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Raman spectroscopy (RS) can be used to identify layers in polymer food packaging films to better understand the laminated plastic’s chemical composition. A Raman spectrum is obtained by illuminating a sample with a laser and collecting and measuring scattered light with a spectrometer. Coupling the spectrometer to a microscope with a mapping stage allows an accurate way to create a chemical map of a film’s composition and structure. The map provides valuable information to better understand the packaging’s barrier properties, structural integrity and layers.

The RS method can be useful for conducting failure analysis (why did a food package fail to meet standards), supply chain validation (is the plastic what the supplier claims), decision making (which plastic should be used), and evaluating package appearance (why is there discoloring, haze or particle inclusions in the film). It provides important information for design, purchasing, product success and other decisions that food manufacturers and packagers regularly face.

Take for example the packaging used for frozen hamburger patties. The film used must be transparent to display the hamburger patties, but it also needs to provide an oxygen barrier in order to prevent the ground beef from turning brown. As such, a polymer layer with low oxygen permeability must be incorporated into the laminated film, along with other components like nylon for strength and polyethylene for heat sealing and water barrier. The most common polymer used as an oxygen barrier is ethylene-vinyl alcohol copolymer (EVOH).

It is important that the film used to package these hamburger patties includes a good heat seal as well as a proper oxygen barrier layer. The possible absence of either of these could result in the undesired effect of ground beef turning brown. Manufacturers may want to test packaging for an EVOH layer to make a purchasing decision or verify a supplier’s claims. Additionally, if the packaging fails, an analysis can determine if the failure was due to having no EVOH barrier layer in the product or if there is a need to investigate other potential issues with the packaging. Regardless of the reason, RS provides a preferable method for rapidly evaluating the plastic for an EVOH oxygen barrier layer.

The RS method can be used to determine the construction of the laminated film and confirm that it meets specifications. Using the combination of RS with microscopy and mapping allows both identification of the polymers and the evaluator to correlate the composition to the layer structure of the laminated film. This method provides a map showing the composition of each layer in the film. In some cases, the Raman map will show layers that are not resolved in the visible micrograph image. Thus, with RS, one test provides both the structure and composition of each layer of the laminated film.

Laminated film, packaging, Intertek — This sample table illustrates composition and thickness of each layer of a laminated film. Table courtesy of Intertek.

To start, a small section of the film (5 x 10 mm) is cut and mounted with a photocuring resin. A cross section of the mounted film is then cut to expose the layers for analysis. This cross-section is placed on the mapping microscope stage of the Raman instrument. A micrograph image with a 100X objective is obtained and a Raman map of the cross-section with 1 µm2 pixel resolution collected.

A map image is obtained by classical least squares (CLS) fitting example spectra to each of the spectra collected from the cross-section. The example spectra for the CLS fits are averages (mean) of the spectra in the center of each layer with a unique composition as determined by the data (see Figure 1). The final result is a color-coded map that can be superimposed on the micrograph image to show the composition and thickness of each layer in the laminated film. For example, a film with six layers composed of Nylon 6, polyethylene or EVOH would have varying thickness and placement of each layer to achieve the desired result for the product.

RS Spectra — Figure 1. Example spectra used to create the CLS model for map image.

The composition map can confirm the presence of an oxygen barrier layer of EVOH, as well as the overall construction of the laminated film. Knowing the thickness of the barrier layer is important since the gas permeability is a function of the film thickness. Determination of the overall film structure allows the end-user to confirm the film meets the specifications from the supplier. In turn, this can be used to make important purchasing decisions or insights into what caused a packaging failure.

While good, successful results will confirm the presence of an EVOH layer, the RS map may also show only polymers that don’t have the required oxygen barrier properties (see Figure 2). The manufacturer would need to check it against a supplier spec sheet. It may ultimately show that the lack of an EVOH layer is what caused the issue with the packaging. If the test is being used for decision-making purposes, the manufacturer would know not to use the product. If a supply chain validation is being run, after checking the spec sheet, the manufacturer may need to correct the situation.

Raman spectroscopy — Figure 2. Raman map overlaid with image of film cross section. Green = nylon; Red = polyethylene; Yellow = ethylene vinyl alcohol copolymer (EVOH).

What if the analysis confirmed that an EVOH layer was present, but the test was done for a failure analysis, meaning the packaging did fail at some point? If the EVOH later is present but the meat is still turning brown and/or spoiling, other potential problems would need to be evaluated. In this case, the issues would most likely be with the heat seal and additional testing of the heat seal would be necessary. Thanks to the RS analysis, the investigation into the packaging failure can proceed, and the issue with the heat seal identified.

