Application Of Chemometrics On Food Discrimination And Determination Of Preservatives

Discrimination of food authenticity and determination of toxic and harmful matter are two crucial issues in food safety and quality control. It is related to the consumers'interests and health. It is realistic significance to develop rapid and effective method for discrimination of food authenticity and determination of toxic and harmful materials.Chemometrics is an object on chemical theory of measurement and methodology. It can optimize the process of measurement, resolve overlapped spectra and extract maximum useful discrimination information of data from chemical measurement using mathematics, statistics, computer science and other related science theories and methods. It offers a new method to solve some problems about food safety and food quality control by using chemometrics and modern analytical means.There are six chapters in this thesis. The focuses of the research work are discrimination of soy sauce samples, vinegar samples and seasoning wine samples of different brands and kinds by NIR, AAS and SFS with the aid of chemical pattern recognition techniques, and resolution of overlapped spectra of four preservatives and determination simultaneously by UV-visible spectrometry with the aid of multivariate calibration.Chapter one The principals of some chemical pattern recognition and multivariate calibration techniques (CA, PCA, DA, PLS, PCR, ANN and PARAFAC), and application of IR, AAS, AES, GC, HPLC and MS in food safety and quality control combined with chemical pattern recognition techniques and in food analysis combined with multivariate calibration were reviewed and summarized. The outlook of chemometrics in food quality and food control was also discussed.Chapter two A new method of discrimination of soy sauce samples of different kinds and brands was developed according to 9 physico-chemical variables using pattern recognition.53 soy sauce samples of three different brands were collected including 26 light soy sauces and 27 dark soy sauces. The values of 9 physico-chemical properties (density, pH, dry matter, ashes, electro-conductivity, amino-nitrogen, salt and total acidity) were determined and acted as the characteristic variables of soy sauce samples. To evaluate the stability of the quality and degree of differentiation of different brands, the similarities of different products were calculated by vector similarity analysis. The results showed that SA was useful to evaluate the stability of soy sauce quality, but limited to differentiate the brands and kinds of soy sauce samples. We used cluster analysis and principal component analysis to study whether it was feasible to discriminate the brands and kinds of soy sauce samples. The results of cluster analysis and principal component analysis showed the effectiveness of discrimination and the correctness of variables selected to predict the brands and kinds of soy sauce samples in verification set. In order to predict the unknown samples, several calibration models were set up, such as partial least squares, linear discrimination analysis and K-nearest neighbor. The results of prediction showed that these models were effective to discriminate soy sauce samples. The variables for LDA and KNN were chosen by means of Fisher F-ratio approach, and the prediction ability of all classifier was evaluated by cross-validation. The first seven variables (density, dry matter, total acidity, pH, salt, electro-conductivity and ashes) were chosen according to the curve of the numbers of variables and correct classification rates. Among the three supervised discrimination analysis, LDA and KNN gave 100% predications according to the categories and brands of samples.Chapter three The research discussed the feasibility of discrimination of vinegar samples of different kinds and brands according to 8 metallic contents and 5 physico-chemical parameters with the aid of pattern recognition techniques.29 vinegar samples, including mature vinegar and white vinegar, were collected. The metallic contents were determined by AAS, and physico-chemical parameters were determined by chemical methods. The data measured were acted as characteristic variables of vinegar samples. To evaluate the stability of vinegar quality similarities of different products were calculated by SA. In order to comparing the contribution of the two kinds of data (metallic contents and physico-chemical parameters) to the discrimination of vinegar samples of different kinds and brands, they were used as variables for principal component analysis, respectively. The loading plot of 13 variables showed that metallic contents contributed greater than physico-chemical parameters.32 vinegar samples were divided into seven groups according to the kinds and brands in the three-dimensional space of the first three PCs.32 vingar samples were correctly clustered according to the distance calculated using Angle Cosine function. The correct prediction rate were 100% and 93% for verification set by PLS model and RBF-ANN models, respectively.Chapter four A new method of fast discrimination of brands of seasoning wine by means of visible-near infrared spectroscopy was developed. The visible-near infrared absorption spectroscopic signals of 37 seasoning wine samples from three different brands were measured between 400 nm and 1400 nm. The spectroscopic data were pretreated by first derivative and WT to denoise and compress data, and the impact of the level of wavelet decomposition on the original spectra was also discussed. In this study we employed second order Daubechies (db2) wavelet function and the fifth decomposition level to denoise and compress the original data. The results of PCA were compared by using original data, data treated by first derivative and WT as characteristic variables. From the clear classification result by PCA and CA, we showed that WT can denoise and compress data effectively. PLS and RBF-ANN calibration models were used to predict the brands of seasoning wine in verification set with 100% accuracy of prediction.Chapter five A new method for fast discrimination of brands of seasoning wine by using characteristic variables extracted from three dimensional SFS was developed. The original three dimensional fluorescence data was compressed and extracted by PCA and WT. The first PC was used as the characteristic fluorescence variables of seasoning wine samples by PCA. By comparing the effects on the signals of the different levels of decomposition, db2 wavelet function and the fourth decomposition level were chosen to extract and compress the data to obtain the original characteristic signal information. From the results of PCA and CA using the characteristic variables extracted by PCA and WT, brands of seasoning wine sample were correctly classified more by WT. The two supervised discrimination analysis calibration models, PLS and RBF-ANN gave 100% predications for unknown samples in the verification set according to the brands of seasoning wine samples. Chapter six Benzoic acid (BA), methylparaben (MP), propylparaben (PP) and sorbic acid (SA) are food preservatives, and they have well defined UV spectra. However, their spectra overlap seriously, and it is difficult to determine them individually from their mixtures without preseparation. The multivariate calibration and RBF-ANN of chemometrics were applied to resolve the overlapping spectra and to determine these compounds simultaneously. The influence of acidity on absorption spectra was investigated. It was discovered that in the acidic buffer solution the sensitivity of detection was higher than in basic buffer solution. Therefore, determination of four preservatives was conducted in pH 2.09 B-R buffer solution. Under the optimum acidic condition, the four compounds, when taken individually, behaved linearly in the 0.25-20 mg L-1 for BA, MP, PP and 0.25-10 mg L-1 for SA concentration range, and the limits of detection (LOD) were 0.22,0.19,0.17 and 0.085 mg L-1 for BA, MP, PP and SA, respectively. Multivariate calibration (CLS, PCR, PLS, DCLS, DPCR, DPLS) and RBF-ANN models were applied to predict the concentration of the four preservatives in the verification set. The results of prediction showed that the calibration models were effective to correctly predict the individual concentration in the mixture, and relative prediction errors (RPET) were under 10%. Among those models, PCR, DPCR and RBF-ANN gave more satisfactory results, and the RPETS were 4.53%,4.55% and 4.67%, respectively. It was obtained satisfactory results to determine the four preservatives simultaneously by UV-visible spectrometry with the aid of chemometrics.