基于E-nose的多类饮料识别：改进的编码Fisher判别分析方法

42 浏览量更新于2024-08-26 收藏 640KB PDF 举报

本文主要探讨了"改进的Fisher判别分析方法在电子鼻（Electronic Nose, E-nose）支持下的饮料识别"这一主题。电子鼻作为食品工业中的重要工具，特别是在饮料识别领域展现出其显著的应用价值。传统Fisher判别分析（FDA）作为一种二分类器，其主要优势在于其简便的模式识别能力，然而在实际应用中，尤其是日常生活中常见的多类别识别问题上，其局限性就显得尤为明显。为了克服FDA在多分类任务中的不足，作者提出了一种基于编码的Fisher判别分析（Encoding Fisher Discriminate Analysis, EFDA）方法。这种方法旨在扩展FDA的适用范围，使其能够处理复杂的多类识别问题。研究者选取了七种常见的饮料样本，包括中国白酒、啤酒、葡萄酒、绿茶、红茶、乌龙茶和普洱茶，通过电子鼻进行客观测试，以此验证EFDA在多类别饮料识别中的性能和有效性。在改进的EFDA方法中，可能包括对原始数据的预处理步骤，如特征选择和降维，以提高分类器的准确性和效率。同时，可能会采用编码策略将多类问题转化为一系列二分类问题，使得FDA的核心思想得以应用。此外，可能还涉及到训练过程中的参数优化和模型评估，以确保EFDA在实际应用中的鲁棒性和稳定性。该研究不仅关注技术层面的算法创新，还考虑到了实用性，即如何使电子鼻在饮料识别场景中发挥更大的作用，这对于食品质量控制、生产监控以及消费者体验提升等方面具有重要意义。通过实验结果的展示，读者可以了解到EFDA在饮料分类中的精确度和性能提升，这为未来电子鼻在更多领域的多分类问题提供了新的思路和技术参考。这篇文章深入探讨了如何通过改进的Fisher判别分析方法来解决电子鼻在多类别饮料识别中的挑战，展示了理论与实践的结合，并为相关领域的研究人员和工程师提供了实用的工具和方法论指导。

Electronic Nose Based Beverage Identification by an Improved Fisher Discriminate

Analysis Method

Tao Liu, Lei Zhang, Yanbin Chen, Dongqi Li, Mengya Wu and Xingrui Cui

College of Communication Engineering, Chongqing University, Chongqing 400044, China

cquliutao@cqu.edu.cn; leizhang@cqu.edu.cn

Abstract—Electronic noses are widely employed in food

industry and play an important role in beverage

identifications. Meanwhile, fisher discriminate analysis is

considered as one of convenient pattern recognition

methodologies for electronic noses. Traditional fisher

discriminate analysis (FDA) is a kind of binary classifiers. In

other words, it is not competent to multi-class classifications

which are common in daily concerns. To solve the limitation of

the binary classifier, we presented a FDA based classification

method, namely encoding fisher discriminate analysis (EFDA),

which can be applied to multi-class recognition. In this paper,

seven kinds of beverages including Chinese liquor, beer, wine,

green tea, black tea, oolong tea and pu’er tea were prepared

and tested objectively by an electronic nose (E-nose) system

with 32 gas sensors. The E-nose system was a prototype for

general detection of complicated gaseous compounds . It

performed all the experiments and tested the volatile

compounds of seven beverages. The experimental results

indicated that the proposed method were more effective than

conventional pattern recognition methods such as principal

component analysis (PCA) and k-nearest neighbor (k-NN).

The highest recognition rate could reach 96.83% by EFDA.

The outcomes achieved demonstrated that EFDA can be

successfully used in multi-class identifications.

Keywords—electronic nose; fisher discriminate analysis;

beverage; binary encoding

I. I

NTRODUCTION

Artificial olfactory sensation is a novel technology to

simulate human-smell process to indentify various gaseous

compounds. Typically, when a gas sensor array associates

with pattern recognition models, an artificial olfactory

system is composed. This kind of machine, namely

electronic nose (E-nose), has great potential in complicated-

ingredient-gas sensing for their evident superiorities such as

high speed, good reproducibility and non-invasion.[1] On

the other hand, food safety and quality become more and

more important in modern society due to increasing concern

on human health and living quality. Thus, instead of

traditional chemical instruments, E-noses supply a

convenient way to perform special detections of food

industry in which beverage discriminations usually play an

important role. [2]

Communities have made quite a few efforts to the

scientific researches about beverage identifications. Among

them, some scholars pay attention to distinguish various

categories of tea according to types, grades, even habitats

with commercial E-nose systems.[3]-[5] Meanwhile, some

studies focus on differentiating kinds of alcoholic drinks on

their different aromatic flavors.[6][7] Furthermore, positive

attempts have been performed to show strong ability of

olfactory machines to recognize different fruit juices and

milks. [8]-[10] Generally speaking, suited pattern

recognition models are needed to infer certain results from

sensor-array responses in E-nose machines. As far as it is

concerned, a few methods such as principal component

analysis (PCA), fisher discriminate analysis (FDA), k-

nearest neighbor algorithm (k-NN) and artificial neural

network (ANN) have been adopted for E-nose based

beverage tests and shown obvious

validity and practicability in the reports aforementioned.

Although FDA with the advantages of simple and easy

realization is frequently used in pattern recognition of E-

nose systems, conventional FDA method only fits for a

binary classification.[11]-[13] That is to say that FDA is

only a binary classifier. In order to extend the applied range

of FDA to multi-class identifications, either one-against-

one or one-against-all strategies[14][15] has been

considered to be a suitable scheme instead of the traditional

one. However multi-class identification of FDA, even other

binary classifiers, is so far an interesting issue for the need

of convenient recognition solutions. The wide applications

and issues of E-nose can be referred to as [16]-[18].

In this paper, the authors proposed a novel multi-type

classification strategy, called Encoding FDA (EFDA),

which combined binary encoding and hamming-distance

measurement with FDA to explore a better FDA based

multi-class classifier. Meanwhile, we also designed and

developed an E-nose platform with 32 gas sensors for gas

tests. Seven types of beverages including Chinese liquor,

beer, wine, green tea, black tea, oolong tea and pu’er tea

were prepared as testing samples. Then EFDA, k-NN and

PCA were adopted as pattern recognition part of E-nose.

Comparison of the three method was presented and the

recognition accuracy of EFDA was the best one among

three adopted methods. It hinted that EFDA had favorable

prospects in multi-class applications. The rest of this paper

is organized as follows: In Section II, we illustrate the

hardware architecture of the proposed E-Nose platform and

describe the principle of the methods used in algorithm

validation. Then the experimental setup, results and

2016 Asia Multi Conference on Modelling and Simulation

DOI 10.1109/AMC.2016.20

下载后可阅读完整内容，剩余5页未读，立即下载

weixin_38702726

粉丝: 10
资源: 930

基于E-nose的多类饮料识别：改进的编码Fisher判别分析方法

电子功用-基于多核Fisher判别分析的电子鼻特征选择优化方法

核Fisher判别分析方法三篇技术文档

matlab费希尔判别分析

fisher判别分析

r语言fisher判别分析

python判别分析费希尔

fisher判别分析matlab代码

fisher线性判别分析

Fisher线性判别方法的原理

R语言中用Fisher判别求判别效率的代码

最新资源