卷烟烟气pH与烟气总粒相物中游离烟碱的关系

对烟气总粒相物pH的测定方法进行了实验研究,确定了准确测定烟气总粒相物pH的方法。对烟气总粒相物中游离烟碱含量的测定方法进行了实验研究,确定了用配备FID的气相色谱仪准确测定烟气总粒相物中的游离烟碱含量的方法。按照国标方法测定烟丝中总植物碱、还原糖、总氮、挥发碱的含量;按照国标方法测定卷烟的焦油、烟碱、水分。请评吸专家对卷烟进行评吸,针对烟气劲头和刺激性进行打分。对测定结果进行分析比较,研究结果表明:烟气中游离烟碱占总烟碱的比例与烟气总粒相物pH呈现显著的线性相关关系;烟丝总氮含量与烟气总粒相物pH呈显著线性相关;烟气烟碱、烟丝总烟碱、还原糖对烟气劲头有较大影响,而对烟气刺激性影响较大的因素有烟气总粒相物pH、烟叶pH、总氮等。      

近红外光谱法快速测定烟草中的钙

采用傅立叶变换近红外(NIR)光谱法建立了烟草中钙含量的NIR数学预测模型。该模型的内部验证决定系数(R2)为92.00,均方差(RMSECV)为0.121;外部验证的决定系数(R2)为90.99,均方差(RMSEP)为0.122,NIR预测值与化学测定值的平均相对误差为6.20%。该法简捷、快速、精密度高,可用于烟草中钙的快速测定。     

主成分分析-支持向量机在卷烟主流烟气分析中的应用

采用主成分分析进行样本集特征的提取,结合支持向量机建立回归模型,并对成品卷烟主流烟气中的总粒相物、焦油量和烟气烟碱含量进行定量预测。结果表明:总粒相物、焦油和烟气烟碱的预测均方差分别为0.61,0.47和0.04,与模型相比分别下降了30.73%,26.12%和8.15%,体现了更高的预测准确度。     

近红外光谱法同时测定涪陵榨菜中果胶和总糖的含量

应用傅里叶变换近红外光谱技术,以涪陵榨菜为材料建立与其品质有关的果胶和总糖的定量分析模型。测定50份涪陵榨菜的近红外光谱数据,得到原始光谱,通过光谱预处理方法消除噪声,最后通过偏最小二乘法(PLS)建立回归模型。最终得到涪陵榨菜中果胶和总糖含量的近红外光谱分析模型,其决定系数(R2)分别为98.31、98.35,交叉验证均方差(RMSECV)分别为0.513、0.0531。用该模型对18份未知涪陵榨菜样本进行外部验证,其果胶和总糖的外部验证决定系数(R2)分别为96.69、95.63,预测集标准偏差(RMSEP)分别为0.572、0.0671。内部交叉验证和外部验证均证明,近红外定量分析有较高的准确度,能满足生产中对涪陵榨菜果胶和总糖同时测定的精度要求。     

小种红茶水浸出物近红外定量分析模型的建立

为实现近红外光谱技术在小种红茶中的快速无损检测,对76份有代表性的小种红茶按现行国家标准测定其水浸出物含量,采集样品的近红外光谱,采用OPUS 7.5软件,结合偏最小二乘法(partial least squares,PLS)建立小种红茶水浸出物含量的近红外定量分析模型。结果表明,所建立的水浸出物定量模型决定系数R2为95.73%,校正均方差(root mean square error of calibration,RMSEC)为0.629,验证均方差(root mean square error of prediction,RMSEP)为0.513。所建立的小种红茶水浸出物含量的近红外定量分析模型较为成功,模型预测效果较好,能够对小种红茶中水浸出物的含量进行快速地分析。     

