Colorimetric sensor arrays for the analysis of beers: a feasibility study

Eighteen commercial beers have been analyzed in both liquid and gas phases using colorimetric sensor arrays made from selected chemically responsive dyes printed on a hydrophobic membrane. Digital imaging of the dye array before and after exposure to the complex analytes in either the liquid phase or the head-gas provides a color change profile as a unique fingerprint for the specific analyte. The digital data libraries generated were analyzed using statistical and chemometric methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). In either liquid- or gas-phase experiments, facile identification of specific beers was achieved using comparison of the color change profiles; using HCA statistical analysis the error rate of identification was <3%. Differentiation between even very similar beers proved to be straightforward. In addition, differentiation of pristine beer from the effects of watering or decarbonation proved to be possible. These results suggest that colorimetric sensor arrays may prove to be useful for quality assurance/quality control applications of beers and perhaps other beverages.     

Colorimetric Artificial Nose and Pattern Recognition Methods for the Concentration Analysis of NH3

An investigation was made to evaluate the capacity of a colorimetric artificial nose to detect toxic gas at low concentration. A low-cost and simple colorimetric sensor array for identification and quantification of NH₃ with different concentrations (30, 90, 150, and 210?ppb) were reported. Using porphyrin, porphyrin derivatives (mainly metalloporphyrins), and chemically responsive dyes as the sensing elements, the developed sensor array of artificial nose showed a unique pattern of colorific change upon its exposure to NH₃ with different concentrations. The dynamic responses of colorimetric sensor array to NH₃ and colorimetric sensor array to various NH₃ concentrations at the same time point showed that there was a positive relationship between the color change values of spots and contractions of NH₃. NH₃ with four concentrations were measured, and the response values at six different collection times were conducted by linear discrimination analysis (LDA) and artificial neural network (ANN). The four concentrations were discriminated completely by LDA. The response value of the colorimetric artificial nose at 0.4?min was optimum for discrimination. The method of ANN was performed and less than 5% of error by using T-S fuzzy neural network.     

基于主成分与聚类分析的苹果加工品质评价

为了探讨苹果品种间理化品质的差异,给选育新品种和苹果合理加工利用提供理论支持,采集了30个苹果品种为试材进行模式识别分析。该试验所用苹果在2011年8-10月份按照苹果可采成熟度(九成熟)在辽宁省兴城市国家种质资源圃进行采集。试验测定了单果质量、体积、密度、果皮颜色、硬度、糖酸比、维生素C等20项理化品质指标,采用描述性统计、主成分和聚类分析方法对苹果品种和品质关系进行了分析。结果显示,30个品种苹果的密度、果型指数和含水率3项指标未表现出差异。对剩余17项品质指标进行了主成分分析,依据主成分解释总变量和碎石图提取了6个主成分反映原变量的82.3%的信息。第一主成分主要综合了可滴定酸、糖酸比及固酸比的信息即口尝品质因子;第二主成分主要综合了L值,a值和b值的信息,命名为颜色因子;其余主成分依次为甜度因子、质构因子、内在品质因子和果个大小因子。结合主成分得分图直观地显示了苹果品种和理化指标间关系:辽伏、理想、早金冠和瑞光分布在PC1和PC2的正向区间,糖酸比和固酸比值较大、口感好,但是果皮颜色较绿,是品质较好的早熟苹果;寒富、赤阳和红富士依次落在PC1和PC2第四区间,食用品质好、果皮颜色较红,是较为常见的晚熟苹果。分布在第二区间的普利阿姆,白星,Pvma果皮颜色绿且口感酸涩,不适宜鲜食,这些品种可能较为适宜进行加工。第三区间品种固酸比、甜酸比取值较小但是果皮颜色红,为选育果皮颜色受消费者喜爱且内在品质好的早熟品种提供了理论支持。聚类分析将30个品种苹果分为5类,聚类结果与主成分得分图结果基本一致,该试验初步判定30个品种苹果是否适宜鲜食,为苹果品种选育和加工应用利用提供理论依据。     

Discrimination and classification of olive tree varieties and cultivation zones by biophenol contents

The peak areas from a high-performance liquid chromatography-diode array (HPLC-DAD) analysis of biophenols extracted from olive leaves have been used as chemotaxonomic markers to construct chemometric models in order to discriminate and classify (1) 13 varieties of Olea europaea olive trees, namely, Alame?o, Arbequina, Azulillo, Chorna, Hojiblanca, Lechín, Manzanillo, Negrillo, Nevadillo, Ocal, Pierra, Sevillano, and Tempranillo, from the same cultivation zone and (2) Arbequina samples from six different geoghaphical origins, namely, Córdoba, Mallorca (north and south), Ciudad Real, Lleida, and Navarra. Models based on principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for discrimination between samples as a function of the tree varieties and cultivation zone, whereas K nearest neighbors (KNN) and soft independent modeling of class analogy (SIMCA) models were generated to classify the samples used to validate the models into one of the groups previously established by PCA and HCA. KNN classified correctly 93 and 92% of the samples into the variety and cultivation zone, respectively; meanwhile, the SIMCA models predicted 85 and 92%, respectively.     

