanis mangoes through sensor fusion
of an electronic nose and acoustic sensor
电子鼻传感器技术判断芒果成熟度和成熟期质量分类应用
ammar zakaria *, ali yeon md shakaff, maz jamilah masnan, fathinul syahir ahmad saad,
abdul hamid adom, mohd noor ahmad, mahmad nor jaafar, abu hassan abdullah andlatifah munirah kamarudin
centre of excellence for advanced sensor technology (ceastech), universiti malaysia perlis
(unimap), 01000, kangar, perlis, malaysia; s: aliyeon@unimap.edu.my (a.y.m.s.);
mazjamilah@unimap.edu.my (m.j.m.); fathinul@unimap.edu.my (f.s.a.s.);
abdhamid@unimap.edu.my (a.h.a.); mohdnoor@unimap.edu.my (m.n.a.);
mahmad@unimap.edu.my (m.n.j.); abu.hassan@unimap.edu.my (a.h.a.);
latifahmunirah@unimap.edu.my (l.m.k.)
* author to whom correspondence should be addressed; : ammarzakaria@unimap.edu.my;
.: +604-979-8931.
abstract:
in recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. however, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. this paper presents the work on the classification of mangoes (magnifera indica cv. harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. principal component analysis (pca) and linear discriminant analysis (lda) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. however, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both pca and lda were unable to discriminate the age difference of the harumanis mangoes. instead of six different groups, only four were observed using the lda, while pca showed only two distinct groups. by applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using lda was improved. however, no significant improvement was observed using pca with data fusion technique. further work using a hybrid lda-competitive learning neural network was performed to validate the fusion technique and classify the samples. it was found that the lda-clnn was also improved significantly when data fusion was applied.
近年来，有许多关于使用非破坏性技术评估和确定芒果成熟度和成熟度水平的应用研究。然而，大多数研究应用都是使用单一算法模式识别传感器系统进行，无论是使用电子鼻、声学还是其他无损测量。本文介绍了哈鲁马尼斯芒果的分类研究)成熟度和成熟度水平使用电子鼻和声波传感器数据融合。三组样本分别来自两个不同的收获时间(第7周和第8周)，通过电子鼻，声波传感器进行评估。主成分分析(pca)和线性判别分析(lda)仅基于芒果释放的香气和挥发性气体，就能够区分第7周和第8周收获的芒果。然而，当六个不同成熟度和成熟度水平的不同组组合在一个分类分析中时，pca和lda都不能区分哈鲁曼尼芒果的年龄差异。与6个不同的组相比，使用lda只观察到4个组，而pca只显示出两个不同的组。通过将低阶数据融合技术应用于电子鼻和声波数据，改进了基于lda的成熟度和成熟度分类方法。然而，采用数据融合技术的pca并没有显著改善。利用混合lda竞争学习神经网络算法对融合技术进行了验证，并对样本进行了分类。结果表明，采用数据融合后，lda-clnn有明显的改善。
keywords:electronic nose; acoustic sensor; volatiles; mango ripeness classification
关键词：电子鼻；声传感器；挥发物；芒果成熟度分类
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of an electronic nose and acoustic sensor
电子鼻传感器技术判断芒果成熟度和成熟期质量分类应用
ammar zakaria *, ali yeon md shakaff, maz jamilah masnan, fathinul syahir ahmad saad,
abdul hamid adom, mohd noor ahmad, mahmad nor jaafar, abu hassan abdullah andlatifah munirah kamarudin
centre of excellence for advanced sensor technology (ceastech), universiti malaysia perlis
(unimap), 01000, kangar, perlis, malaysia; s: aliyeon@unimap.edu.my (a.y.m.s.);
mazjamilah@unimap.edu.my (m.j.m.); fathinul@unimap.edu.my (f.s.a.s.);
abdhamid@unimap.edu.my (a.h.a.); mohdnoor@unimap.edu.my (m.n.a.);
mahmad@unimap.edu.my (m.n.j.); abu.hassan@unimap.edu.my (a.h.a.);
latifahmunirah@unimap.edu.my (l.m.k.)
* author to whom correspondence should be addressed; : ammarzakaria@unimap.edu.my;
.: +604-979-8931.
abstract:
in recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. however, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. this paper presents the work on the classification of mangoes (magnifera indica cv. harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. principal component analysis (pca) and linear discriminant analysis (lda) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. however, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both pca and lda were unable to discriminate the age difference of the harumanis mangoes. instead of six different groups, only four were observed using the lda, while pca showed only two distinct groups. by applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using lda was improved. however, no significant improvement was observed using pca with data fusion technique. further work using a hybrid lda-competitive learning neural network was performed to validate the fusion technique and classify the samples. it was found that the lda-clnn was also improved significantly when data fusion was applied.
近年来，有许多关于使用非破坏性技术评估和确定芒果成熟度和成熟度水平的应用研究。然而，大多数研究应用都是使用单一算法模式识别传感器系统进行，无论是使用电子鼻、声学还是其他无损测量。本文介绍了哈鲁马尼斯芒果的分类研究)成熟度和成熟度水平使用电子鼻和声波传感器数据融合。三组样本分别来自两个不同的收获时间(第7周和第8周)，通过电子鼻，声波传感器进行评估。主成分分析(pca)和线性判别分析(lda)仅基于芒果释放的香气和挥发性气体，就能够区分第7周和第8周收获的芒果。然而，当六个不同成熟度和成熟度水平的不同组组合在一个分类分析中时，pca和lda都不能区分哈鲁曼尼芒果的年龄差异。与6个不同的组相比，使用lda只观察到4个组，而pca只显示出两个不同的组。通过将低阶数据融合技术应用于电子鼻和声波数据，改进了基于lda的成熟度和成熟度分类方法。然而，采用数据融合技术的pca并没有显著改善。利用混合lda竞争学习神经网络算法对融合技术进行了验证，并对样本进行了分类。结果表明，采用数据融合后，lda-clnn有明显的改善。
keywords:electronic nose; acoustic sensor; volatiles; mango ripeness classification
关键词：电子鼻；声传感器；挥发物；芒果成熟度分类
接触式掌纹识别门简介,什么是接触式掌纹识别门
甲醇乙醇燃料油柴油重油热值大卡检测设备推荐
明年市场规模有望突破500亿元！2023年第四季度3D打印领域融资事件频发
JJ48-EP600红外分光测油仪的技术指标
绝热橡塑保温板制品产品性能
电子鼻传感器技术判断芒果成熟度和成熟期质量分类应用
酸碱电位滴定仪需要注意哪些
脱硫脱硝自动烟尘（气）测试仪
光学显微镜的保养和维护
自动混匀超声波催化反应系统应用参数-已有相关保护
如何判断密封圈的材质是不是硅橡胶？
具有生物活性pDNA多链体的静电纺明胶基质
深入了解扫描电镜：扫描电镜（SEM）电子透镜的介绍
无锡液压元件功能及安装提示功能
B2级橡塑海绵板燃烧性能及使用范围
实验室细胞废液真空抽吸系统BV235的特点
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