傅立叶变换近红外光谱法快速检测锅盔中水分含量

应用傅立叶变换近红外光谱技术,建立锅盔水分含量分析模型.测定61份锅盔的近红外光谱,经一阶导数+MSC预处理以滤去噪声,在7 501.9～4 597.6 cm-1谱段范围内,选择维数10,利用偏最小二乘法建立近红外光谱与国标参考方法测得的水分含量之间的相关模型.最终得到水分定量校正模型决定系数(R2)为99.03％,内部交叉验证均方差(RMSECV)为0.409％.用该模型对19个未知锅盔样品进行外部验证,其水分外部验证决定系数(R2)为97.99％,预测标准偏差(RMSEP)为0.341％.结果表明,近红外定量分析技术有较高的准确度,能满足锅盔水分的快速检测精度要求.     

加热卷烟烟气pH值与烟碱含量的关系研究

为探究不同抽吸模式下加热卷烟烟气pH值与烟碱含量的关系，建立了加热卷烟烟气粒相物pH值的测定方法，采用GCFID测定了14种市售加热卷烟样品不同抽吸模式下烟气粒相物中总烟碱和游离烟碱含量，分析烟气粒相物pH值、游离烟碱和总烟碱含量以及加热卷烟劲头之间的关系。结果表明：（1）建立的加热卷烟烟气粒相物pH值的测定方法日内和日间相对标准偏差均小于1%，重复性和再现性良好。（2）加热卷烟烟气粒相物p H值受抽吸模式影响较大，ISO模式（6.02～6.46）下pH值明显高于HCI模式（5.92～6.18）;(3)ISO和HCI两种模式下，烟气中总烟碱含量分别在0.45～0.65 mg/支和1.30～1.88 mg/支，而游离烟碱含量分别在0.12～0.24 mg/支和0.16～0.44 mg/支；（4）不同抽吸模式下的烟气粒相物pH值均与游离烟碱占比、游离烟碱含量呈显著正相关；（5）总烟碱含量与游离烟碱含量也呈显著正相关，但与烟气pH值、游离烟碱占比之间均无相关性；（6）HCI模式下，加热卷烟劲头与烟气粒相物pH值、游离烟碱含量及游离烟碱占比之间均存在一定相关性。     

烤烟烟叶常规化学成分与主流烟气成分的关系

为研究烟叶原料常规化学成分与主流烟气成分之间的相互关系,测定了全国26个烟叶产区的6个品种和进口烤烟烟叶样品的常规化学成分(烟碱、总糖、还原糖、总氯、总钾和总氮)含量、卷烟烟气成分释放量(总粒相物、烟碱、水分、焦油和一氧化碳)和抽吸口数,并对其进行了相关分析、偏相关分析和通径分析。①简单相关分析显示,烟气总粒相物、烟碱和焦油与烟叶烟碱、总钾和总氮含量呈显著相关;烟气一氧化碳与烟叶烟碱呈显著正相关,但与钾含量呈极显著负相关;抽吸口数与总糖、还原糖和总钾呈显著相关。②偏相关分析表明,烟气总粒相物、烟碱和焦油仅与烟叶烟碱含量呈显著的相关关系,烟气一氧化碳和抽吸口数与总钾含量呈显著负相关。③回归和通径分析表明,对烟气总粒相物、烟碱和焦油起主要作用的是烟叶烟碱含量,其次是钾含量,总氮则主要是通过烟碱的间接作用产生影响;烟气一氧化碳主要受烟叶总钾含量的影响;还原糖主要通过总糖的间接作用对抽吸口数造成影响。烟叶中烟碱和总钾含量对烟气成分影响较大。     

