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Click the title above to open the Spectroscopy August 2019 Special Issue, IR Spectroscopy for Today's Spectroscopists, in an interactive PDF format.


Vol 34 No s8 Spectroscopy July 2019 FTIR supplement Issue PDF

Previous studies have shown that soil microbial biomass constitutes as much as 50% organic carbon in soil organic matter, and therefore plays a crucial role in soil-atmospheric chemistry and climate. However, little is known about the fate and transformation of the magnitude of microbial components in the soil. In this study, mid-infrared (IR) spectroscopy is applied as a primary technique to better understand the bio- and ultraviolet-transformation of soil microbial biomass. The spatial distribution of microbial-derived organic structures on kaolinite and montmorillonite clay minerals was investigated to decipher which organic structures preferentially associate with the crystalline and amorphous forms of the minerals, which may be accessible to microbial heterotrophs, and which are physically protected from decomposition.

	Soil organic matter (SOM) is a complex mixture of compounds with varied sources, compositions, and environmental and ecological roles. Generally, the biogeochemical characteristics of SOM represent an integrated signal of multiple processes, including degradation and its interaction with soil minerals and pedogenic oxides. Recently, the microbial contribution to SOM has been shown to be much larger than previously thought, and therefore its role in the carbon and nitrogen biogeochemical cycles may also be underestimated (1,2). Considering the huge amounts of carbon stored in soils, and the unprecedented pressure on land due to rapid population growth, climate change, land degradation, and other environmental stressors, there is an urgent need to better understand the degradation dynamics of SOM, as well as its interactions with pristine and structurally altered (weathered) clay minerals.

	In soils, biological recalcitrance of organic matter (OM) can be attained through molecular structure and formation of cross linkages, but organic matter–mineral interactions have also been shown to play a crucial role in the preservation and distribution of SOM. Overall, organic matter–mineral interactions have significant implications for a number of biogeochemical processes, ranging from the formation, growth, and division of the earliest cells, to agricultural productivity and other ecosystem services, such as enhancing the ability of soils to act as a natural modulator of global climate change (3,4). Despite these important roles and functions, molecular-level information about the mechanisms of such interactions remains poorly understood, and even less is known about the effects of micro-scale weathering of minerals on these interactions. Therefore, these gaps in our knowledge may serve to undermine our ability to predict future carbon cycling under a warming climate, and to reconstruct paleo-environmental conditions based on organic matter-derived proxies.

	In this contribution, mid-infrared (IR) spectroscopy is applied as a primary technique to better understand the bio- and ultraviolet-transformation of soil microbial biomass. Further, the spatial distribution of microbial-derived organic structures on kaolinite (1:1) and montmorillonite (2:1) clay minerals is investigated, in order to decipher which organic structures preferentially associate with the crystalline and amorphous (weathered) forms of the minerals, which may be accessible to microbial heterotrophs, and which are physically protected from decomposition. IR spectroscopy provides a rapid nondestructive means to perform molecular-level investigations on SOM and their interactions with soil minerals and pedogenic oxides. Critically, since samples are not subjected to chemical treatment, there are no side effects of secondary reactions. Additionally, the molecular composition of the sample is determined simultaneously, which simplifies and shortens the time required for spectral processing, including the quantitative determination of major macromolecular components (5).

	A mixed heterotrophic soil biome was prepared in trypicase soy broth (TSB), supplemented with 0.5% (w/v) yeast extract. Initially, 1 g of freshly collected, homogenized agricultural soil was added to 100 mL aliquots of sterile distilled water, and dispersed by stirring. Aliquots (1 mL) of the initial soil suspension were used to prepare serial dilutions of 10

 g soil suspension in a mixture of TSB–yeast extract and sterile distilled water (1:9). Finally, 1-mL aliquots from the serial dilutions were used to inoculate 100 mL of the undiluted growth solution, and the cultures incubated at room temperature for 72 h, with shaking at 150 rpm. Dilutions were done to prevent any possible carryover of soil particles from the initial soil suspension. After incubation, the cultures were harvested by centrifugation, and the cell pellets washed in ultrapure water and freeze-dried (6,7).

	Bio- and ultraviolet degradation of soil microbial biomass and biodegradation of kaolinite-microbial complex were conducted as described by Spence and Kelleher (8). Biodegradation was carried out under ambient conditions for 26 weeks.

	Microscale weathering of untreated (raw) montmorillonite (SWy-2; Source Clays Repository; The Clay Minerals Society) was carried out for 36 h, according to the protocol described by Spence and associates (9).

	Duplicate clay-microbial adsorption experiments were carried using raw kaolinite, as well as raw and acid-treated (weathered/degraded) montmorillonite clay minerals (9).

