Scientists in the food and beverage industry are faced with many different quality control tasks, such as making sure that flavors and fragrances meet certain standards, identifying changes in process parameters that may lead to a change in quality, detecting adulteration in any ingredient and identifying the geographical origin of raw materials. Food scientists who work for regulatory agencies, such as the Food and Drug Administration, are interested in detecting economic fraud due to product substitution and adulteration, as well as health risks from possible contamination.
Many of these quality control issues have traditionally been assessed by experts, who are able to determine a product’s quality by observing its color, texture, taste, aroma, etc. However, it takes years of experience for one to acquire these skills. It would therefore be advantageous if there were a way for food scientists to measure the quality of a product by instrumented means.
Unfortunately, quality is a difficult parameter to quantify. It is impossible to find direct sensors for quality parameters such as freshness or expected shelf life; therefore we are forced to measure an indirect set of parameters which, individually, may be only weakly correlated to the properties of interest. In analyzing this multivariate data, patterns emerge which are related to product quality and can be recognized by either a human interpreter or a computer.
For example, a chromatogram or spectral profile can be thought of as a fingerprint, where a pattern emerges from the relative intensities of the chromatographic sequence or spectrum. If these fingerprints are repeatable for every batch packaged for sale, it is possible for an automated quality control system to interpret those patterns in the data.
Chemometrics is a statistical approach to the interpretation of patterns in multivariate data. When used to analyze instrument data, chemometrics often results in a faster and more precise assessment of composition of a food product or even physical or sensory properties. For example, composition (fat, fiber, moisture, carbohydrate) of dairy products or grain can be quickly measured using near infrared spectroscopy and chemometrics. Food properties (e.g., taste, smell, astringency) can also be monitored on a continuous basis with instrument methods.
In all cases, the data patterns are used to develop a model with the goal of predicting quality parameters for future data. Infometrix has had a long association with the Food and Drug Administration, the USDA and many food-related companies. Our vast experience can help your business deploy instrumental methods to streamline process monitoring, quality control, and consistency of production within your manufacturing plants.