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Champion saves the day: Volume 2 Production

In-process and at-line NIR for production

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Beat the costs in production! Download Volume 2 of the Champions’ Guidebook and find out how to save money while monitoring production lines.

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Our determined (and love-struck) food champion, Max, is back at it. Check out the newest animated video to see how NIR can avoid costly production errors (and increase profitability) after googly-eyed Max’s big goof-up.

One of the greatest assets of on-line and at-line NIR is having a second set of (focused) “eyes” on production operations. The NIR can be trained to measure critical material properties for in-process or finished products, or even do simple identification procedures to confirm questions like: is Product A is actually being produced?

Max may be a little distracted at times, but NIR can still make him a champion!

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Be a Food Analysis Champion!

Save time with efficient incoming goods inspection

New BUCHI campaign delivers 3 e-booklets to create Food Analysis Champions!

beattheclock

Every day, food producers undergo myriad processes and procedures designed to achieve a quality product and (hopefully) a profitable business.

The loading docks and warehouse serve as initial points of contact for ingredients and foodstuffs that will become integrated into delicious (and sometimes nutritious) food products. It is the obligation of the producer to ensure that they are obtaining the highest quality and correctly priced goods prior to feeding those ingredients into the production process.

Our first booklet provides insight into challenges and opportunities related to incoming goods inspection, including:

  • Typical slow-downs in incoming goods receiving
  • Tips to meeting incoming goods inspection requirements efficiently
  • Benefits of using fast, non-destructive NIR analysis for testing incoming goods
  • Improving time-to-result for classical reference methods (i.e. extraction and Kjeldahl)
  • Sample NIR and classical testing applications to help you save time!

Download this complimentary resource, and stay tuned for future additions to the series, including: production and finished goods control!

For some nice (and enlightening) lunch break entertainment, watch our Food Analysis Champion, Max, save the day when production is halted due to QC backlog in the BUCHI animated video short series for “Beat the Clock.”

 

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Best Practices: Sample Planning for Quantitative NIR Methods

The focus of this post is NIR project and sample planning, a critical step in the NIR method development process that often gets rushed through in the eagerness to have our NIR instruments pump out measurement results.  Putting some extra effort in sample planning could pay off big dividends in terms of two things I suspect are really important to you – accuracy and robustness. So, let’s get into it.

Let’s start our discussion by framing it in an example. You work at The Cheese Factory, and you want to measure the fat content in your cheese using NIR.

Fat is incredibly important to you for a few reasons. For one, fat is the vehicle for the flavors of your cheese and creates a creamy mouth feel. If you don’t have enough fat, your cheese will be hard and corky.  No one wants corky cheese.  However, fat isn’t cheap, so you don’t want any more in there than you need—that’s money down the drain.  The formulation people have been tasked to establish what amount of fat is “Just Right” but you need to make sure that’s what comes off the conveyor belt.

What do you do?

Defining a property range

Your first thought might be to grab a few samples of cheese that span a range of fat content, each with reference measurements confirmed by your primary method. That’s a good start.  There are some general recommendations that you should consider when it comes to the range of the property you want to calibrate for. Whether it’s fat in cheese or active ingredient in a pharmaceutical formulation, first consider the primary method you’re using as a reference for the property. That method has a standard error associated with it. Take 20x that error and that is the minimal range over which your calibration property values should span to reduce the impact of the reference error on your NIR model.

Whatever property range you might expect to see as a reflection of normal process variation is what we can your working range. The calibration range should be broader than the working range to avoid extrapolation; that is, avoiding measurement predictions outside of the scope of the calibration.

When possible, the target value (e.g. label claim) of your property should fall in the middle of the calibration range, and all of your concentration points would be more or less evenly distributed across the calibration range. At the very least, avoid a situation where you have one large cluster of points at one end of the range and only a few points at the opposite end.

