电子舌检测不同冷藏时间草莓鲜榨汁的品质变化

为了探讨不同冷藏时间草莓鲜榨汁的品质变化规律,利用电子舌对4℃冷藏草莓鲜榨汁进行逐天的品质检测并进行定性和定量分析。结果显示:通过主成分分析和线性判别分析,电子舌能够很好的反映出不同冷藏时间草莓的鲜榨汁品质的变化趋势,在冷藏最初期草莓鲜榨汁品质下降迅速,其后鲜榨汁品质下降缓慢。采用BP神经网络对草莓鲜榨汁冷藏时间进行预测,训练集的整体识别率达到94.90%,预测集的整体识别率为85.71%。采用偏最小二乘法与多元线性回归对草莓鲜榨汁的主要成分预测可知,偏最小二乘法回归模型的预测效果更好,模型的预测值和实际值的相关系数大于0.9,能够为草莓鲜榨汁品质的预测提供参考。     

基于电子舌技术的绿茶滋味品质评价

该文研究利用电子舌技术快速评价绿茶的滋味品质。试验以“碧螺春”绿茶为研究对象,以绿茶滋味化学鉴定法作为绿茶滋味品质的评价方法,获得的滋味总得分值作为电子舌评价模型的参考测量值。在数据分析过程中,首先对不同生产日期的碧螺春茶汤滋味总得分值和各传感器响应值进行单因素方差分析;然后对比采用偏最小二乘法和最小二乘支持向量机建立电子舌传感器响应值与滋味总得分值之间的相关模型。结果显示不同生产日期对绿茶滋味品质及各传感器响应信号都具有极显著影响;当采用4个主成分时,建立的最小二乘支持向量机模型最优。用独立样本检验模型精度,模型预测值与参考值的相关系数为0.906,预测集均方根误差为4.077。研究结果可为茶叶品质智能化评价提供参考。     

羊肉纯度电子舌快速检测方法

为实现掺假羊肉的快速、客观评价,利用电子舌对混入不同比例鸡肉的掺假羊肉糜进行检测及定性和定量分析。3种浸提溶液分别浸提,样品量均对电子舌传感器的响应影响极显著;以数据点重复性和聚类效果为依据,采用主成分分析方法确定了电子舌检测羊肉糜样品的较佳条件为0.1 mol/L KCl溶液浸提15 g肉糜样品。在此较佳条件下,对混入不同比例鸡肉的掺假羊肉进行检测,结果表明：采用主成分分析和典则判别分析,前2个主成分累积贡献率均超过80%,电子舌均能很好地区分混入不同比例鸡肉的羊肉糜样品;采用多元线性回归分析和偏最小二乘回归分析建立的定量预测模型能有效预测混入的鸡肉比例（R2>0.99,RMSE<3%）。试验表明：电子舌在羊肉掺入鸡肉的鉴别中具有可行性,研究结果可为羊肉掺假鉴别提供参考。     

基于氨基酸组成的黄酒酒龄电子舌鉴别

该研究采用电子舌结合化学计量学方法用于黄酒酒龄的快速鉴别。为确证黄酒样品酒龄,采用氨基酸分析仪分析了1年陈、3年陈和5年陈黄酒中20种氨基酸,并利用主成分分析（principal component analysis,PCA）对氨基酸数据进行了分析。采用电位型电子舌采集了不同酒龄黄酒样品的味觉指纹信息,并采用判别分析（discriminant analysis,DA）方法结合味觉指纹信息建立黄酒酒龄快速鉴别模型。采用偏最小二乘法（partial least squares regression,PLSR）建立电子舌响应信号与氨基酸含量之间的相关关系。氨基酸数据结合PCA分析表明所有样品均标注正确;电子舌结合DA所建黄酒酒龄鉴别模型可将3个年份预测集样品正确区分;异亮氨酸（Ile）、天门冬氨酸（Asp）、酪氨酸（Tyr）和缬氨酸（Val）与电子舌相关性高,模型的相对分析误差（Residual predictive deviation, RPD）高于2。研究表明电位型电子舌结合判别分析是黄酒龄鉴别的稳健方法。     

