Sensing Systems for Flavor Analysis and Evaluation - ACS Publications

Chapter 6

Sensing Systems for Flavor Analysis and Evaluation Xiaogen Yang, JeanneM.Davidsen, RobertN.Antenucci, and Robert G. Eilerman

Downloaded by CORNELL UNIV on September 21, 2016 | http://pubs.acs.org Publication Date: August 14, 2001 | doi: 10.1021/bk-2001-0794.ch006

Givaudan Flavors Corporation, 1199 Edison Drive, Cincinnati, OH 45216 The recent development of sensing systems, such as metal-oxide or conducting polymer based electronic noses and mass spectrometer based chemical sensors, provide valuable tools for flavor chemists for research, development, application, and quality control. These sensing systems are composed of detection devices for multivariate measurements and a chemometric computing package. The concept of sensing systems can be further extended to many analytical techniques involving multivariate measurements. In the present study, some characteristics of a metal oxide based electronic nose system were evaluated, and the possibilities using other sensing systems based on UV, MS and GC/FID for discriminant analysis were explored. Applications and the potential capabilities of various sensing systems for flavor analysis and evaluation were discussed.

Introduction Sensors are detection devices which respond to chemical or biological species in the sample and provide continuous signal output. Higher-order sensors have more than one transduction principle in the same selective layer, while chemical sensing arrays have many selective layers using the same transduction principle ( i ) . Sensors can be loosely categorized into biosensors and chemical sensors based on their selectivity. Biosensors have biologically derived selectivity and chemical sensors are made of synthesized selective matrices. In the applications of sensors, especially higher-order sensors and sensing arrays, multivariate measurements are often required. The complexity of information content makes the data processing and interpretation very difficult or even impossible without the aid of statistical techniques. Therefore, multivariate statistical analysis often becomes an indispensable part of analytical processes involving sensing techniques. Chemical sensing systems are analytical systems incorporating sensors (detectors) or sensing arrays, sample introduction © 2001 American Chemical Society

Takeoka et al.; Aroma Active Compounds in Foods ACS Symposium Series; American Chemical Society: Washington, DC, 2001.

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58 and/or separation (e.g., headspace sampling, chromatography), and advanced data processing techniques (2). In many specific applications, a chemical sensor should ideally be highly selective, responding only to a single analyte or a certain group of chemical species and maintaining minimal interference from other species in the sample. Today, the development of new sensors with better selectivity still remains one of the major tasks in the chemical sensing research field. For flavor analysis and evaluation, however, active odor components often involve a wide range of chemical species. Therefore, it is practically impossible to develop individual sensors responding to individual odor active compounds for each application. A new approach, "Chemical Image" analysis, has been adapted recently for flavor analysis and evaluation (3). In this approach, the focus is on the chemical patterns characterizing an individual or group analytical subjects, rather than on the quantification and identification of each individual chemical component. Therefore, highly selective sensors for specific components are not required for this technique. Odor recognition by the mammalian nose is achieved by a set of odor receptors with broad and overlapping selectivity. The chemical compounds that compose an aroma interact with these receptors eliciting signal response patterns that are transmitted to the brain. The brain processes the signals, conducts pattern recognition analysis, and forms a sensory image of the sample aroma {the sensory profile). This analytical principle can be applied to instrumental analysis. In general, any given analytical sample has a characteristic "chemical image" ("chemical pattern" or "fingerprint") which can be recorded using instrumental techniques. Such "chemical images" can be any collection of physicochemical, spectroscopic, and/or compositional properties of the sample. Classification and discrimination of the samples can be achieved by multivariate analysis of the "chemical image." We can also consider this method as determining the position of the sample in a multidimensional space defined by the analytical measurements. The measurements of "overall impression" may, in many cases, greatly simplify the analytical procedure. Therefore, it allows for developing a simpler, more rapid analytical method, i f separation, which is generally the most time consuming process, is no longer needed. One of the main tasks of flavor analysis is to provide qualitative and quantitative information on the flavor composition of a sample. The major analytical activities in the flavor industry are identification and quantitation of aroma components in samples. However, compositional information is not always necessary for evaluating flavor samples. For example, it is much easier to differentiate apples from oranges according to their odor, taste, color, or appearance, rather than by the analysis of the chemical composition of the fruits. In many cases, the primary analytical objective is not to provide the sample composition, but to answer questions such as: "Does this sample belong to category A or B ? " or "Does this sample pass the given specification?" We can achieve these analytical objectives by recording the "chemical image" or "overall impression" of the samples using instrumental techniques, followed by pattern recognition analysis. Electronic noses are examples of such analytical instruments based on the concept of "Chemical Image."