	The IR spectra of initial and degraded (bio- and ultraviolet) biomass, raw kaolinite, raw and acid-treated montmorillonite, and their related microbial complexes were acquired on a Bruker Tensor Series FT-IR spectrometer, using the KBr pressed disk technique. Thoroughly dried samples (~1 mg) were homogenized in 100 mg of spectroscopic-grade KBr (Sigma). Background KBr spectra were obtained and spectra ratioed to the background. Spectra were recorded by accumulating 256 scans in the 4000 to 400 cm

mid-IR spectral range in the absorbance mode, with a resolution of 4 cm

. To minimize complications arising from unavoidable spectral shifts, baseline correction was applied to all spectra using the automatic baseline correction method. In addition to the use of thoroughly dried samples, it was essential to analyze the disk-like pellets immediately after preparation. This was done to minimize the adsorption of atmospheric water by the hygroscopic KBr, hence reducing the possibility of chemical exchange between the clay and KBr, which could result in a change of the crystalline structure of the mineral (10). Further, rapid analysis of the pellets was important to eliminate the suppression of key signals from the samples by KBr-adsorbed water.


	Figure 1: Stacked infrared spectra of (a) fresh microbial biomass; (b) microbial biomass degraded under ambient light for 26 weeks; and (c) microbial biomass degraded under ultraviolet light for 26 weeks. The spectra are presented to highlight the 4000–2400 cm

 spectral regions. The spectra in each box are offset and shown with similar scaling so that clear comparisons can be made.

	Figure 1 presents the IR spectra of fresh microbial biomass (a), and that degraded under ambient (b) and ultraviolet (c) conditions. General peak assignments are applicable to all spectra, and the characteristic functionalities of major biochemical components are summarized in Table I. After degradation has occurred, major structural changes are observed, particularly the enrichment of polymethylenic-C [(CH

] in long-chained aliphatic structures (CH

), indicating that they are selectively preserved as part of the stable carbon pool. Signals from other biochemical components (proteins and carbohydrates; see Table I) are attenuated, indicating that they have degraded relative to aliphatics. Overall, there is no distinction between bio- and photodegradation products. Bio- and photodegradation are primary decomposition processes involved in organic matter transformation in the terrestrial environment (8).

	The superimposed spectra of raw and acid-treated montmorillonite are presented in Figure 2. A band at 3626 cm

(OH) of structural OH groups coordinated to octahedral cations (Al–O[OH]); Al-Al-OH bending (917 cm

 suggest a relatively low concentrations of Fe

 ions, respectively. After acid-treatment, the 

), the –OH bending vibrations near 917, 881, and 844 cm

 (12) are no longer observed, demonstrating a depletion in the content of Al

 cations in the octahedral sheet. This is supported by the attenuation of spectral hydration peaks in the –OH stretching (3419 cm

, and an incremental shift in the position of the 

) of bending mode of cation hydration water in the raw mineral, is not observed in the acid-treated clay. Microscale weathering of the tetrahedral (Si environment) is also observed, and is characterized by changes in both the shape and position of the Si–O stretching band (1047 cm

, and the enrichment of the asymmetric band 790 cm

. This would suggest that acid hydrolysis (a key process in soil formation) has transformed the crystalline structure of montmorillonite to amorphous silica. All remaining bands (1095, 790, and 463 cm

 [12]) in the acid-treated spectrum are synonymous with Si–O vibrations.


	Figure 2: Infrared spectra of (a) raw montmorillonite, and (b) montmorillonite samples treated with 6 mol/L of HCl for 36 h. The spectra are presented to highlight the 4000–2400 cm

	Figure 3 illustrates the interactions of microbial biomass with raw and weathered montmorillonite. Microbial interactions with raw montmorillonite is dominated by signals from aliphatic and proteinaceous components (Figure 3a; see Table I for full assignment of spectral peaks). The results also clearly demonstrate that only limited quantities of microbial-derived components (primarily proteins [1640 cm

]) are adsorbed to the weathered structure. This would suggest that octahedral and interlayer cations present in a crystalline structure play important roles in the adsorption process.


	Figure 3: Inset spectra: superimposed infrared spectra of the interactions of a mixed heterotrophic soil biome with (a) raw montmorillonite, and (b) sample treated with 6 mol/L HCl for 36 h. The spectra are presented to highlight the 4000–2400 cm

	The IR spectra of raw kaolinite (a), kaolinite–microbial complex (b), and the latter sample degraded under ambient conditions for 26 weeks (c) are presented in Figure 4. Raw kaolinite exhibits four characteristic peaks assigned to 

(OH) stretching vibrations of surface OH groups (3652, 3671, and 3694 cm

(OH) vibrations of inner OH groups (3620 cm

(Si–O) is represented by three strong, well-resolved bands in the 1100–1000 cm

 represent OH-bending vibrations of the outer and inner surface OH groups of the mineral, respectively. Additional bands near 701 and 755 cm

 are associated with the surface OH groups. Bands due to δ(Al-Fe-OH) at 865–875 cm

 and stretching at 3607 cm-1 are typical of Fe-bearing kaolinites, while the absorption bands due to Si–O–Al and Si–O–Si are observed at 544 and 473 cm

, respectively (13). After interactions have occurred, the 

(SiO), and the δ(SiO) bands are now attenuated (Figure 4b and c). Further, the 

 is no longer observed, suggesting that hydrogen bonding with microbial-derived components has occurred.