Qualitative and quantitative control of carbonated cola beverages using ¹H NMR spectroscopy

1H Nuclear magnetic resonance (NMR) spectroscopy (400 MHz) was used in the context of food surveillance to develop a reliable analytical tool to differentiate brands of cola beverages and to quantify selected constituents of the soft drinks. The preparation of the samples required only degassing and addition of 0.1% of TSP in D?O for locking and referencing followed by adjustment of pH to 4.5. The NMR spectra obtained can be considered as "fingerprints" and were analyzed by principal component analysis (PCA). Clusters from colas of the same brand were observed, and significant differences between premium and discount brands were found. The quantification of caffeine, acesulfame-K, aspartame, cyclamate, benzoate, hydroxymethylfurfural (HMF), sulfite ammonia caramel (E 150D), and vanillin was simultaneously possible using external calibration curves and applying TSP as internal standard. Limits of detection for caffeine, aspartame, acesulfame-K, and benzoate were 1.7, 3.5, 0.8, and 1.0 mg/L, respectively. Hence, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of soft drinks and quantification of selected constituents.     

电子鼻快速检测谷物霉变的研究

针对目前我国在谷物的霉变与否的检测上还有一定的滞后性，研制出一套能快速检测谷物是否霉变的电子鼻装置，该装置能快速、准确地分析所测谷物散发的气味，从而判定所测谷物是否霉变。该电子鼻主要由一组厚膜金属氧化锡气体传感器阵列和RBF神经网络组成。用所研制的电子鼻对小麦、水稻、玉米3种谷物进行检测。整个实验过程如下：首先从每个传感器的反应曲线中提取4个特征值，并对所有特征值进行归一化处理，然后用常规的主成分分析和径向基函数(RBF)神经网络对它们进行分析。实验过程中发现，从主成分分析的结果发现很难将霉变谷物与正常谷     

电子鼻传感器阵列优化及其在小麦储藏年限检测中的应用

采用德国Airsense公司的PEN2电子鼻系统对5个陈化年限的小麦进行了检测。对传感器信号进行方差分析和Loading分析去掉差异不显著的传感器。最后选择传感器1、2、8、9、10的响应信号进行模式识别。对优化后的传感器阵列进行主成分分析得到结果显示5个年份的小麦被很好地区分，各个类的集中性也比较强。从BP网络分析结果可以看出network1(优化后传感器阵列数据的BP网络)的预测准确率高于network2(优化前传感器阵列数据的BP网络)，可以更好地区分5个年份的小麦。说明对传感器进行优化去掉一些响应不显著的传感器信号并不影响模式识别结果，反而提高了电子鼻的识别性能。     

基于SERS的三种食源性致病菌的快速检测

为了探究表面增强拉曼光谱(SERS)法在大肠杆菌(ATCC 25922)、肠炎沙门氏菌(CICC 21540)和单核增生李斯特菌(CICC 10982)快速检测中的可行性,本研究利用两步生长法制备银包金纳米颗粒(Au@Ag NPs)作为增强基底,研究Au@Ag NPs分别与3种致病菌偶联后的拉曼增强效果和稳定性,并对3种致病菌的特征拉曼光谱结合主成分分析(PCA)和层次聚类分析(HCA)进行对比分析。结果表明,合成的Au@Ag NPs颗粒大小均匀,拉曼增强效果明显,可与3种致病菌有效结合,且均有稳定的拉曼响应度;基于大肠杆菌、肠炎沙门氏菌和单核增生李斯特菌的特征拉曼光谱的PCA和HCA分析得出,3种致病菌的拉曼特征光谱之间差异明显,能有效区分。本研究结果为利用Au@Ag NPs作为增强基底的SERS方法快速检测大肠杆菌、肠炎沙门氏菌和单核增生李斯特菌提供了参考基础。     