电子鼻和电子舌单独与联合检测掺大豆蛋白或淀粉的鸡肉糜

为实现掺杂掺假鸡肉的快速、客观评价，该研究利用电子鼻和电子舌联合检测技术对掺杂鸡肉糜进行快速检测，通过对采集的数据进行主成分分析和偏最小二乘法分析，所得结果表明，采用主成分分析，电子鼻和电子舌联合检测掺大豆蛋白鸡肉糜和掺淀粉鸡肉糜的主成分总贡献率分别为99.8%和99.1%；采用偏最小二乘法分析，电子鼻和电子舌联合检测鸡肉糜中掺杂大豆蛋白含量的预测值与真实值之间的决定系数为0.992，均方根误差为2.8%；联合检测鸡肉糜中掺杂淀粉含量的预测值与真实值之间的决定系数为0.996，均方根误差为2.4%。表明电子鼻和电子舌联合检测对鸡肉糜的掺杂情况具有良好的区分和预测能力，并且是一种有效、高精度的肉类掺假检测方法。     

基于电磁感应协同野外原位光谱的土壤盐分反演研究

快速准确监测土壤盐渍化可为土地资源合理开发利用与改良提供科学依据。利用 EM38-MK2大地电导仪和野外光谱仪测定的土壤表观电导率和光谱数据，构建表观电导率与土壤电导率的反演模型，依据相关性分析结果进行土壤盐渍化特征波段的提取，并采用反射率、反射率倒数和反射率一阶微分 3种数据变换形式构建土壤电导率的全波段与特征波段的偏最小二乘回归与主成分回归土壤盐分监测模型。研究结果表明，EM38-MK2测定的土壤表观水平电导率和表观垂直电导率相结合建立的电导率解译模型的拟合优度达到 0.89，在土壤盐渍化光谱建模中可快速提供电导率数据。全波段建模精度高于特征波段建模精度，偏最小二乘回归建模精度高于主成分回归建模精度，反射率一阶微分变换后建立的模型精度优于反射率倒数变换与反射率。研究区土壤电导率的预测模型选取经一阶微分变换后的全波段偏最小二乘回归建模方法为最佳模型，精度指标可达到 0.85，相对分析误差可达到2.56。     

基于高光谱遥感土壤有机质含量预测研究

高光谱遥感技术是土壤养分含量监测的一种先进手段,能及时、准确地监测滨海滩涂土壤养分的动态变化,对滨海湿地生态环境保护具有重要意义。以辽河三角洲滨海湿地滩涂为研究对象,在对地面高光谱数据与实验室样本理化性质分析的基础上,经过微分以及连续统去除法相关分析,分别建立了多元线性回归模型和偏最小二乘法(PLSR)模型,并对模型结果进行分析、比较。结果显示,连续统去除法能有效的提高有机质含量与光谱反射率之间相关性,基于此变换所建立的多元线性回归模型R2达到了0.7545,RMSE为1.2216;偏最小二乘模型R2达到了0.8792,RMSE为1.2299。所建模型具有预测土壤有机质含量的潜力,其中偏最小二乘法优于多元线性回归法。     

电子鼻在牦牛肉和牛肉猪肉识别中的应用

为了探索电子鼻对肉类掺假识别的可行性,利用电子鼻对牦牛肉、牛肉和猪肉样品进行了分析。通过对所获得的数据进行主成分分析（principal component analysis,PCA）、判别因子分析（discriminant factor analysis,DFA）和偏最小二乘回归分析（partial least-squares analysis,PLS）。结果表明：几种肉类在电子鼻传感器上有不同的特征性响应图谱,电子鼻能够有效识别猪、牛肉;同时电子鼻能够识别不同部位的牦牛肉和普通牛肉;但不能识别不同部位的猪肉。在牛肉馅中掺入不同比例的猪肉馅时,电子鼻也能进行识别。采用偏最小二乘回归分析对数据进行处理,电子鼻响应信号和猪肉馅掺入比例之间有很好的相关性（决定系数R2为0.9762）,PLS模型预测误差在1.27%～7.00%之间。试验证明电子鼻可用于肉类的识别。     

电子舌检测奶粉中抗生素残留

为了找到能快速检测乳制品中抗生素残留的方法,该文利用电子舌对奶粉中相同质量浓度的6种抗生素进行了辨识,并对新霉素检测质量浓度进行了初步研究。采用铂、金、钯、钨、钛和银6个电极组成的传感器阵列和1、10和100 Hz 3个脉冲频率进行检测,并通过主成分分析、线性判别分析和偏最小二乘法进行数据分析。结果显示：电子舌对不同种抗生素和不同质量浓度的新霉素具有较好的辨识能力,定性分析能够达到国家最高残留限量标准;利用偏最小二乘法（PLS）建立模型定量分析,新霉素最适检测质量浓度范围在300～1 100 μg/L附近。电子舌依据其独特的优点,为食品掺杂掺假的检测提供了新的思路和方法。     