	Conversely, the bands at 3621, 3652, and 3694 cm

 remained unchanged, indicating there was no intercalation of the mineral. Signals form 

(OH) in phenolic and carboxylic groups and 

(N-H) of amides from major biochemical groups appear as a strong broad at 3408–3300 cm

. Microbial–mineral interactions are further evidenced by peaks at 2929 and 2856 cm

 methylene groups, respectively. When coupled with a weak shoulder at 2960 cm

[C–H] of methyl groups), this would suggest a contribution from polymethylenic-C ([C-H

]) with a low degree of branching in the interaction process (14). Contributions from other biochemical components with roles in the interaction process, including bands at 1657 cm

(O–C=O) of metal-coordinate carboxylates, H-bonded 

(C=O) of amides, and amide I band in peptides and proteins. Absorption peaks of moderate to low intensity at 1545, 1425, 1394–1380, and 1235 cm

 denotes δ(N–H) of amide II of proteins; δ(O-H) of carboxylic acids, the CO

stretch of carboxylates and the aliphatic CH

(OCO) of coordinated carboxylate groups; and 

(C-N) of amides (amide III), respectively (14). A weak shoulder at 1099–1095 cm

 corresponds to the C–O–O and C–O ring vibration of carbohydrates, most likely from bacterial cell wall components such as peptidoglycan. Note, these figures remain largely unchanged following degradation of the clay-microbial complex, suggesting that microbial-derived biomolecules are physically protected.


	Figure 4: FT-IR absorbance spectra of (a) pure kaolinite, (b) initial kaolinite-microbial complex, and (c) the latter sample degraded under ambient conditions (4000–400 cm

	In this study, mid-infrared (IR) spectroscopy is applied as a primary technique to better understand the bio- and ultraviolet-transformation of soil microbial biomass. The spatial distribution of microbial-derived organic structures on kaolinite and montmorillonite clay minerals was investigated to decipher which organic structures preferentially associate with the crystalline and amorphous forms of the minerals, which may be accessible to microbial heterotrophs, and which are physically protected from decomposition.

	Bio- and photodegradation produced indistinguishable results, and are both characterized by considerable enrichment in aliphatic components, presumably polymethylenic-C ([C-H

), and a concomitant decrease in the relative concentrations of carbohydrate and protein structures in a clay-free environment. Results also indicate that microbial-derived organic matter (OM), primarily aliphatic components, adsorbed mainly to the external surfaces of the minerals, but also to the interlaminar region of montmorillonite. Similarly, aliphatic structures appear to be dominant after degradation of OM-mineral complexes had occurred. After chemical weathering of montmorillonite, the adsorption of microbial-derived components (in particular lipids) to the amorphous form of the mineral appears to have decreased. Based on this behavior, an immediate conclusion is that spatial co-variation of microbial-derive OM with octahedral cations in the minerals may be a primary mechanism of interaction.

	(1) R. Kindler, A. Miltner, H-H. Richnow, and M. Kastner, 

	(2) A.J. Simpson, M.J. Simpson, E. Smith, and B.P. Kelleher, 

Soil Mineral-Organic Matter-Microorganism Interactions and Ecosystem Health: Dynamic, Mobility and Transformation of Pollutants and Nutrients,

 A. Violante, J-M. Bollag, L. Gianfreda, and P.M. Huang, Eds. (Elsevier Science, Amsterdam, The Netherlands, 2002), pp. 1–36.

Biophysico-Chemical Processes Involving Natural Nonliving Organic Matter in Environmental Systems,

 N. Senesi, B. Xing, and P.M. Huang, Eds. (John Wiley & Sons, Hoboken, New Jersey, 2009), pp. 219–272.

	(5) M. Grube, J. Zagreba, E. Gromozova, and M. Fomina, 

	(6) D. Borrok, J.B. Fein, and C.F. Kulpa, 

	(7) I.V.N Rathnayake, M. Megharaj, G.S.R. Krishnamurti, N. Bolan, and R. Naidu, 

	(9) A. Spence, C. Robinson, and R.E. Hanson, 

 (Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000).

	(12) M. Pentrák, V. Bizovská, and J. Madejová, 

	(14) C.P. Marshall, E.A. Carter, S. Leuko, and E. Javaux, 

 is with the International Centre for Environmental and Nuclear Sciences, at the University of the West Indies, in Mona, Kingston, Jamaica. Direct correspondence to: 

Mid-infrared spectroscopy is applied as a primary technique to better understand the bio- and ultraviolet-transformation of soil microbial biomass. Soil microbial biomass constitutes as much as 50% organic carbon in soil organic matter, and therefore plays a crucial role in soil-atmospheric chemistry. In this study, the spatial distribution of microbial-derived organic structures on kaolinite and montmorillonite clay minerals was investigated.