As an example:

  • Target (Label Claim) = 50% Property (e.g. “Fat”)
  • Standard Error of Lab (SEL) = 1% Property
  • Calibration Range, Suggested =  SEL x 20 = 1% x 20 = 20%
  • Calibration Range, Min. =  Target – (Calibration Range/2)  = 50 – 20/2 = 40% Property
  • Calibration Range, Max. = Target + (Calibration Range/2) = 50 + 20/2 = 60% Property

Accounting for production variation

Keep in mind, even though your primary objective is to build a calibration model for fat in cheese, there is other “stuff” in there that is going to absorb or scatter NIR light, impacting the spectra you collect. And if it impacts the spectra you collect, it could impact your fat measurement by NIR.

In this example, let’s consider other components in the cheese formulation: Moisture, protein, lactose and salt.  Sample temperature, consistency and the mode of sample preparation can also impact the spectra we collect.

So, in total, this slide is suggesting that there are 7 additional factors to consider outside of FAT when developing an NIR calibration for fat in cheese.  Let’s take a look at an example method planning worksheet to see how we can accommodate known product variations into our NIR model.

CalibrationPlanningTable_Quant1b

Here is one tool you might consider when you are looking to start a new NIR project or to optimize a current project. I highly recommend applying this type of table to your method planning. Let’s start with our example of fat in cheese. The first thing to record is your formulation target as input A. This number is based on your label claim and determined by formulation scientists. Next, record your current reference method for that property. Here, we might have an extraction. This method should be kept constant throughout the NIR method development cycle, as different laboratory methods have different accuracy and precision relative to one another.  Input C should reflect the standard error of the laboratory for whatever reference method you select. Take 20 times that number to get your recommended range for that property, as shown in the previous slide.

The working range, input D, is your typical property variation. Regardless of the calibration range recommendation, your working range needs to be within the boundaries of the calibration method.

The sample prep and presentation should be well-defined and kept constant. There may be some trial and error here at the beginning of your project, depending on limitations of practicality and desired calibration performance.  In general, the more uniform the sample is, the better the method precision.

The last column shown here is a catch-all for all other known sources of chemical and/or physical property variation expected in your sample.

Let’s dig a little bit deeper into the product variations that should be captured within the sample plan, as shown in the Table below:

CalibrationPlanningTable_Quant1a

Write out the min and max values for each component in the matrix and be intentional at identifying samples that span the full range of possibilities.  As indicated here, each ingredient or component of a sample has a minimal and maximal value associated with it. Perhaps too, there are various vendors supplying these components and vendors have slightly different particle size specifications that can impact our NIR signal.  Be sure to collect calibration samples that have been produced with materials sourced from multiple vendors to account for any chemical or physical property differences in those materials.

If your production involves heating or cooling, you’ll need to either (a) standardize the temperature at which NIR spectra are collected or (b) build the temperature variation into your model. For the latter, I would suggest collecting spectra of a single sample at multiple temperatures. For actively cooling samples, collect the first spectra when the sample is hot, then collect spectra of the block after it’s reached room temperature.

If your plant has several different production lines, it would be a good idea to collect samples that were produced from each process line, as equipment aging or servicing may impact things like finished product particle size, morphology or packing density.

If your product is very hygroscopic it is likely to be more sensitive to seasonal variations in temperature and humidity, so calibration data collection across seasons may be required in order to optimize model robustness.  If  your sample is very compressible, it is likely to be more sensitive to sample handling and so exhibit greater operator-to-operator variation during sample prep. I use the example of Boris the Strong-Man tapping a powder sample into a vial and the powder forms a near-solid puck. Then there is Gentle Jim, who gently taps the vial so that the powder flows to the bottom of the vial. Boris and Jim’s samples have very different packing density which will show up as baseline offsets in the spectra, so randomizing calibration data collection across several operators is good practice.

As you consider your own project, be sure to include any additional sources of variation. Talk to plant operators and plant managers or formulation chemists to uncover variables that may be impacting your method performance.