大豆中异黄酮含量的测定及其近红外分析

以100份中国核心大豆种质资源为材料,建立高效液相色谱法(HPLC)测定大豆中异黄酮5组分和总异黄酮。扫描大豆的近红外光谱,以傅里叶近红外光谱法(FT-NIRS)与HPLC技术相结合,采用偏最小二乘(PLS)回归和交叉验证法,探讨利用FT-NIRS技术预测异黄酮含量的可行性。总异黄酮近红外预测模型的内部交叉验证其校正决定系数(R2)和交叉检验均方误差(RMSECV)分别为0.8763和0.515,外部验证其校正决定系数(R2)和预测均方误差(RMSEP)则分别是0.9492和0.599。结果表明,利用FT-NIRS预测大豆中总异黄酮含量是可行的,但是各异黄酮组分的近红外模型不能达到准确预测要求。大豆异黄酮近红外模型的建立对今后大豆的异黄酮选育工作可以提供帮助。     

傅立叶变换近红外光谱法测定豆腐干中总酸、蛋白质和水分含量

应用傅立叶变换近红外光谱技术,以豆腐干为材料建立豆腐干中总酸、蛋白质和水分含量分析模型.测定63份豆腐干的近红外光谱数据,得到原始光谱信息,通过光谱预处理方法消除原始光谱噪声,最后采用偏最小二乘法建立回归方程.最终得到总酸、蛋白质和水分含量近红外光谱分析模型的决定系数(R2)依次为98.24%、97.85%、99.17%,交叉验证均方根差(RMSECV)依次为0.0113、0.122、0.152.用该模型对21个未知豆腐干样品进行外部验证,其总酸、蛋白质和水分外部验证的决定系数(R2)依次为93.46%、97.49%、99.39%,预测标准偏差(RMSEP) 次为0.0208、0.121,0.121.内部交叉验证和外部验证均证明,近红外定量分析有较高的准确度,能满足生产中总酸、蛋白质和水分检测的精度要求.     

利用近红外光谱分析方法建立妙府老酒酒精度和总酸模型

该文利用近红外(NIR)透射光谱分析技术结合偏最小二乘法(PLS)建立了妙府老酒酒精度与总酸的定量检测模型,并以该模型对未知黄酒样品的酒精度与总酸含量进行预测,验证模型的可靠性.酒精度建模结果:决定系数为90.36％、均方差为0.043;酒精度建模结果:决定系数为和96.73％,均方差为0.121;外部检验的预测均方差分别为0.058和0.081;结果表明该方法应用于黄酒品质的检测,操作简便、快速、准确.为妙府老酒的现场、即时快速定量分析提供了一种新方法,同时也为生产过程中的在线质量监控奠定了基础.     

Near‐Infrared Spectroscopy for Proficient Quality Evaluation of the Malt and Maize Used for Beer Production

Moisture and total nitrogen content are considered very important factors that influence barley malt quality, as well as moisture and lipid content for maize quality. In the present study, the feasibility of using Near‐Infrared (NIR) spectroscopy to measure the moisture, total nitrogen and lipid content of the whole malt grains and maize grits was examined. The NIR spectra of the following samples were examined: 295 malt whole grains for moisture, 281 malt whole grains for total nitrogen, 128 maize grits for moisture and 102 maize grits for total lipids. Validation was carried out both by means of cross‐validation and test set validation. Coefficients of determination (R²) were higher than 93% for both malt and maize moisture and higher than 71 and 80 for malt total nitrogen and maize total lipids, respectively. The Root Mean Square Error (RMSE) values (both in Cross Validation (RMSECV) and in Prediction through external validation (RMSEP)) ranged from 0.127 to 0.165% for both malt and maize moisture, whereas they ranged from 0.043 to 0.053% for malt total nitrogen and from 0.065 to 0.079% for maize total lipids. Repeatability (r₉₅) ranged from 0.105 to 0.222% and from 0.012 to 0.155% for malt moisture and total nitrogen content, respectively, and from 0.086 to 0.192% and from 0.020 to 0.171% for maize moisture and total lipid content, respectively. The findings showed that NIR spectroscopy has potential for the rapid prediction of quality of whole malt grains and maize grits for brewing.     

