Rapid method for prediction of Escherichia coli numbers using an electronic sensor array and an artificial neural network

An electronic sensor array with 12 nonspecific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth alone and in super broth inoculated with Escherichia coli (ATCC 25922) at 37 degrees C for 2 to 12 h. Using discriminant function analysis, it was possible to differentiate super broth alone from that containing E. coli when cell numbers were 10(5) CFU or more. There was a good agreement between the volatile profiles from the electronic sensor array and a gas chromatography-mass spectrometer method. The potential to predict the number of E. coli and the concentration of specific metabolic compounds was investigated using an artificial neural network (ANN). The artificial neural network was composed of an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.999) between actual and predicted data.     

基于神经网络及电子鼻的虾夷扇贝鲜活品质评价及传感器的筛选

追踪检测虾夷扇贝品质变化过程中的存活指标,生理指标以及电子鼻气味图谱的变化,建立保活流通过程中不同等级的活品虾夷扇贝电子鼻气味指纹图谱,购买市场上不同状态的活品虾夷扇贝,分别通过学习向量量化(learning vector quantization, LVQ)、概率(probabilistic neural networks, PNN)、支持向量机(support vector machine, SVM)神经网络对测试样品快速模式分类,最后通过对电子鼻传感器的筛选探索便携式快速品质鉴别设备的可能性。研究结果表明,24 h的极端胁迫环境放置较为完整的模拟了虾夷扇贝在保活流通过程中状态变差的过程;将电子鼻数据主成分分析、聚类分析结果与存活指标(开口率、缩边率以及死亡率)和生理指标(超氧化物歧化酶活性、耗氧率以及海水浊度)相结合可以把品质变化过程中的虾夷扇贝分成5个等级,并分别得到每个等级的扇贝气味指纹图谱;3种神经网络均可以对测试样品等级进行快速测定,其中支持向量机(SVM)神经网络兼具精确和快速的特点,测试样本T全部预测为等级4,测试样本N全部预测为等级3,从交叉验证到仿真预测所用时间仅为7.652 s;筛选得到的8个电子鼻传感器也可以对不同等级鲜活虾夷扇贝气味特征进行有效区分。     

An alternative freshness index method for modified atmosphere packaged abalone using an artificial neural network

An alternative freshness index method for abalone (Haliotis asinina) muscle packaged under atmospheric air (Air) and modified atmosphere (MA) of 40% CO₂: 30% O₂: 30% N₂ packaging conditions and stored at 2 ± 1 °C was developed. Biochemical indices covering pH, total volatile basic nitrogen (TVB-N), trimethylamine (TMA) and nucleotide degradation products, as well as instrumental texture and color of the packaged abalones, were determined. Sensory characteristics including odor, color and appearance were evaluated and then summarized into overall freshness scores (freshness index). The biochemical and instrumental analyses were then calibrated with the freshness index, using an artificial neural network algorithm. The neural network was shown to be capable of correlating biochemical and instrumental analyses with the freshness index. A useful prediction was possible, as measured by a low mean square error (MSE = 0.092) and a regression coefficient (R² = 0.98) between true and predicted data.     

Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC–MS analysis of volatile compounds

An electronic nose based on an array of 6 metal oxide semiconductor sensors was used, jointly with artificial neural network (ANN) method, to classify Pecorino cheeses according to their ripening time and manufacturing techniques. For this purpose different pre-treatments of electronic nose signals have been tested. In particular, four different features extraction algorithms were compared with a principal component analysis (PCA) using to reduce the dimensionality of data set (data consisted of 900 data points per sensor). All the ANN models (with different pre-treatment data) have different capability to predict the Pecorino cheeses categories. In particular, PCA show better results (classification performance: 100%; RMSE: 0.024) in comparison with other pre-treatment systems.     

Treatment of Escherichia coli O157:H7 inoculated alfalfa seeds and sprouts with electrolyzed oxidizing water