Also keep in mind that method precision is likely to be better when factors outside of your property of interest, such as sample temperature, are held constant (or as constant as is practical) rather than varied.

Sample uniformity and dynamics

Other critical sample characteristics that often get overlooked during the method development process are sample consistency and stability.

Consider the physical state of your samples. Does it phase separate, forming oily or watery layers? Is moisture easily driven off or absorbed? If so, it’s important to create standard operating procedures to limit the impact of those sample characteristics on your method performance. Something as simple as adding a stirring step to a hot or oily sample could pay huge dividends with regard to method performance.

We also want to keep in mind how well the sample sent for reference testing matches the sample analyzed by the NIR.  Ideally, we would take advantage of the non-destructive testing of the NIR and use the actual NIR sample for the reference laboratory testing. Even if you’re able to do that, the sample size for each method may differ, and the following points should be considered to obtain our goal, which is that the reference laboratory sample is representative of the NIR sample:

Below, I have 5 figures representing different sampling situations.  Solid blue represents the sample matrix, yellow circles represent our property of interest, and the light blue drops represent moisture. The solid black box indicates the sample volume by NIR, while the red hashed box represents the sample submitted for reference testing.

SampleUniformity_Quant1

In the leftmost box, the sample is uniform throughout. The sample submitted for reference testing matches the sample for NIR. There is no problem here and the precision of both methods should be very good.

In the second box, the sample is non-uniform. Our property of interest is accumulating at the bottom of the sample cup, maybe due to phase separation or particle segregation. If the reference sample is drawn off the top, the results will not represent the NIR sample measurement well. The precision of our method will be poor unless some sort of mixing or homogenizing step is added.

In the third box we introduce moisture as an added variable.  Here again, moisture is evenly distributed in the sample and there is no issue with reference and NIR data correlation.

In the fourth box we have a hygroscopic material that readily absorbs moisture from the environment. If the reference sample is taken from the top it will be biased toward higher water content than is representative of the sample as a whole. The sample requires stirring prior to removal of the sample for the reference method as well as prior to measurement by NIR.

The final box illustrates non-uniformity of moisture. This could be a hot block of cheese coming off the conveyor belt or a powder pulled from a fluid bed dryer. If water is actively being evaporated or you see water pooling on the surface, you risk biasing your moisture data by simply collecting a sample from the top of your product. In this case, it may be useful to wait until the sample has reached a steady-state temperature and/or mixing the sample bed (e.g. for powders) before analyzing by either the reference or NIR methods, respectively.

Sample collection

After going through these slides with your own products in mind, you may have identified all of the product variations you anticipate in routine production. You are starting to formulate a plan to ensure that the sample submitted for reference testing is representative of your NIR sample.  The next question is… where are these samples coming from?

The first answer is: from production. However, your routine production is likely to have pretty tight control and you’re building the NIR model to look for rare process deviations. You might be able to get some more extreme values of property range or other factors like particle size by pulling samples close to process start-up or run-off.

In many cases, it is not very efficient to wait for out-of-spec samples from production. If your production process is small-scale, you may consider intentionally creating out-of-spec materials using your actual production equipment. For example, creating high-fat cheese by adding an excess of butterfat to one batch or by intentionally over-drying a granulation run.  In other situations, you may find it more economic and efficient to perform “spiking” or dilution steps to your products to produce adequate property ranges.

How many samples are required to build a robust NIR method? The simple answer is… it depends. Typically, the more complex the sample matrix and the more sources of variation you’ve identified using the prior tables, the more samples are needed.  Generally, a start-up model may require 50 unique samples. There are plenty of exceptions to this rule.  For example, if you’re quantifying something with a very unique NIR peak you may be able to get away with fewer samples. If your sample matrix has a lot of ingredients with spectral overlap, as typically seen with foodstuffs, you may need more than 100 samples.

Calibration in itself should be considered a continuous process. You can be reactive or proactive in extending that calibration to improve robustness to unforeseen or un-modeled sources of variation.

