Patent Application: US-58434109-A

Abstract:
the present invention is directed to a method and apparatus that satisfies the need for a bioelectronic tongue for food allergy detection . the method of detecting concentration of food allergen incorporates antibodies into an electronic tongue to create a bioelectronic tongue . additionally the method uses impedance , capacitance , and / or other related electrochemical methods for detecting analyte in complex media . furthermore the method additionally includes methods to subtract out non - specific interactions . the method also subtracts non - specific interactions . the device / apparatus is a bioelectronic tongue for detecting allergen in diluted food samples . the device includes : a sensor array ; an impedance or capacitance analyzer ; a preprocessor ; a feature extractor ; a pattern recognizer ; and an output device indicating an allergen concentration . in order to implement the method of detecting food allergens on a bioelectronic tongue a computer readable medium containing an executable program is used for performing the analysis of a food sample . the executable program performs the acts of : preprocessing data from an impedance analyzer ; extracting a feature pattern ; recognizing a pattern of features of data representing a concentration of food allergen contained is the food sample ; and outputs allergen concentration data .

Description:
fig1 represents the desired biomolecular recognition process between an antibody and a food protein allergen that can occur at an individual electrode within the bioelectronic tongue . fig2 illustrates the flow of detecting a food allergen from a sample with a bioelectronic tongue , which contains many such electrodes . the first step is providing a diluted food sample 10 . the reason for diluting the sample will be understood in the detailed discussion below . the sample ( s ) are made part of a sensor array 12 . the samples are detected by a potentiostat / impedance analyzer which provides an electrical output 14 to a preprocessing portion 14 . the feature extraction 16 selects data from the output of the preprocessing portion 14 . the output of feature extraction 18 portion provides data to a pattern recognition portion 20 that determines the concentration of a food allergen within the sample . the concentration of the allergen of interest may be displayed on a display device ( not shown ). the electronic tongue is an established technology that combines a multi - electrode array of only partly selective sensors with pattern recognition algorithms that separate the “ true ” signal from the detailed sensor response to complex , real - world samples that typically contain interfering species . ( for an overview of electronic research , see f . winquist , c . krantz - rulcker and i . lundstrom , “ electronic tongues ,” mrs bullet . 29 , 726 ( 2004 ), hereby incorporated herein by reference . the principles of an electronic tongue are similar to those of the electronic nose , but the electronic tongue is designed for aqueous samples . like the electronic nose , many of the applications of electronic tongues are related either to environmental monitoring , medical applications , or to food and beverage production . electronic noses / tongues have been used in complex , real world environments where sensitivity as well as specificity must be considered , such as detection of chemical warfare agents , indoor air quality , and bacterial classification . see j . l . perez pavon , m . del nogal sanchez , c . garcia pinto , m . ferna laespada b . m . cordero and a . g . pena , “ strategies for qualitative and quantitative analyses with mass spectrometry - based electronic noses ,” trends anal . chem . 25 , 257 ( 2006 ); k . arshak , e . moore , g . m . lyons , j . harris and s . clifford , “ a review of gas sensors employed in electronic nose applications ,” sens . rev . 24 , 181 ( 2004 ); m . b . pushkarsky , m . e . weber , t . macdonald and c . k . n . patel , “ high - sensitivity , high - selectivity detection of chemical warfare agents ,” appl . phys . lett . 88 , 044103 ( 2006 ); s . zampolli , i . elmi , f . ahmed , m . passini , g . c . cardinali , s , nicoletti and l . dori , “ an electronic nose based on solid state sensor arrays for low - cost indoor air quality monitoring applications ,” sens . actuat . b 101 , 39 ( 2004 ); and r . dutta , j . w . gardner and e . l . hines , “ classification of ear , nose , and throat bacteria using a neural - network - based electronic nose ,” mrs bull . 29 , 709 ( 2004 , all of which are hereby incorporated herein by reference . an electronic tongue combines hardware , an array of electrochemical sensors , with software , automated pattern recognition algorithms such as principal component analysis , linear discriminate analysis , support vector machines , and neural networks . a set of features derived from the sensor array response is used as predictors ( input ), in order to predict the presence and / or concentration of analyte ( s ) ( output ). the algorithms are ‘ trained ’ using samples that include the analyte ( s ) of interest and potential interfering species . training is performed automatically using multiple iterations with a large dataset to create a mathematical relationship between the features with the desired output ( analyte concentrations ). the pattern recognition algorithms are then tested with unknown samples to demonstrate generalization of the approach . most electronic tongues employ voltammetry or potentiometry with an electrode array containing either electrodes made from different materials , or different electrode coatings . see f . winquist , c . krantz - rulcker and i . lundstrom , “ electronic tongues ,” mrs bullet . 