Mid-IR Spectroscopy as a Primary Tool in Carbon Biogeochemistry Research

A defining requirement in biopharmaceutical development, formulation, and manufacturing is to elucidate and maintain the structure of the drug entity. That work relies on relevant protein characterization, a task complicated by how samples evolve through the drug development lifecycle. The requirement to change techniques to cope with progressively more concentrated, and complex, multicomponent formulations is a major issue when it comes to comparing datasets and safeguarding structural parity. In this article, we discuss a new infrared spectroscopy technique, microfluidic modulation spectroscopy (MMS), that delivers reproducible protein characterization over close to four orders of magnitude in protein concentration, from 0.1 to 200 mg/mL. This technique can transition with the sample from the earliest stages of development through to manufacture, and present experimental data demonstrating the concentration-independent data that can be generated.

	Elucidation of the structure-function relationships that define the clinical efficacy of therapeutic biologics such as monoclonal antibodies (mAbs) makes protein characterization critical to their development. Though the primary structure of proteins is relatively stable, higher order structure (HOS)-secondary, tertiary and quaternary-is labile, susceptible to change triggered by chemical or thermal stress. Therefore, delivery of a drug entity to the patient with its structure intact, unchanged by manufacture or storage, relies on detailed protein characterization across the biopharmaceutical lifecycle. This requirement creates the need for an analytical technique that can characterize structure reproducibly across a broad range of concentrations, and that remains viable and reliable in the presence of excipients and additives.

	Secondary structure develops from interactions between hydrogen bond donor and acceptor residues associated with repeating units in the primary peptide chains of the protein, and is typically characterized by Fourier transform infrared (FT-IR) spectroscopy and far ultra-violet circular dichroism (far-UV CD). The most common element of secondary structure is the α-helix, but β-sheet structures are also relatively prevalent, particularly within mAbs, which is one of the most widely used classes of therapeutic proteins. β-sheet structures consist of β-strands and stretched sections of peptide chain, linked via hydrogen bonds (1).

	With FT-IR, information relating to secondary structure is extracted from absorption spectra spanning the amide I band, resulting from the C=O stretch vibrations of peptide linkages along the protein backbone. These modes are highly sensitive to changes in secondary structure (2), with features of the amide I band correlated robustly with specific secondary structure elements; sensitivity to β-sheet structure is a particular benefit (3,4) of using the mid-IR. However, from a practical perspective, conventional FT-IR technology has some limitations, including the relatively high concentration required for measurement, optimally in the range of 10–150 mg/mL, which makes the technique less viable for the earlier stages of drug development, when sample concentration is typically low. In addition, FT-IR tends to exhibit background drift and poor repeatability, due to the complexities of water vapor subtraction and referencing.

	The technique of circular dichroism (CD) characterizes samples on the basis of the differential absorption of left-handed and right-handed circularly polarized light that occurs when a molecule contains one or more chiral chromophores. Such chromophores include the peptide bonds associated with secondary structure, which give rise to a CD signal in the far-UV region (180–240 nm) that can be analyzed to determine the relative quantities of major secondary structural elements (5). Far-UV CD is well-suited to more dilute, simple solutions, operating at a relative low concentration of 0.2 mg/mL, for example, when using a cell of 1-mm pathlength. However, commercial products are typically formulated at higher concentrations, and the presence of chiral molecules in more complex formulations is also problematic. Typically, a dialysis procedure must be performed to remove excipients in the formulation buffer that interfere with CD signals. Phosphate buffered saline (PBS), a buffer used routinely in biological research, is well-characterized for far-UV CD analysis, and can be used to dilute formulation components that interfere with CD measurements (5).

	Switching from far-UV CD to FT-IR as samples become more complex and concentrated, through formulation and into commercial manufacture, is one way to address the limitations of these techniques. However, this introduces the complicating factor of determining whether any conflicting observations are scientifically relevant, or simply an artifact of measurement or data analysis. An alternative strategy is to dilute samples to extend the application of far-UV CD. This is problematic from the perspective of data relevance, given that proteins are highly sensitive to their environment. The limitations of both of these strategies creates a need for innovative spectroscopy platforms for the direct measurement of native proteins.

	An important step forward in this respect is the development of microfluidic modulation spectroscopy (MMS), a technique that harnesses the advantages of IR spectroscopy for robust measurement across a very wide dynamic range, with minimal interference from formulation excipients and additives (see Figure 1). In the MMS system, the sample solution and a matching reference stream are rapidly modulated (1–5 Hz) across the laser path to produce differential absorbance spectra via a process of continuous autoreferencing. The result is nearly drift-free scans background compensated to the buffer of interest, and of superior quality to those produced via conventional FT-IR. Concentration limitations are also directly addressed through the use of a tunable mid-IR quantum cascade laser that generates an optical beam around 100 times brighter than that used in a typical FT-IR. Run in continuous wave mode to generate a very accurate, low noise beam, this laser, in combination with a fully optimized optical configuration, delivers highly repeatable, high sensitivity measurement over a concentration range from 0.1–200 mg/mL. The laser also enables the use of simple detectors with no requirement for nitrogen cooling.


