Freshness and Shelf Life of Foods - American Chemical Society

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Chapter 6

SPME-GC/MS Testing for Monitoring Off-Flavors in Processed Milk: Application of Odor Wheel Plots R. T . Marsili and N . Miller Research Department, Dean Foods Company, 555 Colman Center Drive, Rockford, I L 61108

S P M E - G C / M S is a powerful analytical system for deciphering causes of off-flavors in processed milk. Previous work has demonstrated how S P M E - G C / M S can be used as an electronic-nose (e-nose) instrument to classify milk samples according to type of off-flavor and to predict the shelf life of processed milk. This paper describes how a new data presentation technique can be used in conjunction with e-nose results to better understand the actual causes of off-flavor development in milk samples. Radar plots of concentrations and odor units of "indicator" volatiles reveal which specific chemicals are the most likely contributors to off-flavors in samples and likely mechanisms involved in their formation.

A n analytical technique using solid-phase microextraction, mass spectrometry, and multivariate analysis ( S P M E - M S - M V A ) has been shown to be a useful tool for predicting the shelf life of processed milk samples (1,2). In this work, G C / M S was used as an electronic nose (e-nose). Principal component analysis (PCA) was used to show clustering patterns of milk samples with similar types of off-flavors, and partial least squares (PLS) analysis was used to provide a

80

© 2003 American Chemical Society Cadwallader and Weenen; Freshness and Shelf Life of Foods ACS Symposium Series; American Chemical Society: Washington, DC, 2002.


81 quantitative estimate of processed milks' shelf life — i.e., the period between processing and the time when milk becomes unacceptable to consume because of odor or taste. One problem with estimating shelf life with the e-nose approach is that results are statistical estimates, and it is difficult to make critical quality control decisions (e.g., whether or not to discard a day's production of fluid milk because of possible short shelf life) based solely on statistical estimates from S P M E - M S M V A results. Another problem with the e-nose approach is that it doesn't provide details about which specific chemicals are causing off-flavor. This type of supplementary information would be invaluable in making crucial quality control decisions regarding questionable samples. Supplementing S P M E - M S - M V A results with conventional G C / M S testing shows promise. While conventional G C / M S is a more time-consuming approach than the S P M E - M S - M V A method, it does provide information that is difficult or impossible to derive from e-nose experiments — namely, the amount and identity of the specific off-flavor chemicals that are present. This paper describes how a new graphical technique can be applied to G C / M S peak area data to assist in elucidating the reasons for off-flavor development in milk.

Experimental Sampling and Sensory Evaluation A l l samples were commercially pasteurized and homogenized reduced-fat milk (2% milkfat) free of off-flavors at time of manufacture. Samples were packaged in either pint or half-pint high density polyethylene (HDPE) contoured bottles with screw caps. Thirty samples of reduced-fat milk were sampled consecutively from the production line at a dairy plant on the day of processing. This sampling scheme was conducted on five occasions over a seven-month period. Samples were immediately taken from the dairy plant and refrigerated in a walk-in cooler at 7.2±0.5 °C until the end of shelf life was reached as determined by sensory paneling. During refrigerated storage, a sample was removed from the cooler on a daily basis and evaluated by a taste panel to determine when the samples first developed off-flavors. The fresh initial samples (controls) and the samples at the end of shelf life were subjected to S P M E - G C / M S analysis. Prior to G C / M S analysis, samples were preincubated to allow bacteria to grow and produce enough metabolites to be detected. The preincubation procedure involved placing the sample in a 19±1 °C incubator for 16 h. After 16 h, the preincubated sample was subjected to S P M E - G C / M S analysis. Four judges experienced in tasting dairy products were used for sensory evaluation of milk samples. The method used for sensory scoring was based on

Cadwallader and Weenen; Freshness and Shelf Life of Foods ACS Symposium Series; American Chemical Society: Washington, DC, 2002.

82 a 10-point scale according to the scoring guide of the American Dairy Science Association, Shelf life was ended when a score of 5 or lower was recorded by three of the four judges, and the day before was considered the end of shelf life.


