Tag Archives: NIR

Plant based milk

How Advancements in Analytical Testing Are Supporting the Development of Novel Plant-Based Dairy Alternatives

By David Honigs, Ph.D.
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Plant based milk

Globally, milk and dairy products rank among the top eight allergens that affect consumers across the world. In America in particular, 32 million people suffer from some form of allergy, of which a staggering 4.7 million are allergic to milk. Additionally, it is estimated that around 70% of adults worldwide have expressed some form of lactose intolerance. As such, it is important for key stakeholders in the dairy industry to create novel products that meet the wants and needs of consumers.

Low-lactose products have been available since the 1980s. But in recent years, the demand for plant-based alternatives to dairy products has been on the rise. Some of this demand has come from individuals who cannot digest lactose or those that have an allergy to dairy. However, as all consumers continue to scrutinize their food labels and assess the environmental and ethical impact of their dietary choices, plant-based milk has become an appealing alternative to traditional dairy products.

To adapt to this changing landscape, traditional dairy processors have started to create these alternatives alongside their regular product lines. As such, they need access to instruments that are flexible enough to help them overcome the challenges of testing novel plant-based milk, while maintaining effective analysis and testing of conventional product lines.

 David Honigs, Ph.D. will share his expertise during the complimentary webinar, “Supporting the Plant-Based Boom: Applying Intuitive Analytical Methods to Enhance Plant-based Dairy Product Development” | Friday, December 17 at 12 pm ETLow in Lactose, High in Quality

Some consumers—although not allergic to dairy—lack the lactase enzyme that is responsible for breaking down the disaccharide, lactose, into the more easily digestible glucose and galactose.

Low-lactose products first started to emerge in 1985 when the USDA developed technology that allowed milk processors to produce lactose-free milk, ice cream and yogurt. This meant consumers that previously had to avoid dairy products could still reap their nutritional benefits without any adverse side effects.

Similar to conventional dairy products, routine in-process analysis in lactose-free dairy production is often carried out using infrared spectroscopy, due to its rapid reporting. Additionally, the wavelengths that are used to identify dairy components are well documented, allowing for easier determination of fats, proteins and sugars.

Fourier transform infrared (FTIR) technologies are the most popular of the infrared spectroscopy instruments used in dairy analysis. As cream is still very liquid, even at high solid levels, FTIR can still effectively be used for the determination and analysis of its components. For products with a higher percentage of solids—usually above 20%—near-infrared (NIR) spectroscopy can provide much better results. Due to its ability to penetrate pathlengths up to 20 mm, this method is more suitable for the analysis of cheeses and yogurts. For low-lactose products in particular, FTIR technology is integral to production, as it can also be used to monitor the breakdown of lactose.

Finger on the Pulse

For some consumers, dairy products must be avoided altogether. Contrary to intolerances that only affect the digestive system, allergies affect the immune system of the body. This means that allergenic ingredients, such as milk or dairy, are treated as foreign invaders and can result in severe adverse reactions, such as anaphylactic shock, when ingested.

From 2012 to 2017, U.S. sales of plant-based milk steadily rose by 61%. With this increasing demand and the need to provide alternatives for those with allergies, it has never been a more important time to get plant-based milk processing right the first time. Although the quantification of fat, protein and sugar content is still important in these products, they pose different challenges to processors.

In order to mimic traditional dairy products, plant-based milk is often formulated with additional ingredients or as a blend of two plant milks. Sunflower or safflower oil can be added to increase viscosity and cane syrup or salt may be added to enhance flavor. All of these can affect the stability of the milk, so stabilizers or acidity regulators may also be present. Additionally, no plant milk is the same. Coconut milk is very high in fat content but very low in protein and sugar; on the other hand, oat milk is naturally very high in carbohydrates. This not only makes them suitable for different uses, but also means they require different analytical procedures to quantify their components.

Although many FTIR and NIR instruments can be applied to plant-based milk in the same way as dairy milk, the constantly evolving formulation differences pose issues to processors. For example, the way that protein is determined in dairy milk will vary from the way protein is determined in almond milk. Both will follow a method of quantifying the nitrogen content but must be multiplied by a different factor. To help overcome these challenges, many companies have started to develop plant-based milk calibrations that can be used in conjunction with existing infrared instruments. Currently, universal calibrations exist to determine the protein, fat, solids, and sugar content of novel products. With more research and data, it’s likely in the future these will be expanded to generate calibrations that are specific to soy, almond and oat milk.

