Qualitative NIR Analysis for Ingredients in the Baking Industry

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1 Overview The challenge to all baking companies in today s economy is to operate plants as efficiently as possible, with a focus on quality and keeping costs in check. With regulatory issues becoming more stringent and the need to protect from lawsuits, it is now more important than ever to ensure the quality of all ingredients used for baking finished product or mixes. It is no longer enough to trust the supplier or accept the supplier s Certificate of Analysis. Classical analytical tests and/or baking tests to analyse these ingredients can take hours to complete with high costs for reagents and consumable items. Sites without on-site analytical laboratories may have to send samples to outside laboratories, which can take days to return results. Ingredients held in quarantine can hurt plant efficiency, and cause revenue loss. It is responsibility of the baker to know exactly what ingredients are being put in the product and to ensure these ingredients are correct and safe. Near-infrared (NIR) technology has been used in the food, feed, and agriculture industries for over 50 years as a way to analyse for properties such as moisture, protein, fat, fibre, ash, amino acids, and more. Qualitative NIR analysis is widely used in the food, pharmaceutical, and chemical industries. NIR testing is fast (analysis in seconds), accurate, safe, usually nondestructive and requires minimal sample preparation with no reagents. NIR is extremely flexible and can be configured for the analysis of solids, liquids, oils, slurries, and suspensions. Accuracy is often equivalent to the wet chemical methods that it replaces. Its precision is almost always better. What is NIR Analysis? Developed as a technique for predicting the chemical composition of a variety of unknown samples, near infrared (NIR) uses diffusely reflected light in the 800 to 2500 nanometre (nm) range to make a determination. Specifically, NIR light affects the molecular bonds of C-H, N- H, and O-H. These bonds are directly related to the sample constituents of interest, such as fat, protein, moisture, fibre, starch, sugar, and amino acids, to name a few. Response to these bonds can be found throughout the NIR spectrum, but the primary combination bands for all of these properties, found above 1900 nm, are the most sensitive and generally provide the most accurate calibrations. When NIR light hits a sample, part of the light is absorbed and part is diffusely reflected. The amount of absorbed light, at a particular wavelength, is directly proportional to the concentration of the constituent of interest. In other words, the more NIR light being absorbed at a particular wavelength, the greater the constituent (moisture, fat, protein, etc.) level in the sample. A series of standard samples of known concentration, analysed using a high accuracy reference method is scanned to measure the absorbance values at wavelengths throughout the NIR region. A calibration is then developed by using one of various mathematical models to Page 1 of 6 Copyright Unity Scientific Inc. 2007

2 correlate the reference lab values to the amount of absorbed NIR energy. The calibration can then be used to predict the constituent concentration of unknown samples. What is Qualitative NIR Analysis? Qualitative analysis seeks to differentiate between different materials. All materials (those with C-H, N-H, and O-H interactions) have a fingerprint in the NIR region. By using advanced computer software, different materials can be identified based on their NIR spectrum, much like fingerprint analysis can differentiate between different people. Another use of qualitative NIR analysis is to distinguish between different classes of the same material. This application is more difficult since materials are being differentiated based upon very small differences in their NIR spectrum. These applications require both good, stable instrumentation and powerful software to be successful. To develop qualitative NIR calibrations, different sets of materials are scanned using an NIR scanning instrument. These materials should have previously been subjected to wet chemical tests to verify that they are indeed the correct material. Qualitative calibrations are then developed by creating algorithms to compare the various materials against each other. How can Qualitative NIR be used for testing incoming ingredients? In all baking operations, ingredients are received continuously from suppliers. These ingredients may or may not undergo some form of testing before being used in finished product. An ingredient supplier will issue a Certificate of Analysis (COA) with each batch of material sent, stating what the material is and that it meets a certain quality specification. Most baking companies will use product based on this COA, trusting that the supplier is honest and / or has not made any mistakes with providing this material. This process is extremely dangerous for the manufacturer! If a material is trusted to be correct, by simply taking the COA on faith, and then later turns out to be the wrong ingredient, this mistake can be extremely costly. The wrong ingredient (or poor quality ingredient) can cause entire batches to be thrown away, costing the plant money. If the wrong ingredient causes sickness or death to the consumer, it is the baker that is liable for this mistake for not having control of the operation. The threat of lawsuits can absolutely cripple a company. Mistakes happen. It is always possible that the supplier mislabeled a material. Without checking upon receipt of the ingredient, this material could make its way into the product without anyone knowing it was bad. Suppliers also are smart enough to know which of their customers check their incoming ingredients. If they know a particular company does not check incoming ingredients, then they are more willing to send out marginal product with the high probability that this will never be detected. This ends up costing the manufacturer by having to pay for poor quality ingredients. Page 2 of 6 Copyright Unity Scientific Inc. 2007

