Featured

Things are really getting cheesy at BUCHI.

When prodded, I suppose many at BUCHI would agree that some of the cheesiest members of the team belong to the NIR group.

Maybe that’s something to be proud of!

More cheese, please!

Cheese is delicious, after all. With a global market of around $100 Billion USD, I think there is a general agreement on the matter.

There are many ways to consume cheese, of course. Processed cheese products like fondue have their place on the cheese continuum, particular around special events like graduation parties and weddings that seem to dominate our summer calendars.

Manufacturing processed cheese products like fondue is a complex task, with special emphasis on key quality parameters like total solids and fat content.

These products are typically produced by blending one or more shredded natural cheeses with additional ingredients, such as: emulsifying salts, condiments, flavors and other goodies. This mixture is then heated and sheared until a homogeneous molten mass is obtained which can be poured into heart- or graduation-capped shaped molds or other shapes for future devouring.

In order to consistently deliver the same great-tasting product, real-time control of key quality attributes is a must. Monitoring total solids content, fat, salt, pH, homogeneity and more during a blend can allow for real-time process adjustments to meet all of the quality targets and avoid having to rework a batch. The end result: tasty cheese products (read: make money) and improved manufacturing efficiency (read: save money).

While traditional laboratory methods exist for determining the quality parameters, issues with representative sampling and the method collection times are rate-limiting.

Want to learn more? Contact Us and request Short Note #357, or ask to speak with an Applications Specialist to learn more about how you can implement NIR for better process control in cheese manufacturing.

You can also visit our NIR-Online Solutions page to learn more.

If cheese quality is on your mind, but an on-line solution isn’t a good fit, BUCHI also has milk & dairy solutions for our off-line and at-line NIR. You can use our NIR Applications Finder on-line tool to configure the perfect NIR solution for you. Choose your industry and products, then get a full listing of available pre-calibrated applications, plus a quote.

Among our pre-calibrated parameters, you’ll find: dry matter, moisture, fat, protein, lactose, fatty acids, total sugar, ash and more.

Use our new NIR Application Finder to get the latest in Pre-Calibrated Solutions for your industry.

Interested in an application that isn’t listed? Fear not! We have a team of Application Scientists at the ready. Reach them using our Application Support Request Form .

If you’re looking for the full gamut of our published cheese & dairy applications, check out the BUCHI Application Finder. You’ll find methods related to:

  • Extraction
  • Spray Drying
  • Kjeldahl for protein determination
  • Near-infrared spectroscopy

Stay cheesy, my friends!

Featured

Quality is Going to the Dogs.

Don’t worry, it’s a good thing.

This week, BUCHI Product and Application Specialists mingled with pet food suppliers and manufacturers at the Petfood Forum 2019 in Kansas City, MO. Some key topics on deck for event speakers include nutrition, labeling, product development, safety and manufacturing.

BUCHI Laboratory Solutions can help keep the fur kids happy and healthy.

Did you know BUCHI has its paws in the formulation, quality control and labeling aspects of the pet food industry?

Formulation

Our spray dryer and freeze dryer equipment can be used to develop innovative, nutritious and shelf-stable products for happy, healthy pets. These technologies have been used to optimize stability and bioavailability for pet food ingredients, including: natural products, amino acids, proteins, vitamins and oils.

Quality Control

WATT Global Media conducted a survey and identified raw material ingredient quality as the top concern among surveyed Petfood Forum registrants. BUCHI provides expertise and laboratory and process equipment which helps to address quality standards at various stages along the pet food value chain, from raw material intake, to in-process quality control, to finished product testing to validate label claims.

The multi-axis plot shown below is a type of decision tree to determine which is the most appropriate method to select for protein determination, comparing Kjeldahl (red line), Dumas (yellow dashed line) and NIR (blue dashed line). For example, if your current need is for a high-speed analysis with a small environmental footprint, suitable for moderate sample type variation, then NIR is a good choice. If labeling compliance is of chief concern, with potential to adapt methods to broad variation in sample types, then Kjeldahl is a better selection.

Raw material inspection is an important component of a quality control program. Understanding the actual quality and parameters of incoming materials can help avoid process or nutritional deviations that occur because of out-of-spec ingredients. There is also an economical component: formulate closer to target and minimize issues like “protein give-away,” or avoid product recalls due to mislabeled or contaminated ingredients.

