Survey of ochratoxin A and deoxynivalenol in stored grains from the 1999 harvest in the UK

Three hundred and twenty samples from the 1999 UK harvest comprising wheat (201 samples), barley (106) and oats (13) were analysed for ochratoxin A and deoxynivalenol. A small number of organic samples was also obtained. Samples were collected from farms, central stores, mills, maltings and ports from across the UK from February to April 2000. Ochratoxin A and deoxynivalenol analysis was by affinity column clean up and high-performance liquid chromatography with fluorescence and ultraviolet light detection, respectively, with limits of detection of 0.2 and 20 μg kg^-1. The survey found ochratoxin A at below 5 μg kg^-1 in 97% of the samples indicating satisfactory storage conditions. The remaining 3% of the samples contained ochratoxin A at levels between 5.2 and 231 μg kg^-1, but none of these samples was intended for human consumption. Deoxynivalenol was detected in 88% of all samples, with 83% below 100 μg kg^-1; the maximum level was 600 μg kg^-1. Twenty samples containing deoxynivalenol at or above 150 μg kg^-1 by high-performance liquid chromatography were all confirmed by gas chromatography/mass spectrometry. Nivalenol was also detected by gas chromatography/mass spectrometry at levels of 50 μg kg^-1 or higher in 18 of 20 samples where deoxynivalenol was confirmed.     

基于电子鼻和电子舌的白酒检测

研究利用电子鼻和电子舌技术快速检测白酒品质。以不同品牌、不同香型和不同比例掺假白酒为检测对象,采用主成分分析、线性判别分析和偏最小二乘法对电子鼻整体气味响应图谱和电子舌在1、10、100和1000Hz四个不同频段脉冲激发下的金、银、钨、钛四个工作电极组成的传感电极阵列响应信号进行分析。结果显示:电子舌对不同品牌和不同香型白酒的区分能力要优于电子鼻,且LDA的识别效果较PCA更好;利用偏最小二乘法建立的定量预测模型,在主成分数取5时,电子舌所建模型最优。用独立样品检验模型精度,模型预测值和参考值的相关系数为0.881。研究结果可为白酒生产和销售过程中的质量监控提供支持。     

Volatiles for mycological quality grading of barley grains: determinations using gas chromatography-mass spectrometry and electronic nose

The possibility of using an electronic nose or gas chromatography combined with mass spectrometry (GC-MS) to quantify ergosterol and colony forming units (CFU) of naturally contaminated barley samples was investigated. Each sample was split into three parts for (i) ergosterol and CFU analysis, (ii) measurements with the electronic nose and (iii) identification of volatiles collected on an adsorbent with a GC-MS system. Forty samples were selected after sensory analysis to obtain 10 samples with normal odour and 30 with some degree of off-odour. The data set of volatile compounds and the data collected from the electronic nose were evaluated by multivariate analyse techniques. SIMCA classification (soft independent modelling of class analogy) was used for objective evaluation of the usefulness of the data from the GC-MS or electronic nose measurements for classification of grain samples as normal or with off-odour. The main volatile compounds of grain with normal odour were 2-hexenal, benzaldehyde and nonanal, while 3-octanone, methylheptanone and trimethylbenzene were the main volatile compounds of grain with off-odours. Using data from the electronic nose three samples of 40 were misclassified, while data analysis of the volatile compounds detected with the GC-MS, led to six misclassified samples. Regression models (partial least-squares, PLS) were built to predict ergosterol- and CFU-levels with data from the GC-MS or electronic nose measurements. PLS models based on both GC-MS and electronic nose data could be used to predict the ergosterol levels with high accuracy and with low root mean square error of prediction (RMSEP). CFU values from naturally infected grain could not be predicted with the same degree of confidence.     