基于高光谱的柑橘叶片钾含量快速诊断模型

及时准确地监测柑橘树体钾营养状况,有助于科学施肥,提高柑橘果实的品质和产量。试验对采集的W·默科特叶片钾含量及叶片光谱信息进行相关性分析,利用主成分分析和连续投影算法进行降维处理,同时结合偏最小二乘回归和最小二乘支持向量机回归分别建立了叶片钾含量预测模型。结果显示,柑橘叶片钾含量特征波长主要集中在450～600 nm、700 nm左右、980～1030 nm区域;最小二乘支持向量机回归模型的预测效果明显优于偏最小二乘回归模型,预测集相关系数达0.91。结果表明,利用高光谱成像技术结合最小二乘支持向量机回归可实现对柑橘叶片钾含量的快速诊断。     

基于电子舌的料酒味觉特征辨识与定量分析

为探明电子舌对调味料酒生产工艺的判别能力和理化指标的预测能力,本研究采用电子舌和理化检测手段,结合不同统计方法,对54份料酒样品分别建立定性和定量分析模型。结果表明,应用主成分分析(PCA)可以区分不同生产工艺的料酒样品,第一主成分为鲜味,贡献率62.4%,第二主成分为酸味,贡献率33.2%;应用簇类独立软模式法(SIMCA)可以准确判别酿造料酒和配制料酒,各传感器区分能力(DP)>5,识别率达到100%;应用偏最小二乘法(PLS)将传感器信号与行标方法检测结果进行拟合,总酸、氨基酸态氮和食盐的验证集标准偏差与预测标准偏差的比值(RPD)分别为12.1、6.5和14.1,建立的模型效果良好,可进行准确的定标和预测;酒精度RPD值为2.7,也可进行定量分析,但模型稳定性较弱。本研究结果为应用电子舌对调味料酒进行品质区分和检测提供了理论和实践基础。     

电子舌在红茶饮料区分辨识中的应用

为检验电子舌在茶饮料方面的辨识能力,该文应用法国Alpha M.O.S公司生产的传感器型电子舌对中国市场上已有的7种红茶饮料进行检测,所得数据用主成分分析法（PCA）和聚类分析法（CA）进行分析。结果表明,该电子舌可以很好的区分这7种红茶饮料。且在PCA第1主成分和第2主成分得分图上,区别指数达95.38。由此可见,电子舌在茶饮料的品牌区分、质检以及真伪辨识中有巨大的应用潜力。同时,该研究也初步探讨了聚类分析法在电子舌数据处理方面的应用。     

Performance comparison of UV and FT-Raman spectroscopy in the determination of conjugated linoleic acids in cow milk fat

The determination of conjugated linoleic acids (CLA) in cow milk fat was studied by using UV (210-250 nm) and Fourier transform (FT)-Raman (900-3400 cm (-1)) spectroscopy in order to determine the best spectrophotometric technique for routine analysis of milk fat. A collection of 57 milk fat samples was randomly divided into two sets, a calibration set and a validation set, representing two-thirds and one-third of the samples, respectively. All calculations were performed on the calibration set and then applied to the validation set. The CLA content ranged from 0.56 to 4.70%. A comparison of various spectral pretreatments and different multivariate calibration techniques, such as partial least-squares (PLS) and multiple linear regression (MLR), was done. This paper shows that UV spectroscopy is as reliable as FT-Raman spectroscopy to monitor CLA in cow milk fat. The best calibration for FT-Raman was given by a PLS model of seven factors with a standard error of prediction (SEP) of 0.246. For UV spectroscopy, PLS models were also better than MLR models. The most robust PLS model was constructed with only one factor and with SEP=0.288.     