3 This ultimately leads to the loss of market share because the end consumer does not like the taste of the product made from the poorer ingredients. So, it is both dangerous and costly to simply trust the COA. Having to produce product and then throw it away after the fact to account for poor quality ingredients is not a cost effective way of running a plant. Plus, this method does not lend itself to proper documentation. Government regulations and the threat of future legal action are increasingly requiring companies to provide documentation for each batch that is manufactured and have a paper trail for all ingredients used. A government inspector or court of law will not accept the reasoning that the supplier said it was good. NIR results are generated in approximately 30 seconds, yielding a pass or fail result. A quick decision can be made as to whether the ingredient should be accepted, rejected, or discounted for poor quality. All results are documented and stored with date and time stamps on the instrument, giving documentation for every ingredient used in the plant. What is the cost justification for using Qualitative NIR? NIR provides excellent payback for the baking mix manufacturer. The use of NIR ensures consistent quality of ingredients, verifies the supplier s integrity, and ensures that money is spent for quality ingredients and not add-ins, like moisture or filler. The speed of analysis reduces quarantine time, promotes just in time manufacturing, and increases the efficiency of the plant. Results of using the Unity Scientific SpectraStar 2400 NIR analyser for ingredient analysis. A calibration was developed for a variety of baking ingredients. A set of known, good ingredients was scanned on the Unity SpectraStar These samples are of known quality, determined through independent analysis. Each ingredient has its own finger print in the NIR spectrum. All ingredients are placed into a library and a calibration, which seeks to differentiate between the different materials, is developed using the Unity CalStar software. When an unknown sample is scanned, it is compared to the library and given a pass or fail results based upon how closely it matches the good materials in the library. The following figure shows how qualitative NIR can be used to differentiate between various baking ingredients. The figure shows that the SpectraStar 2400 can see the difference between these ingredients by forming distinct clusters of each material. Page 3 of 6 Copyright Unity Scientific Inc. 2007

4 Corn Meal Cinnamon Salt Brown Sugar Cocoa Powder Baking Soda Dextrose Diagram 1: Library of Various Baking Ingredients The SpectraStar 2400 NIR Made Easy The Unity Scientific SpectraStar 2400 is a NIR scanning monochromator system designed for running quantitative and qualitative applications in the food, feed, and agricultural industries. The SpectraStar 2400 is an economical investment delivering scanning monochromator technology at filter technology pricing. Excellent payback and low cost of ownership make the SpectraStar a sound investment. The SpectraStar has incorporated ease into every aspect of its design. Easy to Optimise Performance The SpectraStar 2400 is a next generation monochromator system that is guaranteed to be accurate. The SpectraStar 2400 has an optimum wavelength range of nm. This range encompasses the primary combination bands of C-H, N-H, and O-H bonds found in all major constituents such as fat, protein, moisture, starch, fibre, carbohydrates, amino acids, and sugar. Using the longer wavelengths from nm improves sensitivity, accuracy, signal to noise ratio, and results at low concentrations. This wavelength range is critical to analysing sugars, amino acids, starch, fibre, and low levels of protein and fat. The SpectraStar measures data at 1 nm increments, for a total of 1200 data points. This leads to improved data smoothing and better signal to noise. Scanning data allows for data pre- Page 4 of 6 Copyright Unity Scientific Inc. 2007

5 processing helping improve calibration performance. More data points equal more accurate calibrations. The SpectraStar 2400 has been designed for operators of all skill levels. The InfoStar software, that controls the system, uses the familiar Windows based operating system and contains both an operator and supervisor level of operation. The SpectraStar 2400 is a next generation, state of the art NIR system designed for superb reliability. With the SpectraStar, data management is an easy and powerful task. The SpectraStar contains a built-in Windows based computer with 20 GB of storage space. All results and raw data are automatically stored by the system with a date and time stamp. Easy to Implement The SpectraStar has been designed for quick and easy implementation, providing ready to go solutions. Existing NIR users can easily transfer calibrations from older or discontinued systems to the SpectraStar. The system is designed to accept MLR calibrations from older filter NIR technology from Bran+Luebbe, Perten, Dickey-john, and more. The TransStar software allows calibrations and databases from NIR monochromator systems from Unity, Bran+Luebbe, Perstorp, Bruker, NIRSystems, and Foss to be transferred to the SpectraStar. It is not necessary to go through all of the costly, time consuming steps of re-creating an entire database from scratch. This transfer procedure can typically be completed in a morning. Once a database has been transferred to the SpectraStar, data can continue to be added to make calibrations even more robust. For customers wishing to develop their own calibrations, the CalStar software provides an easy tool for quantitative and qualitative calibration development and database management. Unity boasts some of the finest technical specialists in the world. Our employees have an average of 18 years of experience implementing NIR technology on most NIR brand models. Our global support staff can provide consulting, installation, training, applications support, calibration development, database management, software support, and hardware repair. Unity understands the various issues that can make implementing and managing NIR a difficult, time consuming task. Our experience can overcome these issues and make implementing and managing the SpectraStar a simple and efficient process. The SpectraStar makes NIR easy by providing a system that is easy to optimise, easy to use, easy to maintain, easy to manage data, and easy to implement. With an economical investment, excellent payback, and low cost of ownership, the SpectraStar 2400 is truly a sound investment How does Unity Scientific deliver better value in NIR analysis? With mounting profit pressures and demands for cost control, every laboratory and process control investment must produce rapid and predictable business value. Unity Scientific s Page 5 of 6 Copyright Unity Scientific Inc. 2007

6 experienced consultants will align your NIR monitoring and control technology with your food and feed production strategy for improved business performance. Customers can outsource applications through Unity. Our expert NIR team certifies and guarantees calibration performance so results are always accurate! Whether your decisions are related to regulatory compliance, material inspection, least-cost formulation, process monitoring, or internal quality control Unity will guide you to the NIR solutions that best meet your needs. Unity has reduced the burdening cost of NIR utilisation. Systems are factory matched for easy calibration transfer. Installations are straightforward. Operation is simple, yet flexible for customisation. Maintenance has been greatly simplified. Results and performance trends can be instantly networked. Fast, accurate and precise information when you need it! The Unity Scientific SpectraStar 2400 is the ideal solution for the baking mix industry. The rapid analysis capability of the SpectraStar is ideal for analysing every mix to tell whether major components have been added properly and that the material has been mixed for the proper amount of time. Page 6 of 6 Copyright Unity Scientific Inc. 2007

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