29 , 726 ( 2004 ), hereby incorporated herein by reference . one common format for an electronic tongue is an array of ion - selective electrodes . ion selective membranes are not 100 % selective , typically suffering from interfering signals from one or more other ionic species , so the pattern recognition algorithms incorporated into an electronic tongue allow dramatic increases in both sensitivity and selectivity . however , as electronic nose / tongue technology has matured , several weaknesses have been attributed to the shortcomings of the fundamental sensor components . see f . rock , n . barsan and u . weimar , “ electronic nose : current status and future trends ,” chem . rev . 108 , 705 ( 2008 ) hereby incorporated herein by reference . for example , the additional information gained from adding sensor elements quickly saturates , in contrast to human sensory systems . this limits the sensitivity and selectivity that can be obtained with an electronic nose or tongue . thus it has been argued that for many applications , sensitivity and selectivity can only be increased through improvements in the sensitivity and selectivity of the individual sensor elements . this approach is illustrated in the first reports of a bioelectronic tongue , in which enzymatic materials are incorporated into the individual sensor elements . see m . gutierrez , s . alegret and m . del valle , “ potentiometric bioelectronic tongue for the analysis of urea and alkaline ions in clinical samples ,” biosens . bioelectron . 22 , 2171 ( 2007 ) and m . gutierrez , s . alegret and m . del valle , “ bioelectronic tongue for the simultaneous determination of urea , creatinine and alkaline ions in clinical samples ,” biosens . bioelectron . 23 , 795 ( 2008 ), both hereby incorporated herein by reference . determination of urea typically employs the enzyme urease , which produces ammonium ions , but selectivity is often compromised by other ionic species that interact with ammonium ion - selective electrodes . the use of a bioelectronic tongue containing an array of electrodes combined with pattern recognition rather than individual sensor elements enables the recognition of the analyte of interest amongst competing background ionic species . the focus of this application is to make a similar advance in the selectivity of antibody electrodes . although antibody electrodes are considerably more selective than ion - selective electrodes , they still suffer from interference arising from non - specific interactions . see j . lahiri , l . isaacs , j . tien and g . m . whitesides , “ a strategy for the generation of surfaces presenting ligands for studies of binding based on an active ester as a common reactive intermediate : a surface plasmon resonance study ,” anal . chem . 71 , 777 ( 1999 ); e . ostuni , r . g . chapman , r . e . holmlin , s . takayama and g . m . whitesides , “ a survey of structure - property relationships of surfaces that resist the adsorption of protein ,” langmuir 17 , 5605 - 5620 , 2001 ; x . qian , s . j . metallo , i . s . choi , h . wu , m . n . liang and g . m . whitesides , “ arrays of self - assembled monolayers for studying inhibition of bacterial adhesion ,” anal . chem . 74 , 1805 ( 2002 ); a . bange , h . b . halsall and w . r . heineman , “ microfluidic immunosensor systems ,” biosens . bioelectron . 20 , 2488 ( 2005 ) and d . r . shankaran , v . k . gobi and n . miura , “ recent advancements in surface plasmon resonance immunosensors for detection of small molecules of biomedical , food and environmental interest ,” sens . actuators b 121 , 158 ( 2007 ) all of which are hereby incorporated herein . these are typically ascribed to interactions of an antibody electrode with interfering protein species , but small molecules and ionic species may also provide interfering signals . the goal of the invention is to integrate multiple antibody electrodes into a bioelectronic tongue , and to use pattern recognition methods to discriminate the desired signal from interfering ones . the specific system to be disclosed is detection of peanut proteins , which the applicants have already demonstrated in ideal solutions using electrochemical impedance spectroscopy . ( see y . huang , m . c . bell and i . i . suni , “ impedance detection of peanut protein ara h 1 ,” anal . chem . 80 , 9157 ( 2008 ) and y . huang and i . i . suni , “ degenerate si as an electrode material for electrochemical biosensors ,” j . electrochem . soc . 155 , j350 ( 2008 ) both hereby incorporated herein by reference .) the “ electronic fingerprint ” for peanut proteins can then be determined by multivariate analysis using automated pattern recognition algorithms . ( see m . l . nogueira , r . mcdonald and c . westphal , “ can commercial peanut assay kits detect peanut allergens ?,” j . aoac int . 87 , 1480 ( 2004 ); r . guitierrez - osuna , “ pattern analysis for machine olfaction ,” ieee sens . j . 