低场核磁共振结合主成分分析法快速检测掺假牛乳

以掺假牛乳样品为低场核磁共振检测对象,利用主成分分析法（PCA）分析处理Carr-Purcell-Meiboom-Gill （CPMG）序列的检测数据,旨在探求一种能有效分析处理样品弛豫数据的快速方法,并用于牛乳品质的监控。对几种常见掺假形式的（掺入水、食盐、尿素、豆浆等）牛乳样品、复原乳以及纯牛乳样品进行了检测和评价。试验结果表明：纯牛乳和掺入不同物质的掺假牛乳在主成分得分图上可以得到很好的区分;纯牛乳、复原乳及其混合乳也得到了有效的辨识;同时各种掺假牛乳样品随掺假物质的掺加比例在图中呈规律性分布。PCA法可用于快速分析处理低场核磁共振数据,低场核磁共振结合PCA法在乳制品的质量控制及评价中具有较大的应用潜力。     

Evaluation of the combined effects of enzymatic treatment and aging on lees on the aroma of wine from Bombino bianco grapes

In this study, two different doses of commercial beta-glucanase enzyme preparation were tested to verify their effect on wines aged on lees. These wines were compared with two samples with no enzymatic treatment. The former was aged on lees (control), and the latter was readily filtered off from the yeast cell biomass (standard). Analysis of variance (one-way ANOVA), the Tukey test, and principal component analysis (PCA) were applied to all of the samples, which were analyzed for aroma composition, along with galacturonic acid, total acidity, pH, and color. Results showed a large number of statistically significant differences among samples. In general, wines treated with beta-glucanase were characterized by higher concentration of many volatile compounds. The presence of lees and even more the exogenous enzymatic action enhanced almost all volatile compounds. Besides the high presence of ethyl esters, it is worth mentioning the behavior of hexanol and trans-3-hexenol, which are strongly enhanced by the presence of lees and by enzymatic treatments.     

Geochemical behavior assessment and apportionment of heavy metal contaminants in the bottom sediments of lower reach of Changjiang River

Heavy metal contamination of bottom sediments of the Changjiang River is widely reported, however, the potential source and methods of transportation of these heavy metals in the contaminated sediments is poorly defined. This paper examines the correlation between heavy metals and geochemical indices, including Fe₂O₃, Al₂O₃, total organic carbon (TOC) and black carbon (BC), as well as magnetic susceptibility (MS). Using these indices we investigate the contamination characteristics of heavy metals in the sediments by with Principal Component Analysis (PCA). Results from 83 sediment samples collected in the lower reach of Changjiang River (from Nanjing to Shanghai) show that the first principal component accounts for 52.23% of the total variance, corresponding to the heavy metals, Co, Cr, Cu, Ni and Zn, and conservative components of Fe₂O₃, Al₂O₃ and TOC. This result indicates that heavy metal distributions are controlled by the transportation and sedimentation of fine particles which is also confirmed by particle size analysis. The second principal component explains 24.81% of the variance and is dominated by Cd, Pb and MS, which, collectively, result chiefly from industrial and transportation activities and, for MS, fly ash production. The third principal component accounts for 7.91% of the variance and corresponds solely to Hg. Principal component analysis/multiple linear regression (PCA/MLR) was used to estimate the contribution of the three principal components to each heavy metal. PCA/MLR results suggest that more than 50% of Co, Cr, Cu, Ni and Zn were influenced by the particle size effect. Particle size effect and fly ash account for 37.1% and 27.7% of As. Cd and Pb were mainly explained by fly ash. 98.9% of Hg was related to PC3, which represented black carbon (BC). Our study indicates that the combination of geochemical and multivariable statistical methods clearly characterizes the geochemistry of heavy metals in sediment of the lower reaches of the Changjiang River and suggests that power plants are the main source of heavy metal pollution.     

Analysis of volatiles in Pinotage wines by stir bar sorptive extraction and chemometric profiling

A fast, simple, cost-effective, and reliable method based on stir bar sorptive extraction (SBSE) in the headspace mode was used for the analysis of 39 volatile components in Pinotage wines. The method was sensitive, with LODs ranging from 50.0 pg/L to 281 ng/L and LOQs between 180 pg/L and 938 ng/L. Precision was between 6 and 20%. The intermediate precision was within the acceptable range. Moreover, good calibration curves with R(2) > 0.99 for all compounds were achieved. The method was successfully applied for the analysis of 87 young Pinotage wines of vintages 2005 and 2006 collected from various South African regions. To characterize the results based on vintage and origin, the obtained concentrations of the compounds were subjected to chemometric analysis. Exploratory factor analysis (FA), principal component analysis (PCA), and analysis of variance (one-way ANOVA) were consecutively done. The chemometrics approach revealed a reasonable correlation among the volatile components of these wines, as well as with respect to their year of production.