Identification and quantification of lactose isomers using raman spectroscopy

Abstract

Methods and apparatus for using Raman spectroscopy to identify and quantify lactose isomers in solid samples. In one example, a method is performed via a computing device for providing support to a Raman instrument and includes receiving from the Raman instrument a set of electrical readout signals representing a low-frequency Raman (LFR) spectrum of a solid sample including lactose. The method further includes the computing device estimating percentages of α-lactose monohydrate and anhydrous β-lactose in the total amount of lactose present in the solid sample using a selected multivariate chemometric model and further using the LFR spectrum.

Description

Docket No.: TP387690WO1IDENTIFICATION AND QUANTIFICATION OF LACTOSE ISOMERS USING RAMAN SPECTROSCOPYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application no. 63 / 713,175, filed October 29, 2024. The entire content of the aforementioned application is incorporated by reference herein.FIELD OF THE DISCLOSURE

[0002] Various example embodiments relate to analytical chemistry and, more specifically but not exclusively, to uses of Raman spectroscopy for chemical analyses.BACKGROUND

[0003] Lactose has a variety of uses in the dairy, food, and pharmaceutical industries.For example, about 60% to 70% of pharmaceutical dosage forms contain lactose and, in volume, it is one of the biggest pharmaceutical excipients. Being aware of the different forms of lactose and their concentrations can be helpful in managing the manufacturing efficiency, product quality, and condition of the stored product.BRIEF SUMMARY OF SOME SPECIFIC EMBODIMENTS

[0004] Various examples provide methods and apparatus for using low-frequency Raman spectroscopy, also sometimes referred to as terahertz (or THz) Raman, to identify and quantify lactose isomers in solid samples. Biomolecules like lactose produce much weaker Raman scattering intensity than non-biomolecules in the traditional Raman fingerprint region (about 200 cm’1and above). However, when Raman modes exist in the THz Raman range, such modes are stronger than those in the fingerprint region. This is also true for lactose isomers (e.g., see FIG. 3). The relatively large difference between the signatures of the two lactose isomers in the THz Raman range is a significant benefit that can be used to establish a chemometric model, thereby helping to convert THz Raman spectroscopy into a rapid, accurate, and non-destructive quality control tool for the detection of lactose isomers. Various embodiments of this quality control tool can beneficially be used offline or inline, e.g., in diary, food, and pharmaceutical industries.Docket No.: TP387690WO1

[0005] An example embodiment utilizes differences in the low-frequency Raman (LFR) spectra of a-lactose monohydrate and anhydrous P-lactose and applies a suitable multivariate chemometric model to estimate the percentages of the two forms of lactose in the total amount of lactose present in a solid sample under test. In some examples, the solid sample can be a food preparation or a drug formulation. At least some embodiments can beneficially be used to provide fast and powerful noncontact, nondestructive, offline or inline quality-control tools for the dairy, food, and pharmaceutical industries.

[0006] In one example, a method performed via a computing device for providing support to a Raman instrument comprises: receiving from the Raman instrument a set of electrical readout signals representing an LFR spectrum of a solid sample including lactose; and estimating percentages of a-lactose monohydrate and anhydrous P-lactose in the lactose of the solid sample using a selected multivariate chemometric model and further using the LFR spectrum.

[0007] In another embodiment, a method is performed via a computing device for supporting quality control applications. The method includes receiving a set of electrical readout signals from a Raman instrument, which represent an LFR spectrum of a solid sample containing lactose. The computing device then processes these signals to estimate the percentages of a-lactose monohydrate and anhydrous P-lactose present in the solid sample. This estimation is achieved using a selected multivariate chemometric model that interprets the LFR spectrum. Based on the estimated percentages, the computing device initiates one or more quality control actions. These actions may include adjusting manufacturing parameters or storage conditions to ensure the product maintains its desired quality and consistency. This method provides a robust framework for real-time quality control directed at optimizing industrial processes involving lactose.

[0008] In another example, an apparatus comprises: a Raman instrument; and a computing device configured to: receive from the Raman instrument a set of electrical readout signals representing an LFR spectrum of a solid sample including lactose; and estimate percentages of a-lactose monohydrate and anhydrous P-lactose in the lactose of the solid sample using a selected multivariate chemometric model and further using the LFR spectrum.Docket No.: TP387690WO1

[0009] According to yet another example embodiment, provided is a non-transitory computer-readable medium storing instructions that, when executed by the computing device, cause the computing device to perform operations comprising the above method.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other aspects, features, and benefits of various disclosed embodiments will become more fully apparent, by way of example, from the following detailed description and the accompanying drawings, in which:

[0011] FIG. 1 schematically illustrates the molecular structures of a-lactose (a-D- galactopyranosyl-(l— >-4)-D-glucose), a-lactose monohydrate (a-D-galactopyranosyl- (1— >-4)-D-glucose monohydrate), and P-lactose (P-D-galactopyranosyl-(l— >-4)-D-glucose).

