Research On Quality Identification Method Of Wine And Baijiu

In China,Baijiu has always been the most important type of consumption of spirits,while wine is the one to take the liquor market in a fast-growing speed in recent twenty years,both of which are supported with extensive groups of consumers.As is known,the quality of Baijiu and wine keeps being the main concern to consumers,producers and regulators.Although there are related provisions for inspecting the quality of Baijiu in accordance with national standards,there are lacks of detection methods corresponding closely to those provisions.This study precisely centered on some challenges and frequent topics of the quality inspection and the market supervision in relation to Baijiu and wine production in China,which was on the base of the modern instrumental analysis method to detect characteristic components of Baijiu and wine in Chinese market,with Chemometrics supporting the detailed data analysis.Also,it discussed about the identification method of Baijiu by traditional fermentation and Baijiu by liquid fermentation for monitoring Baijiu quality,based on high performance liquid chromatography-coulometric electrochemical array detection;it talked about the wine quality,based on diode array high performance liquid chromatography;it evaluated the entire bunches of grape quality and geographical origins of wines,based on near infrared spectroscopic technique.The quality identification model of Baijiu and wine were established to fill the gaps of the national standard detection method,provided technical means for controlling supervising the quality,and gave proposals for improving the quality identification methods and quality indicators in corresponding national standards.The main results are as follows:1.In this study,in order to respond to the difficulties of identifying the place of origin,the variety and the vintage from Chinese quality detection and market supervision,and features and real conditions of regulators in Chinese wine market,a new method for determining the content of 7 kinds of organic acids simultaneously existing in wines with HPLC was established.Using this method,all 7 organic acids were successfully separated,presenting a good linear relationship(R2,0.9959?0.9999),precision and repeatability,which leaded to a low limit of detection(0.025?0.05 mg/ml)and a high degree of accuracy of recoveries(95%?102%)relatively.After applying the method,there were results showing that both variety and vintage had significantly influence on organic acids in red and white wines.On the one,variety played a greater effect than vintage;on the other,vintage played a more severe impact on white wines than on red wines.Meanwhile,being different from red wines' tartaric acid and lactic acid,white wines' the main organic acids were malic acid and tartaric acid.Furthermore,this method was used to analysis the organic acids quality condition in Beijing market wines,illustrating that the overall quality condition in Beijing market wines was relative good.Among 102 wine samples,only 5 samples were found containing an overdose of citric acid,with a relevance ratio of 4.9%.Total organic acids could be effectively used to detect fake wines,especially these fake wines using "alcohol,essence,saccharin and water." Furtherly,organic acid characteristics were different when they were compared between imported wine and domestic wine,or between wines from different regions and wineries.Through comparing and analyzing organic acids and total amount of original wine and commercial wine,the preliminary conclusion is made about the quality of wine,which provides a basis for the establishment of organic acid indicators in the standards of wine products in China.2.The fundamental task of fermenting well-qualified wine is the identification of optimum ripeness of grapes timely and accurately.This research also aimed at evaluating grape' s soluble solid content(SSC),which was nondestructively based on near infrared spectroscopic technique,using a detection probe designed in house and focusing on optimization of three detection parameters.Those included light power(P),outer diameter of the probe(D),distance between the light source to the probe(L).Statistical models of diffusing transmittance spectra with grape' s SSC were developed with the application of partial least square(PLS)regression.Additionally,orthogonal experiment was applied to choose optimal parameters.Comprehensive performance of different models under each combination of factor levels was assessed in terms of correlating coefficient of calibration(rc),root mean square error of prediction(RMSEP)and differential values of RMSEP and root mean square error of calibration(?).The PLS model obtained the best results with the rc of 0.83,the RMSEP of 0.76°Brix and the ? of 0.84°Brix,on the condition that P,D and L were 70 W,70 and 85 mm respectively.The results in the study show that SSC prediction of grape using the testing probe is feasible and parameter L has more impact on the performance of models than others.More advanced models can be obtained through combining reasonable parameters.This research can provide reference for the fast nondestructive detection equipment of the entire bunches of grapes.3.Undoubtedly,wine produced in excellent sites are more appreciated in the market,which triggers the occurrence of counterfeit-wine events from time to time.This research develops a more rapid and nondestructive detection method of wine based on near-infrared spectroscopy.This research describes how near-infrared(NIR)spectroscopy could be combined with radial basis function neural networks(RBFNN)and least-squares support vector machines(LS-SVMs)based on principal component analysis(PCA)to classify origin of grapes used to produce wine.There were also comparisons about the effects of different preprocessing methods(standard normal variate(SNV)and multiplicative scattering correction(MSC))on classification results.The data shows that the use of NIR preprocessing spectral data with optimum RBFNN parameters produced a very high level of correct classification rate,from 90.16 to 98.36%.For LS-SVM,identification rates were from 91.80%to 98.36%.According to the results,supported by Chemometrics with appropriate spectral data pretreatment,NIR spectroscopy has the potential to differentiate wine produced in varies grape origins rapidly and non-destructively.The results of this study are helpful in developing a more effective and nondestructive detection method of wine.4.Considering about the growing demand of Baijiu in Chinese market,there were kinds of production standards established in China,which by contrast expanded difficulties of supervisions.Nevertheless,how to distinguish Baijiu by traditional fermentation and Baijiu by liquid fermentation in accordance with national standards has remained to be an unsolved question.In this study,a high-performance liquid-phase electrochemical detection method for Baijiu was established.Under the optimal chromatographic conditions,when using 8 channel,102 characteristic peak in Chinese Baijiu could be identified.All peaks are well separated with high precision(RSD<2.40%)and stability(RSD<2.01%).Using this method,Baijiu by traditional fermentation and Baijiu by liquid fermentation could be distinguished.A variety of active substances in these two kinds Baijiu could be detected.The fingerprint information is rich,and there are obvious differences between the two kinds of Baijiu.Furtherly,cluster analysis and principal component analysis were also used for the detection of data processing,and could achieve an accurate distinction between these two kinds Baijiu,but cannot accurately distinguish between different brands of Baijiu.Furthermore,the method was used to analyze the Baijiu with different blending ratio,and the results were in accordance with the true circumstances.The Baijiu was divided into four groups precisely:the proportion was divided into 10%,15%,75%,and 100%.All these data showed that this method could provide a suitable selection for classifying and determining the quality and group of Baijiu in the future.