Milk has enjoyed increasing popularity in China, however, the milk supply chain is still vulnerable to fraud throughout the country. Milk can be adulterated in variety of ways, from dilution with water to the addition of carbohydrate- or nitrogen-based and protein-rich adulterants as well as a variety of unapproved (sometimes hazardous) additives. This study used Fourier transform-infrared spectroscopy to determine fraud in 52 ultra-high-temperature commercial milk samples. Twenty-three percent of the samples turned out to be adulterated and some of the samples were even flagged for multiple issues.
Pasta is widely consumed around the world, and prices have increased because people have been stockpiling it during the COVID-19 pandemic. Durum wheat, the basic wheat for pasta, is the second most cultivated wheat around the world after common bread wheat, claiming 15–30% higher prices, and therefore an attractive target for food fraud. Out of 150 Argentinian pasta samples that were analyzed with a new method based on Fourier transform infrared spectroscopy (FTIR), in combination with Partial-Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA), 112 were found to be altered with common wheat. Argentinian labeling law requires durum wheat pasta to be based on 100% durum wheat.
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.
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.
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.
Strictly Necessary Cookies
Strictly Necessary Cookies should be enabled at all times so that we can save your preferences for these cookie settings.
We use tracking pixels that set your arrival time at our website, this is used as part of our anti-spam and security measures. Disabling this tracking pixel would disable some of our security measures, and is therefore considered necessary for the safe operation of the website. This tracking pixel is cleared from your system when you delete files in your history.
If you visit and/or use the FST Training Calendar, cookies are used to store your search terms, and keep track of which records you have seen already. Without these cookies, the Training Calendar would not work.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.
A browser cookie is a small piece of data that is stored on your device to help websites and mobile apps remember things about you. Other technologies, including Web storage and identifiers associated with your device, may be used for similar purposes. In this policy, we say “cookies” to discuss all of these technologies.
Data generated from cookies and other behavioral tracking technology is not made available to any outside parties, and is only used in the aggregate to make editorial decisions for the websites. Most browsers are initially set up to accept cookies, but you can reset your browser to refuse all cookies or to indicate when a cookie is being sent by visiting this Cookies Policy page. If your cookies are disabled in the browser, neither the tracking cookie nor the preference cookie is set, and you are in effect opted-out.
In other cases, our advertisers request to use third-party tracking to verify our ad delivery, or to remarket their products and/or services to you on other websites. You may opt-out of these tracking pixels by adjusting the Do Not Track settings in your browser, or by visiting the Network Advertising Initiative Opt Out page.
You have control over whether, how, and when cookies and other tracking technologies are installed on your devices. Although each browser is different, most browsers enable their users to access and edit their cookie preferences in their browser settings. The rejection or disabling of some cookies may impact certain features of the site or to cause some of the website’s services not to function properly.
The use of online tracking mechanisms by third parties is subject to those third parties’ own privacy policies, and not this Policy. If you prefer to prevent third parties from setting and accessing cookies on your computer, you may set your browser to block all cookies. Additionally, you may remove yourself from the targeted advertising of companies within the Network Advertising Initiative by opting out here, or of companies participating in the Digital Advertising Alliance program by opting out here.