基于高光谱成像技术快速检测茶叶茶多酚含量

目的 为了快速、无损的检测茶叶中茶多酚含量,建立一种精确、高效的多元校正模型。方法 首先利用高光谱成像技术采集单纵茶叶的光谱数据,其次通过二维相关光谱(two-dimensional correlation spectroscopy techniques,2D-COS)波段筛选算法提取特征光谱,最后结合极限学习机(extreme learning machine,ELM)建立茶多酚的预测模型,并与全波段模型进行对比。结果 经二维相关光谱算法所提取后的特征波段所建立的模型预测效果优于全波段模型。茶多酚的决定系数(correlation coefficient of cross-validation,R2)从0.89上升到0.94,预测值均方根误差(root mean square error of prediction,RMSEP)也从2.37%下降到2.16%。结论 表明二维相关光谱波段筛选算法有效的提取茶多酚的特征波段,对茶叶茶多酚含量的快速、无损预测具有可行性。     

基于太赫兹—衰减全反射光谱的甘氨酸溶液定量研究

采用太赫兹衰减全反射光谱,研究20～450cm-1范围内不同浓度甘氨酸水溶液的光谱信息,将光谱进行二阶导数预处理后,选择不同波数进行随机组合,确定最适三波数组合模型,并建立最适拟合三元线性回归方程。结果表明,分别在65.56,127.28,173.56,308.55cm-1处出现清晰的吸收峰;回归方程R2Adj为0.990 3,均方根误差为0.005 7,预测均方根误差为1.658 9。     

红外光谱对纺织助剂中烷基酚聚氧乙烯醚的快速定性和定量

为快速排查纺织助剂中APEO,建立了红外光谱对纺织助剂中APEO的定性和定量方法。应用红外二阶导数谱图中1608±4cm-1, 1510±3 cm-1一组特征吸收峰筛查纺织助剂中不同分子结构形式的APEO。红外光谱对APEO的定性方法不需标准品、不需对样品进行分离,简单快速。同时,应用定量软件TQ Analyst中的偏最小二乘法,选择二阶导数红外光谱并选取（1529.27cm-1,1496.45cm-1）峰范围建立APEO定量模型,回归系数R为0.99946,均方根误差RMSEC为0.970。应用该定量模型对APEO定量,结果显示其绝对偏差在-1.56~3.18%范围, 回收率为82~117%。     

基于介电谱法的油茶籽油掺伪检测方法研究

为检测油茶籽油的掺伪程度,对5种食用油及两组油茶籽油掺杂混合油进行了介电谱测量.运用主成分分析法(PCA)开展了5种食用油的区分分析,结果表明PCA法不仅能够显著区分单不饱和脂肪酸(MUFA)型和多不饱和脂肪酸(PUFA)型食用油,对于同一类型的不同油品也具有良好的区分度.运用基于交叉验证的偏最小二乘法(PLS),对两种油茶籽油掺杂混合油的介电谱数据建立定量分析模型,经外部验证集验证,混合油的预测均方根误差(RMSEP)小于2.10％,决定系数(R2)大于0.998 9.最后进行了油茶籽油介电谱的温度特性测试,给出了温度补偿的相应算法.试验结果表明,介电谱法为食用油的掺伪鉴别和纯度检测提供了一种快捷、准确的方法.     

卷烟焦油测试嵌套析因设计分析

为了研究卷烟焦油测试过程中的各变异源的差异,采用完全嵌套方差分析和嵌套析因设计分析对各变期源进行分析.结果表明:①具有明确原因的变异源中,总粒相物的变异性最大,为42.0％;总粒相物和滤片含水量(W&W)的变异性较大,为30.6％;空白水分变异性为25.3％;烟碱和气相色谱仪引入的变异性仅占2.3％;在进行减小测量变异性改进时应从前3个变异源考虑;②由日期和时间引起的变异性为42.9％,在卷烟焦油比对测试时应参考日期和时间方向上的变异进行设计.     

