Chocolate. The quality side.

QC of cocoa & chocolate using NIR

Last week the BUCHI Group gathered in the mountains of Pennsylvania for our annual national meeting. Jerry Richardson, our Product Manager for BUCHI Kjeldahl, Dumas and Extraction, decided to lure the sales group in with a session modeled around a topic near and dear to so many – chocolate.

Chocolate

Being a Swiss company, you know we have had our hands in the chocolate industry. In fact, one of the largest Swiss chocolate makers has been using BUCHI NIR in their quality control program for years. (If you aren’t familiar with NIR yet, please start here!)

Quality

Just as is echoed across most of the food industry, cocoa and chocolate manufacturers rely on analytical methods to monitor and control quality parameters such as moisture, fat, protein and sugar content of their incoming, in-process and finished products. These critical quality parameters impact the taste, texture, shelf-life and cost of our beloved confections.

So, we circle back to the obvious question – how would NIR support the quality and profitability of a cocoa or chocolate manufacturer?

It starts with the bean

Cocoa beans of course are the most important ingredient in chocolate, but the imported bean quality will vary depending on the – sometimes dynamic – environmental conditions of the region where they were grown. Quantification of the fat content in the beans and intermediate products can help ensure consistency in final products. Another important quality parameter is moisture content, which can be used to monitor the roasting process.

The reference method for fat is the Weibull-Stoldt method, a traditional acid hydrolysis followed by Soxhlet extraction in ether; the reference method for moisture is Karl Fischer titration. Both methods require sample preparation, chemical reagents, skilled technicians and extended analysis time. In contrast, beans can be placed in a sample cup on the NIR and both fat and moisture can be measured simultaneously in as little as 30 seconds. The non-destructive, rapid NIR method can be used to make decisions regarding cocoa bean processing – for example, whether or not roasting is complete.

While it’s easy to think of the NIR as a magical black-box, these measurements are based on the interaction of light with your sample. The carbon-hydrogen and hydrogen-oxygen functional groups representative of the sample fat and water content, respectively, are readily measured using NIR spectroscopy. Applying a calibration model, we can quickly relate the sample spectra back to its composition (e.g. fat and moisture). Of course, the calibration model is based on samples of known composition, and the primary reference methods (Weibull-Stoldt, Karl Fischer) need to be employed to generate and validate the relationship between spectra and the quality parameter of interest.

Cocoa Mass, Cocoa Butter and Cocoa Powder

The theme of quick, non-destructive measurements doesn’t end with the bean. NIR has also been applied to measure moisture and fat in nibs and cocoa mass, free fatty acids and iodine value in cocoa butter, and moisture and fat in cocoa powder. These measurements can be used to maximize the cocoa butter yield from the cocoa liquor, ensure the standard of identity specifications are met without excess addition of expensive ingredients like cocoa butter, and to determine the fat content on which the products should be sold; these applications could have a significant impact on production efficiency and profitability.

Confectionery products

Calibration models using NIR have also been developed for key confectionery product categories, including: milk and dark chocolate. Parameters include: moisture, fat (including solid fat at room temperature), lactose, sucrose and theobromine.

As it turns out, the session’s brainchild, Jerry, was himself a closet chocolatier. He built his own chocolate lab in his home. I have yet to quality-check his product portfolio, though I’ve heard good reviews. After hearing him talk about chocolate, I can at least vouch for his devotion to his craft.

Additional information

For additional information, take a look at the BUCHI Application Finder to see what we have published in way of chocolate analysis. You’ll find applications using extraction and hydrolysis, speed extraction, Kjeldahl and NIR. Please note that not all applications are published; if you have an application in mind, consult a BUCHI representative to see if we have experience within our local or global network!

 

Feed Manufacturing: Profit with In-line NIR Process Control

The BUCHI Crew is hitting the road this week for the 2017 International Production & Processing Expo (IPPE) in Atlanta, Georgia. Sounds peachy! Find us in Hall C, Booth 205.