2 , 189 . ( 2002 ) and s . ampuero and j . o . bosset , “ the electronic nose applied to dairy products : a review ,” sens . actuat . b 94 , 1 ( 2003 ) all of which are hereby incorporated herein by reference .) to the best of the applicants &# 39 ; knowledge , only three research groups have reported the use of electrochemical impedance spectroscopy in an electronic tongue . ( see a . riul , r . r . malmegrim , f . j . fonseca and l . h . c . mattoso , “ an artificial taste sensor based on conducting polymers ,” biosens . bioelectron . 18 , 1365 ( 2003 ); m . ferreira , a . riul , k . wohnrath , f . j . fonseca , o . n . oliveira and l . h . c . mattoso , “ high performance taste sensor made from langmuir - blodgett films of conducting polymers and a ruthenium complex ,” anal . chem . 75 , 953 ( 2003 ); a . riul , a . m . gallardo soto , s . v . mello , s . bone , d . m . taylor and l . h . c . mattoso , “ an electronic tongue using polypyrrole and polyaniline ,” synthet . met . 132 , 109 ( 2003 ); d . s . dos santos , a . riul , r . r . malmegrim , f . j . fonseca , o . n . oliveira and l . h . c . mattoso , “ a layer - by - layer film of chitosan in a taste sensor application ,” macromol . biosci . 3 , 591 ( 2003 ); a . riul , h . c . de sousa , r . r . malmegrim , d . s . dos santos , a . c . p . l . f . carvalho , f . j . fonseca , o . n . oliveira and l . h . c . mattoso , “ wine classification by taste sensors made from ultra - thin films and using neural networks ,” sens . actuat . b 98 , 77 ( 2004 ); c . e . borato , f . l . leite , l . h . c . mattoso , r . c . goy , s . p . campana filho , c . l . de vasconcelos , c . g . da trindade neto , m . r . pereira , j . l . c . fonseca and o . n . oliveira , “ layer - by - layer films of poly ( o - ethoxyaniline ), chitosan and chitosan - poly ( methacrylic acid ) nanoparticles and their application in an electronic tongue ,” ieee trans . dielect . electr . insul . 13 , 1101 ( 2006 ); n . k . wiziack , l . g . paterno , f . j . fonseca and l . h . c . mattoso , “ effect of film thickness and different electrode geometries on the performance of chemical sensors made of nanostructured conducting polymer films ,” sens . actuat . b 122 , 484 ( 2007 ); f . j . ferreira , r . c . t . perreira , a . c . b . delbem , o . n . oliveira and l . h . c . mattoso , “ random subspace method for analyzing coffee with electronic tongue ,” electron . lett . 43 , 1138 ( 2007 ); m . cortina - puig , x . munoz - berbel , m . del valle , f . j . munoz and m . a . alonso - lomillo , “ characterization of an ion - selective polypyrrole coating and application to the joint determination of potassium , sodium and ammonium by electrochemical impedance spectroscopy and partial least squares method ,” anal . chim . acta 597 , 231 ( 2007 ); m . cortina - pig , x . munoz - berbel , m . a . alonso - lomillo , f . j . munoz - pascual and m . del valle , “ eis multianalyte sensing with an automated sia system — an electronic tongue employing the impedimetric signal ,” talanta 72 , 774 ( 2007 ); pioggia , g ., di francesco , f ., marchetti , a ., ferro , m ., and ahluwalia , a . a composite sensor array impedentiometric electronic tongue . part i . characterization . biosens . bioelectron ., 22 : 2618 - 2623 , 2007 ; and g . pioggia , f . di francesco , a . marchetti , m . ferro , r . leardi and a . ahluwalia , “ a composite sensor array impedentiometric electronic tongue . part ii . discrimination of basic tastes ,” biosens . bioelectron . 22 , 2624 ( 2007 ) all of which are hereby incorporated herein by reference .) another novel aspect is the use of antibodies , which do not appear to have been employed previously in an electronic tongue . two recent reports mention the use of antibodies as sensing elements in an electronic nose , but do not actually construct such a device . ( see d . d . stubbs , s . h . lee and w . d . hunt , “ molecular recognition for electronic noses using surface acoustic wave sensors ,” ieee sens . j . 2 , 294 ( 2002 ) and d . d . stubbs , s . h . lee and w . d . hunt , “ investigation of cocaine plumes using surface acoustic wave immunoassay sensors ,” anal . chem . 75 , 6231 ( 2003 ) both hereby incorporated herein by reference . to the best of our knowledge , neither the electronic nose nor the electronic tongue has been used to detect allergens . the bioelectronic tongue is for detection of a wide range of food allergens in a wide variety of food products . much of the discussion herein is related to detection methods for peanut allergens , the technology may be applied to other allergens such as β - lactoglobulin , which is believed to be the main allergenic protein in cow &# 39 ; s milk , and tri a bd 27k , which is believed to be the main allergenic protein in wheat . requirements for food allergen biosensors include : sufficient sensitivity to detect trace quantities of proteins ( 1 - 100 mg / kg ). ( see r . krska , e . welzig , and s . baumgartner , “ immunoanalytical detection of allergenic proteins in food ,” anal . bioanal . chem . 378 , 63 ( 2004 ) hereby incorporated herein by reference .) adequate specificity in complex food matrices . rapid , simple apparatus that can be used by inexperienced personnel . ideally , continuous monitoring of analytes . the detection limit given above , which is only approximate , 34 can be translated into molarity using the density of water , yielding a detection limit ranging from 2 . 5 × 10 − 8 to 2 . 5 × 10 − 6 m . this compares favorably to the detection limits reported for impedance - based biosensors , which range from the nm to pm range . 