Near-infrared spectroscopy is one tool in the analytical toolbox that has been useful for establishing quality in raw ingredients, from grains to raw meats. The speed of analysis is well-suited for a quick quality check against Certificates of Analysis upon receipt of supplied goods.

Typical parameters measured by NIR in meat products include: protein, fat and moisture. For meat applications, color, pH, salt, starch and collagen content may also be implemented. These and other calibrations may be further refined with the addition of samples representative of the ingredient suppliers used within any production scheme.

Click to view a webinar highlighting ways to manage pet food production & quality using NIR

Properties of raw meat ingredients can be monitored at the time of their production, with installation points over a conveyor belt, directly in product pipes or processing equipment including deboners, grinders or mixers. An example of online measurements of protein, moisture and fat content of minced meat at a mixer has been described in a BUCHI short note . These same calibrations can be applied in-line or off-line for the pet food manufacturer who sources meat from a supplier. Large premium meat producers such as Mircana have successfully implemented this equipment to make real-time corrections to processing deviations at the mixer.

Watch this short clip to see how single or multipoint inline NIR sensors can help you control your production process

Labeling

Kjeldahl is the most established reference method for protein determination in feed, and commonly serves as a reference for NIR. You can find applications for protein and fat determination by Kjeldahl and Soxhlet extraction using our BUCHI Application Finder. Some of the content you’ll find includes:

The BUCHI Booth at Petfood Forum is getting packed up later today. If you missed us, Contact Us to schedule a chat with an Application Specialist, or even a virtual demo!

Featured

Champion saves the day: Volume 2 Production

In-process and at-line NIR for production

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

E-MAIL_2_898x529

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!

Featured

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.”

 

Featured

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!

Featured

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.

Featured

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!”

NIR: a Spring-y subject

Winter felt brutal and eternal, as it always does for someone who doesn’t ski or care for hot chocolate, I suppose. What a relief it is to see signs of Spring emerging from my brownish-colored yard and hear birds chirping outside once again.

Did you know NIR is quite Spring-y as well. This blog will explore some spring-themed theory.

beautiful bird bloom blossom
Photo by Pixabay on Pexels.com

At temperatures above absolute zero (i.e. even in the dead of a Northeast USA snowmageddon), all of the atoms in a molecule are in continuous vibration with respect to each other.

The behavior of molecular vibration is analogous to a mechanical model in which two masses connected to the ends of a spring! A disturbance of one of these masses along the axis of the spring results in a vibration called the simple harmonic oscillation.

close up of metal
Photo by Pixabay on Pexels.com

Vibration, or the displacement of an atom relative to its equilibrium position, produces potential energy proportional to the work required to displace the mass.  This energy is at its maximum when the spring is stretched or compressed to its maximum amplitude. It is at its minimum (i.e., zero) at the equilibrium position.

The fundamental requirement for infrared (Far-IR, mid-IR, near-IR) activity, leading to the absorption of infrared radiation, is that the energy of incident radiation matches the vibrational energy levels exactly, and that the vibration itself causes a change in dipole moment.  The frequency of radiation that will bring about this change can be calculated by Hooke’s Law:

Hookes Law

where c is the speed of light (3×103 cm/s), f is the force constant of the bond (dyne/cm), and Mx and My are the masses of  the atom x and atom y involved in the bond, respectively.  The force constant is positively correlated to properties such as bond order or bond strength (i.e. the “springiness” of the bond).  In accordance with the Boltzmann distribution, frequencies which correspond to fundamental transitions between the ground state and first vibrational level (n = 1) dominate the vibrational absorption spectrum.  Because the majority of absorption bands of chemical compounds correspond to fundamental vibrations at infrared frequencies, it is a common tool for structural elucidation.

How does thee vibrate? Let me count the ways.

The number of possible or theoretical fundamental vibrations is determined by the total degrees of freedom of the molecule.  Each atom requires three degrees of freedom in order to describe its position relative to other atoms in the molecule.  Therefore, a molecule of N atoms has 3N degrees of freedom.  For nonlinear molecules, six degrees of freedom are used to describe translation and rotation; the remaining 3N – 6 degrees of freedom are vibrational degrees of freedom (i.e., fundamental vibrational modes).  For linear molecules, only two degrees of freedom are required to describe rotation, resulting in 3N-5 normal modes.