电子鼻和气质联用法分析普洱茶香气成分

为了解不同品牌普洱茶香气成分的特点和差异性,本研究采用电子鼻(electronic nose,E-nose)和顶空固相微萃取(headspace solid phase microextraction,HS-SPME)结合气相色谱-质谱联用(gas chromatography-mass spectrometry,GC-MS)对3个品牌普洱茶香气成分进行分析和鉴定。结果表明,E-nose能够较好区分3个品牌的普洱茶,主成分分析显示不同品牌样品间差异明显,区分度良好。进一步采用HS-SPME-GC-MS对普洱茶香气构成进行分析,结果共检测出74种化合物,共有成分38种,其中大益普洱茶、老同志普洱茶和澜沧古茶分别检测出66、53和48种,主要包括:醛类、醇类、酮类、甲氧基苯类化合物等物质,且物质组成和含量差异显著,主要差异性物质包括2-羟基-6-甲基苯甲醛、藏红花醛、芳樟醇氧化物、4-萜烯醇、甲基庚烯酮、(E,E)-3,5-辛二烯-2-酮、1,4-二甲氧基苯、1-甲氧基-4-(1-丙烯基)-苯、邻异丙基甲苯、α-松油烯、邻苯二甲酸二甲酯、咖啡因。香气活性值(odour active values,OAV)分析表明造成香气差异的物质主要是(E)-2-辛烯醛、(E,E)-2,4-壬二烯醛、壬醛、α-紫罗兰酮、香叶基丙酮。     

应用电子鼻和GC-MS比较牛肉不同部位的挥发性物质组成

实验的目的是探讨不同部位牛肉对不同加热温度的气味影响。实验运用电子鼻对60、120℃加热后不同部位的牛肉（牛腱子肉、牛后腿肉、牛前腿肉、牛肩肉和上脑牛肉）进行电子鼻检测,所得数据采用线性判别式分析法（LDA）分析,获得不同部位和不同温度下牛肉气味的指纹图谱;并进一步运用GC-MS分析导致气味差异的挥发性物质组成,结果表明:通过GC-MS分析,在60℃下,牛后腿肉,牛前腿肉,牛腱子肉,牛肩肉,上脑牛肉中分别鉴定出28、27、24、24、17种挥发性物质,120℃下,牛后腿肉,牛前腿肉,牛腱子肉,牛肩肉,上脑牛肉中分别鉴定出20、33、16、20、21种挥发性物质,而气味的差异主要由醛类和醇类物质的含量和组成所造成。      

基于电子鼻和气质联用技术的浓香型白酒分类

该研究分别利用电子鼻和气质联用(gas chromatography-mass spectrometry,GC-MS)分析7种不同品牌浓香型白酒的差异。结果表明,电子鼻的S2、S6、S7和S9 4个传感器对不同品牌浓香型白酒具有较好的响应信号,经主成分分析(principal component analysis,PCA)筛选后可以作为浓香型白酒差异的特征指标来衡量。基于传感器信号,比较了PCA和线性判别对不同各品牌浓香型白酒的分类效果,PCA分析能够对不同品牌白酒进行较好区分。GC-MS分析表明,不同品牌浓香型白酒风味物质含量存在明显差异,而PCA分析中关系密切的样品在风味成分层面存在相似性。该研究提供了一种基于电子鼻、GC-MS技术和数理统计分析相结合的浓香型白酒分类方法,为浓香型白酒的快速质量分类方法的开发提供了理论和数据支撑。     

基于电子鼻和GC-MS对优质稻谷挥发性成分差异性分析

采用电子鼻和GC-MS对两种优质稻谷苏香粳3号和南粳46挥发性物质进行鉴定并结合主成分分析法分析它们储藏期间挥发性组分的变化和差异。结果表明：电子鼻可以有效区分不同储藏时间的稻谷样品,使用GC-MS测定稻谷的挥发性成分主要有烷烃类、苯烯烃类、醛类、酮类、醇醚类、酸酯类和杂环类 7 大类。温度条件对不同挥发性物质的含量占比有很大影响,两种稻谷低温时烷烃类和酮类含量占比较高,高温时酸酯类含量占比更高。对两种稻谷的挥发性成分进行主成分分析可知,两种稻谷的特征性挥发物质有所不同,苏香粳3号拥有更多种酸酯类和杂环类特征性物质,而南粳46在烯烃类和醛酮类有更多种独特的物质。可以根据特征性挥发物质来区分苏香粳3号和南粳46,为通过寻找特征性挥发物质来区分不同稻谷提供参考依据。     

Occurrence of mycotoxins (ochratoxin A, deoxynivalenol) and toxigenic fungi in Moroccan wheat grains: impact of ecological factors on the growth and ochratoxin A production