	Figure 1: With MMS, rapid modulation with a matched reference stream produces highly sensitive differential IR scans, eliminating any potential interference from formulation excipients and additives; a tunable, cascade laser and optimized optical set-up enable measurement across a very wide concentration range.

	MMS effectively shapes IR spectroscopy into an efficient tool for protein characterization from early drug development through to manufacture. The following experimental study of a mAb in clinical development demonstrates the capabilities of MMS for the measurement of protein samples in the presence of different buffers and across a very broad concentration range.

	Dilution series were prepared from a mAb stock sample of concentration of 157 mg/mL made up in a formulation buffer. A first dilution series was prepared by mixing volumes of the stock solution with the formulation buffer to create a samples series covering a concentration range from 1 to 80 mg/mL (specifically 1, 5, 10, 20, 40, and 80 mg/mL). A second dilution series was prepared in a strictly analogous way, and covering the same concentration range, by mixing samples of the stock solution with a PBS buffer at pH 7.4. In addition, a series of samples of formulation buffer diluted in PBS buffer were prepared as matching buffers for the PBS dilution series. In MMS measurements, the sample is modulated with a relevant buffer, so there is a need for a series of PBS diluted formulation buffer samples matched to the PBS dilution series samples.

	All samples from both dilution series were analyzed by MMS (AQS

pro, RedShiftBio), generating differential absorbance spectra for each sample. All samples were tested at a modulation rate of 1 Hz. The instrument automatically determines the back pressure required for optimal fluid flow through the flow cell, to enhance data quality. For samples in the 1–40 mg/mL concentration range, the applied back-pressure was 5 psi and duplicate measurements were carried out for each sample. For the most concentrated 80 mg/mL samples, which were more viscous, higher back pressures were automatically determined (25 psi for the sample in the formulation buffer, and 10 psi for the sample in the PBS buffer). One measurement was carried out for both of these samples. The measurements made were subject to comprehensive data analysis using the instruments integral software package (AQS

	Analysis of the dataset for the first dilution series highlights key features of the data generated by MMS, and how it can be processed to reveal information about secondary structure, and to compare samples.

	Figure 2a shows the differential absorbance spectra produced for the first dilution series; the replicate measurements indicate the high repeatability and accuracy. A plot of the maximum diffAU value as a function of concentration (see Figure 2b) shows excellent linearity with an 

value of 0.9997, illustrating the potential of the technique for protein quantification. Samples of unknown concentration can be accurately quantified using this plot.


	Figure 2: In MMS, continuous modulation between the sample and reference streams produces (a) differential absorbance spectra, shown here for a dilution series; and (b) the plot of maximum diffAU against protein concentration showing strict linearity, highlighting the potential of the technique for quantification.

	With the buffer subtracted and concentration normalized, the absolute absorption spectra for each of the samples can be compared directly (see Figure 3). The high degree of overlap in these spectra indicate that the secondary structure of the protein is essentially unchanged by the dilution process across the full concentration range, which is not the case for all proteins.


	Figure 3: Buffer subtraction and concentration normalization produces absolute absorbance spectra for the mAb that can be used for detailed structural analysis.

	One way to accentuate structural differences between samples is to generate second-derivative spectra from absolute absorbance spectra, as a function of wavenumber. This data processing step produces the traces shown in Figure 4, which provide further evidence of the close similarity of the samples, particularly those in the 5 to 80 mg/mL range.


	Figure 4: One way to highlight difference, or conversely confirming similarity, between samples is to analyze the second derivative plots, as shown.

	Protein similarity can also be directly compared via area of overlap (AO) plots (data not shown) which quantify the extent to which the spectra for two samples are similar (see Table I). Using the average AO plot for the two 5 mg/mL samples as the reference for comparison, this analysis indicates that the 1 mg/mL samples have a similarity of 98.2 to 98.5%, while all the remaining samples, across the remainder of the concentration range, have a similarity in the range 99.6 to 99.9%.

	Finally, because absorption at specific wavenumbers is well-correlated with specific elements of secondary structure, the relative quantities of the different elements can be determined (see Figure 5). The secondary structure of the mAb is shown to consist predominantly of beta sheets (~60%) and beta turns (~30%), with a much smaller fraction of unordered structure and minimal levels of alpha-helix. Levels of each element are extremely consistent across each of the samples.


	Figure 5: Higher order structure (HOS) analysis determines the quantity of specific elements of secondary structure present in the mAb, including beta sheet, beta turn, unordered structure, and alpha helix.

	The data processing steps illustrated in the preceding stage were applied to elucidate the impact of diluting the mAb stock with PBS buffer. Figure 6 shows the protein concentration plot for the PBS dilution series alongside the absolute absorbance spectra.