[0012] FIG. 2 is a block diagram illustrating a Raman instrument with which various embodiments can be practiced.

[0013] FIG. 3 graphically illustrates Raman spectra of a-lactose monohydrate and anhydrous P-lactose acquired with the Raman instrument of FIG. 2 according to some examples.

[0014] FIG 4 is a flowchart illustrating a method performed via a computing device for providing support to the Raman instrument of FIG. 2 according to some examples.

[0015] FIG. 5 is a block diagram illustrating a computing device according to some examples.DETAILED DESCRIPTION

[0016] Lactose is a disaccharide including P-D-galactose and D-glucose moieties and is a reducing sugar. These features influence many of lactose’s chemical interactions. The D isomer of lactose is commonly found in nature. For example, D-lactose is abundantly present in mammal milk.

[0017] The industry produces two significant lactose derivatives: a-lactose monohydrate and anhydrous P-lactose. There exists a transition temperature of 93.5 °C for lactose aqueous solution at and above which lactose will crystalize into anhydrous P- lactose and below which it will crystalize into a-Lactose monohydrate. Because of theDocket No.: TP387690WO1 significant difference in solubility of the two isomers, further purification can be achieved by multiple recrystallization processes. a-Lactose monohydrate is the more common form, characterized by its crystalline structure and water molecule inclusion, making it prevalent in various food and pharmaceutical applications, such as its use in inhalers. Conversely, anhydrous P-lactose, lacking water in its crystalline structure, exhibits distinct physical and chemical properties, making it suitable for other applications including, but not limited to, its use in tablet pharmaceutical preparations. Understanding and distinguishing between these two forms is important for quality control and product development, necessitating deployment of adequate analytical techniques.

[0018] However, rapid and accurate noncontact identification and quantification of lactose isomers may be challenging. Example existing methods include optical activity measurements, water solubility testing, calorimetry testing, X-Ray diffraction measurements, Raman optical activity measurements, and nuclear magnetic resonance (NMR) measurements. Each of these methods has disadvantages in some specific applications.

[0019] For example, water solubility testing may be labor intensive and may take minutes to hours to obtain one data point. Water solubility testing also requires specific preparation, including temperature control, measurement of lactose quantities, and monitoring of at least some parameters over time. It often involves the use of specialized equipment to ensure accurate temperature and concentration control. Each solubility measurement may take anywhere from several minutes to several hours, depending on the isomer and testing conditions. This makes it difficult to adapt for high-throughput analysis. Because both lactose isomers of interest can coexist in equilibrium in water, meaning the two isomers can mutually convert into each other depending on the specific conditions, two equilibria tend to be reached in the solution, one for the dissolving process, and another one for the mutual conversion process. This complexity may result in added time for the solubility measurement. The solubility test is destructive in nature, as the lactose sample is consumed during the solubility process, making it unsuitable for cases where sample preservation is needed.

[0020] Calorimetry testing can distinguish lactose isomers due to distinct thermal behaviors of alpha-lactose monohydrate and anhydrous beta-lactose. The involved technique is differential scanning calorimetry (DSC), where the heat flow to increase theDocket No.: TP387690WO1 temperature of a lactose sample is measured, thereby revealing specific thermal transitions, such as melting points, crystallization, and phase changes. The technique involves specific sample preparation and further involves a relatively lengthy heating process taking minutes to hours. The lactose sample needs to be accurately weighed and placed in a specialized calorimeter cell. Calorimetry testing is destructive as well, as the sample is subjected to increasing temperatures that typically cause irreversible physical and chemical changes, including melting, crystallization, degradation, and transitions between alpha lactose and beta lactose. Once tested, the sample may not be reused.

[0021] X-ray diffraction (XRD) can be used for the identification and characterization of lactose isomers because of the inclusion of one water molecule in alpha lactose monohydrate. Due to the use of high-energy X-rays, XRD needs to be performed in a well-shielded, enclosed environment to protect operators from radiation exposure. This safety measure is important, as improper handling or shielding can result in harmful radiation exposure. XRD analysis typically takes minutes to hours depending on the desired resolution and the size of the sample. Specialized sample preparation is needed for this type of analysis.

[0022] Lactose isomers can also be distinguished based on their optical activity, which arises from their chirality. The corresponding optically testable samples are prepared by dissolving the input samples in solvents. The dissolution consumes the input sample and may also be rather time consuming. When polarized light passes through a solution containing lactose, the plane of polarization is rotated. Both alpha-lactose and beta-lactose are optically active but exhibit different respective rotation values. While this method is useful for distinguishing between isomers, one difficulty is that the optical activity measurements can be sensitively affected by temperature, concentration, impurities, etc. In addition, the dissolution process may change the stereo-structure, thereby adversely affecting the inaccuracy of measurements. Furthermore, in at least some cases, this technique may yield limited structural information, thereby prompting further analysis and / or the use of complementary methods to fully disambiguate the results.