Sample failure, calibration update

Samples failing your NIR method may indicate that calibration update is necessary! But, not every time. So, how do you know a measurement has failed, and what does that failure mean?

The NIRWare Operator software makes it fairly easy to identify which samples should be used to update an existing calibration model – see the samples with the red X! There are two types of outliers that the Operator software will flag: spectral residuals and property outliers.

When you have a spectral residual outlier, the Operator will not obtain an NIR measurement result, only a red X.  Spectral residuals indicate that the sample that was just measured had spectral features – that is, the peaks and valleys – that did not match up with the calibration data set. This could be the result of the original calibration being over-fit, leading to very tight tolerances, or it could be that the sample that was just measured has property combinations (like high fat, low moisture) that were not part of the calibration.  Worse case is that a spectral residual is due to a contaminant that was not present in the calibration data set.

A property outlier indicates that the current sample has a property value prediction that is outside of the calibration range.  This is considered an extrapolation.

However, the “failed” result may also be an issue with the way the sample was collected!  In order to verify that you truly have a calibration outlier, please run through the following check-list.

  1. Check that sample (or probe) is positioned properly during the measurement
  2. Check that the optical path (window, sample container) is clean and retry measurement
  3. Check that a good reference was collected and that reference material is clean
  4. Sample may have new variation that wasn’t used in the calibration training set (e.g., higher moisture content due to seasonal humidity, new vendor with different particle size)

If all signs are pointing to the sample truly being unique (i.e. out-of-specification and out of the range of the calibration model), then send this sample for primary analysis, add to the calibration data set and recalculate the model.

Identification of spectral outliers or range extrapolations is one way to plan for samples for calibration model update! This is a reactive approach but reasonable.

To be more proactive when time and resources allow, you can look for gaps in your current design space. Take a look at your reference vs. predicted plot to see if you are adequately covering the calibration range with samples or if gaps exist. Create scatter plots of the calibration properties (e.g. Property 1 vs. Property/Variable 2)  to identify gaps in the design space when multiple variables are considered. Once holes are identified, flag samples that match your missing criteria in routine production, or manually create those samples using small batch processing, spiking or dilution experiments, when possible.

SampleSpread_Quant1

 

I hope this was helpful toward planning (or updating) your quantitative NIR methods. Be sure to check out our other FT-NIR user Best Practice Blogs for qualitative method development and quantitative methods for pre-calibration users. Should you have additional specific topics that you would like to see covered in this blog, please submit your ideas!

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BUCHI NIR is Pro-Food Quality at ProFood Tech

The BUCHI wagon got put back on the road for the ProFood Tech conference in Chicago this week. Hopefully you’ll catch us at our booth at Lakeside Upper Hall #3113 (vs. catching our booth attendants just lurking the show floor devouring free samples all day).

ProFood Tech is an event, and BUCHI is a laboratory equipment manufacturer, but you might be interested in the overlap between us. We serve many of the same industries. NIRSolutions_bread

Baking and Snack

We already blogged about some of the sweet stuff BUCHI can do in the chocolate industry, but we offer analytical measurements for many raw materials used by the baking and snack industries:

  • Whole & ground cereals (e.g. wheat, semolina, barley, rice, corn/maize)
  • Hulls & bran
  • Oil seed meals
  • Fats & oils (e.g. vegetable oils and animal fats)
  • Egg &  milk derivatives (e.g. egg powder, liquid egg, milk powder)
  • Dry pasta & noodles
  • Ready-meals (e.g. lasagna, frozen pizza)
  • Confectionary (e.g. chocolate, cocoa & derivatives)

Meat, Poultry and Seafood

Protein builds muscle, and BUCHI has flexed some muscles in the QC of many meats and meat products, including:

  • Animal meat (e.g. beef, pork, turkey, wild animals)
  • Fish meat
  • Sausage
  • Animal flour
  • Fish meal
  • Pig adipose tissue