By giving a chemical image of the packaging, RS analysis provides a wealth of information about a film that can be vital to a food manufacturer or processor. Knowing why certain films may not be working, either due to faults in chemical makeup or the need to look elsewhere, such as the heat seal, RS quickly and efficiently provides information and answers to help get products to market and meet consumer demand.

Producers of food-based products are faced with challenges of maintaining the safety and quality of their products, while also managing rapid screening of raw materials and ingredients. Failure to adequately address both challenges can be costly, with estimated recall costs alone starting around $10 million, in addition to any litigation costs.¹ Long-term costs can accumulate further as a result of damage to brand reputation. A vast array of methods has been employed to meet these challenges, and adoption continues to increase as technology becomes smaller, cheaper and more user friendly. One such technique is Fourier transform infrared (FTIR) spectroscopy, an analytical technique that is widely used for quick (typically 20–60 seconds per measurement) and non-destructive testing of both man-made and natural materials in food products. The uniformity and physical state of the sample (solid vs. liquid) will dictate the specifics of the hardware used to perform such analyses, and the algorithm applied to the identification task will depend, in part, on the expected variability of the ingredient.

Infrared spectral measurements provide a “compositional snapshot”— capturing information related to the chemical bonds present in the material. Figure 1 shows an example of a mid-infrared spectrum of peppermint oil. Typically, the position of a peak along the x-axis (wavenumber) is indicative of the type of chemical bond, while the peak height is related either to the identity of the material, or to the concentration of the material in a mixture. In the case of peppermint oil, a complex set of spectral peaks is observed due to multiple individual naturally occurring molecular species in the oil.

Mid-infrared spectrum, peppermint oil — Figure 1. Mid-infrared spectrum of peppermint oil. The spectrum represents a “chemical snapshot” of the oil, as different peaks are produced as a result of different chemical bonds in the oil.

Once the infrared spectrum of an ingredient is measured, it is then compared to a reference set of known good ingredients. It is important that the reference spectrum or spectra are measured with ingredients or materials that are known to be good (or pure)—otherwise the measurements will only represent lot-to-lot variation. The comparative analysis can assist lab personnel in gaining valuable information—such as whether the correct ingredient was received, whether the ingredient was adulterated or replaced for dishonest gain, or whether the product is of acceptable quality for use. The use of comparative algorithms for ingredient identification also decreases subjectivity by reducing the need for visual inspection and interpretation of the measured spectrum.

Correlation is perhaps the most widely used algorithm for material identification with infrared spectroscopy and has been utilized with infrared spectra for identification purposes at least as early as the 1970s.² When using this approach, the correlation coefficient is calculated between the spectrum of the test sample and each spectrum of the known good set. Calculated values will range from 0, which represents absolutely no match (wrong or unexpected material), to 1, representing a perfect match. These values are typically sorted from highest to lowest, and the material is accepted or rejected based on whether the calculated correlation lies above or below an identified threshold. Due to the one-to-one nature of this comparison, it is best suited to identification of materials that have little or no expected variability. For example, Figure 2 shows an overlay of a mid-infrared spectrum of an ingredient compared to a spectrum of sucrose. The correlation calculated between the two spectra is 0.998, so the incoming ingredient is determined to be sucrose. Figure 3 shows an overlay of the same mid-infrared spectrum of sucrose with a spectrum of citric acid. Notable differences are observed between the two spectra, and a significant change in the correlation is observed, with a coefficient of 0.040 calculated between the two spectra. The citric acid sample would not pass as sucrose with the measurement and algorithm settings used in this example.

Mid-infrared spectrum, sucrose — Figure 2. An overlay of the mid-infrared spectrum of sucrose and a spectrum of a different sample of sucrose.

Mid-infrared spectrium, sucrose, citric acid — Figure 3: An overlay of the mid-infrared spectrum of sucrose and a spectrum of citric acid.

When testing samples with modest or high natural variability, acceptable materials can produce a wider range of infrared spectral features, which result in a correspondingly broad range of calculated correlation values. The spread in correlation values could be of concern as it may lead to modification of algorithm parameters or procedures to “work around” this variation. Resulting compromises can increase the potential for false positives, meaning the incorrect ingredient or adulterated material might be judged as passing. Multivariate algorithms provide a robust means for evaluating ingredient identity for samples with high natural variability.

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Tag Archives: chemical composition

Using Raman Spectroscopy to Evaluate Packaging for Frozen Hamburgers

Food Fraud and Adulteration Detection Using FTIR Spectroscopy

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