S P M E Analysis A Saturn 3 G C / M S was used (Varian Analytical Systems, San Fernando, C A ) . The G C was equipped with a split/splitless model 1078 injector. The injec tor was operated in the split mode (6:1 split ratio) at a temperature of275 °C. The S P M E fiber used was 75-μηι Carboxen/PDMS (Supelco, Bellefonte, PA). For thermal desorption, the S P M E fiber remained in the injector for 3 min. Helium was used as the carrier gas. A 30 m χ 0.25 mm i.d. DB-5 fused-silica capillary column with a film thickness of 1 μιη (J&W Scientific, Folsom, C A ) was used, and the flow rate of the helium carrier gas was 1.0 mL/min. The following column temperature programming sequence was used: A n initial temperature of 50 °C was maintained for 0.5 min, increased to 180 °C at a rate of 6 °C/min, and held at 180 °C for an additional 4 min. Three milliliters of milk sample, 5 μΐ, of internal standard solution (10 μg/mL chlorobenzene), and a micro-stirring bar (Fisher Chemical Co., Cat. No. 09-312-102) were placed in a 6 m L glass G C vial (38 mm high and 22 mm in diameter) and capped with 20 mm PTFE/silicone septa (Wheaton Scientific Products, Cat. No. 224173). The stock chlorobenzene solution that was used to prepare the 10 μg/mL working internal standard solution was purchased as a 5000 μg/mL in methanol standard solution from Supelco (Cat. No. 4-0006). Clogging the 10 μΐ, syringe after internal standard addition to milk was common. This was prevented by rinsing the syringe with distilled water after each addition of internal standard to milk. The setting on the S P M E holder assembly scale was adjusted to 0.8 scale units to ensure that the fiber was positioned in the headspace above the sample in exactly the same way from run to run. With the fiber exposed, the sample vial was placed in a 50 °C water bath (fiber exposure started immediately with sam ple at 19 °C), and the sample was stirred at 350 rpm. Excessive stirring speed (i.e., greater than 1,000 rpm) sometimes caused milk to splash onto the fiber. When fibers contaminated with small amounts of milk were injected, unusual peaks from the thermal decomposition of lactose were observed. Examples included 2-furanmethanol, 5-methyl-2(3H)-furanone, 5-hydroxymethyl-2-furan carboxaldehyde, and 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one. After a 15 min exposure time, the fiber was retracted into the needle assem bly and removed from the vial. The setting on the S P M E holder assembly was changed to 4.0 scale units prior to injection into the G C injector port, which was fitted with a special insert for S P M E analysis (Varian, Cat. No. 03-925330-00).


83 M S Analysis The Varian Saturn M S detector was used in the electron impact (EI) mode with a 1 sec scan time and a 1 count peak threshold. The mass range used was mlz 40 to m/z 350. The temperature of the ion trap manifold was 180 °C.


Generation of Graphs: Indicator Volatiles and Odor Wheel Samples at the beginning (day of manufacture) and end of shelf life were used to prepare graphs. A l l graphs were made using the radar plot option with Microsoft® Excel 97. Radar plots display changes in values relative to a center point. Two general types of radar plots based on G C / M S analyte data were made: (a) Plots of indicator volatiles. Indicator volatiles are the chromatograhic peaks that indicate a potential off-flavor problem and provide clues as to mechanisms of off-flavor formation. Indicator volatiles (IV) graphs are radar plots of the area of the analyte peak divided by the area of the internal standard peak (chlorobenzene) multiplied by a scaling factor. The scaling factor is a value between 0.1 and 10. Multiplication by a scaling factor allows for analytes of widely varying concentrations to be plotted and visualized on the same graph. (b) Odor Wheel plots. Odor Wheels are radar plots of the log of the odor unit value for each analyte. These plots show which indicator volatiles are most likely to contribute to a sample's off-flavor and malodor.

Quantitation of Volatiles Quantitation of analytes, which was necessary for computing odor unit values, was accomplished using the method of additions technique in which fresh, control milk samples were spiked with internal standard and various levels of standard analyte. Standard response factors for each series of analytes were used to quantitate levels of off-flavor volatiles in the samples tested.