	Figure 6: The protein concentration plot for the PBS dilution series showing (a) a very high degree of linearity; and (b) absolute absorbance spectra that are very closely matched, indicating a high degree of structural similarity in the samples.

	These data show that dilution in the PBS has a closely comparable impact to dilution in the formulation buffer. The plot of maximum diffAU against protein concentration, for example, shows similar, comparably high linearity, exhibiting an 

 value of 0.9998. The absolute absorbance spectra indicate that the structure of the protein is closely similar across the full concentration range, as confirmed by the HOS analysis (see Figure 7).

	These results are directly useful when it comes to comparing measurements with far-UV CD with MMS, or, indeed, FT-IR. The mAb stock solution was supplied at a concentration of 157 mg/mL, which is substantially outside the measurement range for far-UV CD, and there are limitations with respect to diluent choice, since formulation buffer cannot be used if it contains a significant level of chromophores. These data confirm that diluting the sample with PBS, an acceptable, well-characterized buffer for far-UV CD analysis, has no impact on the structure of the protein.


	Figure 7: The HOS of the mAb in the PBS dilution series is closely similar to the formulation dilution series with beta sheet and beta turn the predominant structural elements.

Analyzing Similarity across Both Dilution Series

	In a final analysis, the two datasets were merged to look at similarity with respect to both concentration and buffer. Figure 8 shows the second derivative spectra for all of the samples; Table II shows the associated HOS data.


	Figure 8: Combining second derivative spectra for all of the samples in both buffers confirms the exemplary similarity of secondary structure across both dilution series.

	These data illustrate that the secondary structure of the mAb is extremely consistent, regardless of concentration or buffer. When compared with the 5 mg/mL sample in the formulation buffer, the similarity of the 1 mg/mL samples in formulation buffer are in the range of 98.2 to 98.5%, while those of all the other samples, in both the formulation and PBS buffer, are between 99.0 and 99.9%, indicating that the structure of the mAb is highly comparable across all the samples. The ability of MMS to measure across the full concentration range, without interference from either buffer, makes it extremely valuable for elucidating differences between data sets and for effective buffer screening. Here, the results indicate that any discrepancies observed between far-UV CD and MMS data are not attributable to either concentration or buffer components.

	Instrumentation developed to characterize the secondary structure of proteins across a wide range of conditions, most especially over a broad concentration range, is particularly helpful to biopharmaceutical scientists. The development and manufacture of biopharmaceuticals calls for a detailed understanding and control of the drug entity, within the native formulation environment, and through manufacture and storage to the point of patient delivery. The use of different measurement techniques, especially under different protein conditions, significantly complicates the interpretation of and the analytical workflow. In particular, the comparison of datasets produced using alternative technologies can lead to discrepancies between results that cannot be attributed to the sample with any certainty.

	The ability of MMS to measure with high reproducibility and repeatability, across a very wide concentration range, and to generate data unaffected by formulation excipients and additives, is extremely valuable within this context. This capability significantly reduces the need to compare and transfer specifications from one technique or instrument to another, thereby simplifying and nullifying risk in the analytical workflow, while at the same time reducing the cost associated with the purchase and ongoing maintenance of multiple instruments. Such flexibility makes MMS superior to alternatives such as far-UV CD and FT-IR for the essential task of characterizing the secondary structure of protein, from the earliest stages of drug delivery through to commercial biopharmaceutical manufacture and release testing.

https://proteinstructures.com/Structure/Structure/secondary-sructure.html

, J.M. Chalmers and P.R. Griffiths, Eds. (John Wiley & Sons, Ltd., Chichester, United Kingdom, 2002), pp. 3399–3425.

Analytical Applications of FTIR to Molecular and Biological Systems, 

J.R. Durig, Ed. (D. Reidel, Boston, Massachusetts, 1980), pp. 241–255.

	(4) A. Dong, P. Huang, and W.S. Caughey, 

 are with RedShift BioAnalytics, Inc., in Burlington, Massachusetts. 

Ioannis A. Papayannopoulos and Shannon Renn-Bingham

 are with Celldex Therapeutics, in Fall River, Massachusetts. Direct correspondence to: 

A new infrared spectroscopy technique, microfluidic modulation spectroscopy (MMS), delivers reproducible protein characterization over close to four orders of magnitude in protein concentration (from 0.1 to 200 mg/mL). This technique characterizes samples from the earliest stages of development through to manufacture.

Native Measurement of a Biotherapeutic without Interference from Excipients Using Microfluidic Modulation Spectroscopy

A combination of near infrared (NIR) reflection spectroscopy and multivariate analysis has been used to characterize materials relating to an injection mold barrel purging process. Reference spectra from unprocessed mold and purge materials have been compared with samples prepared after purging to estimate the content of residues in samples after a series of purging steps. Based on the results of principal component analysis and multivariate curve resolution, injection molded samples compare best with reference nylon after two purges of the barrel. Comparison of analysis of the results from the NIR and attenuated total reflectance-mid-infrared (ATR-MIR) spectral measurement was carried out to assess whether the NIR measurement was relevant to either the surface or the bulk of the hub samples.

