The implications of poor product quality spreading in today’s social media environment, as well as the financial consequences of a product recall, can be devastating to a company’s bottom line. These factors have created a demand for new and objective quality control methods. Traditional methods are costly and time-consuming. Developing rapid, non-invasive, cost-effective, and environmentally sound methods for quality testing has become a priority for meat companies. One such method with potential for both laboratory and industrial testing of meat products is NIR spectroscopy.
Fat, Protein, and Moisture
Meat products that have been studied using NIR spectroscopy include ground beef, poultry, sausage, lamb, and seafood. Fat, protein, and moisture content are of paramount importance in meat products and there have been extensive studies documenting the feasibility of measuring these parameters using NIR spectroscopy. While results vary from study to study, there is no question that NIR spectroscopy has the potential to replace laborious and often expensive wet chemistry methods to measure these parameters successfully. It is important to incorporate both a wide range of chemical composition variability and proper sample homogenization to create accurate calibration models for any parameter
More recently, consumers have become interested in the fat content and fatty acid profile in meat products. For many, meat is the primary contributor of lipids to the human diet, and meat products are marketed based on nutritional guidelines for reducing total fat, saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA) while increasing polyunsaturated fatty acids (PUFA). Studies have included scanning beef, chicken, and lamb (both intact and ground). While scanning intact samples is more advantageous from a practical perspective, the heterogenous nature of intact muscle likely limits the ability to generate accurate reference data and calibration models for fatty acids. Some studies have shown exceptional ability to predict SFA and MUFA using NIR spectroscopy. PUFA has been more difficult to measure (most likely due to low concentrations and a greater number of double bonds in PUFA than MUFA). One strategy which has been successfully deployed is to measure PUFA indirectly from the difference between the total fatty acids and the sum of SFA & MUFA.
Adulteration and Food Fraud
Adulteration and food fraud are major problems for meat producers and many other companies in the global food industry. Food fraud is defined as the intentional act of substituting, adding, tampering of food products as well as misrepresentation of food, food ingredients, and food packaging. It is often done for economic purposes, and in the case of meat, it usually consists of adulterating high-quality meat with cheaper components. Such components can be animal or plant proteins (e.g., soy flour and wheat gluten) or other kinds of meat (e.g., beef fat, turkey, beef offal). Studies measuring the feasibility of meat adulteration detection have shown the best results when first using a classification model to determine the presence of adulteration and if the presence is detected, a quantitative model to determine the level of adulterant in the meat. This approach is known as local modeling and is used in many NIR spectroscopic applications. Using such a technique to measure adulterants presents new and evolving challenges as the types and amounts of adulterants are constantly being changed by those adding them. To make NIR spectroscopy a feasible long-term tool for detecting food fraud, models must be kept up to date by adding data including new types of adulterants.
Process Analytical Technology (PAT) & Online Measurements
Process Analytical Technology is a framework for innovative process manufacturing and quality assurance. Critical points and parameters during manufacturing of a product are defined, and the process is designed in a way that such points and parameters can be measured using analytical tools and instruments for real-time process feedback and control. Such instruments must be able to measure online and in a non-invasive manner. Many vendors have developed instruments that can measure multiple points in a process with a single instrument, usually using optical fibers and probes. PAT has become an important part of pharmaceutical manufacturing and is beginning to acquire hold in the food industry. One such analytical tool with great potential for use in PAT is NIR Spectroscopy.
There are significant challenges to implementing PAT in a meat manufacturing environment. NIR spectroscopy has been proven as a useful tool for measuring parameters of interest in meat measurement. Vendors are coming up with new and innovative ways to make online measurements a feasible solution for companies. Advances such as improved fiber-optics, in-situ sampling, a transition to integrated automation, improved data management systems, and communication systems in the Internet and Cloud age have all contributed to implementing PAT. The meat and food industries also present particular challenges due to natural product variability that must be accounted for when calibrating NIR spectrometers and other analytical PAT tools.
Feasibility studies have been conducted for measuring meat parameters online as well as comparing the results of laboratory and at-line measurements vs. online measurements. Results have been good and show that online measurements are a feasible tool for meat analysis using PAT. Full adoption of PAT in the food industry will require a collaborative effort from process engineers, food scientists, and other contributors to provide the industry with a manufacturing framework for the 21st century.
A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products – Prieto, Pawluczyk, Dugan, Aalhus, Applied Spectroscopy. 2017, Vol. 71 (7) 1402-1426 http://journals.sagepub.com/doi/pdf/10.1177/0003702817709299
Assessing Different Processed Meats for Adulterants Using Visible-Near-Infrared Spectroscopy – Rady, Adedeji, Meat Science 136 (2018) 59-67
Process Analytical Technology for the Food Industry -O’Donnell, Fagan, Cullen, et al., Springer, Food Engineering Series (2014)