【摘要】：黃酒營養(yǎng)豐富,市場發(fā)展前景廣闊,并享有"國酒"美譽(yù),但因質(zhì)量評價(jià)手段較少,導(dǎo)致諸如虛報(bào)酒齡、亂貼知名品牌標(biāo)簽和以次充好冒充地理標(biāo)志產(chǎn)品等非法行為時(shí)常發(fā)生,嚴(yán)重侵犯了正規(guī)廠家利益和消費(fèi)者權(quán)益。復(fù)合材料修飾電極因表面能夠固定化學(xué)性質(zhì)優(yōu)良的納米材料和聚合物等,克服了單一材料修飾電極和裸電極反應(yīng)微弱、靈敏度低等缺點(diǎn),近年來已在酒類酒齡、地域和品牌鑒別中有所應(yīng)用。基于此,本文首次將自主研制的金屬納米復(fù)合材料修飾電極陣列用于黃酒質(zhì)量真?zhèn)舞b別,結(jié)合模式識別方法成功實(shí)現(xiàn)了不同酒齡、品牌和地域黃酒的區(qū)分和預(yù)測,從而為凈化黃酒市場、維護(hù)消費(fèi)者和正規(guī)廠家的利益提供了一套全新可靠的解決方案。本文具體研究內(nèi)容、方法和結(jié)論如下:(1)采用自制的PACBK/Au/GCE、PABSA/Au/GCE和PASP/Pt/GCE組成修飾玻碳電極陣列成功實(shí)現(xiàn)了對3年陳、5年陳、8年陳、10年陳、15年陳和20年陳6種酒齡紹興古越龍山黃酒的區(qū)分和預(yù)測。本部分選取在黃酒陳釀過程中含量變化較大的三種呈味物質(zhì):維生素C(維生素,酸味)、酪氨酸(氨基酸,澀味)和葡萄糖(糖類,甜味),采用循環(huán)伏安法(CV)和電流時(shí)間法(i-t)等對應(yīng)制備了 PACBK/Au/GCE、PABSA/Au/GCE和PASP/Pt/GCE三種聚合物/金屬納米復(fù)合材料修飾電極,并在優(yōu)化pH、掃速和緩沖液濃度等檢測條件下實(shí)現(xiàn)了對三種呈味物質(zhì)在一系列濃度梯度溶液中的定量測定,通過對比三種物質(zhì)在電極上的檢測限和在黃酒中的含量,發(fā)現(xiàn)物質(zhì)在黃酒中含量遠(yuǎn)大于檢測限,這說明黃酒樣品滿足了修飾電極的檢測條件,即證明了修飾電極鑒別黃酒的有效性�；陔姌O有效性的基礎(chǔ)上,在6種酒齡黃酒樣品中采用復(fù)頻多電位階躍法作為激發(fā)信號施加于電極陣列,選取響應(yīng)電流信號曲線與時(shí)間軸所圍區(qū)域的面積值作為特征值,結(jié)合主成分分析(PCA)、局部保留投影(LPP)、線性判別分析(LDA)和支持向量機(jī)(LSSVM、LIBSVM)等模式識別方法對黃酒酒齡進(jìn)行區(qū)分和預(yù)測,PCA、LPP和LDA三種區(qū)分模型中,LDA區(qū)分效果最好,在二維圖和三維圖中,6種酒齡黃酒都能夠明顯分開;LSSVM和LIBSVM兩種酒齡回歸模型中,LIBSVM預(yù)測效果要優(yōu)于LSSVM,特別是均方差更小。(2)采用自制的PACBK/Au/GCE、PABSA/Au/GCE和PGA/Cu/GCE組成修飾玻碳電極陣列成功實(shí)現(xiàn)了對3種古越龍山、3種塔牌和會稽山總計(jì)7種品牌紹興黃酒的區(qū)分和預(yù)測。本部分選取在各品牌黃酒間含量差異相對較大的三種呈味物質(zhì):維生素C(維生素,酸味)、酪氨酸(氨基酸,澀味)和沒食子酸(酚類,苦味),采用循環(huán)伏安法等電化學(xué)方法對應(yīng)制備了 PACBK/Au/GCE、PABSA/Au/GCE和PGA/Cu/GCE三種聚合物/金屬納米復(fù)合材料修飾電極,在優(yōu)化pH、掃速、富集電位和時(shí)間等檢測條件下應(yīng)用線性掃描伏安法(LSV)和差分脈沖伏安法(DPV)等實(shí)現(xiàn)了三種呈味物質(zhì)在一系列濃度梯度中的定量測定,并將三種電極對維生素C、酪氨酸和沒食子酸的檢測限與三種呈味物質(zhì)在黃酒中實(shí)際含量進(jìn)行了比較,結(jié)果顯示實(shí)際含量均遠(yuǎn)大于檢測限,說明基于修飾電極陣列對黃酒品牌進(jìn)行鑒定是可行的。之后在7種品牌黃酒中采用方波和梯形波兩種多電位階躍法施加于電極陣列得到響應(yīng)電流信號曲線,選取電流曲線與時(shí)間軸所包圍區(qū)域面積值作為特征值,結(jié)合PCA、LPP、LDA、LIBSVM、ELM(極限學(xué)習(xí)機(jī))和BPNN(BP神經(jīng)網(wǎng)絡(luò))等模式識別方法對黃酒品牌進(jìn)行了區(qū)分和預(yù)測,區(qū)分模型顯示PCA、LPP和LDA三種區(qū)分效果均存在3種塔牌黃酒類間距過小的問題,ELM區(qū)分效果也不太理想,但LIBSVM模型效果較佳,訓(xùn)練集和測試集區(qū)分正確率分別為100%和99.05%;預(yù)測模型顯示ELM和LIBSVM預(yù)測效果一般,而BPNN效果較好,預(yù)測準(zhǔn)確率達(dá)到了 97.14%。(3)采用自制的 SMWCNT/Au/GCE、PABSA/Au/GCE 和 PGA/Cu/GCE 組成修飾玻碳電極陣列成功實(shí)現(xiàn)了對江蘇丹陽(鎮(zhèn)江)、青島即墨(青島)、浙江汾湖(嘉興)、浙江同康(臺州)和古越龍山(紹興)5種地域黃酒的區(qū)分和預(yù)測。本部分選取在各地域黃酒中比較有代表性的三種呈味物質(zhì):5'-GMP(添加劑,鮮味)、酪氨酸(氨基酸,澀味)和沒食子酸(酚類,苦味),采用滴涂法和循環(huán)伏安法等制備了 SMWCNT/Au/GCE、PABSA/Au/GCE和PGA/Cu/GCE三種金屬納米復(fù)合材料修飾電極,在優(yōu)化的最佳pH和掃速等條件下應(yīng)用LSV和DPV等電化學(xué)方法實(shí)現(xiàn)了三種呈味物質(zhì)在一系列濃度梯度溶液中的定量測定,并將三種物質(zhì)在對應(yīng)電極上的檢測限與其在黃酒中的含量進(jìn)行了比較,結(jié)果顯示含量均遠(yuǎn)大于檢測限,說明黃酒中的三種物質(zhì)含量滿足修飾電極響應(yīng)條件,電極的有效性符合要求。之后在5種地域黃酒中采用復(fù)頻多電位階躍法施加于電極陣列獲得響應(yīng)電流信號曲線,選取電流曲線與時(shí)間軸所包圍區(qū)域的面積值作為特征值,結(jié)合PCA、LPP、LDA、LIBSVM和ELM等模式識別方法對黃酒地域進(jìn)行了區(qū)分和預(yù)測,區(qū)分結(jié)果顯示PCA、LPP和LDA均存在浙江汾湖和浙江同康黃酒類間距過小以及古越龍山黃酒樣本點(diǎn)較分散等問題,ELM和LIBSVM區(qū)分效果較好,訓(xùn)練集和預(yù)測集區(qū)分正確率都較高;預(yù)測結(jié)果顯示ELM和LIBSVM兩種回歸模型表現(xiàn)較好,特別是決定系數(shù)R~2較大。