35 - 40 a more complete bibliography of research into impedance biosensors is available in recent reviews . 41 - 44 despite the excellent sensitivity of impedance biosensors , their applications have been limited so far by selectivity limitations arising from non - specific interactions . however , by combining the use of impedance detection at antibody - coated electrodes with pattern recognition in a bioelectronic tongue constructed of many such electrodes , we remove the effect of such background interference . the main advantage of such a method relative to elisa is the seamless ability to create a multi - analyte sensor , by including one antibody electrode for each analyte of interest . in other words , although non - specific interactions are quite complex , their complexity does not increase substantially with an increase in the number of antibody - coated electrodes ; rather , an increased number of electrodes provides additional information through which the pattern recognition algorithm can achieve improved performance . the fundamental basis for detecting non - specific interactions includes the use of electrodes at which antibodies are immobilized of varying isoelectric point , and the inherent heterogeneity of protein surfaces , which already average out non - specific interactions , allowing background subtraction . as discussed above , current methods for detecting peanut proteins are based on enzyme linked immuno - sorbent assays ( elba ), 6 - 9 which are time consuming , require trained personnel , are difficult to automate and miniaturize , and are not fully standardized . 10 for example , a recent study compared five different elisa tests for peanut proteins , and these tests differ widely in their procedures , in their analytical targets , and in their performance in both biscuits and dark chocolate . 45 elisa techniques also suffer interferences , which are often attributed to matrix effects and cross - reactivities . 46 as a result , elisa methods suffer from false positives , 47 - 50 so they are often used only for preliminary screening . another practical problem with elisa techniques is that users typically need a separate elisa test for each analyte of interest , as demonstrated by detection of different aflatoxins in foods . 51 the proposed bioelectronic tongue is inherently capable of multi - analyte detection through an electrode array . cross - reactivity of food allergens with each other , and with environmental allergens , is well - known . 52 - 53 cross - reactivity is believed to arise from the existence of identical or highly similar linear or conformational epitopes in two different protein allergens . cross - reactivity arising from pre - sensitization by a secondary protein allergen should not affect the sensor proposed here . however , the disclosed sensor may detect directly cross - reactive food and other allergens , likely resulting in a modest number of false positive results , as with other types of sensors . however , in some cases this false positive will not really be false , since many individuals will be allergic to both species anyway . it is most important that a sensor should not suffer from false negatives . one of the applicants recently published two reports of impedance detection of peanut protein ara h 1 after surface immobilization of its monoclonal mouse antibody onto either au or si electrodes . 15 , 16 peanut protein ara h 1 and its monoclonal mouse antibody were purchased from indoor biotechnologies . following antibody immobilization , described later , this biosensor interface was exposed to increasing concentrations of peanut protein ara h 1 , as shown in fig3 . fig3 illustrates an impedance response of au electrode with increasing concentrations of peanut protein ara h1 ( from reference # 15 ). the concentrations of ara h1 from the innermost to the outermost semicircular arcs are 0 , 0 . 02 , 0 . 04 , 0 . 08 , 0 . 16 , 0 . 24 μg / ml , respectively . the test solution also contains 50 mm pbs and 5 mm fe ( cn ) 6 3 -/ 4 - at ph 7 . 3 . these results are presented as a nyquist plot , with the highest frequency at the left and the lowest frequency at the right . these results have been fit by complex nonlinear least squares ( cnls ) analysis to the simplified randles equivalent circuit model shown in fig4 . here r s is the solution phase resistance , r ct is the charge transfer resistance , c d is the capacitance , and z w is the warburg impedance , which arises from mass transfer limitations and was not fit . the results of this data fit are shown in table 1 . a clear trend of increasing charge transfer resistance ( r ct ) with increasing peanut protein concentration can be observed . this corresponds approximately to the diameter of the semicircles in fig3 . this trend in r ct allows us to identify the ac probe frequencies ( 1 - 10 hz ) with the greatest sensitivity to the presence of peanut protein ara h . at these frequencies , the response time is of the order of sec . one advantage of impedance detection is the low noise level that can be obtained even at room temperature . although single frequency experiments were not conducted with peanut protein ara h 1 , single frequency experiments in another system yielded a noise level less than the digital increment in the analog - to - digital converter . 