农业工程学报
農業工程學報
농업공정학보
2013年
20期
263-269
,共7页
尹芳缘%黄洁%王敏敏%郑海霞%杨月%陈静%曾小燕%童春霞%王绿野%姜燕%沈凤%惠国华
尹芳緣%黃潔%王敏敏%鄭海霞%楊月%陳靜%曾小燕%童春霞%王綠野%薑燕%瀋鳳%惠國華
윤방연%황길%왕민민%정해하%양월%진정%증소연%동춘하%왕록야%강연%침봉%혜국화
传感器%优化%非线性分析%霉变燕麦%电子鼻%随机共振
傳感器%優化%非線性分析%黴變燕麥%電子鼻%隨機共振
전감기%우화%비선성분석%매변연맥%전자비%수궤공진
sensors%optimization%nonlinear analysis%mildew oat%electronic nose%stochastic resonance
应用电子鼻对燕麦（Avena sativa L）霉变程度进行区分，为了提高区分准确度，对电子鼻传感器阵列进行了优化的研究。每天随机选择10个燕麦样品进行电子鼻检测，试验连续进行5 d，将检测数据耦合入非线性双稳态随机共振系统，以外部Gaussian白噪声激励系统产生共振，选择输出信噪比特征值进行主成分分析，初期试验主成分1和主成分2贡献率之和为96.43%，且相同霉变程度样品离散度较大，不同霉变程度样品之间距离较近。为了提高电子鼻对霉变燕麦样品区分效果，进行了电子鼻传感器负荷加载分析，优化选择了传感器阵列，优化后主成分1和主成分2贡献率之和为99.31%，相同霉变程度燕麦样品的聚合度更高，使不同霉变程度燕麦样品之间的区分更加明显，为进一步的定量化检测奠定了基础。
應用電子鼻對燕麥（Avena sativa L）黴變程度進行區分，為瞭提高區分準確度，對電子鼻傳感器陣列進行瞭優化的研究。每天隨機選擇10箇燕麥樣品進行電子鼻檢測，試驗連續進行5 d，將檢測數據耦閤入非線性雙穩態隨機共振繫統，以外部Gaussian白譟聲激勵繫統產生共振，選擇輸齣信譟比特徵值進行主成分分析，初期試驗主成分1和主成分2貢獻率之和為96.43%，且相同黴變程度樣品離散度較大，不同黴變程度樣品之間距離較近。為瞭提高電子鼻對黴變燕麥樣品區分效果，進行瞭電子鼻傳感器負荷加載分析，優化選擇瞭傳感器陣列，優化後主成分1和主成分2貢獻率之和為99.31%，相同黴變程度燕麥樣品的聚閤度更高，使不同黴變程度燕麥樣品之間的區分更加明顯，為進一步的定量化檢測奠定瞭基礎。
응용전자비대연맥（Avena sativa L）매변정도진행구분，위료제고구분준학도，대전자비전감기진렬진행료우화적연구。매천수궤선택10개연맥양품진행전자비검측，시험련속진행5 d，장검측수거우합입비선성쌍은태수궤공진계통，이외부Gaussian백조성격려계통산생공진，선택수출신조비특정치진행주성분분석，초기시험주성분1화주성분2공헌솔지화위96.43%，차상동매변정도양품리산도교대，불동매변정도양품지간거리교근。위료제고전자비대매변연맥양품구분효과，진행료전자비전감기부하가재분석，우화선택료전감기진렬，우화후주성분1화주성분2공헌솔지화위99.31%，상동매변정도연맥양품적취합도경고，사불동매변정도연맥양품지간적구분경가명현，위진일보적정양화검측전정료기출。
Oats (Avena Sativa L.) are one of the important food crops. It contains some rich nutrients. Oats easily gets mildew affected by environmental factors during storage, which is getting to be one of problems in the food safety field. As one artificial olfactory analysis method, the electronic nose technique is widely applied in crop quality detecting fields. This technique utilizes a gas sensor array to imitate a human’s olfactory system. The detecting signals measured by a gas sensor array is discriminated and recognized by an artificial pattern recognition method. Then the species of the detecting objectives can be determined. In this paper, electronic nose system was utilized to discriminate mildewed oat samples. The diagram includes three main parts:data acquisition and transmitting unit, sensor array and the chamber unit, and power and gas supply unit. The sensor array consisted of eight semiconductor gas sensors. Polytetrafluorethylene (PTFE) material was utilized to fabricate the chamber. Each sensor room was separated, which helped to eliminate the cross-influence of the gas flow. At the same time, gas sensor array optimization was also studied. 25 g of oat samples were weighed and placed into an experimental container. The container was tightly sealed with parafilm. 40 samples were prepared. All samples were stored under room temperature and standard atmospheric pressure. In order to accelerate the mildew procedure of the samples, 4 mL deionized water was sprayed on all samples every day. 10 samples were randomly selected in an electronic nose measurement. The measurement time of each oat sample was 45 s. The experiments lasted for five days. The measurement data was measured and transmitted to the computer. The stochastic resonance had three principal parts:a weak input signal, a non-linear bistable system, and an additional dose of external Gaussian white noise. The experimental data was coupled into a non-linear bistable stochastic resonance model. Stepping external Gaussian stimulating white noise was utilized to modulate the stochastic resonance system for resonance generation. Finally, stochastic resonance signal-to-noise ratio (SNR) was calculated and exported as signal-to-noise ratio curves. Eigen values of systematic output signal-to-noise ratio were selected for principal component analysis (PCA). The total degree of contribution of the first principal component and the second principal component was 96.43%. In order to improve the mildewed oat discrimination rate, sensor loadings analysis was used to evaluate the contribution rate of all gas sensors. The optimized gas sensor array included S1, S2, S3, S4, S5, S6 and S7. After an optimization procedure, the total degree of contribution of the first principal component and the second principal component was 99.31%. These results demonstrated that an electronic nose system presents a discriminating ability for mildewed oat samples. Sensor array optimization based loadings analysis improved the discriminating rate. The proposed method is promising in the crop quality and safety analysis field.