IPPE is hailed as the largest annual trade show for the poultry, meat and feed industries. The show focuses on innovation, education, global reach and networking. So, what does BUCHI have to offer these industries? Let’s talk about real-time process monitoring and how it plays into profitability. 

Is moisture content in feed synonymous with profit margin? Talk to a feed producer and you’re likely to see a head nodding in agreement.

How valuable would it be to have a continuous read-out of the moisture content of your feed at points along the process where you can still adjust your process to reach a moisture target? Sounds like money in the bank, and BUCHI near-infrared (NIR) spectroscopy is a proven tool for the job. It’s also possible to monitor other critical feed parameters, like protein and fat, simultaneously.

Check out this short note showing how a BUCHI NIR-Online® process analyzer was used to monitor fat, protein, moisture and crude fiber after the mixing step in a feed operation. Or, read below for a case study at the German RKW mill that produces 75,000K tons of mixed feed annually and uses BUCHI IR-Online® to improve profitability.

The production process

At the RKW Kehl production facility the process begins with a check of raw materials for upcoming orders. The feed is predominately composed of soy and maize cereals, with minerals, vitamins and amino-acid supplementation. Feed composition is obtained by the required moisture, fat, protein, crude fiber and starch of each recipe. Due to the inherent variability of these nutrients in the incoming raw materials, the ratio of raw components needed to fulfill the nutrient profile of each recipe must be recalculated daily.

Mixtures under scrutiny

Raw materials are selected, scaled and mixed according to ordered batch recipes using the process control system. The quality control group determines whether a feed mixture falls below a required protein or fat content, or if moisture needs adjusted to meet specifications. Prior to the installation of the NIR-Online® process analyzers, the answers to these questions came from traditional laboratory analysis with long wait times. Now, the answers are streaming in real-time using the NIR-Online® device at the end of the mixing unit.

Near-infrared (NIR) light emitted from the sensors illuminates product through an installed window resulting in absorption by the sample, which is characteristic of its composition.  These readings are visualized in process charts by personnel in the switch room. If preset parameters are over- or under-run, corrective adjustments can be made immediately. Integration of the NIR-Online® products with the existing process control system provides the opportunity for automatically generated, highly detailed process documentation.

In-line control

“[Because the process can be stopped or adjusted early in production if specifications are not met] the utilization of NIR-Online® has minimized expensive rejects and complaints,” said Mr. Lühr, as he described the financial advantages of the NIR-Online® solution. “This benefit is further increased with the possibility of adjusting moisture content in real time.”

The NIR-Online® process software calculates the difference between the in-line property measurement and the batch set point without stopping production, ensuring moisture addition is precisely controlled for each batch.

“If we are able to converge within 0.5% of the moisture set point of a batch, we can sell more than 375 tons of additional mixed feed per year – a substantial benefit. “The investment in NIR-Online® technology will be paid for in a few months”, Mr. Lühr stated.

Find out more

Use the BUCHI Application Finder to explore additional application notes related to NIR in the feed industry. Or, Contact Us to get details on the broad array of feed materials and properties that can be measured by NIR.

Looking for a different technology? Click here to see results that include applications for all of the BUCHI product solutions for the feed industry, including: Kjeldahl, Dumas, extraction and spray-drying.

 

Training an NIR?

Sample planning & calibration

Buy a whistle and some orange cones and lace up your high-tech sneakers. Time to break a sweat.

NIR spectroscopy is a secondary technique. That means that the analyzer isn’t directly measuring water content in pet food kibble or fat in cream cheese. But with a good chemometric software package  and some quality reference lab data, you can train it better than a Best in Show German Shepard at Westminster.

BUCHI has already done some heavy-lifting, developing calibrations for key quality parameters across various industries. Check out the BUCHI Application Finder to see if we have a Plug & Play solution already developed for you!