54 fig5 illustrates the dependence of the fit impedance parameters on the concentration of peanut protein ara h 1 . in fig5 the variation in the charge transfer resistance ( r ct , left side ) with concentration of peanut protein ara h 1 is illustrated . the relative change in the charge transfer resistance ( rd ) with concentration is greater than that in the differential capacitance ( c d ). this suggests that monitoring frequencies ( 1 - 10 hz ) most sensitivity to r et will yield the most sensitive detection . the bioelectronic tongue hardware consists of an array of electrodes at which different antibodies are immobilized . this array might include the following antibodies : antibody to peanut protein ara h 1 , as demonstrated above . antibody to peanut protein ara h 2 . since peanut proteins ara h 1 and h2 are considered the most important food allergens , detection of both should be included in the bioelectronic tongue . cockroach - specific antibody from the same subtype ( igg1 ) as the antibodies to peanut protein ara h 1 and ara h 2 . antibodies to other proteins that cause food allergies . antibodies to other environmental allergens not expected in food samples . other blank antibodies chosen to have a range of isoelectric points . if the bioelectronic tongue contains antibody electrodes with different charging behavior , then it will be more effective in discriminating between different charged or partially charged species . as will be described later , the cockroach - specific mouse igg1 antibody plays an important role in the development of a bioelectronic tongue , since one can assume that this antigen will not be present in food samples . 55 - 57 since this antibody is also from the same mouse igg1 sub - type as the antibodies to peanut proteins ara h 1 and ara h 2 , this can be used as a reference element for background subtraction of non - specific interactions from the analytes of interest . 55 - 57 an exemplary bioelectronic tongue contains eight electrodes , as illustrated in fig2 , coated with the eight different antibodies : two different target analytes , peanut proteins ara h 1 and ara h 2 ; reference antibody from the same igg1 sub - type , the cockroach - specific antibody ; two other food protein allergens from apple , carrot , or celery ; and three other non - food protein allergens from dust mite , mold , or animal . all antibodies can be immobilized onto an au electrode by the following procedure : formation of an organic film with a carboxylate termination by immersing the au - coated electrode into 11 - mercaptoundecanoic acid ( 11 - mua ). carboxylate activation by n -( 3 - dimethylaminopropyl )- n ′-( ethylcarbodiimide hydrochloride ) ( edc ) and n - hydroxysulfosuccinimide sodium salt ( nhss ). reaction of activated carboxylate groups with amine groups on the peanut protein ara h 1 surface at ph 8 , which ensures that the amine groups are deprotonated , resulting in formation of an amide bond . 58 this technique for amide bond formation by carbodiimide coupling has become a standard method . 58 this immobilization chemistry was used for detection of peanut protein ara h 1 , as described in the above , and for other species . in some cases , this amide bond formation chemistry must be altered somewhat by varying the ph . 58 standard methods exist for antibody immobilization onto other conductive electrode surfaces . following antibody immobilization , the electrodes are dipped in bovine serum albumin ( bsa ) to block the remaining active sites on the electrode , a method that has been widely employed . 59 this will likely be only partly effective , but will reduce interference effects in the bioelectronic tongue . dramatic further reduction in interfering signals is accomplished by sample dilution . 60 , 61 typically , the concentration at which an antibody - based sensor exhibits a linear response is far lower than the sensitivity desired for detection of food allergens , which ranges from 1 - 100 μg / ml , 33 assuming that the sample density is that of water . this suggests that all liquid food matrices be strongly diluted by water by a factor of 100 - 1000 × before being introduced into the bioelectronic tongue in order to prevent the surface immobilized antibodies from becoming saturated with peanut protein , or their corresponding antigen . sample dilution has the added benefit of reducing the importance of non - specific interactions by that same factor of 100 - 1000 ×. solid food matrices can be similarly diluted following dissolution into water . sample dilution has other advantages , such as correcting for effects of varying sample viscosity and ionic strength . in other words , the properties of the testing fluid are determined largely by the dilutent . the pattern recognition algorithms remove the remaining effects of non - specific interactions , as described below . pattern recognition algorithms are applied to the bioelectronic tongue to discriminate between real detection of peanut protein and false positives . as described , an array of electrodes , as well as the patterns across frequencies generated from each electrode , creates a fingerprint for a specific analytes of interest , peanut proteins ara h 1 and h2 . the difficulty is that electrodes may also respond to other interfering species . a pattern recognition algorithm is ‘ trained ’ using data collected that includes the patterns of interest , as well as potential non - specific interactions . however , the problem diverges from a ‘ fingerprint ’ type of pattern recognition problem , in that multiple analytes of interest can be present at the same time . by including many presentations of peanut protein ( with and without non - specific interactions ) in the training data , the pattern recognition algorithm can learn the complex patterns . this has been demonstrated in other electronic nose / tongue applications , such as , for example in detection of chemical warfare agents in complex air environments where hundreds of gases are present of which several dozen may cause false positives . 