Dairy

If I could survive on cheese and ice cream alone, I would. Our BUCHI NIR products are used to make sure that the stuff that goes into milk and milk products are in-spec. We can help you analyze important sample properties for things like:

  • Milk
  • Yogurt and fresh cheese
  • Hard, semi-hard and soft cheese
  • Processed cheese
  • Butter
  • Milk creams
  • Milk powders

Frozen and Prepared Foods

When you don’t have time to cook or time for long laboratory analysis methods.  BUCHI NIR has methods developed for:

  • Dry pasta/noodles
  • Ready-meals (e.g. lasagna, meat pie, meat & fish ready noodles, frozen pizza)

Beverage

Drink up! BUCHI NIR can be used for quality control of beverages:

  • Distillers grains
  • Milk powders
  • Chocolate (e.g. cocoa & derivatives)

Getting hungry for more information?

Check out our Application Finder on the website or Contact us to talk about your specific application needs.

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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!”

Chemical Industry QC with NIR

 

Read this post, or watch the webinar instead!

Quality control for many labs involves a heavy dose of wet chemistry methods, things like titration and separation techniques that take skill, time and (even more) chemicals to execute. Luckily, some of these traditional testing methods can be replaced by simple, fast and safe NIR spectroscopy.

While this blog title indicates applicability to the Chemical Industry, “chemical” is one broad umbrella. There are myriad products and processes that fall under the chemicals category, from natural products like wood and pulp to personal care products to standard bulk chemicals. Reaching all of these audiences with one blog post seemed a little daunting until we broke it down to some common key themes for implementation of NIR for the chemical (or any!) industry:

  • Raw material qualification
  • Intermediate/in-process testing
  • Finished product testing

Of course, the typical applications that might fall into any one of these categories will differ based on the products being produced. Some of the more common applications include:

  • Material identification
  • %-Moisture or %-solvent quantification
  • Reaction extent or %-polymerization
  • Hydroxyl and acid number determination

As with many other industries, the raw materials used for production of chemical products are often non-discrete, sourced from various parts of the galaxy, and labeled–sometimes correctly, sometimes not.  If you follow product recalls, you’ll find that millions of dollars have been lost due to mislabeled containers being poured into mixers, placed on trucks for distribution to other producers, or stocked on store shelves.

NIR is one quick tool used for identity testing of routinely received materials. There is potential to differentiate isomers, crystalline forms, chemical analogs, fatty acids, and even contaminated materials. Because identity testing with NIR takes seconds and can be done in the warehouse, more frequent testing can be accomplished without backlogging the QC guys and gals.

On the quantitative side, there is plenty to measure keeping in mind the inherent sensitivity of NIR to particular molecular bonds, including O-H, C-H, N-H and C-O bonds. So, if those bonds are changing in type or in number, NIR could be a great fit. This is the case in the typical chemical application of determining hydroxyl number, where we observe a decrease in NIR signal attributed to O-H bonds as those O-H end groups are consumed during polymerization. In fact, determining hydroxyl number of polyols by NIR is a standard practice per ASTM and ISO.

BUCHI Market Manager and former BUCHI NIR Applications Specialist Ryanne Palermo produced a short webinar on these topics, including a fiery example of tracking nitrogen substitution in nitrocellulose. Tune into the webinar by clicking here.

Find more free, streaming content on our BUCHI Webinar On-Demand page, including information about preparative chromatography, laboratory and industrial evaporation, drying, encapsulation and more.

 

Be a Champion of Final Goods Inspection

Max won’t let a pile of untested final goods (or third wheel) stand between him and a coffee date with his lady love. Check out the newest and last installment of the Food Quality Champion Series animated videos, then download the Guidebook and become a Final Goods Inspection Champion, yourself!