	Nylon hubs, used in the construction of neurovascular catheters, may be manufactured using injection molding. After repeated use, nylon residues build up in the barrel, and these must be removed to prevent contamination. This is accomplished by using a purging material that is fed through the injection mold barrel, removing residues as it is passed through. In doing so, the barrel becomes contaminated temporarily by the purge material, and this is removed by running several blanks of the material that is normally injection molded using the purged barrel (1).

	The industrial client for whom we carried out this analysis has switched from one purging material to another. They wished to determine the point at which the hubs are clear of residues associated with the barrel and the purge material. Determination of the point at which the barrel residues and purging materials are no longer present, as contamination in the molded pieces is often estimated using visual inspection alone. However, this method has an inherent uncertainty. We present a method based on the combination of near-infrared (NIR) spectroscopy and multivariate analysis techniques that allows for the clear identification of the point at which no further purging is necessary.

	NIR spectroscopy is an established technique used for analyzing polymers. Absorption of NIR light by a polymer sample may occur by interaction with vibrational overtones and sum tones of chemical functional groups such as N-H, C-H, and O-H. These functional groups are common to a range of polymers, and contribute to a given polymer's spectral fingerprint. The spectrum may be used to discriminate between materials and to measure concentrations of mixtures. Measurement of the NIR spectrum is non-destructive, and sample preparation is generally minimal or unnecessary (2). Principal component analysis (PCA) is a multivariate analysis technique that is sensitive to changes in process conditions. The scores calculated from PCA of a data set allow for visualization of the relationships between the samples in a set. Clusters, patterns, and trends are revealed, and these are interpreted based on the context of and prior knowledge relating to the sample set (3). Multivariate curve resolution (MCR) is a complementary multivariate analysis technique to PCA. It differs from PCA in that the information used in the analysis is not abstract. Loadings describing components calculated using PCA are abstract solutions, whereas those calculated using MCR or inputted into an MCR model are related to the actual physical characteristics of the sample. It is often used in the spectroscopic analysis of mixtures (4).

	Nylon pellets, polystyrene pellets, and sample hubs were supplied by Stryker Neurovascular. Sample hubs or "blanks" were molded and collected after purging of the mold barrel. Blanks were collected from each of the five moldings after the first purge. Blanks collected after the second and third purges were taken from the first and fifth moldings.


	Figure 1: Visual inspection of the sample hubs; (a) through (f) show different conditions (as indicated for each inset figure).

	Optical imaging of the hubs was carried out using an Ash Inspex inspection camera and a Leica M165 inspection microscope. NIR spectra were recorded using a Perkin Elmer Spotlight 400 fitted with a NIRA reflection NIR accessory. Each spectrum was accumulated from 32 scans at 16 cm

 resolution. ATR-MIR spectra were recorded using a Perkin Elmer UATR accessory. Each spectrum was averaged from 16 scans at a resolution of 1 cm


	Figure 2: Imaging of the hub area measured using the NIR spectrometer. The sampling area of the NIR measurement is shown on sample (e); (a) through (f) show different conditions (as indicated for each inset figure).

	Multivariate analysis of spectral data was carried out using Camo Unscrambler software. Principal component analysis (PCA) was calculated using a singular value decomposition (SVD) algorithm. PCA was calculated using the first derivative of the NIR spectra. The derivative spectra were calculated using a Savitzky-Golay algorithm with a second order polynomial. Derivative spectra were processed using standard normal variate (SNV) normalization. Multivariate curve resolution (MCR) was calculated using an alternating least squares algorithm with a maximum number of 50 iterations. The constraints of non-negativity and closure were applied to the MCR model. The spectra of the reference polystyrene and nylon were used as pure components in an "initial guess" model. MCR was performed on spectra processed using SNV normalization.


	Figure 3: NIR spectra (as absorbance vs. wavenumbers) for reference materials nylon, polystyrene, and P1 B5. The spectra presented here are offset, and have been processed using standard normal variate (SNV) correction.

	Initial assessment of the hubs was carried out visually. In the case of the blanks prepared after the first purge, purge residue carried over from the process is visible as cloudy material distributed heterogeneously through the hub body. The fourth and fifth blanks are included in Figure 1 as a reference to visible hub contamination after purging. The first and fifth blanks prepared after two subsequent purges showed no visible signs of purge residue carry over. With visual examination alone, it is not certain whether the polystyrene used to purge the injection mold barrel is completely absent from these sets of blanks. For example, the cloudy material observed for the blanks prepared after the first purge may be attributed to the purge polystyrene, residues from the barrel or a combination of both.


	Figure 4: ATR-MIR spectra (as absorbance vs. wavenumbers) for reference materials nylon, polystyrene, and P1 B5. The spectra presented here are stacked, and have been processed using standard normal variate (SNV) correction.