59 Electronic noses are composed of an array of gas sensors to generate patterns for classification and discrimination of sample groups. Consequently, headspace sampling is required. Most of the gas sensors used in commercially available electronic noses are based on metal oxides, conducting polymers, acoustic and optical devices, quartz resonator, etc. Data analysis is often performed using statistical techniques. The gas sensor array based sensing systems have been applied successfully for discriminating complex mixtures such as foods and beverages (4). In some cases, a correlation between the response of the human nose and the pattern obtained from electronic noses can be established, and the information can provide objective means to evaluate odor qualities (5). However, this happens merely by chance and that useful correlation is just as likely not to occur. The concept of using a sensor array to obtain a chemical image of an analytical sample can be easily extended to other types of detectors. For example, the analysis of a sample using mass spectrometry provides a characteristic chemical profile of the mixture. This mass spectrum can be utilized for classifying or for discriminating sample groups with the help of pattern recognition analysis (6, 7). A new sensing system based on mass spectrometry, the so-called "Chemical Sensor," has been introduced recently. This instrument is composed of a quadrupole mass selective detector (MSD) and a headspace sampling device as used in headspace G C / M S analysis. The mass spectrum of a sample headspace is recorded and the obtained ion peaks are subject to pattern recognition analysis. Because the operation and the results presented by the "Chemical Sensor" are similar to the sensor array-based electronic noses, the chemical sensor sometimes is also referred as "Mass spectrometry-based electronic nose." The concept of "Chemical Image Analysis" can be further extended to many existing analytical techniques. For example, the M S D based chemical sensor can be viewed as G C / M S system without a G C column. In the same way, an H P L C / P D A system can be converted into a " P D A sensing system" by replacing the H P L C column by empty tubing. The obtained "chemical image" from the P D A sensing system is a U V spectrum of the sample. A method based on non-composition "chemical images" can be treated as a black box, which simplifies the analytical procedure and data analysis to a great extent. We still need to dig into the root of the cause, the chemical composition, in order to answer the question "why is the odor of apple different from orange?" In the present study, we evaluated some characteristics of a metal oxide-based electronic nose system, and explored the possibilities using sensing systems based on U V , M S and GC/FID for pattern recognition analysis.

Experimental Sample Preparation. A l l chemicals used in this study were obtained from commercial sources. The same sample preparation procedure was used for both E nose and Fast G C analyses. 100 piL of each compound were accurately weighed into a 10 m L headspace vial and immediately capped. For very volatile flavor components,