Disclaimer: Sometimes 1+1 doesn’t equal 2

The number of theoretical bands will not necessarily equate to the number observed experimentally.  The number of theoretical bands observed may be reduced by: lack of a change in the molecule’s dipole as it vibrates or rotates, fundamental frequencies that fall outside of the infrared region or are too weak to be observed, vibrations that coalesce, or the occurrence of a degenerate band from several absorptions of the same frequency in highly symmetrical molecules.  On the other hand, vibrations at integer-multiples of a given frequency and combination tones will increase the actual number of bands observed.  It is from the combination and overtones transitions that NIR spectra arise.

The NIR region of the electromagnetic spectrum covers the range of approximately 14,000 to 4,000 cm-1, or about 700 to 2,500 nm.   The most prominent absorption bands occurring in the NIR region include overtones and combinations of fundamental vibrations of the IR-active –CH, –OH, -CO, –NH and –SH functional groups present in most pharmaceutical drug molecules.  Due to the relatively weak molar absorptivities of the transitions responsible for the peaks observed, sample dilution is not required.  This characteristic also provides for relatively deep sample penetration up to several millimeters thick, especially at shorter wavelengths (e.g., 700-1500 nm).  Even though NIR spectroscopy is characterized by spectra which are typically broad, overlapping and of low intensity relative to the fundamental mid-IR absorption bands, it has some practical advantages.  The richness and utility of NIR spectra is a consequence of anharmonic oscillation.

Anharmonicity: where the simple rules start to break down and things start to get interesting

Bonds which share a common atom seldom behave as independent oscillators.  As the interatomic distance separating two atoms decreases, coulombic repulsion between the nuclei results in an additional force which acts in the same direction as the force restoring the system toward equilibrium.  Thus, the potential energy of the system increases more rapidly than predicted by the harmonic oscillator.  On the other hand, as the interatomic distance approaches that at which dissociation of the atoms takes place, a decrease in the restoring force and potential energy of the system occurs.  The intramolecular interactions produce non-symmetric vibrations about the equilibrium position.  The anharmonicity results in non-equivalent energy changes between vibrational states, where ΔE becomes smaller at higher quantum numbers.   Moreover, the selection rule is not rigorously followed (because rules are made to be broken), thus allowing the overtones responsible for much of the NIR spectra, where Δn = ±2, ±3 and ±4 represent the first, second and third overtones, respectively.

The degree of anharmonicity determines the extent of the displacement from an integer multiple of the fundamental frequency, as well as the intensity of the overtones.   Vibrations stemming from intramolecular hydrogen-bonding vibrations have the highest anharmonicity constants, leading to their prevalence and high intensity in the NIR region. That’s why NIR is so great at measuring low levels of water in samples!

More interesting stuff in the spectra:

NIR spectra are further enriched when vibrational modes interact to give absorptions at frequencies that are the approximate sums or differences of their fundamental frequencies.  These combination bands, which generally occur between 1900 and 2400 nm, are a consequence of energy absorption by two bonds rather than one, allowing the photon to excite two vibrational modes simultaneously.  As with overtones, the intensities of combination bands are weaker than their fundamental frequencies.

A special type of interaction called Fermi resonance occurs as a consequence of accidental degeneracy of different vibrational modes have the same symmetry and approximately the same frequency as a fundamental vibration.  This results in two relatively strong absorbance bands which are displaced at slightly higher and lower frequencies than expected, respectively.

Darling and Dennison resonance affects vibrations which have identical symmetry species and similar energies, leading to several pairs of absorption bands.

Coupling between oscillators results in slight to moderate shifts in the absorption frequency of the molecules involved.  In general, coupling requires that vibrations be of the same symmetry species and a common atom or bond between the two vibrations or vibrating groups, respectively.  The interaction is greatest when the coupled groups have nearly equivalent energies; little to no interaction is observed by groups separated by two or more bonds.  Despite the fact that coupling leads to uncertainties in functional group identification, it is this phenomenon that provides the unique features of a spectrum enabling compound identification.