基于电子鼻传感器阵列优化的甜玉米种子活力检测

针对甜玉米种子活力传统检测方法操作繁琐、重复性差等不足,该研究利用电子鼻技术建立甜玉米种子活力快速检测方法。利用电子鼻获取不同活力甜玉米种子的气味信息,再结合主成分分析(PCA,principal component analysis)、线性判别分析(LDA,linear discriminant analysis)、载荷分析(loadings)和支持向量机(SVM,support vector machine)对气味信息进行提取分析,建立甜玉米种子活力的定性定量分析模型。结果显示:PCA和LDA分析均无法区分不同活力的甜玉米种子,而SVM的鉴别效果较好。全传感器阵列数据集SVM分类判别模型训练集和预测集正确率分别为97.10%和96.67%,建模时间为30.75 s,回归预测模型训练集和预测集决定系数R~2分别为0.993和0.913,均方差误差分别为2.23%和8.50%。经Loadings分析将10个传感器阵列优化为6个。优化后传感器阵列数据集SVM分类判别模型训练集和预测集正确率分别为98.55%和96.67%,建模时间为21.81 s,回归预测模型训练集和预测集决定系数R~2分别为0.982和0.984,均方差误差分别为3.80%和3.01%。结果表明:基于SVM的电子鼻技术可以实现对不同活力甜玉米种子的高效判别和预测,将传感器阵列优化为6个,判别和预测效果均有所提升。该研究为电子鼻技术应用于甜玉米种子活力检测提供理论依据。     

Biochemical profiling and chemometric analysis of seventeen UK-grown black currant cultivars

Black currant fruits are recognized as being an important dietary source of health-related compounds, such as anthocyanins and ascorbic acid. In the present study, the biochemical composition (viz., nonstructural carbohydrates, individual anthocyanins, total anthocyanins, total phenolics, and organic acids, including ascorbic acid) from 17 UK-grown black currant cultivars was analyzed. Berry composition was significantly affected by genotype. Nonstructural carbohydrates ranged from 85.09 to 179.92 mg g(-1) on a fresh weight (FW) basis, while concentration for organic acids ranged from 36.56 to 73.35 mg g(-1) FW. Relative concentrations of cyanidin 3-glucoside, cyanidin 3-rutinoside, delphinidin 3-glucoside and delphinidin 3-rutinoside were 3.1-7.9%, 35.4-47.0%, 7.6-12.5% and 36.9-50.9%, respectively. Differences in the biochemical profile among cultivars were emphasized by principal component analysis (PCA) and hierarchical cluster analysis (HCA). PCA was able to discriminate between cultivars, especially on the basis of health-related compounds. Initial exploration revealed that individual anthocyanins, total phenolics, and ascorbic acid could be used to characterize and classify different cultivars. HCA showed that the biochemical composition of the different cultivars was related to parentage information.     

Multielement analysis of soft drinks by X-ray fluorescence spectrometry

Several commercial soft drinks and respective plastic bottles were analyzed for their multielement contents employing the synchrotron radiation total reflection X-ray fluorescence spectrometry technique (SRTXRF). The SRTXRF method has been developed and validated, and about 20 elements were detected in the investigated samples, including some trace elements, which can be toxic for human beings, such as Ti, Cr, Sb, As, and Pb in soft drinks and Al, Sb, As, and Pb in poly(ethylene terephthalate) (PET) containers. Statistical analysis was performed using chemometric techniques (principal component analysis and cluster analysis), and similarities were verified in the multielement contents of the samples. The results demonstrated that the SRTXRF offers a good multielemental approach for the quality control of food products. Moreover, on the basis of enrichment factors, the possibility of the trace elements in the PET container may be leached to the beverages under normal commercial situations and other results were discussed.     

电子鼻在饮料识别中的应用研究

为解决传统的饮料检测所采用的理化分析方法烦琐而实时性差的问题，研制了一套能够实时、准确地检测饮料散发气味的电子鼻系统。该系统主要由气敏传感器阵列和数据处理软件组成，并采用氮气作为载气以减少测试环境因素的影响。为了提高信噪比，从每个传感器与气体反应曲线中提取了4个特征值，然后用主成分分析法和BP神经网络对样本特征值进行处理。识别结果表明，这种检测方法快速、准确，识别正确率高达95.2%。     