Electrolyzed oxidizing water is a relatively new concept that has been utilized in agriculture, livestock management, medical sterilization, and food sanitation. Electrolyzed oxidizing (EO) water generated by passing sodium chloride solution through an EO water generator was used to treat alfalfa seeds and sprouts inoculated with a five-strain cocktail of nalidixic acid resistant Escherichia coli O157:H7. EO water had a pH of 2.6, an oxidation-reduction potential of 1150 mV and about 50 ppm free chlorine. The percentage reduction in bacterial load was determined for reaction times of 2, 4, 8, 16, 32, and 64 min. Mechanical agitation was done while treating the seeds at different time intervals to increase the effectiveness of the treatment. Since E. coli O157:H7 was released due to soaking during treatment, the initial counts on seeds and sprouts were determined by soaking the contaminated seeds/sprouts in 0.1% peptone water for a period equivalent to treatment time. The samples were then pummeled in 0.1% peptone water and spread plated on tryptic soy agar with 5 microg/ml of nalidixic acid (TSAN). Results showed that there were reductions between 38.2% and 97.1% (0.22-1.56 log(10) CFU/g) in the bacterial load of treated seeds. The reductions for sprouts were between 91.1% and 99.8% (1.05-2.72 log(10) CFU/g). An increase in treatment time increased the percentage reduction of E. coli O157:H7. However, germination of the treated seeds reduced from 92% to 49% as amperage to make EO water and soaking time increased. EO water did not cause any visible damage to the sprouts.     

Detection of Escherichia coli in meat with an electrochemical biochip

Detection of foodborne pathogenic and spoilage bacteria by RNA-DNA hybridization is an alternative to traditional microbiological procedures. To achieve high sensitivity with RNA-DNA-based methods, efficient bacterial lysis and release of nucleic acids from bacteria are needed. Here we report the specific detection of the hygiene indicator microorganism Escherichia coli in meat by use of electrochemical biochips. We improved RNA isolation from bacteria in meat juice from pork and beef. Samples, either naturally or artificially contaminated by E. coli, were enriched by incubation in full or minimal medium. A combined treatment of the samples with lysozyme, proteinase K, and sonication resulted in efficient cell disruption and high total RNA yields. Together with optimization of enrichment time, this ensures high sensitivity of electrochemical measurements on biochips. A short enrichment period and the triple-lysis regimen in combination with electrochemical biochip measurement were tested with 25 meat samples. The lower limit of detection of the biochip was approximately 2,000 CFU of E. coli per ml. The entire analysis procedure (5 h of enrichment, triple lysis, and biochip detection) has a lower limit of detection of 1 CFU of E. coli per ml within a total time needed for analysis of 7 h.     

Detection of age and insect damage incurred by wheat, with an electronic nose

Wheats of five storage ages and with 15 degrees of insect damage were evaluated and classified by the static-headspace sampling method using an electronic nose (E-nose). A commercial E-nose (PEN2) comprising 10 metal-oxide semiconductor (MOS) sensors was used to generate a typical chemical fingerprint of the volatile compounds present in the samples. Principal-component analysis (PCA) and linear-discriminant analysis (LDA) were applied to the generated patterns to achieve classification into the five groups of different storage-age wheats and the 15 groups of different degrees of insect-damaged wheat. The results obtained indicated that the E-nose could discriminate successfully among wheats of different age and with different degrees of insect damage.     

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。     

Detection of lard adulteration in RBD palm olein using an electronic nose

The use of surface acoustic wave (SAW) sensing electronic nose (zNose™) for detection of lard as an adulterant in refined, bleached, deodorized (RBD) palm olein was investigated. Mixing of animal fats, especially lard in any form in food products, is a cause of concern for certain religions. RBD palm olein spiked with lard at levels ranging from 1% to 20% (w/w) was analyzed. The zNose™ produced a two-dimensional olfactory image called VaporPrint™, which could be used for immediate detection (qualitatively) of lard substances in sample admixtures. Lard adulteration could be determined by a few distinct peaks in the zNose™ chromatogram. The best relationship between percentage of lard in adulterated RBD palm olein and SAW detector response was observed in adulterant peak E (R² = 0.906). Pearson’s correlation coefficient (r) was calculated using this parameter. An ideal correlation was observed between the zNose™ data and other chemical tests (r > 0.90).     

Study on classification of soy sauce by electronic tongue technique combined with artificial neural network

Electronic tongue as an analytical tool coupled with pattern recognition was attempted to classify 4 different brands and 2 categories (produced by different processes) of Chinese soy sauce. An electronic tongue system was used for data acquisition of the samples. Some effective variables were extracted from electronic tongue data by principal component analysis (PCA). Backpropagation artificial neural network (BP-ANN) was applied to build identification models. PCA score plots show an obvious cluster trend of different brands and different categories of soy sauce in the 2-dimensional space. The optimal BP-ANN model for different brands was achieved when principal components (PCs) were 2, and the identification rate of the discrimination model was 100% in both the calibration set and the prediction set, and the optimal BP-ANN model for different categories had the same result. This work demonstrates that electronic tongue technology combined with a suitable pattern recognition method can be successfully used in the classification of different brands and categories of soy sauce.     