4 non - specific interactions are likely to occur on multiple electrodes , whereas the peanut protein will respond only to the two electrodes with antibodies to ara h 1 and ara h2 proteins . non - specific interactions may also cause changes in the frequency pattern at the peanut electrodes . through pattern recognition , we will distinguish between these patterns through the use of the training set that includes examples of each . the bioelectronic tongue can incorporate an expected relationship between the electrode response and the concentration of peanut protein , or alternatively , the relationship can be determined phenomenologically . for detection in only one food matrix , impedance measurements from the individual antibody electrodes are recorded in : 10 different cookies ( prepared by the investigators to control ingredients ) baseline measurement , plus 3 concentration levels of peanut proteins ( antibodies to ara h 1 , 2 ) added to cookie preparation tests are performed for each food without protein ( 10 tests ), then repeated for 3 different concentrations ( 30 tests ). each measurement is made three times so to train and test the pattern recognition algorithm . prior research suggests the use of 5 - 10 samples per class for training . 18 , 62 models to detect the presence of peanut protein will have a binary class ( that is , 30 samples without and 90 samples with protein ). to detect the concentration of peanut protein , a total of four classes ( three concentration levels plus one without protein ) are needed , where there are 30 samples for each of the four classes . the remaining samples are used for validation and testing . two additional food matrices ( soup , ice cream ) 10 different samples each ( purchased locally ) baseline measurement ( no peanut protein ), plus 3 concentration levels of peanut proteins ( antibodies to ara h 1 , 2 ) added to cookie preparation . the three concentrations of peanut protein ara h 1 and h2 that are tested are 0 . 04 , 0 . 08 and 0 . 16 μg / ml , after dilution . 3 repeat experiments ( above ) for training and validation of models tests without peanut protein correspond to the negative control , while the tests with known peanut protein concentrations correspond to the positive control . negative control experiments with an additional non - peanut allergenic protein are an additional control . tests performed for each food matrix individually ( soup , ice cream ), as in the cookie dataset above , first for presence of peanut protein ( binary class — with or without ) and then for concentration level . lastly , data from cookies , soup , and ice cream are combined to further extend the model to more complex environments . as the complexity of the problem increases , the electrode array size may need to increase , as might the number and types of samples required to establish the pattern recognition method . complex pre - treatment and separation methods are typically employed when biosensors are applied to food matrices . see s . s . haughey and g . a . baxter , “ biosensor screening for veterinary drug residues in foodstuffs ,” j . aoac int . 89 , 862 ( 2006 ); v . andreu , c . blasco and y . pico , “ analytical strategies to determine quinoline residues in food and the environment ,” trends anal . chem . 26 , 534 ( 2007 ); and c . blasco , c . m . torres and y . pico , “ progress in analysis of residual antibacterials in food ,” trends anal . chem . 26 , 895 ( 2007 ) all of which are hereby incorporated herein by reference . these methods can be applied to peanut protein detection according to the procedures used by those familiar in the art . the steps involved in pattern recognition for detection of peanut protein allergens are described in detail below . these include preprocessing , feature selection , preprocessing , performance evaluation , and validation . the preprocessing stage prepares the data for classification . 17 in this , baseline adjustment and drift compensation are performed where we consider utilizing the response to an additional reference sensor . this is an electrode that is coated with the cockroach allergen specific antibody , as described above . this reference sensor is expected to suffer the same baseline drift associated with chemically interfering species , but without any specific response to the peanut proteins of interest . the baseline of the reference can generally be subtracted , divided , or both . if drift over time is seen , then periodic recalibration can be performed and used to determine and accommodate for the drift for the sensor array . 