QCM气敏传感器阵列的制备及其对鸡蛋货架期的检测

为实现鸡蛋货架期的无损检测,该文提出了基于石英晶体微天平(quartz crystal microbalance,QCM)传感器阵列的检测方法。采用浸涂法制备了4个QCM气敏传感器,分别修饰有碳纳米管、石墨烯、氧化铜以及聚苯胺敏感材料薄膜;优化传感器的敏感材料层数后,分别选择4层碳纳米管、4层石墨烯、5层氧化铜和5层聚苯胺修饰的传感器构成QCM传感器阵列,其灵敏度分别为2.05、1.37、2.31与1.70 Hz/(mg/kg),长期稳定性均高于85%,4个传感器表现出良好的重复性、回复性以及动态响应特性。进一步将所制备的QCM传感器组成阵列应用于鸡蛋货架期的检测中,采用主成分分析法(principal component analysis,PCA)、线性判别分析法(linear discriminant analysis,LDA)对不同货架期的鸡蛋样品进行区分,LDA法能够有效区分不同货架期的鸡蛋样品,区分效果优于PCA;采用偏最小二乘回归法(partial least squares regression,PLSR)建立鸡蛋货架期的回归模型,能够很好预测鸡蛋样品的货架期(R~2=0.954 7,RMSE=1.666 1 d)。结果表明,所制备的QCM传感器阵列能够实现不同货架期鸡蛋的区分和预测,研究结果为鸡蛋货架期的无损检测提供参考。     

基于味觉传感器阵列的玉米汁饮料分类辨识

为了快速辨识不同口味的玉米汁饮料,确保同一种饮料质量的均一性,构建了包含12个传感器的味觉传感器阵列。使用代表酸、甜、苦、咸、鲜的呈味物质检测味觉传感器阵列对5种基本味觉辨识的能力。使用主成分分析和概率神经网络考察了该阵列对基本味觉的辨识效果,该阵列对基本味觉表现出良好的辨识能力。将该阵列应用于玉米汁饮料的分类辨识中,区分来自不同品牌的9种玉米汁。系统聚类分析表明了同一种玉米汁样本的味觉特征非常接近,可聚合为一类。通过主成分分析法实现数据降维,提取前3个主成分作为概率神经网络的输入神经元。试验结果表明:该味觉传感器阵列对不同种玉米汁饮料具有较好的辨识能力,辨识的正确率为95.06%。     

Molecularly imprinted photonic hydrogels as colorimetric sensors for rapid and label-free detection of vanillin

A novel colorimetric sensor for the rapid and label-free detection of vanillin, based on the combination of photonic crystal and molecular imprinting technique, was developed. The sensing platform of molecularly imprinted photonic hydrogel (MIPH) was prepared by a noncovalent and self-assembly approach using vanillin as a template molecule. Morphology characterization by scanning electron microscope (SEM) showed that the MIPH possessed a highly ordered three-dimensional (3D) macroporous structure with nanocavities. The vanillin recognition events of the created nonocavities could be directly transferred into readable optical signals through a change in Bragg diffraction of the ordered macropores array of MIPH. The Bragg diffraction peak shifted from 451 to 486 nm when the concentration of the vanillin was increased from 10?12 to 10?3 mol L?1 within 60 s, whereas there were no obvious peak shifts for methyl and ethyl vanillin, indicating that the MIPH had high selectivity and rapid response for vanillin. The adsorption results showed that the hierarchical porous structure and homogeneous layers were formed in the MIPH with higher adsorption capacity. The application of such a label-free sensor with high selectivity, high sensitivity, high stability, and easy operation might offer a potential method for rapid real-time detection of trace vanillin.     

基于气敏传感器阵列特征优化的储粮害虫赤拟谷盗检测

为实现储粮中害虫赤拟谷盗（Tribolium castaneum（Herbst））的检测，该研究使用自主开发的储粮害虫电子鼻检测装置，采集了小麦中不同虫口密度梯度的赤拟谷盗挥发性气味信息，根据10个气敏传感器采集到的响应曲线，提取了各个传感器的相对变化值（Relative Change，RC）、相对积分值（Relative Integral，RI）、平均微分值（Mean Difference，MD）作为原始特征矩阵（10×3），使用遗传算法（Genetic Algorithm，GA）作为特征选择方法，获得样本的特征信息，通过建立预测回归模型，实现了对小麦中赤拟谷盗虫口密度的预测。以识别准确率作为评价指标，对原始的特征矩阵进行了多特征优化，优化后的特征矩阵的识别准确率由原始的82.85% 提升至97.14%，优化后的特征数量由原始的30个减少为12个，特征数量减少60%，传感器数量减少至8个。最后通过采用偏最小二乘回归（Partial Least Squares Regression，PLSR）、主成分回归（Principal Components Regression，PCR）和支持向量机回归（Support Vector Machine Regression，SVR）3种回归方法进行回归预测，研究结果表明：基于偏最小二乘回归（PLSR）的预测模型达到了较好的预测效果，预测集回归模型的相关系数r和均方根误差RMSE分别为0.828和11.293。研究证明了气敏传感器阵列多特征优化方法的可行性和有效性，同时为实现粮食虫害快检测提供一种方法和参考。     