Results and Discussion A n example of a graphical IV presentation is shown in Figure 1 for a fresh, reduced-fat milk sample (sampled at day of production) and for another sample from the same day's production but after storage in a 7.2±0.5 °C cooler for 18



84 days when the first sign of off-flavors was noted. The primary metabolites produced in this particular sample are 2-pentanone, hexanoic and octanoic acids, pentanal, hexanal, and dimethyl sulfide. Based on the chromatographic profiles for the five end-of-shelf-life samples and numerous results from spoiled milk samples tested over the past 20 years in our laboratory, 25 volatiles plotted in the IV graphs were selected as "indicator volatiles" — i.e., volatiles that most consistently appear in samples with off-flavor (OF). Identification of the presence of these indicator volatiles can help in the determination of the mechanism involved in OF formation — for example, whether the OF originates from microbiological causes, sanitizer that hasn't been properly flushed from processing lines, lipid oxidation (of linoleic and linolenic acids in the butterfat) caused by exposure to light or prooxidant metals (e.g., copper or iron), or residual organic solvents from packaging materials. Furthermore, in some cases it may be possible to identify the type of organism (psychrorrophic bacteria) involved in spoilage. For example, the production of 3-methyl butanal, which contributes a malty OF to milk, is produced by Streptococcus lactis maltigenes. Determination of the mechanism of OF formation, however, is not always straightforward because many of the indicator volatiles can be produced by multiple mechanisms. Table 1 illustrates a few examples (3). Figure 2 shows IV plots for the other four end-of-shelf-life samples tested. Plots for the four control samples are not shown because they appear essentially identical to the control sample in Figure 1. While control samples contain numerous volatiles (e.g., acetone, 2-butanone, limonene, etc.), these compounds are usually present in good-tasting milk and impart imperceptible flavors to the milk at concentrations normally present. These non-indicator volatiles are not plotted in IV graphs because they provide no insight into what is causing OF. The primary metabolites detected in the end-of-shelf-life samples shown in Figure 2 include dimethyl sulfide, 1-butanol, 1-propanol, ethyl acetate, methyl butyrate, 2-heptanone, 2-nonanone, and free fatty acids (C4-C10). It is interesting that, while many of the plots contain similar types of indicator volatiles, no two end-of-shelf-life plots are identical. Additional reduced-fat milk samples with nonmicrobiological OF were prepared to evaluate how well the IV plots depict the specific indicator volatiles produced. One sample of reduced-fat milk was spiked (under sterile conditions) with 1300 ppm of Matrixx sanitizer (Ecolab, St. Paul, M N ) . This level of sanitizer was chosen since it represents the lowest level that could be detected by sensory analysis. A second sample was spiked (under sterile conditions) with 5 ppm copper. Both samples were preincubated at 19±1 °C for 16 h as previously described. After 16 h, the preincubated samples were subjected to S P M E G C / M S analysis, and the IV plots were made from peak area data. The same multiplication factors for each indicator volatile were used in this set of samples as was used for the microbially spoiled samples. Figure 3 shows the expected



!

2-pentanone butyric acid/

hexanoic acid

octanoic acid

decanoic acid

2-nonanone

2-heptanone

2-pentanone

hexanal

pentanal

ethyl acetate _ dimethyl sulfide. , methyl butyrate methyl hexanoate 3-methyl-biitanal

End of Shelf Life (18 Days Later) Reduced-Fat Milk

Figure 1: IV plots of control (fresh, no off-flavor) reduced-fat milk and another sample from the same production day at end of shelf life (Le., first day with detectable off-flavor development).

** propanol

uto

butyric acid

octanoic acid

decanolc acid

heptanai

ethyl acetate methyl butyrate , methyl hexanoate , 3-methyt-butanal

Control (Fresh, 0 Days) Reduced-Fat Milk



86



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Figure 2: IV plots of the remaining four microbially-spoiled, reduced-fat milk samples at end of shelf life. Chemicals shown are psychrotrophic metabolites. All samples tested at first detection of OF by trained sensory panel (from 17 to 25 days after production).

ethyl acetate


88 Table L Indicator Volatiles in M i l k : Multiple Mechanisms of Formation Chemical/Chemical Type


Free fatty acids (C4-C10) Acetic acid Methyl ketones Toluene Dimethyl sulfide Dimethyl disulfide 3-Methyl butanal