63 one possible source of sensor drift is the presence of interfering protein species . these interactions would cause a small baseline drift which are differentiable from the response to the peanut protein . since the sensor output is not simply a single response , but a response versus time , the next step is a final response from this time series called compression . two methods include utilization of the steady response to the sensor as well as transient response . in this application , we expect for simple testing setups with peanut protein only that we expect to utilize steady state response . for the case with baseline drift , even when accounted for by using a reference electrode , we can use signal processing to compute the transient response for a response that is recognized as different from drift . each sensor generates one or more features which are used as inputs to the pattern recognition algorithm . for impedance - based biosensors , the measurement at a sensor can be repeated for multiple frequencies . for example , in the results shown in fig3 , 20 points within the frequency spectrum were measured . pattern recognition can be considered with one frequency as input to the classifier , 27 , 28 with the entire spectra as input , 26 or with some portion of the spectra . electrochemical intuition suggests that three frequencies may be sufficient , as they may contain all possible information about peanut protein binding . as described above , the two equivalent circuit elements sensitive to peanut protein binding are the charge transfer resistance ( rd ) and the differential capacitance ( c d ). since the solution resistance is essentially constant , only two frequencies must be measured to determine r ct and c d . however , the complex low - frequency behavior in fig3 may also be sensitive to peanut protein binding , so inclusion of a third frequency might be useful . regardless , pattern recognition is performed with additional frequencies to ensure that information is not lost . a trade - off exists with the time taken for data collection , which increases with the number of frequencies monitored , and with inclusion of low frequency data , which takes longer to acquire . the entire frequency spectrum in fig3 takes about 2 . 5 min . to acquire . the next step is normalization of the vector of features ( measurements at multiple frequencies for each of eight sensors . one can consider normalizing the entire vector or individual sensors by utilizing the mean / std , range , or utilizing logarithmic approaches . with a large array , it may be necessary to perform feature selection or reduce the dimensionality of the data . automated approaches for feature selection are typical next steps for large datasets . one can use standard approaches such as principle component analysis ( pca ) and linear discriminate analysis ( lda ) which map the multiple dimensional feature space into a projection of features ordered by variability . typically the features with the most variability are selected as features since they carry the most information . one can also consider using random search , sequential searching , where features are added or removed to determine impact on performance , and other supervised approaches such as between - class pairwise distance , linear separability , overlapped feature histogram , hill climbing , simulated annealing , threshold accepting optimization , and expectation maximization . 64 as an example we have an array of two peanut biosensors , combined with other six other protein biosensors . the additional array elements are used to capture information on non - specific interactions . since the biosensor array contains only eight sensors , our feature set may not require reduction and the features could be used directly . one can expand our feature set by considering the detailed impedance spectra and the inclusion of additional frequencies . this may increase the feature set , provide more information to allow for separation , and potentially require feature selection . pattern recognition is a mature field in electrical and computer engineering and computer science . a variety of pattern recognition tools have been developed and utilized for many applications where simple thresholds achieve poor performance . one of the chief advantages of many pattern recognition tools is that they can create non - linear thresholds to separate features in multi - dimensional feature space and these non - linear models can be determined automatically through the use of a training set . to develop a pattern recognition algorithm , we divide the data into a training , test , and validation sets . the training set is used to create the model and select the thresholds . this is usually an iterative process where weights / model is updated to minimize the classification error . a test set is used to verify that the model will generalize to an unseen dataset . once the model is selected based on the training / test stages , the validation set provides final evidence that the model will be successful in the device &# 39 ; s application . the main requirement for automated supervised pattern recognition is data that represents the variability likely to been seen in a given problem and that has enough examples available . initially , since the dataset is small , one would use cross - validation , where a small set is withheld for validation while the remaining data is used for training / testing . the process is repeated many times ( 100 × or more ) repeatedly on small subsets of the data and the average results are used to estimate the predicted performance of the classifier on an unseen data set . one may consider multiple pattern recognition models , including artificial neural networks , support vector machines , classification trees , and nearest neighbor classifiers . most of these have been applied to electronic nose and tongue applications . 