利用GC-MS分析妙府老酒中的低级醇酯

采用乙醚萃取妙府老酒中的低级醇酯类物质,运用GC-MS(气质联用仪)初步鉴定出12种物质,分析结果表明:妙府老酒中的低级醇酯类物质主要有乙醇、丙醇、β-苯乙醇、2-甲基丙醇、丁醇、2-甲基丁醇、3-甲基丁醇、戊醇、2,3-丁二醇、乳酸乙酯、2-己烯酸乙酯和乙酸乙酯。该方法为妙府老酒中低级醇酯的构成提供了定性参考。     

近红外光谱法快速检测黄酒的酒精度、总糖和总酸

通过研究黄酒的近红外光谱和利用化学计量学的技术,采用偏最小二乘法建立快速检测黄酒的酒精度、总糖和总酸的近红外模型。用相关系数(R)、交叉验证均方差(RMSECV)和相对分析误差(RPD)衡量模型的预测精度和稳定性,R值分别为0.9994,0.9989,0.9780,RMSECV值分别为0.0891,0.6980,0.0898,RPD值分别为27,21,4.8,RPD≥3表明建立的模型效果良好。研究结果表明,近红外光谱法可用于快速检测黄酒酒精度、总糖和总酸,为黄酒食品安全质量控制体系的建立提供了快速检测手段。     

在线近红外光谱在保健酒酒精度检测上的应用

采用短波近红外光谱仪器在线检测保健酒调配液生产线上产品的酒精度。通过使用一阶倒数(First derivative,FD)和平滑处理(Norris derivative filter,ND),对近红外图谱进行预处理,使用偏最小二乘法(Partial least square,PLS)建立了酒精度检测近红外模型。模型的校正集均方根误差(Root mean square error of calibration,RMSEC)为0.737,交互验证相关系数为0.9189;预测集均方根误差(Root mean square error of prediction,RMSEP)为0.788,交互验证相关系数为0.9425。实验数据显示,近红外计算酒精度数值与标准法测量数值相对偏差主要集中在±2%之间,该方法可以满足生产过程中在线检测酒精度的要求。      

基于支持向量机的食醋总酸近红外光谱建模

为了得到稳定可靠的食醋总酸光谱模型,以不同产地、不同种类的95个食醋样品为研究对象,应用基于统计学原理的最小二乘支持向量机(LS-SVM)对食醋总酸含量进行光谱分析.对预处理后的光谱进行主成分分析(PCA),以主成分信号作为输入变量建立食醋总酸含量的近红外光谱模型,并与偏最小二乘法(PLS)和向后区间偏最小二乘法(biPLS)模型进行比较.结果表明,LS-SVM模型中的校正集中的相关系数(rc)和交互验证均方根误差(RMSECV)分别达到0.9614和0.2192,预测集相关系数(rp)和预测均方根误差(RMSEP)分别达到和0.919和0.3226,均优于PLS和biPLS模型.研究表明,近红外光谱与食醋总酸含量呈非线性关系,采用LS-SVM建立的模型预测性能更好,精度更高.     

近红外光谱技术结合iPLS_SPA波段筛选在黄水酒精度预测模型中的应用

为实现白酒发酵过程中黄水酒精度的快速检测,研究采用傅里叶近红外光谱（FT-NIR）技术对黄水进行光谱采集,并且采用偏最小二乘回归（PLSR）法建立酒精度预测模型。为减少全光谱的数据冗余降低复杂度,提升建模准确率,将连续投影算法（SPA）与间隔偏最小二乘法（iPLS）联用,对整个谱区进行特征波段筛选,并用决定系数R2与预测均方根误差（RMSEP）评价预测模型。结果表明：与原始数据集相比,经过异常样品剔除、预处理、特征光谱筛选后预测模型,预测集R2也从最开始的0.702变为0.952,提升35.61%;预测RMSEP从3.812变为1.367,降低64.14%;变量数也从2,203逐步下降到99,降低了95.51%。说明在减少非相关信息与噪声的同时,模型的复杂度也得到极大改善,并且模型的稳定性与准确度得到了有效提升,最终实现黄水酒精度的快速无损检测,以期为白酒发酵领域提供一种新的可能性,为近红外在白酒发酵副产物中的检验提供理论基础。     

妙府老酒中总氮及氨基酸含量的测定

分别采用凯式定氮法、隆丁区分法与全自动氨基酸分析仪分析测定了妙府老酒中的总氮、蛋白质分子质量分布及氨基酸含量。结果表明,妙府老酒氨基酸总含量为3287.52 mg/L,是富含氨基酸型保健饮品。该方法操作简便、检测快速、结果准确。     