基于BP神经网络的电子鼻羊奶贮藏时间的预测

利用电子鼻PEN3系统判定室温和冷藏条件下羊奶的贮藏时间。通过电子鼻系统采集羊奶室温贮藏及冷藏期间挥发性成分的响应值,并采用PCA(主成分分析法)、LDA(线性判别分析法)和LM算法优化的BP神经网络(LM-BP)、遗传算法优化的神经网络(GANN)、4层BP神经网络进行模式识别。结果表明PCA和LDA均可区分室温贮藏及冷藏1～6d的生鲜羊奶,LDA方法还可以明显体现出羊奶贮藏期间挥发性成分的变化趋势,并且与羊奶酸度的变化有很好的一致性。采用LM-BP神经网络、GANN神经网络和4层神经网络均能较好地预测不同贮藏时间的羊奶,其中4层神经网络的预测正确率高于LM-BP神经网络和GANN神经网络。     

Efficacy of a post enrichment acid treatment for isolation of Escherichia coli O157:H7 from alfalfa sprouts

The enrichment, detection and isolation procedure in the current US FDA BAM have been shown effective for Escherichia coli O157:H7 in a wide variety of foods. Recently reported modifications to the enrichment protocol, including post-enrichment immunomagnetic separation (IMS) procedures have improved sensitivity of the method for alfalfa sprouts. However, cultural isolation on selective agar plates still presents a challenge in this food matrix.     

Inactivation of Escherichia coli O157:H7 and Salmonella by gamma irradiation of alfalfa seed intended for production of food sprouts

Inonizing irradiation was determined to be a suitable method for the inactivation of Salmonella and Escherichia coli O157:H7 on alfalfa seed to be used in the production of food sprouts. The radiation D (dose resulting in a 90% reduction of viable CFU) values for the inactivation of Salmonella and E. coli O157:H7 on alfalfa seeds were higher than the D-values for their inactivation on meat or poultry. The average D-value for the inactivation of Salmonella on alfalfa seeds was 0.97 +/- 0.03 kGy; the D-values for cocktails of meat isolates and for vegetable-associated isolates were not significantly different. The D-values for nonoutbreak and outbreak isolates of E. coli O157:H7 on alfalfa seeds were 0.55 +/- 0.01 and 0.60 +/- 0.01 kGy, respectively. It was determined that the relatively high D-values were not due to the low moisture content or the low water activity of the seed. The D-values for Salmonella on alfalfa seeds from two different sources did not differ significantly, even though there were significant differences in seed size and water activity. The increased moisture content of the seed after artificial inoculation did not significantly alter the D-value for the inactivation of Salmonella. The results of this study demonstrate that 3.3- and 2-log inactivations can be achieved with a 2-kGy dose of ionizing radiation, which will permit satisfactory commercial yields of sprouts from alfalfa seed contaminated with E. coli O157:H7 and Salmonella, 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。     

Detection of mite infestation in wheat by electronic nose with transient flow sampling

A total of 71 wheat samples of 70 g each, to which were added 0–70 flour mites (Acarus siro L), were scanned, 1–2 weeks after preparation, by a conducting polymer-type electronic nose with transient flow sampling. The device uses an array of non-selective sensors to produce a digital fingerprint (odour profile) relating to the volatile composition of the sample. The results demonstrate the ability of the device to detect the mites in wheat at concentrations of relevance to the cereal trade, of the order of just 1000 A siro kg⁻¹ (around 0.001% by weight). One week from preparation, discrimination between samples with no added mites and samples with 70 added mites achieved a classification accuracy of over 83%. Evidence is presented which suggests the known mite-produced volatile undecane as the origin of the electronic nose response. © Crown copyright 1999     

Behavior of enterohemorrhagic Escherichia coli O157:H7 on alfalfa sprouts during the sprouting process as influenced by treatments with various chemicals.