21 , 26 , 65 each has their own advantages and disadvantages . supervised feedforward neural networks , called multilayer perceptrons , are trained with the standard back - propagation algorithm . 66 nearest neighbor classifier using a training set of both positive and negative cases performs classification by calculating the normalized euclidean distance to the nearest training case . 67 classification trees derive a sequence of if - then - else rules using the training set in order to assign a class label to the input data . 68 lastly , support vector machines ( svm ) map input vectors to a higher dimensional space where a maximal separating hyperplane is constructed . 69 by using the cross - validation approach across the models described above , the approximate performance can be estimated for a particular pattern recognition problem . one would also consider factors such as overfitting , model order selection , early stopping , and regularization . 7 the aim of the method and device is to detect the presence or absence of peanut protein above a specific minimum concentration . in this case , the classes are presence of peanut protein or not , i . e . a binary classification problem ( 0 , 1 ). additionally the method and device will further assess the pattern of the features in order to relate the magnitude to concentration . in this case , the ‘ classes ’ to be detected would be level of concentration where the classes would increase according to the desired concentration resolution . the algorithms developed above are only as good as the data used to train them . each component can be developed and considering the impact of different preprocessing , feature selection , and classification algorithms . however , its development is iterative as the data becomes available . the applicants suggest a point - of - use biosensor for detecting peanut protein allergen . the following practical requirements must also be considered : rapidity of the overall testing process . portability , possibly including miniaturization of all components . regeneration of the biosensor interface between sample tests . for the chemical part of this method and device , sensor , antibody - antigen interactions are quite rapid , and do not slow detection of peanut protein . impedance detection of peanut protein ara h 1 is most sensitive at frequencies of 1 - 10 hz . at such frequencies , impedance detection is quite rapid , and the slowest chemical step is mass transfer . with a well designed fluid handling system , the time response is on the order of seconds . the time response of the pattern recognition algorithms is expected to be within 0 . 5 second or much less and is not a limiting factor . the sample dilution step discussed above will of course slow the response time somewhat . however , dilution is a relatively simple process , and there are several ways in which this can accomplished rapidly . for example , if the sensor electrodes are stored in an aqueous solution , the analyte can be introduced into that pre - existing aqueous solution as only a small volume , maintaining the dilution factor give above of 100 - 1000 ×. mixing could also be accomplished using short bursts of acoustic energy , for example . the electrochemical detection system is relatively easy to miniaturize . if one takes apart conventional potentiostats and impedance analyzers , they are essentially a collection of integrated circuits . as semiconductor electronics are continuously made faster and smaller , the price and the quality of “ electrochemistry - on - a - chip ” is continuously improving . the remainder of the detection process involves reagent mixing and reagent transport , which are easily handled , for example , within lab - on - a - chip technologies . regeneration of elisa and other immunosensors can allow reuse for 50 - 60 cycles and is typically achieved by introduction of a chaotropic agent , often an acidic or basic solution . 70 this is as effective for an impedance biosensor as for traditional elisa tests . in addition , one can anticipate multiplexing between many sensor electrodes since they are constructed out of inexpensive starting materials . as discussed above , even though peanut allergens have been the main focus of this application , the concepts and techniques presented are applicable to other food allergens , so this application is not limited to peanut allergens . many variations and modifications may be made to the preferred embodiments of the disclosure as describe above . all such modifications and variations are intended to be herein within the scope of the present invention . it is therefore wished to have it understood that the present invention should not be limited in scope , except by the terms of the following claims . the following references are hereby incorporated in their entirety herein by reference . 1 . s . a . bock , “ prospective appraisal of complaints of adverse reactions to foods in children during the first three years of life ,” pediatrics 79 , 683 ( 1987 ). 2 . w . burks , g . a . bannon , s . sicherer and h . a . sampson , “ peanut - 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