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100 |LXL of each chemical were dispensed into a pre-capped, tared 10-mL headspace vial, then accurately weighed. Following capping of the vial, all samples were allowed to equilibrate overnight at room temperature. Electronic Nose System. All "electronic nose" measurements were carried out using a Fox 4000 system (Alpha M.O.S.). The Fox 4000 was equipped with a CTC HS50 Headspace Autosampler, and the Alpha M.O.S. Model 701 Air conditioning Unit. The electronic nose instrument was equipped with three sensor chambers, each containing six metal oxide sensors of various types. A flow rate of 300 mL/min of humidified air was maintained through the sensor chambers. Data was collected for 120s following the injection of a sample with a data sampling rate of 1 Hz. A 13minute delay between the end of data acquisition and the next sample injection was allowed for the instrument equilibration. Fast G C and MS sensing system. A Hewlett Packard 6890 GC was equipped with an FID detector and a CTC Combi-Pal Headspace Autosampler. A 10-m, 0.1-mm i.d., 0.1-urn film thickness DB-1 capillary column (J & W Scientific) was employed with hydrogen as carrier gas at a linear velocity of 40 cm/s. The oven temperature was programmed as follows: 40 °C held for 0.1 min, increased to 200 °C at a rate of 50 °C/min. Thefinaltemperature was held for 0.2 minutes, resulting in a 3.50 minute total run time. For external calibration, standard solutions were made for each flavor chemical, using acetone or ethanol as the solvent. The same GC was used as an MS sensing system. The injection port was directly connected to the MSD via a one-meter length of uncoated silica tubing. The injection port temperature was 250 °C and the GC oven was kept at the same temperature. The split ratio was 1:100 and the injection volume was 1 |iL. The mass spectra (m/z 50 to 300) were averaged over the sample peak. Headspace Sampling Conditions. The headspace sampling conditions were identical for both the E-nose and GC analyses. All samples were thermostatted at 35 °C for 30 minutes, with agitation. The autosampler syringe temperature was held at 40 °C. PDA sensing system. An HP 1100 HPLC instrument equipped with PDA detector was used for the experiments. The HPLC column was replaced with empty tubing (0.12 mm i.d.). Methanol and water (75:25) were used as solvents. Theflowratewas set at 0.5 mL/min. 20 fiL Beverage sample was injected into the system. UV spectra were acquired from 190 nm to 400 nm. For statistical analysis, data points of the UV spectra at peak maximum were taken every 4 nm. Statistical Analysis. The electronic nose data were processed using the built-in software package of the instrument. All other statistical analyses were performed using SPSS statistical software.


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Results and Discussion


Characteristics of Electronic Noses To study the sensitivity and selectivity of an electronic nose based on metal oxide semiconductors to common flavor components, we analyzed four homologous straight carbon chain series: alcohols, aldehydes, esters and ketones. Sensor responses were recorded at four different levels for each analyte. Comparison of the sensor responses of different analytes at identical headspace injection volumes reveals differences in both magnitude and pattern. However, the compounds analyzed also exhibit a wide range of volatility. To eliminate the volatility effect, we determined the sample headspace concentration by GC/FID under the same headspace conditions, and the injected sample amount was calculated by external calibration. The relationships between the response of the electronic nose and the injected sample amount are shown in Figure 1. Based on the sensor selectivity, we can divide the 18 sensors into three types: high, medium, and low selectivity. The highly selective sensors can clearly differentiate the four compound classes. They have the highest sensitivity to aldehydes and ketones, medium sensitivity to alcohols, and the lowest sensitivity to esters. The second type of sensors has limited selectivity to the four compound classes. The difference of their responses to compounds with different function groups is small, but can be statistically significant. The third type of sensors exhibits virtually the same sensitivity to all compound classes tested. Figure 1 also demonstrates that the sensors have a non-linear response as a function of sample concentration. The metal oxide sensors studied were not sensitive to C-10 molecules and larger due to their low volatility at 35 °C. The selectivity of sensors to adjacent molecules in a homologous series is very limited, although they may be distinguishable in some cases (8). In comparison with GC/FID analysis using a narrow-bore column, the sensitivity of the sensors tested is lower by at least 2-3 orders of magnitude. Electronic noses are a headspace based technique and therefore are not sensitive for less volatile compounds at lower thermostatting temperature. Flavor analysis of food products often requires a sampling temperature at 40 °C or below in order to avoid artifact formation. This insufficient sensitivity limits the applications of electronic noses.

M S Sensing System A n M S based sensing system called "Chemical Sensor" is available commercially. It is composed of a headspace sampling device coupled to a mass selective detector and data processing software. The sample headspace vapor is directly introduced to a mass selective detector without chromatographic separation. The obtained mass spectrum of the mixture is subjected to statistical analysis. A similar system, comparable to the commercial instrument, can be established using a typical G C / M S system by connecting the G C injection port to the M S detector with a short piece of deactivated silica tubing instead of a coated G C column. This Takeoka et al.; Aroma Active Compounds in Foods ACS Symposium Series; American Chemical Society: Washington, DC, 2001.

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Sensing Systems for Flavor Analysis and Evaluation - ACS Publications

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