Let’s say you want your at-line NIR to measure protein in dry kibble. First, you make a plan to gather samples from several production batches or across a number of kibble product skews so that the samples you’ve collected have a decent range in protein (and other variables that NIR is sensitive to, like moisture, fat and ash). The rule of thumb for the target range is about 20x the standard error of your reference lab technique. Pour kibble into a sample container, collect the NIR spectra, then send each sample off for analysis by Kjeldahl. Once the Kjeldahl protein measurements come back, you plug that information into your NIR software. Now, each spectrum has a reference property of protein associated with it. The next step is to build a calibration model. It’s the mathematical equation that relates your multivariate X-data (spectra) with univariate Y-data (protein). Luckily for you, the software does all of the heavy lifting, typically using partial least-squares regression, and spits out a calibration equation that you can then use to measure the protein content in future production samples. Splendid.

When  you’re looking to do qualitative testing, like 100% inspection of all of your incoming raw materials, the idea is the more or less the same: sample plan, collect NIR data, collect primary data, assign properties, create calibration model using chemometric software. The sampling plan should include a note to gather multiple lots of every raw material (rule of thumb: 5 lots or more). You want to use those lots to train your NIR to “see” and be desensitized to all of the acceptable and expected sources of variation, like vendor, particle size, or moisture content. Once you’ve collected NIR spectra for each lot, ship the samples off to some legit lab that can validate their identity and quality. If the samples pass the test, go back in the software and assign a chemical identity as a property for each spectrum (e.g., “sucrose” or “alanine”).  Then, sit back as your chemometric software does something fancy like Soft Independent Modeling of Class Analogy (SIMCA) so that you can use your NIR to test the identity of future incoming samples. This type of analysis also works to establish blend uniformity or finished product conformity. Way quicker than HPLC.

What if the calibration performance takes a hit?

Things could roll along pretty smoothly for awhile and you’ve cut way back on the number of kibble samples you’ve been checking by Kjeldahl for protein. Then something changes; a new sources of variation has entered the fold. Maybe your kibble got an extra boost of fiber in the formulation to keep the terriers regular. Or maybe some new dryer equipment is reducing the moisture content of your kibble lower than when you developed the first calibration model. All of a sudden, your NIR measurements aren’t as accurate as they used to be. Or maybe the analyzer software is spitting out measurements, but they are marked with red flags.

The fact is, formulations evolve, plant equipment ages or is replaced, a record humidity summer sets in. Things change, and when they do, it’s time for a calibration update.

The effort in a calibration update is essentially proportional to the magnitude of change affecting the sample/product/process. If there is a new kibble skew that has slightly higher fiber, add 10-20 sample spectra with Kjehldal reference data to the calibration set, recalculate the model and test the updated model with some new lots. If it works, meaning you’re getting an acceptable standard error of prediction, you’re back off to the races.

If you’re a current BUCHI customer needing support in calibration development or calibration update, contact us!

If this post was enough to wet your whistle, be sure to click the [FOLLOW] button on your browser to get access notifications of future content where we will delve deeper into the details of calibration development, performance and maintenance.

What is NIR?

More alphabet soup

Near-infrared spectroscopy. “N-I-R.”  Let’s illuminate the subject a bit, shall we?

Spectroscopy is a branch of science interested in the interaction of light with matter. Near-infrared (NIR) spectroscopy happens when the light used to do the measurements falls within a certain energy or frequency range; typically, 12000 – 4000 cm-1 (or about 700 – 2500 nm in terms of wavelength).

This idea isn’t new. The first observations of NIR light were made by Herschel in 1800, and Coblentz was considered its pioneer in the early 1900’s. However, this small but mighty portion of the electromagnetic spectrum didn’t debut commercially until the 1970’s, coinciding with advancements in PC computing power that radically simplified it’s application.

Why do people use NIR?

Everything you’ve come to love in your life: people, places, baked goods… they are all made up of molecules. Those molecules are made of atoms, and those atoms are moving and grooving (i.e. the bond lengths and bond angles aren’t static, but rather wagging and scissoring and bending and stretching). We can use NIR to measure that molecular dance party, or more technically, molecular vibrations. Those vibrational modes can tell us stuff about the sample that most QC departments like, think: sample identity or composition. 