[Abstract]:Yellow rice wine has abundant nutrition, broad market prospects and enjoys the reputation of "national wine". However, due to the lack of quality evaluation methods, illegal behaviors such as false declaration of wine age, labeling of well-known brands and faking geographical indications as inferior products often occur, which seriously infringe the interests of regular manufacturers and consumers. Surface can immobilize nano-materials and polymers with good chemical properties, overcome the weakness and low sensitivity of single material modified electrode and bare electrode. In recent years, it has been used in wine age, region and brand identification. The identification of authenticity and falsity of yellow rice wine quality and the identification and prediction of different wine ages, brands and regions have been successfully realized by pattern recognition method, thus providing a new and reliable solution for purifying the yellow rice wine market and safeguarding the interests of consumers and regular producers. U/GCE, PABSA/Au/GCE and PASP/Pt/GCE modified glassy carbon electrode arrays were successfully used to distinguish and predict six kinds of Shaoxing Guyuelongshan yellow wine aged 3 years, 5 years, 8 years, 10 years, 15 years and 20 years. PACBK/Au/GCE, PABSA/Au/GCE and PASP/Pt/GCE polymer/metal nanocomposites modified electrodes were prepared by cyclic voltammetry (CV) and current-time method (i-t) with acid (amino acid, astringent taste) and glucose (sugar, sweet taste). The electrode was modified by PACBK/Au/GCE, PABSA/Au/GCE and PASP/Pt/GCE. Under the conditions of optimizing pH, sweeping rate and buffer concentration, the three flavoring substances were detected. Quantitative determination in a series of concentration gradient solutions showed that the content of the three substances in yellow rice wine was much higher than the detection limit by comparing the detection limit of the three substances on the electrode and the content in yellow rice wine. On this basis, the complex frequency multi-potential step method was applied to the electrode array as excitation signal in six kinds of rice wine samples. The response current signal curve and the area around the time axis were selected as eigenvalues, combined with principal component analysis (PCA), partial retention projection (LPP), linear discriminant analysis (LDA) and support vector machine (LSSVM, LIBSVM). Pattern recognition method was used to distinguish and predict the age of yellow rice wine. Among the three models, PCA, LPP and LDA, LDA was the best. In the two-dimensional and three-dimensional charts, six kinds of yellow rice wine could be separated obviously; LSSVM and LIBSVM were better than LSSVM in predicting the age of yellow rice wine, especially the mean square deviation was smaller. BK/Au/GCE, PABSA/Au/GCE, PGA/Cu/GCE and PGA/Cu/GCE modified glassy carbon electrode arrays were successfully used to distinguish and predict three kinds of Shaoxing rice wine from three kinds of Guyue Longshan, three kinds of Tapai and a total of seven brands of Jishan. In this part, three flavoring substances, vitamin C (vitamin, acid), tyrosine (amino acid), with relatively large content difference among the different brands of rice wine were selected. Polymer/metal nanocomposites modified electrodes PACBK/Au/GCE, PABSA/Au/GCE and PGA/Cu/GCE were prepared by cyclic voltammetry and gallic acid (phenols, bitters) respectively. Linear sweep voltammetry (LSV) and differential pulse voltammetry (DPV) were used to optimize pH, sweep rate, enrichment potential and time. The quantitative determination of three flavoring