| Chinese rice wine is a kind of traditional alcoholic beverage in China. Shaoxing rice wine is the best representation of Chinese rice wine, and it is popular among consumers due to its low consumption of grain, low percent alcohol and high nutritional value. Shaoxing rice wine is in the lead in production and consumption (especially export) of Chinese rice wine. With increasing consumer concern of nutrient components in Shaoxing rice wine, it becomes a demand of quality control departments of government, breweries, sealers and consumers to find a rapid technology or method to detect nutrient components in Shaoxing rice wine. At the same time, because the aging mechanisms is uncertain, the intensive research on discrimination of Shaoxing rice wine age can not be carried out. So making a study of nutrient components in Shaoxing rice wine based on chemical method and near infrared is needed urgently in order to develop Shaoxing rice wine scientific research, improve its quality and grade, accordingly to promote Shaoxing rice wine industry toward standardization, scientization and internationalization. This study is a further extension of the former research projects done by our group members (quantification of alcoholic degree, soluble solids content, pH and total acid; discrimination of Shaoxing rice base wine with age of 1, 2, 3, 4 and 5 years old). The aim of this study is to rapidly detect flavor components, base wine ages (1, 1-3, 5 and 8-10 years old) and blended wine ages (3, 5, 6 and 8 years old) of Shaoxing rice wine.The objects of this research are base wine and blended wine of Shaoxing rice wine. Detection of flavor components were carried out based on fourier transform near infrared spectroscopy (FT-NIR), chemometrics techniques combined with modern physical-chemical analysis techniques. Quantitative models for total sugar, nonsugar solid, glucose, isomaltose, isomaltotriose, maltose, panose, acetic acid, citric acid and fifteen amino acids, were established using NIR spectra and reference data determined by physical-chemical methods. In this dissertation, age classification and discrimination of base wine and blended wine were also studied using FT-NIR spectroscopy technique and pattern recognition methods. Qualitative models were established for classification and discrimination of base wine ages (1, 1-3, 5 and 8-10 years old) and blended wine ages (3, 5, 6 and 8 years old) respectively. The explanation for discriminant results of base wine ages using concentration of flavor components in Shaoxing rice wine samples were carried out too.The main contents and conclusions were:1. The sampling plan of continuously investigating flavor components in Shaoxing rice wine in ten years, and the combination of continuous data and non-continuous data to analyze changing trends of flavor components and NIR spectra of base wine samples, were put forward. The blending formulas of Shaoxing rice wine blended wine samples, which were used to establish qualitative models for discrimination of blended wine with different marked ages, were designed.2. The curve characteristics of original and pretreatment (smooth and derivative) spectra of base wine and blended wine samples were analyzed. Spectra of base wine and blended wine had a very similar shape, and mainly showed absorption bands at around 982, 1185, 1460, 1692, 1776, 1934, 2265 and 2302 nm. The absorption at 982, 1185, 1460, 1934 nm might be related with O-H group; 1692,2265 and 2302 nm with C-H group; 1776 nm with some sugars. The absorption at these bands directly related with flavor components such as sugars, organic acids and amino acids in Shaoxing rice wine.3. Changing trends of five sugars, three acid and sixteen amino acids in Shaoxing rice wine base wine with age of 1, 1-3, 5 and 8-10 years old, were analyzed. Concentrations of isomaltotriose, citric acid and methionine in 1 year old and aged (1-3, 5, 8-10 years old) base wine were absolutely different, with which 1 year old and aged Shaoxing rice wine can be discriminated.4. After spectra outliers being eliminated, sixty-three base wine samples with age of 1, 1-3, 5 and 8-10 years old were analyzed qualitatively:Comparison results of seven optical path-lengths (0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 5.0 mm) indicated that classification correctness of the model with optical path-length of 1.0 mm was better than models with other optical path-lengths.Comparison results of DA, DPLS, SIMCA, with six modeling bands (800-2500 nm, 800-1250 nm, 1250-1650 nm, 1650-2200 nm, 2200-2500 and 1250-2200 nm) and seven pretreatment methods (5, 15 and 25 points smooth, MSC and SNV), and LS-SVM input by different principal components indicated that classification correctness of the model established using LS-SVM with input of the first eight principal components were optimal. The optimal model had accurate rates of 97.87% for calibration set and 93.75% for prediction set; 100% for base wine samples with age of 1, 1-3 and 8-10 years old, and 75% for samples of 5 years old.5. After spectra outliers being eliminated, ninety-nine blended wine samples with age of 3, 5, 6 and 8 years old were analyzed qualitatively:Comparison results of DA, DPLS, SIMCA, with six modeling bands (800-2500 nm, 800-1250 nm, 1250-1650 nm, 1650-2200 nm, 2200-2500 and 1250-2200 nm) and seven pretreatment methods (5,15 and 25 points smooth, MSC and SNV), and LS-SVM input by different principal components indicated that classification correctness of the model established using LS-SVM with input of the first six principal components were optimal. The optimal model had a accurate rate of 100% for calibration set and