The aim of the present work was to evaluate the contamination of some samples, taken from Moroccan wheat grains, by ochratoxin A (OTA), deoxynivalenol (DON) and the associated toxigenic fungi. Moreover, we focused on the influence of environmental factors on both the growth and OTA production by three strains of Aspergillus. The results showed that only few samples were contaminated by the two mycotoxins (2 samples for OTA and 7 for DON). The main isolated fungi belong to the Aspergillus, Penicillium and Fusarium genus; 74 Aspergillus and 28 Penicillium isolates were tested for their ability to produce OTA. Only 2 A. alliaceus and 14 A. niger were able to synthesize OTA. However, none of Penicillium isolates can produce this toxin under the conditions mentioned. In respect of the effects of the temperature and water activity (aw), the optimal conditions for the growth and OTA production were different. While the optimal conditions of growth for A. alliaceus and A. terreus are 30 degrees C and 0.98 aw, A. niger preferred 0.93-0.95 aw at 25 degrees C, whereas the optimal production of OTA was observed at 30 degrees C for both A. alliaceus and A. niger at 0.93 and 0.99 aw, respectively.     

仿生电子鼻在芝麻油掺伪检测中的应用研究

研究并设计了一套电子鼻系统,并将基于生物嗅觉的模糊神经网络作为其模式识别算法。将该仿生电子鼻系统应用于芝麻油掺伪的检测中。实验结果显示,该系统在预测精度、收敛速度及运行时间上都取得了较好的效果,可为芝麻油以及其他农产品的在线动态监测及保真提供快速、有效的手段。     

谷物霉菌挥发性物质的电子鼻与GC-MS检测研究

为建立粮食受霉菌污染的快速检测方法,本研究利用电子鼻与气相色谱质谱联用(GC-MS)技术对6种谷物中常见霉菌在不同生长阶段(1、2、5、13 d)的特征挥发性气味物质进行了检测分析。GC-MS结果显示不同霉菌的挥发性物质成分存在差异,且在生长后期差异更加显著。基于电子鼻信号的主成分分析(PCA)法能够有效区分生长中后期(5、13 d)不同菌属的霉菌样品。线性判别分析(LDA)和偏最小二乘判别分析(PLS-DA)模型对黄曲霉类、寄生曲霉类和青霉类样品的整体判别正确率分别达到100%和97.4%。结果表明,运用电子鼻与GC-MS技术对粮食霉菌污染情况进行快速鉴定具有一定可行性。     

电子鼻和气质联用技术分析不同酒龄酱香型白酒挥发性成分

以5种不同酒龄的酱香型白酒为研究对象,研究了不同酒龄的酱香型白酒品质以及风味变化规律,通过电子鼻和气相色谱-质谱联用（GC-MS）技术对酱香型年份酒的香气化合物进行整体区分及定性定量分析。结果表明,电子鼻检测到不同酒龄的酱香型白酒香气成分差异明显;在5种不同酒龄的酒样中共定性出52种物质。随着酒龄的增加,酯类物质含量呈先减少后增加趋势,醇类物质呈现波浪形变化趋势;酸类物质呈上升趋势;呋喃类物质上升趋势明显;其中有8种物质（丁酸乙酯、己酸乙酯、庚酸乙酯、戊酸乙酯、丁酸、戊酸、己酸、乙偶姻）的含量上升趋势明显,5种物质（苯乙酸乙酯、2-甲基丙醇、2-甲基丁醇、呋喃、四甲基吡嗪）的含量下降趋势明显。聚类分析（CA）、主成分分析（PCA）与线性判别分析（LDA）的结果也表明,5个不同酒龄的酒样在挥发性成分上具有较为明显的差异,初步证实了通过电子鼻和气质联用技术区分酱香型白酒酒龄的可行性。     

化学发光免疫分析方法检测粮食谷物中赭曲霉毒素A残留

目的建立粮食谷物中赭曲霉毒素A(ochratoxin A,OTA)残留检测的化学发光免疫分析方法。方法人工改造OTA半抗原、制备包被原和多克隆抗体工作液,优化检测步骤,验证各项评价指标,比较化学发光免疫分析方法和高效液相色谱法(high performance liquid chromatography,HPLC)的实际样本检测结果的一致性,以此来实现化学发光免疫分析方法的建立。结果 50%抑制浓度(IC_(50))为0.278μg/L;OTA的最低检出限为5.0μg/kg,样本添加回收率在65.4%到115.8%之间,批内、批间相对标准偏差15%。同时,通过对实际样本检测,证明化学发光免疫分析方法具有显著的准确度和可靠性。结论基于化学发光免疫分析方法能够实现粮食作物中的OTA残留的快速检测,灵敏度高、成本低、时间短,为食品中霉菌毒素残留的快速检测提供了新的技术参考。     