When the molecules of a sample are hit with NIR light, the light is either absorbed or scattered. When the light is absorbed, we see a peak in our NIR spectrum. When light scatters due to the physical properties of the sample (e.g. particle size, particle morphology, bulk density), the overall slope of the spectrum is impacted. Chemical bonds that absorb NIR well are: oxygen-hydrogen (O-H), carbon-hydrogen (C-H), carbon-oxygen (C-O), nitrogen-hydrogen (N-H), and sulfur-hydrogen (S-H). While NIR isn’t the magic bullet for every analysis, we see samples that are dominated by these types of bonds in many industries, from pharmaceuticals to pet food.

The series of peaks and valleys that appear in an NIR spectrum is the summation of molecular vibrations of the sample. Consider the spectra of 5 different solutions in the figure below, where Absorbance is shown as a function of wavelength (nm). The red spectrum is pure methanol, the green spectrum is pure water. Peaks resulting from the -OH vibrations of both the alcohol and water, as well as the -CH vibration of the alcohol, are labeled.

water-alcohol

Since the energy of the -OH (and -CH) bonds of water and methanol (or donuts) differ, they produce unique spectra, as illustrated in the figure above. Combine that with the fact that spectra (of water, methanol or donuts) is repeatable, and we’re going somewhere. That means, if I scan water with my NIR analyzer 10x, I will get (more or less) the same spectrum, and that spectrum is unique from the other stuff I want to analyze (here, methanol). For those reasons, NIR would be a good tool for identification purposes.

Take another look at the figure above. The peak intensities corresponding to each molecular vibration reflect the relative composition of the molecules (water and alcohol) contributing those bonds to the solution. More specifically, you can see the peak corresponding to the water -OH vibration around 1450 nm increase as the relative proportion of water increases in solution. In the same way, the peak corresponding to the -CH combination vibration around 2250 nm increase as the proportion of methanol increases in the solution. That’s Beer’s Law working for us, where Absorbance at a given wavelength is proportional to the pathlength * molar absorptivity * concentration of the absorbing analyte. Note to the academics: if you dig into the theoretical research, you will see that Beer’s Law applies strictly to ideal solutions. Oh, and if you hadn’t heard, almost nothing is ideal in real life. However, we have mathematical ways to address non-linearity, and in the end, most methods work well. And by work well, we mean produce satisfactory standard errors of prediction. 

But before you get too excited about the infinite possibilities of NIR, let me give you some fine print. Our mortal bodies typically can’t run marathons without training first… and an NIR can’t run a qualitative or quantification application without being trained, either.

Additional resources:

For a good historical, theoretical and applications overview, see the Handbook of Near-Infrared Analysis, edited by Donald A. Burns and Emil W. Ciurczak

Entering the Blogosphere

Why are we here?

Nearly everyone has a blog these days. An internet connection plus a few taps on the keyboard can expose you to myriad blogs on health, finance, technology, world affairs or how to cook exclusively in a crock pot.

We weren’t blogging about any of those things, mostly because we aren’t experts in those categories (certainly not in cooking, although perhaps some of us are very good at speedily consuming those slow-cooked meals). However, there is one blog-worthy topic near and dear to us: near-infrared (NIR) spectroscopy. We’ve been doing it for a few decades at BUCHI, and so we’ve accumulated some knowledge on the subject. Rather than keep those insights all to ourselves, we wanted to drop some here in our shiny, new blog.

Our goal is to create and share content that will be useful for the information seekers, the inquisitive and questioning people out there scouring in the inter-webs to improve their efficiency, productivity or bottom line. Whether you are in the market for, or already own NIR equipment, we hope that you will find something in this blog that will help you along your journey toward successful implementation and laboratory or process data domination.

If you can’t find that golden nugget of information you’re seeking, consider contacting us with questions or to request some feasibility studies.

We hope you’ll come away from this blog thinking something along the lines of this lyric brought to us by the classic American band the Beach Boys, who harmonized:

“I’m picking up good vibrations… good, good, good, good vibrations!”