 

For more reading on this spring-y subject:

Skoog D.A. and Leary J.J. Principles of Instrumental Analysis. 4th Edition. 1992.

Silverstein R.M, Spectrometric Identification of Organic Compounds, 5th Edition. 1991.

D. Burns, and E. Ciurczak,Handbook of Near-Infrared Analysis 2nd Edition, Marcel-Dekker, Inc. New York, 2001.

E. Ciurczak and J. Drennen, Near-Infrared Spectroscopy in Pharmaceutical and Medical Applications, Marcel-Dekker, Inc. New York, 2002.

L. Weyer and S.-C. Lo, “Spectra-Structure Correlations in the Near-infrared,” In Handbook of Vibrational Spectroscopy, Volume 3, Wiley, U.K., 2002.

 

 

Evolution of BUCHI NIR

February 12 was Darwin Day. Darwin, of course, was known for his theories of evolution. While his theories continue to be hotly debated 137 years after his passing, none could argue that products and companies must evolve in some capacity to keep pace with, and meet the demands of customers-at-large.

Charles-Darwin-1880-631

Borrowing the words of Spanish soccer player Gerard Pique, “Evolution is all about looking forward.” The BUCHI NIR portfolio shows evidence of evolution in that sense. 

The BUCHI NIR “big bang” occurred in 1999 with the acquisition of Buhler NIR, with incremental steps leading up to the dramatic upgrade to the NIRFlex series, and so far culminating with the N-500 FT-NIR product.

N-500_Solids_left_312093-1104

The NIRFlex bench-top instrumentation was designed to meet consumer demands in both R&D and routine testing labs for true flexibility and high performance. Hot-swap modules accommodate nearly any sample type (i.e. solid, liquid, semi-solid or slurry), while the Fourier-transform (FT) technology provides exceptional precision. A novel single-beam FT design further propels instrument performance, stability and robustness, while hardware and software components continue to meet even the demands of the  pharmaceutical regulatory agencies.

NIRMaster_NIRMaster_ProNext, the NIRMaster was added to the portfolio specifically to serve the demands of the food and feed industries, bringing with it IP-54 and IP-65 ingress protection and features designed to ensure food safety. This revolutionary design was the first to marry the accuracy of FT-NIR with a robust and hygienic standalone at-line design suited for the production floor.

NIR-Online

In 2005, the BUCHI portfolio expanded with the acquisition of German on-line sensor manufacturer NIR-Online, just a modest train ride away from company headquarters in Flawil, Switzerland. With this acquisition, BUCHI was finally able to offer a true in- and on-line NIR solution for process control. Since its introduction, the NIR-Online product has further evolved, expanding capabilities to include multiplexing, and continuing to meet requirements for workplace safety, including explosion-proof options for hazardous environments.

In late 2018, the BUCHI NIR portfolio saw its next evolution: the ProxiMate. This at-line workhorse took the speed and agility of the NIR-Online solution at-line, creating an affordable option for the typical food and feed industry customer without compromise in quality or performance.

ProxiMate_masterProxiMate offers 3 main benefits for food and feed industry users: applicative fit, extreme robustness and simple operation.

  • BUCHI application chemists have developed many ready-to-use pre-calibrations typically required by the food and feed industries, enabling accurate results with minimal effort and by users of any ability level

Of course, the idea of the “survival of the fittest” comes to mind on Darwin Day. It seems that the more recent of our evolutionary steps in the NIR product portfolio has been hyper-focused on robustness–survival in the dirtiest, harshest, or most hazardous environments. Learn more about ProxiMate and join us on this Extreme Journey along the NIR evolutionary path!

Near vs. Mid-IR: pick your poison

Is there a simple answer?

Of course not! When it comes to the debate regarding which infrared spectroscopy reigns superior, near-infrared (NIR) or mid-infrared (IR), the answer should be a reflect the merits of the technology in light of the application of interest. It’s like asking whether a knife is better than a spoon. Well, are you trying to cut an apple or eat ice cream? You see my point.

Basic theory

Put simply, infrared spectroscopy is the study of the interaction of infrared light with matter, where infrared light is characterized by wavenumber range spanning from 12,800 to 10 cm^-1 (or wavelengths of 0.78 to 1000 micron). Mid-IR is typically defined as light between 4000 and 400 cm^-1, and NIR as light between 10,000 and 4,000 cm^-1, give or take. Mid- and near-IR are included under the umbrella of molecular spectroscopy.