	NIR measurements were made on the blank hubs, the purge material and the nylon pellets. Figure 2 shows the part of the hub at which the NIR measurements was made. Figure 3 shows examples of the NIR absorption spectra recorded from the hub samples. The reference spectra for the purge and hub materials have distinct peaks associated with their chemical composition that allow them to be distinguished from one another. The NIR spectrum of polystyrene exhibits absorption peaks related to the first and second overtones of the aromatic C-H stretch vibration at 5945 cm

, respectively (5). The NIR spectrum of nylon exhibits absorption peaks assigned to the amide N-H group found at 4880 cm

. C-H absorption peaks relating to the first and second overtones of the stretching absorption are observed in the nylon spectrum at 5786 cm

, respectively. In the polystyrene spectrum, these peaks are observed at 5792 cm


	Figure 5: Principal component analysis (PCA) of "blank" hubs after purging and reference materials (nylon and polystyrene).

	PCA of the spectral data is shown in Figure 5. The blank hubs from the first five runs after the first purge show the largest scatter in the plot. The first blank after the first purge groups closest with the score calculated for the purge material. Scores of subsequent samples converge toward that of the nylon reference material. Of the blank samples, the fifth hub from the second purge shows the closest score to that of the nylon reference. This is more clearly presented in Figure 6, where the scores for each sample in the first principal component are presented. One can see there that 94% of the variance in 

 is explained by the first principal component in the PCA model, and it is interpreted as the component that differentiates between the purge residue and the nylon. This result indicates that the fifth blank of the second purge has the least post-purge contamination relative to the others measured. In the case of both the second and third purges, the fifth blank grouped more closely with the nylon reference spectrum than the first, indicating that the fifth blank has less contamination than the first for each purge.


	Figure 6: Scores for the hub test samples and materials from the first principal component.

	MCR analysis of the spectral data was carried out in order to compare the results with those obtained using the PCA (Figure 7). The trend observed agrees with those found in the results of the PCA. The blanks prepared after the first purge show the largest amount of purge material present relative to samples from subsequent purges. For the second and third purges, the first blank after purging shows a larger degree of purge residue contamination relative to the fifth.


	Figure 7: Multivariate curve resolution calculated concentrations of the polystyrene and nylon in the samples hubs. Components 1 and 2 are the polystyrene and nylon, respectively.

	It is assumed that the NIR beam penetrates into the bulk of the hub during measurement and, in this manner, reveals bulk properties of the sample. Evidence supporting the validity of this assumption was revealed by performing PCA on ATR spectra of the blank hub samples (Figure 4). Figure 8 shows the scores plot calculated for ATR spectra recorded from the surface of each hub blank. The first principal component (PC 1) describes the difference between the spectral response of the nylon and the polystyrene reference materials. It accounts for 98% of the variance in the data. The first hub of the first purge is the only hub that shows any significant trending toward the polystyrene region of the scores plot. The spectral data of the remaining hubs score similar to the nylon reference for this component. This indicates that the NIR measurements have a better sensitivity to the composition of the bulk of the polymer sample than the ATR measurement.


	Figure 8: Scores plot calculated for the ATR spectra recorded at the surface of selected hub samples from the first and second principal components.

	A combination of NIR and multivariate analysis techniques has allowed for a detailed characterization of changes in material composition during a purging process. The information revealed allows for a reliable identification of the point at which the purging process is complete, and no further purging is necessary. This information is useful in the context of process management for routine implementation and efficiency; in particular, in an industrial context where unnecessary process steps can lead to large waste in the context of annual accumulation. The technique described is non-destructive, and the results are relevant to the bulk composition of the materials of interest.

Plastic Product Material and Process Selection Handbook 

 (Elsevier Science & Technology Books, Oxford, United Kingdom, 2004).

Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy 

 (CRC Press, Boca Raton, Florida, 2nd ed., 2012).

Killian Barton, Michael McAuliffe and Liam Lewis

 are with the Centre for Advance Photonics and Process Analysis (CAPPA), at the Cork Institute of Technology, in Bishopstown, County of Cork, Ireland. Direct correspondence to: 

Near infrared (NIR) reflection spectroscopy and multivariate analysis have been used to characterize materials relating to an injection mold barrel purging process. NIR was compared to attenuated total reflectance-mid-infrared (ATR-MIR) spectral measurements to assess whether the NIR measurement was relevant to either the surface or the bulk of the samples.

Special Issues-08-01-2019

Vol 34 No s8 Spectroscopy July 2019 FTIR supplement Issue PDF

Mid-IR Spectroscopy as a Primary Tool in Carbon Biogeochemistry Research

Native Measurement of a Biotherapeutic without Interference from Excipients Using Microfluidic Modulation Spectroscopy

The Application of FT-NIR and Multivariate Analysis Methods for Characterization of an Industrial Mold Purging Process