Food Authenticity Testing: Concepts and Best Practices You Should Know

Quality control (QC) samples need to be created from the model samples. These are pools of samples from the different groups in the model, e.g., types of honey, and a matrix pool of all the samples, e.g., all honey samples. The samples should be pooled before sample preparation, and the QC should undergo the same sample preparation as the model samples. It is possible to also make an adulterated QC by mixing the group QCs in a known manner. Injecting the same pooled QC sample multiple times at the beginning of development and periodically through the development is advised to ensure that reproducible retention times, mass accuracies, and area counts are achieved. If not, it’s appropriate to adjust the methodology at this stage to make those values as reproducible as possible.

Data Acquisition

Consistent and reliable methods are required to produce robust measurements for using a model. For this purpose, MS-only data acquisition is sufficient when using high resolution mass spectrometers. Compound identifications generally aren’t required for food authenticity modeling, but if identification is required, MS/MS experiments can be done with a Q-TOF. The most important thing to optimize is the acquisition rate, or scan speed, so that enough data is collected across the chromatographic peak widths for robust integration.

Diverting the flow from the mass spectrometer to the waste line is an important aspect of an acquisition method that is often overlooked. In reversed phase LC, the first 0.5 min, the high percent organic and equilibration portions of the run are dirtier, irreproducible fractions. Diverting these to waste can go a long way toward maintaining the performance of the mass spectrometer. Besides this, features eluting at these time points can be inconsistent and not desirable for building the model.

Capturing variation in the method development is crucial to building a good model. Not only does variation in the model samples need to be captured, but so does variation in the sample prep and data acquisition. This is accomplished by acquiring your model samples in different batches processed on different days. Additionally, if you use more than one mass spectrometer, analyzing the model sample set on both systems is important.

Chemometric Statistics and Model Building

The model is built by evaluating the relative intensity of only a certain number of features, which proved to be significantly different between the classes based on the statistical analysis. Feature extraction, statistics, and model building need to be done to develop the full method before moving on to validating. The model samples will go through this process as a batch of data, while the unknown samples will be processed individually using the developed method and routine software, MassHunter Classifier.

Features in the model samples should be extracted using a recursive extraction methodology, such as the one in Profinder, for a high-quality extraction of the features in your model samples. All the discovered features are moved into a chemometric software such as Mass Profiler Professional (MPP), where they are filtered down and a model is built. The statistics performed should result in very robust features that can resist instrument or method drift over time. Often, simple statistical methods such as t-test and fold change are all that is needed to figure out what features are significant to the groups. Using a high threshold at the fold change step is important to remove low abundant features, as these will likely be the least reproducible over time. Models then use only these features in a supervised fashion, using the groups of samples known to the model. Varying the filtering and statistical analysis parameters is suggested to optimize the separation of the classes. Using these strategies will help you build robust, longer-lasting models, but it cannot overcome variability from experimental design, sample preparation, and data acquisition as discussed above. Once a statistical workflow in MPP is established, these steps can all be automated and easily shared with colleagues and collaborators. It also permits you to execute and build models easily, more frequently, and with less error.