The behavior of Escherichia coli O157:H7 on alfalfa seeds subjected to conditions similar to those used commercially to grow and market sprouts as it is affected by applications of NaOCl, Ca(OCl)2, acidified NaClO2, acidified ClO2, Na3PO4, Vegi-Clean, Tsunami, Vortexx, or H2O2 at various stages of the sprouting process was determined. Application of 2,000 ppm of NaOCl, 200 and 2,000 ppm of Ca(OCl)2, 500 ppm of acidified ClO2, 10,000 ppm of Vegi-Clean, 80 ppm of Tsunami, or 40 and 80 ppm of Vortexx to germinated seeds significantly reduced the population of E. coli O157:H7. With the exception of acidified NaOCl2 at 1,200 ppm, spray applications of these chemicals did not significantly reduce populations or control the growth of E. coli O157:H7 on alfalfa sprouts during the sprouting process. Populations of E. coli on alfalfa sprouts peaked at 6 to 7 log10 CFU/g 48 h after initiation of the sprouting process and remained stable despite further spraying with chemicals. The population of E. coli O157:H7 on sprouts as they entered cold storage at 9 +/- 2 degrees C remained essentially unchanged for up to 6 days. None of the chemical treatments evaluated was able to eliminate or satisfactorily reduce E. coli O157:H7 on alfalfa seeds and sprouts. Observations on the ability of E. coli O157:H7 to grow during production of alfalfa sprouts not subjected to chemical treatments are similar to those from a previous study in our laboratory on the behavior of Salmonella Stanley. Our results do not reveal a chemical treatment method to eliminate the pathogen from alfalfa sprouts. We have demonstrated that currently recommended procedures for sanitizing alfalfa seeds fail to eliminate E. coli O157:H7 and that the pathogen can grow to populations exceeding 7 1og10 CFU/g of sprouts produced using techniques not dissimilar to those used in the sprout industry.     

Monitoring the authenticity of low-fat yogurts by an artificial neural network

The growing consumption of low- and reduced-fat dairy products demands routine control of their authenticity by health agencies. The usual analyses of fat in dairy products are very simple laboratory methods; however, they require manipulation and use of reagents of a corrosive nature, such as sulfuric acid, to break the chemical bounds between fat and proteins. Additionally, they generate chemical residues that require an appropriate destination. In this work, the use of an artificial neural network based on simple instrumental analyses, such as pH, color, and hardness (inputs) is proposed for the classification of commercial yogurts in the low- and reduced-fat categories (outputs). A total of 108 strawberry-flavored yogurts (48 probiotic low-fat, 36 low-fat, and 24 full-fat yogurts) belonging to several commercial brands and from different batches were used in this research. The statistical analysis showed different features for each yogurt category; thus, a database was built and a neural model was trained with the Levenberg-Marquardt algorithm by using the neural network toolbox of the software MATLAB 7.0.1. Validation with unseen data pairs showed that the proposed model was 100% efficient. Because the instrumental analyses do not require any sample preparation and do not produce any chemical residues, the proposed procedure is a fast and interesting approach to monitoring the authenticity of these products.     

基于电子鼻技术的烟丝霉变检测

霉变是影响烟丝质量的重要因素之一,研究探索建立基于电子鼻技术的烟丝霉变检测方法。构建的电子鼻系统主要由5只SnO2半导体气敏传感器形成反应阵列,采用BP神经网络(back propagation neural network,BPNN)为主的模式识别方法。从每个传感器响应曲线中提取2个特征值,使用主成分分析和BP神经网络对传感器阵列的所有特征值进行处理。主成分分析结果显示:非霉变烟丝和霉变烟丝存在可区分趋势,但不同霉变程度的烟丝间存在部分重叠。进一步利用BP神经网络对霉变烟丝判别,识别正确率达到90.00%。试验表明,使用电子鼻技术可以客观、有效地区分霉变和非霉变烟丝,为有效控制烟丝质量提供了可靠途径。     

Neural network based electronic nose for classification of tea aroma

This paper describes an investigation into the performance of a Neural Network (NN) based Electronic Nose (EN) system, which can discriminate the aroma of different tea grades. The EN system comprising of an array of four tin-oxide gas sensors was used to sniff thirteen randomly selected tea grades, which were exemplars of eight categories in terms of aroma profiles. The mean and peak of the transient signals generated by the gas sensors, as a result of aroma sniffing, were treated as the feature vectors for the analysis. Principal Component Analysis (PCA) was used to visualise the different categories of aroma profiles. In addition, K-means and Kohonen’s Self Organising Map (SOM) cluster analysis indicated there were eight clusters in the dataset. Data classification was performed using supervised NN classifiers; namely the Multi-Layer Perceptron (MLP) network, Radial Basis Function (RBF) network, and Constructive Probabilistic Neural Network (CPNN) were used for aroma classification. The results were that the three NNs performed as follows: 90.77, 92.31, and 93.85%, respectively in terms of classification accuracy. Hence the performance of the proposed method of aroma analysis demonstrates that it is possible to use NN based EN to assist with the tea quality monitoring procedure during the tea grading process. In addition the results indicate the possibility for standardization of the tea aroma in numeric terms.