Even with exciting advancements in analytical testing for plant-based milk, the downtime for analysis is still a lot higher than traditional dairy. This is due to the increased solid content of plant-based milk. Many are often a suspension of solid particles in an aqueous solution, as opposed to dairy milk, which is a suspension of fat globules in aqueous solution. This means processors need to factor in additional centrifuge and cleaning steps to ensure results are as accurate and repeatable as possible.

In addition to the FTIR and NIR instruments used for traditional dairy testing, plant-based milk can also benefit from the implementation of diode array (DA) NIR instruments into existing workflows. With the ability to be placed at- and on-line, DA instruments can provide continual reporting for the constituent elements of plant-based milk as they move through the processing facility. These instruments can also produce results in about six seconds, compared to the 30 seconds of regular IR instruments, so are of great importance for rapid reporting of multiple tests across a day.

Keeping It Simple

Although the consumption of dairy-free products is on the rise, lots of plant-based milk are also made from other allergenic foods, such as soy, almonds and peanuts. Therefore, having low-lactose alternatives on the market is still valuable to provide consumers with a range of suitable options.

To do this, dairy processors and new plant-based milk processors need access to instruments that rapidly and efficiently produce accurate compositional analysis. For dairy processors who have recently started creating low-lactose or dairy-free milk alternatives, it is important that their instrumentation is flexible and used for the analysis of all their product outputs.

Looking towards the future, it’s likely both dairy products and their plant-based counterparts will have a place in consumers’ diets. Although there is some divide on which of these products is better—both for the environment and in terms of health—one thing that will become increasingly more important is the attitude towards the labeling of these products. Clean labels and transparency on where products are coming from, and the relative fat, protein and sugar content of foods, are important to many consumers. Yet another reason why effective testing and analytical solutions need to be available to food processors.

Susanne Kuehne, Decernis
Food Fraud Quick Bites

A New Way Of Greenwashing

By Susanne Kuehne
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Susanne Kuehne, Decernis
Pistachio, food fraud
Find records of fraud such as those discussed in this column and more in the Food Fraud Database. Image credit: Susanne Kuehne

Turkish delight, baklava, halva, biscotti, mortadella, ice cream and many more delicious foods from around the world contain pistachios, which are pricey and therefore a popular target for food fraud. A recent article describes a method to detect spinach and green peas that often are used as a pistachio replacement due to their color and low price. The technique combines NIR (near infrared) spectroscopy and chemometric analysis and provides a method that is precise, fast and non-destructive.

Resource

  1. Genis, H.E., et al. (August 15, 2020). “Determination of green pea and spinach adulteration in pistachio nuts using NIR spectroscopy”. Science Direct. LWT.
Susanne Kuehne, Decernis
Food Fraud Quick Bites

Fraud Is the Spice Of Life

By Susanne Kuehne
No Comments
Susanne Kuehne, Decernis
Food Fraud, cumin
Find records of fraud such as those discussed in this column and more in the Food Fraud Database. Image credit: Susanne Kuehne.

The high value of spices makes them one of the most popular targets for intentional adulteration. Researchers in Brazil developed an efficient method for fraud detection: Near-infrared spectrometer (NIR) associated with chemometrics. This method is able to detect adulterants like corn flour and cassava in spice samples, revealing a high rate of adulteration, between 62% for commercial black pepper and 79% for cumin samples.

Resource

  1. Amanda Beatriz Sales de Lima et al. (January 2020). “Fast quantitative detection of black pepper and cumin adulterations by near-infrared spectroscopy and multivariate modeling”. Food Control. Vol. 107.
Megan Nichols
FST Soapbox

Machine Vision Training Tips to Improve Food Inspections

By Megan Ray Nichols
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Megan Nichols

As machines become more intelligent, every industry on earth will find abundant new applications and ways to benefit. For the food industry, which has an incredible number of moving parts and is especially risk-averse, machine vision and machine learning are especially valuable additions to the supply chain.

The following is a look at what machine vision is, how it can play a role in manufacturing and distributing foods and beverages, and how employers can train workers to get the most out of this exciting technology.

What Is Machine Vision?

Machine vision isn’t a brand-new concept. Cameras and barcode readers with machine vision have long been capable of reading barcodes and QR codes and verifying that products have correct labels. Modern machine vision takes the concept to new levels of usefulness.

Barcodes and product identifiers have a limited set of known configurations, which makes it relatively straightforward to program an automated inspection station to recognize, sort or reject products as necessary. Instead, true machine vision means handlers don’t have to account for every potential eventuality. Machine vision instead learns over time, based on known parameters, to differentiate between degrees of product damage.