The Final Goods Inspection Guidebook is ripe with information to understand or expedite quality control operations in the food and feed industry. Topics include:

  • Regulations impacting final product quality control
  • Representative sampling & sample preparation
  • Tips for optimizing Kjeldahl workflow for protein determination
  • Tips for optimizing extraction and hydrolysis workflow for fat determination
  • Tips for optimizing NIR methods for proximate determination in food and feed products

Download the guidebook for helpful insights, then start a conversation with your local BUCHI Application Specialists to see how you can be a Champion!

 

 

Next at bat: BUCHI @ PROCESS EXPO

Summer might be coming to a close, but our PROCESS EXPO pre-game is just heating up!

HOF Weekend 1957 Game_action_no acc #_CSU

The global food equipment and technology show PROCESS EXPO is being held in Chicago, home of the defending World Series Champions. You can catch us there September 19-22. In the spirit of the game and in anticipation for the Fall Pennant Races, the BUCHI Booth will be hosting a Wii Sports Home Run Derby competition! Be sure to stop by and take a swing for a chance to win some swag.

While you wait to step up to the plate, check out the NIR-Online, our in-line near-infrared sensor that will help you hit a Grand Slam in process control!

Not into baseball? BUCHI has something for you industry, including classical Kjeldahl reference methods and near-infrared spectroscopy (NIRS) for food analysis, in addition to spray-drying, encapsulation and freeze-drying.

Looking for a way around those long QC queues? Check out our NIRSolutions for the PROCESS EXPO industry sectors: 

Confectionery, Baking and Snack

We already blogged about some of the sweet stuff BUCHI can do in the chocolate industry, but our products can provide quantitative measurements for much more:

  • Whole & ground cereals (e.g. wheat, semolina, barley, rice, corn/maize)
  • Hulls & bran
  • Oil seed meals
  • Fats & oils (e.g. vegetable oils and animal fats)
  • Egg &  milk derivatives (e.g. egg powder, liquid egg, milk powder)
  • Dry pasta & noodles
  • Ready-meals (e.g. lasagna, frozen pizza)
  • Confectionary (e.g. chocolate, cocoa & derivatives)

Meat, Poultry and Seafood

Protein builds muscle, and BUCHI has flexed some muscles in the QC of many meats and meat products, including:

  • Animal meat (e.g. beef, pork, turkey, wild animals)
  • Fish meat
  • Sausage
  • Animal flour
  • Fish meal
  • Pig adipose tissue

Dairy

Our BUCHI NIR products are used to make sure that the stuff that goes into milk and milk products are in-spec, including:

  • Milk
  • Yogurt and fresh cheese
  • Hard, semi-hard and soft cheese
  • Processed cheese
  • Butter
  • Milk creams
  • Milk powders

Frozen and Prepared Foods

When you don’t have time to cook or time for long lab turn-around times, BUCHI NIR has methods developed for:

  • Dry pasta/noodles
  • Ready-meals (e.g. lasagna, meat pie, meat & fish ready noodles, frozen pizza)

Beverage

Drink up! BUCHI NIR can be used for quality control of beverages:

  • Distillers grains
  • Milk powders
  • Chocolate (e.g. cocoa & derivatives)

Getting hungry for more information?

Check out our Application Finder on the BUCHI website or Contact us to talk about your specific application needs.

 

Webinar! Best Practices in NIR Method Sample Planning

Missed our summer web-training series? Scratching your chin about how to get started on your next NIR project? Looking for something to do other than Farmville on your lunch hour?

Whether you are a dedicated BUCHI NIR power user or just looking to dip your toe in the NIR waters before taking the plunge, you are cordially invited to attend our September 27 webinar on NIR Method Sample Planning, part of our BUCHI Best Practices webinar series.  Use this link to register for the event!

During this webinar, you will receive tips and tricks on:

  • Identifying variables impacting method performance
  • Learning how to maximize calibration “learning” with fewer samples
  • Navigating calibration project workflow

Both qualitative and quantitative methods will be discussed.

Can’t wait til September 27? Contact us to discuss your upcoming/ongoing projects, or check out our BrightTALK Channel or blog archives for existing content to support your efforts.