substances in a series of concentration gradients was realized by DPV. The detection limits of vitamin C, tyrosine and gallic acid were compared with the actual contents of three flavoring substances in yellow rice wine. The results showed that the actual contents were far greater than the detection limits, indicating that the brand of yellow rice wine was based on modified electrode array. The response current signal curve was obtained by applying square wave and trapezoidal wave multi-potential step method to the electrode array in seven brands of yellow rice wine. The current curve and the area around the time axis were selected as the eigenvalues, combined with PCA, LPP, LDA, LIBSVM, ELM (Extreme Learning Machine) and BPNN (BP Neural Network) etc. The discriminant model showed that there were three kinds of tower rice wine with too small spacing among PCA, LPP and LDA, and the ELM discriminant effect was not ideal, but the LIBSVM model had better effect. The correct discriminant rate of training set and test set was 100% and 99.05% respectively. SVM has a good prediction effect, but BPNN has a good prediction accuracy rate of 97.14%. (3) Using self-made SMWCNT / Au / GCE, PABSA / Au / GCE and PGA / Cu / GCE modified glassy carbon electrode arrays, we have successfully realized five regions: Jiangsu Suanyang (Zhenjiang), Qingdao Jimo (Qingdao), Zhejiang Fenhu (Jiaxing), Zhejiang Tongkang (Taizhou) and Guyue Longshan (Shaoxing). In this part, three typical flavoring substances, 5'-GMP (additive, delicious), tyrosine (amino acid, astringent taste) and gallic acid (phenolic, bitter taste), were selected to prepare SMWCNT/Au/GCE, PABSA/Au/GCE and PGA/Cu/GCE metal nanocomposites by trickling and cyclic voltammetry. The modified electrode was used to quantitatively determine three flavoring substances in a series of concentration gradient solutions by electrochemical methods such as LSV and DPV under the optimum pH and sweeping speed conditions. The detection limits of the three substances on the corresponding electrode were compared with the contents in rice wine. The results showed that the contents were far greater than the detection limits. The content of the three substances in rice wine satisfies the response condition of the modified electrode and the validity of the electrode meets the requirements.Then the response current signal curve was obtained by applying the complex frequency multi-potential step method to the electrode array in the five regional rice wine.The current curve and the area around the time axis were selected as the eigenvalues and combined with PCA, LPP, LDA, LIB as the eigenvalues. SVM and ELM pattern recognition methods were used to distinguish and predict the regions of yellow rice wine. The results showed that PCA, LPP and LDA had the problems of too small distance between Fenhu and Tongkang yellow rice wine in Zhejiang Province, and scattered sample points of Guyuelongshan yellow rice wine. ELM and LIBSVM had better distinguishing effect, and the training set and prediction set had higher accuracy. It shows that the two regression models of ELM and LIBSVM perform well, especially the determination coefficient R~2 is larger.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS261.7

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