HPLC法测定啤酒原料中的脱氧雪腐镰刀菌烯醇毒素

建立了一种利用多功能净化柱-高效液相色谱检测啤酒大麦和麦芽中脱氧雪腐镰刀菌烯醇毒素（DON）含量的方法。把该方法和GC／ECD方法进行了对比,线形回归分析表明两种方法的检测结果接近。分析了10种国产、进口的大麦和麦芽,大麦和麦芽中DON最高污染水平分别为1．23μg／g、0．24μg／g。该方法可应用于啤酒酿造原料中DON污染情况的检测分析。     

Intake of ochratoxin A and deoxynivalenol through beer consumption in Belgium

Estimations of ochratoxin A (OTA) and 4-deoxynivalenol (DON) exposure of the Belgian population through beer consumption were made using the results of the recent Belgian food survey and the compiled data set of OTA and DON levels in conventionally and organically produced beers in 2003-05. For the consumers of organic beers, the daily intake of OTA was 0.86 (in 2003), 1.76 (in 2004) and 0.72 (in 2005) ng kg^-1 body weight (bw), considering the mean beer consumption (0.638 litres) and the average level of OTA in 2003, 2004 and 2005, respectively. Using the 97.5th percentile of beer consumption (1.972 litres), the corresponding OTA daily intakes were 2.65, 5.44 and 2.24 ng kg^-1 bw, which are close or above the tolerable daily intake (TDI) of 5 ng kg^-1 bw. For the consumers of conventional beers, the OTA intakes were low: 0.23, 0.23 and 0.11 ng kg^-1 bw day^-1 for the average beer consumption, in 2003, 2004 and 2005 against 0.72, 0.73 and 0.34 ng kg^-1 bw day^-1 when the 97.5th percentile level was considered. As for the DON intake, the estimates were quite low for both conventional and organic beer consumers when the provisional maximum TDI (PMTDI) of 1 µg kg^-1 bw was considered. Average consumption of organic beer led to daily intakes of 0.05 and 0.04 µg DON kg^-1 bw in 2003 and 2004, respectively, whilst for conventional beer, daily intakes were 0.07 and 0.05 µg DON kg^-1 bw. At the 97.5th percentile level of beer consumption, daily intakes of 0.15 and 0.13 µg kg^-1 bw were obtained for organic beers against 0.23 and 0.17 µg kg^-1 bw for conventional ones. The results showed that beer could be an important contributor to OTA exposure in Belgium, even though a declining trend seems to be apparent during the last year of monitoring. Therefore, efforts should be devoted to maintain the OTA levels as low as reasonably achievable, especially for organic beer.     

Validation of a UHPLC-FLD method for the simultaneous quantification of aflatoxins,ochratoxin A and zearalenone in barley

A fast and simple UHPLC-FLD method has been developed for the simultaneous determination in barley of aflatoxins (B1, G1, B2 and G2), ochratoxin A (OTA) and zearalenone (ZEA), some of the most important mycotoxins due to their toxicity and occurrence. The procedure is based on the extraction of the six mycotoxins with a mixture of acetonitrile and water, and the purification of the extract with immunoaffinity columns before analysis. Detection of AFB1 and AFG1 is improved using a photochemical reaction. The method has been validated with satisfactory results. Limits of detection were 340 ng kg⁻¹ for ZEA, 13 ng kg⁻¹ for OTA and varied from 0.5 to 15 ng kg⁻¹ for aflatoxins. Recovery percentages were between 78.2% and 109.2%. After being validated, the method has been successfully applied to 20 barley samples cultivated in a region of northern Spain (Navarra).     

电子鼻定量检测淡水鱼新鲜度的方法研究

挥发性盐基氮(total volatile basic nitrogen,TVB-N)值是定量评价淡水鱼新鲜度的重要指标之一,为了寻求更加准确检测TVB-N值的有效方法,自行设计了电子鼻系统。该系统由金属氧化物感器阵列、数据采集卡、信号调理电路以及数据采集与处理程序构成。以鲢鱼为研究对象,利用电子鼻系统对其新鲜度进行检测。以传感器阵列响应值作为自变量,以鱼肉TVB-N值作为因变量,分别采用多元线性回归(multiple linear regression,MLR)、主成分回归(principal component regression,PCR)和反向传播神经网络(back propagation neural network,BPNN)建立了TVB-N值的预测模型。通过测试样本对3种模型进行验证,MLR预测模型对TVB-N的预测值与测量值之间的相关系数R、预测标准误差SEP、最大误差百分比RE-max及平均误差百分比RE-mean分别为0.65、5.11、7.45%和5.04%;PCR预测模型分别为0.80、2.77、5.64%和3.15%;BPNN预测模型分别为0.97、1.56、3.51%和2.18%。结果表明:BPNN预测模型性能最优,PCR预测模型性能次之,MLR预测模型性能最差。该研究为定量化检测淡水鱼新鲜度的检测提供了一种有效的方法。     

