By Casey Thomson, Senior Application Engineer, KPM Analytics
As the price of doing business rises, processed food producers have a solution to mediate costs in many ways.
Food production costs are a significant concern for any food producer. However, many may be transitioning into full panic mode with today’s economy. For food production companies to ensure profitability and stay competitive, finding ways to reduce production costs while maintaining product quality is essential.
Naturally, food ingredients are vital to manufacturing processed foods like baked goods, snack foods, breakfast cereals, and many others. They also comprise some of the greatest expenses a food production company accounts for routinely. Suppliers typically price their ingredients based on specific quality parameters like moisture, fat content, protein, sugar, crude fiber, and others. Many food production companies trust their suppliers to deliver ingredients at these specifications but do not necessarily implement quality assurance measures to verify the supplier is keeping their promise of quality.
As a result, once in production, producers may find that the variable quality of ingredients may yield production issues or a poor-quality final product. At this point, it is usually too late for the producer to make corrections in their process, and they must accept a loss on their bottom line.
By implementing routine at-line or in-process quality analysis, food producers can take the necessary measures to maintain end-to-end control of product quality to verify quality specifications from their supplier, reduce over-formulation (or over-using) of ingredients, and save costs.
One effective method of doing so is through near-infrared (NIR) analysis.
What is NIR analysis?
NIR analysis is a type of spectroscopic analysis that uses light in the near-infrared region of the electromagnetic spectrum to identify and quantify chemical compounds in a product sample. NIR analysis is based on the interaction of light with chemical bonds in the sample. The chemical bonds absorb some of the light, which results in a spectrum that can help identify and quantify the chemical compounds in the sample. This data includes essential quality parameters like moisture, fat content, protein, and more complex constituents like fibers, sugar, fatty acids, starch, and more.
NIR analysis is a non-destructive method that does not damage the sample. This feature is vital in food production, where food or ingredient samples are often small, and the product must be preserved for quality control. NIR analysis is also fast; in most cases, sample results are presented to the operator in under a minute.
Every NIR calibration is a mathematical correlation between a sample and a chemical or property of interest. With more product samples, the more robust and repeatable an NIR calibration becomes.
NIR analysis is not a new technology in food production. Thanks to continued research and application customization, food producers today can access dependable and repeatable calibrations to analyze specific product traits important to brand standards. Another value of NIR technology is that calibrations are transferable to multiple instruments or production sites. This means a single company can control product quality with the highest possible standards at any location.
Benchtop NIR analyzers, such as the SpectraStar XT Series Analyzer shown here, rapidly measure ingredients or finished foods for important vital constituents like moisture, fat, fiber, and more.
How NIR helps food producers save costs
Reducing dependence on outsourced quality analysis
Many food production sites do not have the equipment to test ingredients and finished products for specific quality parameters. As a result, they will routinely submit ingredient or product samples to quality laboratories for analysis.
Because sample analysis typically cost $20-50 USD per sample, this effort adds up over time. Additionally, it can take a few days for a company to receive results from lab analysis. At that point, the ingredients or food product samples sent to the lab could have already been used in production and distributed to consumers, giving the company little opportunity to take necessary action.
Because NIR instruments are simply designed, many can perform quality analysis of ingredients and finished products to lab standards with very little training, with results typically available in under 1 minute. A single instrument has helped companies reduce their reliance on lab analysis, helping improve the efficiency of their operation, make better decisions, and save costs.
Assessing ingredients before production
As mentioned earlier, suppliers typically price their ingredients based on the availability of certain compositional constituents. For instance, chocolate is a vital yet expensive ingredient used in many kinds of baked goods and snack foods. This is due to the price of cocoa butter – a volatile commodity with frequent price swings.
It is not uncommon for bakeries or snack food companies to have to rework chocolate products due to quality inconsistencies, which can affect operational efficiency and lead to wasted ingredients. Cocoa butter introduces fat to the mixture of sugar and cocoa liquor, influencing the desired product’s taste and texture. Fat content is not the only constituent that can affect the quality of cocoa butter; sugar, moisture, lactose, and others play important roles.
More companies are turning to NIR technologies to verify chocolate quality before production begins. This analysis helps significantly reduce rejected or reworked products while holding suppliers accountable for their promise of quality.
Moisture & fat control on the production line
Even if incoming ingredients meet specifications for quality, their formulations can change in the production process.
In-process NIR sensors are a reliable solution for food products where moisture and fat (or oil) control are necessary at various production stages. In-process NIR sensors operate in challenging environments with expected high temperatures and airborne particles. A typical application for in-process NIR technologies is in snack food production.
Take potato chips, for example. After frying, potato chips typically require moisture content of 1-2.5% depending on specifications set by the manufacturer. Too much moisture can lead to a soggy final product, while too little moisture can cause the product to crumble too easily. Also, since potato chips are usually packaged and sold by weight, a final product that is too dry is lighter, which can lead to potential waste due to the increased quantity required to fill the package.
In-process NIR sensors allow food producers to measure moisture, fat, and product temperature at critical stages in the production process.
Cooking oil is another significant expense for snack food operators. Still, it is essential to the flavor and texture of the final product. A fat content that is too high (above 35%) leads to an excessively greasy product. Too little fat content (below 25%) can negatively affect palatability.
Not only can real-time data on product moisture and fat content help ensure product quality, but this information can also help the food producer manage their energy costs. Industrial fryers and ovens require significant power to operate. NIR technologies can help operators make data-driven decisions to adjust temperatures depending on the moisture or fat content of products entering the cooking process. A difference of a few degrees results in a significant amount of money one way or the other.
Equip your operation to succeed through challenging economic times
Unfortunately, the increasing costs of doing business today are mainly out of the food producer’s control. Nevertheless, incorporating methods to control quality at critical stages of production can help producers effectively manage these necessary expenses, reduce waste, and protect their brand value. NIR technology offers this capability with relative ease, whether used in a lab, at-line, or integrated into the production process.
Casey Thomson is a Senior Applications Engineer with KPM Analytics. Casey provides technical expertise across KPM Analytics’ full range of product offerings for the food production industry. Prior to joining KPM Analytics, Casey worked for Hanna Instruments for eight years as an Applications Engineer and has experience as a lab technician.