Rapid Determination of Bitterness in Beer Using Fluorescence Spectroscopy and Chemometrics

Two fluorescence spectroscopic methods with the aim to develop a fast quantitative determination of bitterness in beer were tested. The first method was based on autofluorescence of the diluted and degassed beer samples without any further processing. A total of 21 dark and light beer samples were analyzed and multivariate Partial Least Squares (PLS) regression models to bitterness in form of international bitter units (IBU) were performed. A prediction error in the form of Root Mean Square Error of Cross‐Validation (RMSECV) of 2.77 IBU was obtained using six PLS components. Focusing only on the light beer samples the RMSECV was reduced to 1.81 IBU. The second method developed was based on addition of europium to induce delayed fluorescence signals in the beer samples. PLS models yielded an RMSECV of 2.65 IBU for all beers, while a model on the light beer samples gave an RMSECV of 1.75 IBU. The obtained prediction errors were compared to the errors given in the literature for the traditional extraction method of determining IBU.     

Systematic evaluation of land use regression models for NO₂

Land use regression (LUR) models have become popular to explain the spatial variation of air pollution concentrations. Independent evaluation is important. We developed LUR models for nitrogen dioxide (NO(2)) using measurements conducted at 144 sampling sites in The Netherlands. Sites were randomly divided into training data sets with a size of 24, 36, 48, 72, 96, 108, and 120 sites. LUR models were evaluated using (1) internal "leave-one-out-cross-validation (LOOCV)" within the training data sets and (2) external "hold-out" validation (HV) against independent test data sets. In addition, we calculated Mean Square Error based validation R(2)s. The mean adjusted model and LOOCV R(2) slightly decreased from 0.87 to 0.82 and 0.83 to 0.79, respectively, with an increasing number of training sites. In contrast, the mean HV R(2) was lowest (0.60) with the smallest training sets and increased to 0.74 with the largest training sets. Predicted concentrations were more accurate in sites with out of range values for prediction variables after changing these values to the minimum or maximum of the range observed in the corresponding training data set. LUR models for NO(2) perform less well, when evaluated against independent measurements, when they are based on relatively small training sets. In our specific application, models based on as few as 24 training sites, however, achieved acceptable hold out validation R(2)s of, on average, 0.60.     

Prediction of white button mushroom (Agaricus bisporus) moisture content using hyperspectral imaging

Hyperspectral imaging is a non-contact, non-destructive technique that combines spectroscopy and imaging to extract information from a sample. This technology has recently emerged as a powerful technique for food analysis. In this study, the potential of hyperspectral imaging (HSI) to predict white button mushroom moisture content (MC) was investigated. Mushrooms were subjected to dehydration at 45 ± 1 °C for different time periods (0, 30, 60 and 120 min) to obtain representative samples at different moisture levels (93.40 ± 0.62%, 82.76 ± 2.11%, 73.20 ± 2.60% and 60.89 ± 4.32% wet basis [wb]). Hyperspectral images of the mushrooms were obtained using a pushbroom system operating in the wavelength range of 400–1000 nm. Hunter L, a and b colour values of the mushrooms were also measured. The average reflectance spectra of samples at different MC levels were obtained and Partial Least Square Regression (PLSR) models were built to predict mushroom moisture content. To reduce the spectral variability caused by factors unrelated to MC such as scattering effects and differences in sample height, different spectral pre-treatments were applied. The Standard Normal Variate (SNV) transformation was found to be the best approach among the wavelength range studied, resulting in the greatest reduction in Root Mean Square Error of Cross Validation (RMSECV) and Root Mean Square Error of Prediction (RMSEP) for a 4-component PLSR model. RMSECV of 5.50 (% wb) and RMSEP of 5.58 (% wb) were obtained for the calibration and test sets of data, respectively. Prediction maps were generated from hyperspectral data to show the predictive model performance at pixel level. This study shows the potential of hyperspectral imaging for prediction of mushroom moisture content in the studied wavelength range. The implemented method highlighted contrast between areas of different moisture content to achieve better knowledge of dehydration distribution over the mushroom surface.