Fabrication and optimization of a conducting polymer sensor array using stored grain model volatiles

During storage, grain can experience significant degradation in quality due to a variety of physical, chemical, and biological interactions. Most commonly, these losses are associated with insects or fungi. Continuous monitoring and an ability to differentiate between sources of spoilage are critical for rapid and effective intervention to minimize deterioration or losses. Therefore, there is a keen interest in developing a straightforward, cost-effective, and efficient method for monitoring of stored grain. Sensor arrays are currently used for classifying liquors, perfumes, and the quality of food products by mimicking the mammalian olfactory system. The use of this technology for monitoring of stored grain and identification of the source of spoilage is a new application, which has the potential for broad impact. The main focus of the work described herein is on the fabrication and optimization of a carbon black (CB) polymer sensor array to monitor stored grain model volatiles associated with insect secretions (benzene derivatives) and fungi (aliphatic hydrocarbon derivatives). Various methods of statistical analysis (RSD, PCA, LDA, t test) were used to select polymers for the array that were optimum for distinguishing between important compound classes (quinones, alcohols) and to minimize the sensitivity for other parameters such as humidity. The performance of the developed sensor array was satisfactory to demonstrate identification and separation of stored grain model volatiles at ambient conditions.     

Application of gas chromatography-mass spectrometry metabolite profiling techniques to the analysis of heathland plant diets of sheep

Little is known about how plant biochemistry influences the grazing behavior of animals consuming heterogeneous plant communities. The biochemical profiles of grassland species are mostly restricted to major nutritional characteristics, although recent developments in analytical techniques and data analysis have made possible the detailed analysis of minor components that may influence animal feeding preferences, performance, and health. In the present study, gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF/MS) was used to profile the abundances of metabolites in nine specific heathland plant groups and in three mixed forage diets containing 10, 20, or 30% heather (Calluna vulgaris) and also in plasma and feces from sheep offered one of the three diets. Statistical and chemometric approaches, that is, principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used to discriminate between these diets and between individual animals maintained on these diets. It is shown that GC-TOF/MS analysis of sheep plasma allowed distinction between the very similar diets by PCA and HCA, and, moreover, the plant metabolites responsible for the differences observed have been identified. Furthermore, metabolite markers of herbage mixtures and individual plant groups have been identified, and markers have been detected in sheep plasma and feces.     

用于山核桃陈化时间检测的电子鼻传感器阵列优化

为更好地进行山核桃陈化时间检测,论文拟通过传感器阵列优化来有效提高电子鼻对其区分预测能力。该文依据响应曲线保留响应明显的传感器,并在提取传感器特征值构成初始特征矩阵的基础上,结合均值分析、变异系数分析、聚类分析、相关性分析和多重共线性分析进行逐步优化以获取最终优化传感器阵列。对优化前后的数据采用主成分分析法(principal component analysis,PCA)和偏最小二乘回归(partial least squares regression,PLSR)进行样品区分和预测能力的对比。结果表明:通过优化,经不同人工陈化时间(0、5、10、15d)处理的山核桃能有效区分开,且在PCA得分图中更为聚集;优化后的陈化时间回归模型(R2=0.933 4)较优化前(R2=0.888 7)具有更好的预测能力。说明所给出的阵列优化方法有效可行,为电子鼻针对性检测提供了一种思路。     

Determinants of adolescents' soft drink consumption

OBJECTIVE: To identify determinants of adolescents' consumption of carbonated soft drinks (regular and diet), both of total consumption and of consumption at school. DESIGN/SETTING/SUBJECTS: Regular and diet soft drink consumption was measured by food frequency questions that were dichotomised. Several potential environmental and personal determinants of consumption were measured. A total of 2870 (participation rate: 85%) 9th and 10th graders, within 33 Norwegian schools, participated in the study. Multilevel logistic regression analyses were preformed for total soft drink consumption (twice a week or more vs. less) and for consumption at school (once a week or more vs. less). RESULTS: A total of 63% and 27% of the participants reported to drink respectively regular and diet soft drinks twice a week or more, and 24% and 8%, respectively, reported to drink soft drinks once a week or more at school. Preferences, accessibility, modelling and attitudes were the strongest determinants of both regular and diet soft drink consumption. In addition, gender, educational plans and dieting were related to both total soft drink consumption and consumption at school. Pupils with longer distance from school to shop and those in schools with rules concerning soft drink consumption tended to have lower odds of drinking both regular and diet soft drinks at school. CONCLUSION: This study shows that gender, educational plans, dieting, accessibility, modelling, attitudes and preferences all seem to be strong determinants of adolescents' soft drink consumption. Parents and the home environment appear as great potential intervention targets.