农业工程学报
農業工程學報
농업공정학보
2014年
6期
256-262
,共7页
侯俊才%侯莉侠%胡耀华%郭康权%佐竹隆顕
侯俊纔%侯莉俠%鬍耀華%郭康權%佐竹隆顕
후준재%후리협%호요화%곽강권%좌죽륭현
无损检测%主成分分析%石英晶体微天平%香蕉%成熟度%线性判别分析
無損檢測%主成分分析%石英晶體微天平%香蕉%成熟度%線性判彆分析
무손검측%주성분분석%석영정체미천평%향초%성숙도%선성판별분석
sensors%nondestructive detection%principal component analysis%quartz crystal microbalance%banana%ripening stage%linear discriminant analysis
为了对采后香蕉的成熟度进行无损检测，提高货架品质和减少过熟造成的损失，以巴西香蕉为研究对象，以1,2-二油锡甘油-3-磷-L-丝氨酸、半乳糖神经酰胺、纤维素乙酯、乙基纤维素做为敏感材料，采用滴涂法修饰了4个石英晶体微天平（quartz crystal microbalance，QCM），测试了1～7级成熟度的香蕉的挥发性有机化合物的响应特性，并用主成分分析法和线性判别分析法对响应结果做了分析。试验表明，在4种敏感膜中纤维素乙酯对香蕉的7个成熟阶段的挥发性气体的响应最明显，响应值先随香蕉成熟度增加而增加，在4级成熟阶段时最大，随后降低。用主成分分析法对7个成熟阶段香蕉的挥发物的响应值聚类分析，97%能够正确聚类；用线性判别分析法可获得100%的识别率。据此，4种材料修饰的石英晶体微天平传感器可以用于香蕉成熟度的识别。该研究结论对香蕉成熟度自动化分级研究有一定实用价值。
為瞭對採後香蕉的成熟度進行無損檢測，提高貨架品質和減少過熟造成的損失，以巴西香蕉為研究對象，以1,2-二油錫甘油-3-燐-L-絲氨痠、半乳糖神經酰胺、纖維素乙酯、乙基纖維素做為敏感材料，採用滴塗法脩飾瞭4箇石英晶體微天平（quartz crystal microbalance，QCM），測試瞭1～7級成熟度的香蕉的揮髮性有機化閤物的響應特性，併用主成分分析法和線性判彆分析法對響應結果做瞭分析。試驗錶明，在4種敏感膜中纖維素乙酯對香蕉的7箇成熟階段的揮髮性氣體的響應最明顯，響應值先隨香蕉成熟度增加而增加，在4級成熟階段時最大，隨後降低。用主成分分析法對7箇成熟階段香蕉的揮髮物的響應值聚類分析，97%能夠正確聚類；用線性判彆分析法可穫得100%的識彆率。據此，4種材料脩飾的石英晶體微天平傳感器可以用于香蕉成熟度的識彆。該研究結論對香蕉成熟度自動化分級研究有一定實用價值。
위료대채후향초적성숙도진행무손검측，제고화가품질화감소과숙조성적손실，이파서향초위연구대상，이1,2-이유석감유-3-린-L-사안산、반유당신경선알、섬유소을지、을기섬유소주위민감재료，채용적도법수식료4개석영정체미천평（quartz crystal microbalance，QCM），측시료1～7급성숙도적향초적휘발성유궤화합물적향응특성，병용주성분분석법화선성판별분석법대향응결과주료분석。시험표명，재4충민감막중섬유소을지대향초적7개성숙계단적휘발성기체적향응최명현，향응치선수향초성숙도증가이증가，재4급성숙계단시최대，수후강저。용주성분분석법대7개성숙계단향초적휘발물적향응치취류분석，97%능구정학취류；용선성판별분석법가획득100%적식별솔。거차，4충재료수식적석영정체미천평전감기가이용우향초성숙도적식별。해연구결론대향초성숙도자동화분급연구유일정실용개치。
The ripeness stage is one of the important factors of banana quality, which will affect the sale price of bananas. To consumers, inadequate ripeness means low quality, and over-ripeness means a short shelf life, and both of them will lead to low income to sellers. In order to obtain optimal quality, keep a long shelf period, and minimize losses, it is necessary to automatically detect banana ripeness nondestructively. One promising strategy is using a quartz crystal microbalance (QCM) system to detect volatile organic compounds (VOCs) released during banana ripening. The advantages of this method are quick detection, high sensitivity, and low cost. A QCM sensor is a mass sensor, which is a device of an oscillating circuit. When the mass on its surface is changed, the frequency of an oscillating circuit will change simultaneously. When coated by cross-sensitivity materials, which can absorb VOCs. QCM sensors can be used to detect the change of ambient VOCs. Therefore, it is possible to use QCM sensors for recognizing the banana ripeness stage. This research aimed to discriminate the ripeness stage of banana based on QCM sensors. The measurement system was constructed with 9 MHz of gold electrodes quartz crystals, which were modified by sensitive materials of 1, 2-dioleoyl-sn-glycero-3-[phosphor-L-serine] (DOPS), galactosylceramide (GC), cellulose acetate (CA), and ethyl cellulose (EC). The sensitive materials were dissolved in chloroform (CHL) and tetrahydrofuran (THF). The QCM sensors were fabricated by dropping 5 mg/mL DOPS/CHL, GC/CHL, and CA/THF, EC/THF on quartz crystal electrodes, respectively. Bananas were purchased from the Philippines in green condition, and they were ripened in a room with a constant temperature of (22±1)℃. During ripening, the bananas were divided into 7 ripening stages according to a Von Loesecke HW chart, the banana VOCs were obtained by static head-space through putting bananas into a 2000 mL teflon chamber for 1 hour at every ripening stage. The measurement was conducted by injecting 50 mL VOCs into a 150 mL measurement chamber. The response of these 4 sensors to VOCs of bananas from the 1 to 7 ripening stage was recorded by LabVIEW software. The experiments were repeated 8 times, and the response value was analyzed by principal-components analysis (PCA) and linear discriminant analysis (LDA). The results showed that the response of a sensor modified by CA was more sensitive to banana VOCs than DOPS, GC, and EC. For the sensor coated by CA, the response tended to increase with the ripening stage from stage 1 to stage 4, the frequency shift reached its peak at ripening stage 4 due to an increase of aldehydes and esters in the banana VOCs. After ripening stage 4, the frequency shift declined with a decrease of acetates and an increase of butyrates and alcohols. The other three sensitive materials modified sensors’ response were lower than CA, though their trends were not the same. The frequency shift of 4 sensors coated by 4 sensitive materials to banana VOCs of 7 ripening stages was classified by PCA and LDA, with accuracies of 97%and 100%, respectively. The research showed that it was feasible to classify the ripening stage of banana using four materials as sensitive film, and that the LDA is a potential classification method. Accordingly, this research is helpful for banana automatic grading.