Consider the problem of appraising an apple for its salability. Is it bruised or discolored? Machine vision recognizes that no two bruises look precisely alike. There’s also the matter of identifying different degrees of packaging damage. To tackle these problems, it’s not possible to program machine vision to recognize a fixed set of visual clues. Instead, its programming must interpret its surroundings and make a judgment about what it sees.

Apples, machine vision
On an apple, no two bruises are alike. Machine vision technology can help. Photo credit: Pexels.

The neural networks that power machine vision have a wide range of applications, including improving pathfinding abilities for robots. In this article, I’ll focus on how to leverage machine vision to improve the quality of edible products and the profitability of the food and beverage industry.

Applications for Machine Vision in the Food Industry

There are lots of ways to apply machine vision to a food processing environment, with new variations on the technology cropping up regularly. The following is a rundown on how different kinds of machine vision systems serve different functions in the food and beverage sector.

1. Frame Grabbing and 3-D Machine Vision
Machine vision systems require optimal lighting to carry out successful inspections. If part of the scanning environment lies in shadow, undesirable products might find their way onto shelves and into customers’ homes.

Food products sometimes have unique needs when it comes to carrying out visual inspections. It’s difficult or impossible for fallible human eyeballs to perform detailed scans of thousands of peas or nuts as they pass over a conveyor belt. 3-D machine vision offers a tool called “frame grabbing,” which takes stills of — potentially — tens of thousands of tiny, moving products at once to find flaws and perform sorting.

2. Automated Sorting for Large Product Batches
Machine vision inspection systems can easily become part of a much larger automation effort. Automation is a welcome addition to the food and beverage sector, translating into improved worker safety and efficiency and better quality control across the enterprise.

Inspection stations with machine vision cameras can scan single products or whole batches of products to detect flaws. But physically separating these products must be just as efficient a process as identifying them. For this reason, machine vision is an ideal companion to compressed air systems and others, which can carefully blow away and remove even a single grain of rice from a larger batch in preparation.

3. Near-Infrared Cameras
Machine vision takes many forms, including barcode and QR code readers. A newer technology, called near-infrared (NIR) cameras, is already substantially improving the usefulness and capabilities of machine vision.

Remember that bruised apple? Sometimes physical damage to fruits and vegetables doesn’t immediately appear on the outside. NIR technology expands the light spectrum cameras can observe, giving them the ability to detect interior damage before it shows up on the exterior. It represents a distinct advantage over previous-generation technology and human inspectors, both of which can leave flaws undiscovered.

Tips on Training Workers to Use Machine Vision

Implementing machine vision into a productive environment delivers major benefits, but it also comes with a potentially disruptive learning curve. The following are some ideas on how to navigate it.

1. Take Advantage of Third-Party Training Courses
Don’t expect employees to hit the ground running with machine vision if they’re not familiar with the fundamentals of how it works. Google has a crash course on machine learning, and Amazon offers a curriculum as well to help companies get their employees up to speed on the technology and how to use it.

2. Get the Lighting Right
Having the appropriate intensity of light shining on the food product is essential for the machine vision cameras to get a clear photo or video. The most common types of lighting for machine vision are quartz halogen, LEDs, metal halide and xenon lights. Metal halide and xenon are better for larger-scale operations because of their brightness.

Train employees to check the amount and positioning of the lighting before each inspection station starts up for the day, so that no shadows obscure products from view.

3. Single Out Promising Subject Matter Leaders
Companies today don’t seem to have much confidence in how well they’re preparing their workforce for tomorrow, including future innovations. According to Deloitte, just 47% of companies in the world believe they’re doing enough to train their employees on the technologies and opportunities of Industry 4.0.

Machine vision does not involve buying a camera or two, setting them up, then slapping the “autopilot” button. As products turn over, and manufacturing and distribution environments change and grow over time, machine vision algorithms require re-training, and you might need to redesign the lighting setup.

Employers should find individuals from their ranks who show interest and aptitude in this technology and then invest in them as subject matter experts and process owners. Even if an outside vendor is the one providing libraries of algorithms and ultimately coming up with machine vision designs, every company needs a knowledgeable liaison who can align company needs with the products on the market.

Machine Vision Is the Future of Food Inspections

The market for machine vision technology is likely to reach $30.8 in value by 2021, according to BCC Research.

It is important to remember that neither machine learning nor machine vision are about creating hardware that thinks and sees like humans do. With the right approach, these systems can roundly outperform human employees.

But first, companies need to recognize the opportunities. Then, they must match the available products to their unsolved problems and make sure their culture supports ongoing learning and the discovery of new aptitudes. Machine vision might be superior to human eyesight, but it uses decidedly human judgments as it goes about its work.