Mechanisms of Formation Microbial, enzymatic, sanitizers Microbial, sanitizers Microbial, heating Packaging solvent, decomposition of β-carotene Microbial, heat induced Microbial, light with methionine Microbial, Strecker degradation reaction involving leucine

Hexanal Lactones

Microbial, lipid oxidation (caused by light, copper, etc.) Microbial, animal feed, heating

indicator volatiles for milk contaminated with sanitizer and for milk contaminat ed with copper. In the case of Matrixx sanitizer contamination, peroxyacetic acid decomposes in milk to form acetic acid and hydrogen peroxide. The hydrogen peroxide is capable of oxidizing milkfat components; hexanal and heptanal are produced by this mechanism. Octanoic acid, a component used in the Matrixx formulation, also appears in the IV plot. The IV plot for the sample contaminat ed with copper shows hexanal as the major indicator volatile (from oxidation of linoleic acid), with lesser quantities of pentanal and octanal produced.

Enhanced I V Plots That Incorporate More Information The IV plots shown in Figures 1, 2, and 3 provide a quick visual indication of what types of volatiles are being generated in milk during shelf life. However, they fail to show how the chemicals were formed or the extent that each chemi cal actually contributes to the sample OF detected.

IV Plots That Show Likely Mechanisms of OF Formation Figure 4 is one way more useful information can be incorporated into IV plots. The addition of color-coded balls at the terminus of each axis (or in this case, balls with differing patterns) provides a simple way of indicating possible mechanisms of formation. Figure 4 is a replot of the end-of-shelf-life sample in Figure 1 but includes the addition of coded balls to provide insights into how the off-flavors were formed.



89

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90 ©Packaging ^Microbial Ο Oxidation β Sanitizer © Thermally solvent metabolite induced ethyl acetate lylbutyrate methyl hexanoate

dimi dimethyl dfsul


trirnethyfcenzei

3-methyf-butana!

xylenes

ipentanal

styrene^^^

hexanal

acetic ackf

heptanal 1

decanoic acid

octanal

octanoic acid

2-pentanone 2-heptanone '2-nonanone dijicetyl

hexanoic add butyric acid 14i
1 are indicative of chemicals that are present at concentrations that greatly exceed their thresholds and are likely to contribute signficant flavor impact (5). A n example of an Odor Wheel plotted with log U values is shown in Figure 5(a). The sample plotted in Figure 5(a) is the same sample plotted in Figures I and 4. B y examining the IV plot in Figure 4 and the Odor Wheel in Figure 5(a), the following type of information is readily revealed about the sample: The primary chemicals produced during shelf life include 2-pentanone, hexanoic acid, octanoic acid, pentanal, hexanal, dimethylsulfide, and 3-methylbutanal; these chemicals are most likely produced as metabolites by psychrotrophic bacteria; and, of these chemicals, dimethylsulfide and 3-methylbutanal are the biggest contributors to the sample's OF. It is noteworthy that this useful information is readily obtained from the two types of plots and is acquired with little or no interpretation of data by die dairy plant Q.C. technician. Another example of a Odor Wheel plot based on log U values is shown in Figure 5(b). The sample used for this plot is the same Matrixx-contaminated sample used in Figure 3. Figure 5(b) shows that the primary contributers to off0

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92 flavor in milk contaminated with Matrixx sanitizer are hexanal and pentanal (from oxidation of unsaturated fatty acids by hydrogen perioxide in the Matrixx). The octanoic and acetic acids, which are present at higher concentration levels than either pentanal or hexanal, do not contribute detectable OF. It should be noted that Odor Wheels based on log U values are always plotted with the axis values labeled. This is useful for two reasons. First, it helps to distinguish this type of plot from IV plots based on peak area ratios. Second, knowledge of the actual log U value of an indicator volatile is important for assessing its odor/flavor contribution.