prediction set; for blended wine samples with age of 3, 5, 6 and 8 years old.6. After spectra outliers being eliminated, total sugar, nonsugar solid, glucose, isomaltose, isomaltotriose, maltose, panose, acetic acid, citric acid and fifteen amino acids in Shaoxing rice wine samples were analyzed quantitatively.Comparison results of seven optical path-lengths (0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 5.0 mm) indicated that for total sugar, nonsugar solid, glucose, maltose, aspartic acid, threonine, serine, glycin, alanine, vlaine, isolecine, leucine, tyrosine, phenylalanine and acetic acid, the quantitative results with optical path-length of 0.5 mm were better than those results with other optical path-lengths; for panose was optical path-length of 2.0 mm; for lysine and arginine was 2.5 mm; for isomaltose, isomaltotriose, citric acid, glutamine and proline was 5.0 mm.After concentration outliers being eliminated by leverage and student residual testing, the number of samples used for quantitative analysis of total sugar, nonsugar solid, glucose, isomaltose, isomaltotriose, maltose, panose, acetic acid, citric acid were 83, 84, 123, 123, 119, 120, 122, 123, 124; of aspartic acid, serine, glutamine, proline and glycin were 90 respectively; of alanine were 91; of threonine, leucine and histidine were 89 respectively; of vlaine, isolecine, tyrosine, phenylalanine and arginine were 88; of lysine was 87.Comparison results of PLSR with six modeling bands (800-2500 nm, 800-1250 nm, 1250-1650 nm, 1650-2200 ran, 2200-2500 and 1250-2200 nm) and seven pretreatment methods (5, 15 and 25 points smooth, MSC and SNV), PCR with seven pretreatment methods, SMLR with different wavelength numbers and seven pretreatment methods, and LS-SVM input by different principal components indicated that:(1) The models established using PLSR with 1) 800-2500 nm combined with 25 points smooth for total sugar, nonsugar solid, maltose, aspartic acid, glycin, isolecine, phenylalanine and proline; combined with 15 points smooth for glucose, panose, serine, alanine, vlaine, leucine and lysine; combined with 5 points smooth for threonine; combined with MSC for tyrosine; combined with original spectra for isomaltotriose and histidine; 2) 800-1250 nm combined with MSC for citric acid and with SNV for glutamine, were optimal.(2) The models established using SMLR with five wavelengths (958, 1182, 947, 998 and 989 nm) combined with SNV for arginine, were optimal.(3) The models established using LS-SVM with input by the first ten principal components for isomaltose and acetic acid, respectively, were optimal.The parameters, the correlation coefficient of calibration set and cross-validation, the root mean square error of calibration, prediction and cross-validation, of the optimal models for twenty-six flavor components were as follows: for total sugar, 0.98677, 0.96119, 0.741 g/L, 1.1 g/L and 1.26 g/L; for nonsugar solid, 0.98264, 0.94269, 1.18 g/L, 1.24 g/L and 2.12 g/L; for glucose, 0.91173, 0.79683, 1330 mg/L, 1550 mg/L and 1950 mg/L; for isomaltotriose, 0.97566, 0.84996, 223 mg/L, 320 mg/L and 537 mg/L; for maltose, 0.81837, 0.699, 665 mg/L, 748 mg/L and 841 mg/L; for panose, 0.94010, 0.82750, 428 mg/L, 506 mg/L and 706 mg/L; for tartaric acid, 0.87063, 0.81603, 21.8 mg/L, 34.6 mg/L and 25.7 mg/L; for citric acid, 0.98147, 0.93231, 224 mg/L, 410 mg/L and 423 mg/L; for aspartic acid, 0.96745, 0.8554, 109 mg/L, 215 mg/L and 226 mg/L; for threonine, 0.96552, 0.66033, 69.6 mg/L, 153 mg/L and 202 mg/L; for serine, 0.90998, 0.78218, 201 mg/L, 290 mg/L and 304 mg/L; for glutamine, 0.94025, 0.8184, 281 mg/L, 377 mg/L and 479 mg/L; for proline, 0.96682, 0.70022, 244 mg/L, 604 mg/L and 693 mg/L; for glycin, 0.9416, 0.82303, 193 mg/L, 305 mg/L and 328 mg/L; for alanine, 0.98693, 0.83257, 121 mg/L, 450 mg/L and 417 mg/L; for vlaine, 0.9374, 0.83179, 84.7 mg/L, 182 mg/L and 136 mg/L; for isolecine, 0.87051, 0.71624, 66.8 mg/L, 75.9 mg/L and 96.6 mg/L; for leucine, 0.93946, 0.86623, 149 mg/L, 266 mg/L and 217 mg/L; for tyrosine, 0.97965, 0.78817, 48.8 mg/L, 157 mg/L and 150 mg/L; for phenylalanine, 0.92269, 0.77895, 77.8 mg/L, 157 mg/L and 133 mg/L; for lysine, 0.92269, 0.77895, 101 mg/L, 122 mg/L and 167 mg/L; for histidine, 0.96504, 0.81526, 86.9 mg/L, 177 mg/L and 192 mg/L; for arginine, 0.78312, 0.72322, 399 mg/L, 592 mg/L and 445 mg/L; the correlation coefficient of calibration and prediction set, the root mean square error of calibration and prediction, for isomaltose, 0.91417, 0.70894, 210 mg/L and 264 mg/L; for acetic acid, 0.93979, 0.79075, 157.5 mg/L and 283.6 mg/L.PLSR loading spectra on the full spectra were investigated to extract absorption features of twenty-four flavor components in Shaoxing rice wine samples. The absorption of these components overlapped due to the structure similarity, so the speciality of the absorption features should be confirmed in future studies. The precision and robustness of quantitative models for flavor components need improved, and the accurate rate and adaptability of qualitative models respectively for base wine and blended wine with different ages should be promoted through adding samples and modeling methods. The results obtained in this study indicated the potentiality of NIR spectroscopy technique to rapidly detect flavor components in Shaoxing rice wine, and discriminate base wine and blended wine with different ages. The study laid foundation for developing an instrument for rapid detecting flavor components and ages of commercial Shaoxing rice wine, and provided bases for establishing computer blending system of Shaoxing rice wine.
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