Inspection And Evaluation Of Tomato And Rice Wine By Electronic Nose

Nondestructive inspection of farm produce has been an interesting field in the past of 20 to 30 years. Without damaging the farm produce, the nondestructive method for the evaluation of fruits quality can be used to detect the internal quality and ingredients, and so on, which are related with fruits' quality, and to evaluate internal quality, including sugar content, acidity, firmness, inner pathological changes, etc. By far, a great number of studies have been done on the nondestructive detection methods in domestic and overseas. And the results of many researches indicate that the firmness and aroma are important indexes for evaluating the fruit quality. However, the measurements for the fruits firmness by current methods usually damage fruits and operate slowly, in other words, it is not appropriate for the online measurement In order to overcome these disadvantages, many researchers have been studying on the nondestructive measurement techniques for the fruits firmness depending on the physical properties of fruits, such as optics, acoustics, electrics, and so on. Up to now, these methods have not been widely used in the commerce.Besides some routine examination such as physical and chemical variables, the quality of drink is evaluated by the experts through senses traditionally. The sensitivity of human's sense, however, is liable to be changed by external factors, therefore, it is not easy to make an accurate evaluation through human's sense. Alternatively, instrumental techniques such as gas chromatography (GC) with headspace sampling, and techniques such as GC combined with mass spectrometry (GC/MS) can be used to identify and quantify individual aroma components. These techniques usually have following disadvantages: 1) time consuming and/or labour intensive; 2) require sample preparation, 3) difficult to automate. The electronic nose (E-nose) offers a fast and non-destructive alternative to the measurement of volatile emission of samples. Commercially available E-noses use an array of sensors combined with pattern recognition software.This research work is focused on the quality evaluation of tomato and rice wine with E-nose. Based on the research, the correlation between the inner characteristic and response of sensors has been analyzed, and prediction models of tomato firmness and wine-year have been established. It provides a new method of nondestructive evaluation of food industry for further research.The electronic nose (PEN2) and enrichment unit (a sample pretreatment including adsorption sampling and subsequent thermal desorption can be applied with PEN2) produced by Airsense Analysis Gmbh corporation was used. This system is provided with objectivity, reliability and repetition.In the same time, the electronic nose responses to the fruit and wine aroma, so it is a non-destructive method to evaluate quality of fruit and wine. It has a convenience operation and celerity inspection.1 Study on the maturity, shelf life and damage of tomato with Electronic noseThe electronic nose was used to assess the different mature stages of tomato. When the maturity was distinguished according to color, the score plot of the principal component analysis(PCA) and linear discriminant analysis (LDA) for the E-nose measurements show that immature tomatoes can be distinguished from mature and over mature tomatoes. But the result is not good for mature and over mature tomatoes. When firmness was considered, the PCA analysis was able to classify the 100 % of the total samples in each different mature stage. The result shows that the difference of firmness can bedistinguished by E-nose. In order to assess the potential of electronic nose technique to predict fruit quality parameter, the electronic nose signal and the result derived from well-established traditional technique such as firmness were related using a multivariate technique. The calculations were carried out using 'The Unscrambler V9.1 1986-2004' (CAMO, PROCESS, AS, OSLO, Norway), a statistical software package for multivariate calibration. Partial least square (PLS) which used Full Cross Validation test was used to build the prediction models. When signal of E-nose at 55S was used to PLS analysis, the calibration model for the firmness with four latent variables was determined through the residual variance analysis by each different component, the correlation for the firmness model was 0.950 and average standard error of prediction (RMSEP) was 0.129. The firmness could be well predicted with the electronic nose signal.The experiment of the changes of two different mature state tomatoes had been monitored during shelf life (Dayl-Day 11, Dayl4, Dayl7). Based on E-nose, a clear distinction among mature tomatoes on Dayl-Day6, Day7-Dayll and Dayl4-Dayl7 is available. And it is also possible to distinguish over mature tomatoes on Dayl-Day5, Day6-Dayll and Dayl4-Dayl7. The calibration models for the shelf life with eight latent variables were determined through the residual variance analysis by each different component, the correlation coefficient (R=0.989 and R= 0.980), average standard error of prediction (RMSEP=0.666 and RMSEP=0.912) for mature and over mature tomato respectively.The electronic nose was also used to assess the feasibility of damage inspection on tomatoes. Before sensors were optimized, the score plot of the principal component analysis (PCA) for the E-nose measurements had showed that undamaged tomato groups (5 undamaged tomatoes per group) could not be well distinguished from damaged groups (4 undamaged tomatoes and 1 damaged tomato per group), the score plot of linear discriminant analysis (LDA) for the E-nose measurements showed that it could be distinguishedon7th day after daunage. Based on loading analysis and the evolution of the signals generated by the sensor array, it can be inferred that the sensor MOS2> MOS6> MOS7> MOS8 and MOS9 have higher values, MOSK MOS3. MOS4^ MOS5 and MOS10 are not sensitive to the tomato volatile components. After sensors were optimized, seven-sensor-array with MOSK MOS2n MOS3^ MOS6. MOS7> MOS8 and MOS9 was used for analysis, it could be clearly distinguished on 1st day after damage based on LDA analysis, and the sum of variance was improved by 2.04%. So a subset of few sensors can be chosen to explain all the variance. This result could be used in further studies to optimize the number of sensors.2 Study on rice wine with Electronic nose and EDUIn order to analyze the potential of electronic nose with EDU to assess the rice wine in different year (same brand), different feature parameters had been extracted from the response curve from training samples measurement files and were applied to PCA analysis. The average during first five seconds signal is most appropriate for pattern identification from PCA analysis, the average during last five seconds and the average during sixty seconds take second place, the seventh second and the maximum responses of all sensors are unsuitable to pattern identification. It can be gotten that feature parameter is important determinant of pattern identification. Pattern file used to identify wine in different year (same brand) was created through the average of first five seconds signal. After the training, the pattern file was investigated in order to evaluate the discrimination capabilities with measurement files from measurement samples. The plots of the cluster analysis (CA) showed that samples were able to classify the 100 % of the total measurement samples. In order to assess the electronic nose to predict the different year old wine, the calculations were carried out using PLS analysis. Signal of the average of first five seconds was used to PLS analysis and to build the prediction models. Through the residual variance analysis by each different component, the calibration...