基于近红外光谱建立纸张表面施胶量测定模型

采集不同施胶量纸张的近红外光谱,利用偏最小二乘法建立测定纸张表面施胶量基于近红外光谱的校正模型。得到校正模型的交叉验证均方差（RMSECV）和外部验证均方差（RMSEP）分别为0.0928和0.1460,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别为0.9609和0.9294,表明所建立的校正模型具有较高的预测精度和较好的推广性,为纸张无损伤、无预处理的快速、简便、准确的检测提供了新的途径,并且可望实现纸机上的在线检测。     

近红外光谱技术快速检测黄酒的前发酵过程中总酸含量

黄酒是我国最古老的独有酒种,其生产原料来源广,生产工序复杂等因素对黄酒的质量有很大的影响。目前黄酒生产过程中还存在着利用生产经验来判断某段工序是否可以结束的情况,为了探索快速检测黄酒生产过程中各段工序的各项指标的含量的方法,取代人工判别前发酵终点来更准确的控制黄酒的质量,利用近红外光谱仪对黄酒生产过程中前发酵工段所得样品进行扫描,对其光谱进行预处理和波段选择,并结合偏最小二乘法 (PLS)建立各工段快速无损检测方法。最终得到了较高的决定系数R为0.9348,RMSECV值为0.118的模型。结果表明,黄酒前发酵过程选取的总酸含量所建立的模型能很好的用来进行快速检测。运用这些模型对验证集样品进行预测并统计分析,可知预测值与真实值之间无显著差异。本研究为黄酒生产过程的控制提供了方法基础。     

拉曼光谱快速监测荔枝酒发酵过程酒精度的研究

酒精度是荔枝酒发酵过程及成品酒质量控制的关键因素,该研究使用拉曼光谱仪快速采集样本信号,并对拉曼光谱数据进行2阶导预处理后,分别采用偏最小二乘法（PLS）和向量角偏最小二乘法（VAPLS）建模,同时对模型效果进行对比分析。结果表明,VAPLS酒精度预测模型效果最好,该模型预测值与真实值的相关系数（R2）为0.993 1,均方根误差（RMSE）为0.285 6,验证集预测相对误差为-5.0%～1.6%,优于PLS建模方法。所建分析方法简便快速,能满足生产中荔枝酒酒精度的快速检测精度要求,并可以拓展实现酒类发酵过程中多个性能指标的同时分析。     

基于近红外光谱快速定量检测面粉中曲酸的方法建立

利用近红外光谱技术快速定量检测面粉中非法添加的褐变抑制剂曲酸。选取市场上常见3?种基本类型的面粉（高、中、低筋面粉）,分别制备曲酸质量分数为0.0%、0.5%、1.0%、3.0%、5.0%、10.0%的面粉样品,并采集其在1?000～2?400?nm波段下的光谱数据。对比不同预处理下高筋面粉样品数据所建偏最小二乘（partial least squares,PLS）回归模型效果,选取Savitzky-Golay一阶导数为最优预处理方法。采用区间偏最小二乘（interval partial least squares,iPLS）法选取1?088.8～1?153.5?nm为最佳光谱区间。结果表明,基于最佳光谱区间所建PLS回归模型预测效果优于基于全波段光谱数据所建模型。进一步,基于所选最优区间对中、低筋面粉和混合样品集分别建立PLS回归模型。高、中、低筋面粉及混合样品集基于最优区间的PLS模型的决定系数为0.949～0.972,标准误差为0.581%～0.830%,验证集标准偏差与预测标准偏差的比值为4.171～4.830。结果表明,基于最优区间的近红外光谱方法对不同类型面粉中曲酸质量分数为1.0%～10.0%的样品具有较好的预测结果,结合具有低检测限的化学检测方法,在对大批量样品的检测中可提高检测效率。     

乙醇传感器法测定葡萄酒和黄酒中乙醇的含量

建立了生物传感器法测定葡萄酒和黄酒中乙醇含量的分析方法。结果表明:乙醇含量在2~80μL/100mL浓度范围内线性关系良好,相关系数为0.9997;加标回收率(n=3)在99.22%~101.59%之间;精密度和重复性(n=4)的RSD%低于1%。与国标酒精计法测定结果对比,两种方法无显著差异。该法具有无需样品前处理、特异性好、灵敏度高、测定速度快等优点,适用于企业对发酵过程控制和产品的质量检测。      