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A G C - M S Strategy for Off-Flavor Testing in Dairy Q . C . Labs Figure 6 illustrates a possible analytical strategy for Q.C. monitoring of processed milk. Initially, G C / M S would be used as an e-nose instrument to conduct high throughput screening. Instrumentation could include a less expensive benchtop G C / M S system (e.g., a Varian 2100) equipped with a S P M E autosampler (e.g., the Combi PAL from Leap Technologies). With such an analytical system, minimal involvement would be required by the Q.C. technician. It would be advantageous to apply a fast G C technique that would provide resolution of all chromatographic peaks but would require only 2-5 minutes of chromatography time. The G C / M S data file could be subjected to P L S in order to predict the sample's shelf life as previously described. Samples that are predicted to have an unusually short shelf life could be subjected to further scrutiny by a corporate analytical research laboratory. Since a complete high-resolution total ion chromatogram is obtained by fast-GC/MS, the sample would not have to be retested. The data file could simply be e-mailed to the corporate lab, which could then perform further data analysis such as P C A or, most importantly, IV plots and Odor Wheels of individual chromatographic peaks. Based on additional detailed information about which indicator volatiles are present, their likely mechanism of formation, and their specific contribution to OF, a more intelligent decision could be made concerning whether production lots of suspect processed milk should be discarded to waste. Future developments may include the addition of more types of indicator volatiles and more types of OF mechanisms in Figure 4-type plots.

Conclusion A major advantage of S P M E for studying O F in milk is its ability to extract a wide range of potential OF chemicals, including alcohols, aldehydes, ketones, free fatty acids, lactones, and volatile sulfur compounds. A significant problem is how to manage all the chemical data generated by G C / M S testing. The graphical techniques reported here are a good supplement to e-nose information


Cadwallader and Weenen; Freshness and Shelf Life of Foods ACS Symposium Series; American Chemical Society: Washington, DC, 2002. Q

Figure 5: Odor Wheels of log U values (rather than peak area ratios) of indicator volatiles showing: (a) most odiferous bacterial metabolites produced for the same end-of-shelf-life reduced-fat milk sample shown in Figures 1 and 4 and (b) most odiferous indicator volatiles contributed by Matrixx sanitizer when spiked in reduced-fat milk (same sample as shown in Figure 3).


94 GC-MS Based Strategy for Off-Flavor Testing in Dairy Q.C. Lab

Rapid-MS (I0m χ 0.53


Analyze pre-incubated milk at dairy Q.C. lab is processing plant using SPME-MS-MVA based on the low cost Varian Saturn 2100 equipped with a Combi PAL SPME autosampler.

mm); Saturn 2100 ion-trap

High Productivity Screening Further Examination of Problem Samples Identify individual chromatographic peaks by conventional GC/MS and create ODOR WHEEL to determine cause of off-flavor.

E-mail .MS file to corporate lab for further interpretation (no need to re-analyze sample) GC/MS

τ

MVA*

Classify sample as to cause of off-flavor (type of abuse) using PCA* and/or HCA*

•MVA based on mass intensity data

Figure 6: How Odor Wheel plots fit into a dairy Q.C. testing program.

obtained by S P M E - G C / M S and provide insights into which chemicals are caus ing OF in a particular sample and their mechanism of formation.

References 1. Marsili, R.T.J. Agric. Food Chem. 1999, 47, 648-654. 2. Marsili, R.T. J. Agric. Food Chem. 2000, 48, 3470-3475. 3. Azzara, C .D.; Campbell, L . B . Off-Flavors of Dairy Products. In Off-Flavors in Foods and Beverages; Charalambous, G . , Ed.; Elsevier: The Netherlands, 1992, pp 329-374. 4. van Gemert, L.J. Compliations of Odour Threshold Values in Air and Water; TNO Nutrition and Food Research Institute: Zeist, The Netherlands, 2000. 5. McGorrin, R.J.; Gimelfarb, L. Comparison of Flavor Components in Fresh and Cooked Tomatillo with Red Plum Tomato. In Food Flavors: Formation, Analysis and Packaging Influences; Contis, E.T.; Ho, C.-T.; Mussinan, C.J.; Parliment, T.H.; Shahidi, F.; Spanier, A .M., Eds; Elsevier: The Netherlands, 1998, pp 295-313.


Freshness and Shelf Life of Foods - American Chemical Society

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