Imagine your sample at the molecular level, with carbon, hydrogen, oxygen and nitrogen atoms coordinated by chemical bonds in such a way as to produce the water, fat and protein content in that sample. The relative positions of the atoms in the molecules of your sample are not fixed; they fluctuate continuously as a consequence of a multitude of different types of vibrations (i.e. stretching and bending) and rotations about the bonds in the molecule. Check out this page for some nice illustrations and more in-depth theory. When the frequency of a specific vibration is equal to the frequency of the IR radiation directed on the molecule (*and the molecule undergoes a net change in dipole moment as a consequence of the vibrational or rotational motion), the molecule absorbs the radiation. A plot of the measured infrared radiation intensity versus wavenumber is known as an infrared spectrum.

Consider the difference in the wavenumber range (and hence, energy) of mid- and near-IR radiation. The higher-energy mid-IR is exciting fundamental vibrations; that is, when energy is absorbed by the molecule in its ground state to the first vibrational state. NIR spectroscopy is comprised of combination bands of overtones of those fundamental vibrations.  The latter are of much lower intensity than their fundamental analogs, owing to their lower transition probabilities. This can be an advantage OR disadvantage – depending on what you’re trying to do (keep reading!).

The bonds defining functional groups (structural fragments within the molecule, like C=O, N-H or C-H), tend to absorb IR radiation at predictable wavenumber ranges, regardless of the rest of the molecule’s structure. Organic functional groups have characteristic and well-delineated absorption bands in the mid-IR, lending the technique to structural elucidation and compound identification, especially when paired with other analytical methods like NMR. While the broad peaks and overlapping of the overtone and combination bands strongly decrease the specificity of NIR spectroscopy for spectral interpretation, low absorptivity and efficient light scattering by NIR radiation can be advantageously exploited. In other words, because the absorption intensity is low, NIR samples do not need to be diluted (as with mid-IR) to avoid saturation at the detector; sample thickness interrogated by NIR light can be extended from millimeters up to centimeters, depending on the sample composition. This large sampling volume is valuable for quantitative analysis of samples with some degree of heterogeneity.

Let’s now consider a common application where both mid-IR (FT-IR) and NIR methods are commonly employed: raw material identification.

Mid-IR Advantages

  • Characteristic and well-delineated absorption bands  for organic species in the mid-IR lend the technique to structural elucidation and compound identification; detailed tables of characteristic group frequencies facilitate structural elucidation efforts

Mid-IR Disadvantages

  • The need for sample dilution (e.g. KBr pellets, salt plates) is common, requiring extra time for material evaluation, as well as effective “destruction” of the sample (i.e. the sample cannot be used beyond the mid-IR measurement)
  • The small sampling volume of mid-IR when using attenuated total reflectance (ATR) is small, thus limiting method repeatability for less homogeneous samples

NIR Advantages

  • NIR spectra are impacted by both chemical and physical attributes of the sample; therefore, NIR can be used to discriminate between grades of the same chemical substance
  • NIR radiation achieves more sample penetration; increased sampling volume may increase sensitivity to contaminants
  • No sample preparation  (i.e. no pellets or salt plates), nor purge gas is required, thus reducing the sampling efforts and costs
  • Spectra are collected in seconds (typically 4 to 30s)

NIR Disadvantages

  • Some functional groups having both fundamental and first order (or higher) overtones in the mid-IR region will not appear in the NIR region, potentially limiting the discriminatory power of NIR for certain sample sets
  • Due to the more complex (i.e. broad and overlapping) signal of NIR spectroscopy, chemometric procedures are required for qualitative discrimination.  The superposition of bands However, software capable of handling these procedures is widely available and quite capable when paired with solid experimental design

Conclusion

What’s the moral of the story? If you have a label on a bag of white powder and you want to quickly see if that label is correct, then NIR is likely to be the right choice for you. You’ll complete your analysis quicker and be able to retain or use the NIR sample as you see fit. However, if you are synthesizing compounds in the lab and you want to know what you brewed up, mid-IR is the clear choice.

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!