3,5-二硝基水杨酸法测定发酵型果露酒中总糖含量

通过3,5-二硝基水杨酸（DNS）法建立发酵型果露酒中总糖的检测方法,并对总糖的水解条件和还原糖的检测条件进行了优化。结果表明,果露酒中还原糖的检测条件为：吸收波长540 nm,DNS显色剂3 mL,沸水浴加热9 min,显色20 min;总糖的水解条件为：1∶1（V/V）盐酸添加量7 mL,80 ℃水浴加热20 min。葡萄糖质量浓度在0.2～1.0 mg/mL范围内与吸光度值呈良好的线性关系（相关系数R2=0.999 8）,精密度、重复性和稳定性试验结果的相对标准偏差（RSD）均＜2%,平均加标回收率为98.53%,RSD值为0.50%,表明该方法可靠、准确、重现性好,可用于发酵型果露酒中总糖含量的测定。     

Prediction of sugars and acids in Chinese rice wine by mid-infrared spectroscopy

The use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the rapid determination of sugars and acids in Chinese rice wine was presented in this study. Calibration models were developed by partial least squares regression (PLSR) for eleven parameters related to sugar content and acidity—namely, total sugar, non-sugar solid, glucose, maltose, isomaltotriose, isomaltose, panose, total acid, amino acid nitrogen, pH and lactic acid. In the calibration step, most of the parameters were accurately determined, obtaining regression coefficients of calibration (r_cal) ranging from 0.821 to 0.991. In validation, regression coefficients of validation (r_val) obtained for most parameters were higher than 0.85. Unsatisfactory predictions were obtained for isomaltotriose and isomaltose with r_val being 0.488 and 0.716, respectively. The residual predictive deviation (RPD) values were also higher than or close to 2.0 for all the parameters except for isomaltose and isomaltotriose. Overall, the results indicate that MIR spectroscopy can be applied to the quality determination of Chinese rice wine.     

Correlation between the amino acid content in rice wine and protein content in glutinous rice

Chinese rice wine is a fermentation product of glutinous rice that contains high levels of protein and amino acids. The turnover and catabolism of amino acids by fermentative microorganisms plays an important role in wine quality. The fermentation of Chinese rice wine, using 34 different varieties of glutinous rice, and the analysis of the protein and amino acid content of the resultant rice wine using precolumn derivatization via high‐performance liquid chromatography, are reported. A model of correlation and regression analysis of the protein content in the glutinous rice and the amino acids in rice wine was established. Results showed that the correlation coefficient between the total protein in glutinous rice and the total free amino acids in rice wine was 0.557, indicating a significant relevance. The population correlation coefficient between the total protein in the glutinous rice and the amino acids in the rice wine was high, i.e. R= 0.928. The correlation between the soluble protein content in the glutinous rice and the total free amino acids in the rice wine (or individual amino acids) was negligible. The total protein content in the rice variety was positively related to the sensory performance and free amino acid content of the resultant Chinese rice wine. Copyright © 2016 The Institute of Brewing & Distilling     

Prediction of Cadmium content in brown rice using near‐infrared spectroscopy and regression modelling techniques

The feasibility of prediction of cadmium (Cd) content in brown rice was investigated by near‐infrared spectroscopy (NIRS) and chemometrics techniques. Spectral pretreatment methods were discussed in detail. Synergy interval partial least squares (siPLS) algorithm was used to select the efficient combinations of spectral subintervals and wavenumbers during constructing the quantitative calibration model. The performance of the final model was evaluated by the use of root mean square error of cross‐validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficients for calibration set and prediction set (R_c and R_p), respectively. The results showed that the optimum siPLS model was achieved when two spectral subinterval and fifty‐two variables were selected. The predicted result of the best model obtained was as follows: RMSECV = 0.232, R_c = 0.930, RMSEP = 0.250 and R_p = 0.915. Compared with PLS and interval PLS models, siPLS model was slightly better than those methods. These results indicate that it is feasible to predict and screen Cd content in brown rice using NIRS.     

基于近红外光谱与组合间隔偏最小二乘法的稻米镉含量快速检测

采用近红外光谱(near infrared spectroscopy,NIRS)结合组合间隔偏最小二乘法(synergy interval partial least squares,siPLS)建立稻米镉含量快速检测的方法。收集并分析72个稻米样品的NIRS谱图。对光谱前处理方法进行优化,确定平滑、多元散射校正与均值中心化处理为最优方法。采用siPLS法筛选特征波数,建立稻米镉含量的定量模型。稻米镉siPLS模型交叉验证均方根(RMSECV)与预测均方根(RMSEP)值分别为0.247与0.261,训练集相关系数(Rc)与预测集相关系数(Rp)值分别为0.919与0.895。结果表明:运用siPLS法选择特征波长后,不但可以降低模型的复杂度,同时还能够提高预测精度。NIRS作为一种快速、无损与便捷的初筛方法,可用于检测稻米中镉含量是否超标。