Examining Food Authenticity with Sensitive Carbohydrate Analysis

Food adulteration, whether intentional or accidental, poses a risk to consumer health and defames food manufacturers. In addition to maintaining best manufacturing practices, it is crucial for food scientists to develop reliable methods to test food quality, detect traces of unauthorized adulterants, and remain compliant with regulatory requirements. For a variety of food products, carbohydrate components serve as authenticity markers and are often used to validate food quality.

Despite their widespread use, analytical techniques such as liquid chromatography (LC) or gas chromatography (GC) often present challenges when it comes to obtaining accurate carbohydrate measurements, compromising important information at the expense of public health. Here, we explain why it’s necessary to choose sensitive and robust methods for carbohydrate analysis within the food industry. We also discuss how using high-performance anion-exchange chromatography coupled with pulsed amperometric detection (HPAE-PAD) can identify food adulterants with increased confidence.

Carbohydrate Detection: The Need for Sensitive and Robust Methods

Carbohydrate profiles in certain foods, such as honey, agave syrups, fruit juices, and coffee, act as markers for authenticity and can be used to detect food fraud. Adulteration of honey or agave syrup can involve their dilution with cheaper, often unhealthy alternatives, such as high fructose corn syrup or saccharose syrup, produced from beets or canes. In these instances, analytical methods that can accurately measure sucrose levels in honey or perform oligosaccharide profiling in agave syrup help distinguish the genuine food products from their fraudulent counterparts.

The familiarity and widespread use of LC and GC prompt scientists to use these techniques as a default approach for carbohydrate analysis; however, these methods aren’t the best choice to detect, measure, and study carbohydrates. The high polarity of carbohydrates makes them difficult to reliably retain and separate using reverse phase chromatography. As carbohydrates are weakly acidic, dissolving high concentrations of them imparts higher acidity to the samples. At extreme pH levels, metals from the column’s surface strip away and adhere to the packing materials, tampering with the column’s integrity. Moreover, the inherent viscosity of these samples will also require optimized column heating to ensure a consistent flow through the column. Any fluctuations to lower temperatures can result in changes in viscosity of the sugar samples, causing them to stick to the column surface, generating backpressure and making the method irreproducible.

Additionally, carbohydrates tend to have very few chromophore groups and cannot, therefore, be detected with adequate sensitivity using ultraviolet (UV)-based detectors. Switching to refractive index or low-wavelength UV detection methods prevents the use of gradients due to their sensitivity to the eluent and sample matrix components. Gradients can also increase the baseline noise, thereby reducing the signal-to-noise ratio and decreasing the sensitivity of measurements.

Relying on discrete elution times is also challenging as the monosaccharide components, glucose and fructose, both have the same molecular weights of 180.16 g/mol. When in solution, they also both exist in ring forms, making them indistinguishable, especially given the lack of chromophores. Using a strong base can push the equilibrium to one side and stop the interconversion between ring and chain structures, providing a slight difference in retention time. However, at higher base concentrations, the monosaccharides are not retained for too long and will elute out very quickly.

One option to retain carbohydrates and boost sensitivity for measurement is to derivatize the samples. Though several isomers and chains of a carbohydrate molecule can be derivatized, requiring a summation to yield the total result for one carbohydrate can make method validation complicated, laborious, and time-consuming. Furthermore, due to the diversity of sample matrices used in the food industry, a thorough sample cleanup prior to injection is often necessary to prevent any assay carryovers, making the sample preparation process more tedious.