The production of ochratoxin A and citrinin in barley

The production of ochratoxin A by Aspergillus ochraceus and of ochratoxin A and citrinin by Penicillium viridicatum growing on previously sterilised barley for 200 days at 5, 10 and 20°C and a water activity of 0.85 is reported. A. ochraceus did not grow at 5°C, multiplied slowly at 10°C but did not produce toxin. At 20°C the organism multiplied more quickly and produced ochratoxin after 19 days, which slowly disappeared over the next 150 days. P. viridicatum grew slowly at 5°C but did not produce any toxin. It multiplied at 10°C and produced ochratoxin A which was only detectable during the period from 100 to 150 days. At 20°C both ochratoxin A and citrinin were produced. Ochratoxin A was detected after 10 days and was still present after 240 days, whereas citrinin was produced in large quantities between 118 and 129 days and then rapidly disappeared.     

电子鼻结合气质联用法分析大菱鲆冷藏过程中挥发性成分变化

通过测定大菱鲆在4℃冷藏过程中的感官品质、菌落总数和TVB-N值等指标的变化,确定贮藏20d时已达腐败阶段。利用电子鼻检测新鲜及腐败鱼肉的气味差异,并对所获数据进行主成分分析,同时结合顶空固相微萃取(HS-SPME)和气质联用技术(GC-MS)对新鲜及腐败鱼肉的挥发性成分进行分析鉴定。结果表明:电子鼻对新鲜及腐败鱼肉整体风味的区分快速、灵敏,其分析结果与常规新鲜度评价指标的变化致;采用GC-MS法在新鲜及腐败鱼肉中分别检测出27及28种挥发性物质,主要为醛类、酮类、醇类、酯类、烃类和胺类化合物等,其中醛类、酮类和醇类物质在新鲜鱼肉中的含量较高,而胺类物质在腐败鱼肉中含量较高。     

Reduction of ochratoxin A in extruded barley meal

The objective of this work was to determine the effects of extrusion cooking on the stability of ochratoxin A (OTA) in an artificially contaminated hulled barley meal (0.73-mm grain diameter) using a single screw extruder. The extrusion cooking parameters were temperature (140, 160, and 180 degrees C), initial moisture content of barley meal (24, 27 and 30%), and residence time (30, 40, 50, 60, and 70 s). Both unextruded and extruded samples were analyzed for OTA by high-performance liquid chromatography. Extrusion cooking variables significantly affected the stability of OTA (P < 0.05). Greater OTA reductions were achieved at higher residence time (70 s), medium temperature level (160 degrees C), and either high (30%) or low moisture (24%) content of samples. The amount of OTA destroyed during the extrusion process ranged from 17 to 86% depending on the studied parameters. The decrease in the amount of OTA after extrusion cooking followed first-order kinetics, showing that the fastest treatment in OTA reduction was that at 140 degrees C and 24% of moisture content.     

基于电子鼻与GC-MS技术研究萨兹酒花葡萄酒挥发性风味成分

采用电子鼻和气相色谱-质谱（GC-MS）联用技术,并结合化学计量分析方法分析添加不同浓度的萨兹酒花对葡萄酒挥发性风味的贡献。结果表明,通过采集各样品气味信息,电子鼻能够灵敏地检测到各酒样的香气差异,经主成分分析（PCA）,可以很好地区分不同浓度的萨兹酒花葡萄酒。添加0.8 g/L、1.0 g/L萨兹酒花葡萄酒风味较为接近,但与空白酒样有明显差异,萨兹酒花的加入能使葡萄酒风味产生变化。采用GC-MS进一步分析,共检测到24种香气成分,萨兹酒花的加入丰富了酯类物质种类,提高了酒样中酒花特征香气物质含量,整体萨兹酒花葡萄酒香气以花果香为主。对香气成分建模分析,得出添加0.5 g/L萨兹酒花葡萄酒香气品质最好。