[0023] Raman Optical Activity (ROA) measures the small intensity difference for a chiral isomer when the sample is excited by left and right circularly polarized light, respectively. Since it is based on conventional Raman spectroscopy with the addition ofDocket No.: TP387690WO1 other pertinent optical elements, it is an excellent technology in molecule identification plus the differentiation of pure isomers. However, large measurement errors may occur due to inherently low Raman scattering signal intensities and the following subtraction operation. Furthermore, ROA may not be suitable for the analysis of some isomer mixtures because the ROA intensity difference signal may be canceled out in the mixture of isomers.

[0024] NMR is a powerful tool in composition and stereochemistry analysis. It detects the nuclei resonance in strong magnetic fields in the presence of a weak oscillating magnetic field. However, NMR is highly sensitive to motion, which makes it difficult to apply in inline quality control systems involving moving parts. In addition, an NMR instrument may be relatively expensive to procure and maintain.

[0025] Although the above-described existing analytical methods may provide valuable insights into the identification and quantification of lactose isomers, each of methods has respective limitations in terms of labor intensity, time consumption, sample loss, operating costs, and complexity. These challenges underscore the need for improvements in analytical methodologies directed to achieving rapid, accurate, and nondestructive analysis of lactose isomers.

[0026] FIG. 1 schematically illustrates the molecular structures of a-D- galactopyranosyl-(l— >-4)-D-glucose or a-D-lactose (hereafter a-lactose), a-D- galactopyranosyl-( l ^4)-l)-glucose monohydrate or a-D-lactose monohydrate (hereafter a-lactose monohydrate), and P-l)-galactopyranosyl-( l ^4)-l)-glucose or P-D-lactose (hereafter P-lactose or anhydrous -lactose). The shown a-lactose monohydrate is the main crystalline form of a-lactose, whereas the main crystalline form of P-lactose is anhydrous P-lactose. The a and anomers of lactose differ in the steric configuration of the C1substituent groups (OH and H) belonging to the glucose moiety. Evaluation and control of the ratio of a-lactose and P-lactose are important to the uses of lactose in foods and drugs, e.g., because the a and forms exhibit significantly different physicochemical properties, such as solubility, hardness, pH dependence, and crystallization parameters, leading to different respective behaviors when employed as ingredients in food preparations and drug formulations. For example, the -form is more soluble than the a- form, whereas the a-form has a higher hardness than the P-form.Docket No.: TP387690WO1

[0027] Solubility is an important factor concerning dissolved lactose because of lactose’s mutarotation properties. For example, the a and P forms typically coexist in aqueous solution and are interconvertible through the open-chain form of the glucose moiety. When a-lactose is added to water at 20 °C, it dissolves until saturation. The addition of more a-lactose to the solution causes mutarotation, due to which some of the a-form converts into the P-form. This conversion continues until an equilibrium is established between the two anomers. In the equilibrium at room temperature, the a and P forms represent 37% and 63% of the solute, respectively.

[0028] Temperature has a direct influence on the amount of lactose dissolved in the solution, which reaches 100 g of lactose in 100 g of water at 80 °C. The solubilities of the two lactose isomers, and thus their relative amounts at equilibrium, are also strongly temperature dependent. With increasing temperature, the solubility of the a-form increases, shifting the equilibrium towards the P-form. Indeed, at 93.5 °C, the latter form is the prevalent anomer in the solution. It should be noted, however, that lactose does not dissolve as easily in water as some other simple sugars. Also, in liquids containing protein and fat (e.g., dairy liquids), the dissolution of lactose is slowed down due to the scarcity of available water, which can be bound by those components of the liquid.

[0029] pH does not significantly affect the ratio of the lactose anomers at equilibrium, whereas the rate at which mutarotation occurs is pH dependent. For example, at pH 5, the mutarotation rate is at its minimum. The mutarotation rate increases when pH shifts toward higher or lower pH values.

[0030] Another important aspect concerning lactose is its crystallization, which can occur when water is removed from a lactose supersaturated solution or when the temperature is lowered. In general, several different crystalline forms can originate from the lactose solution, depending on the specific parameters of the crystallization process.

[0031] As already mentioned above, the main crystalline forms of lactose are the a- lactose monohydrate and anhydrous P-lactose (also see FIG. 1), which originate from the dissolved a-lactose and P-lactose, respectively. In particular, when the crystallization conditions are met, a-lactose crystallizes in its monohydrate form at temperatures below 93.5 °C. As the crystallization process continues, the equilibrium in the solution shifts from the P-form to the a-form, thereby causing more and more a-lactose monohydrate toDocket No.: TP387690WO1 be formed. Depending on the temperature, the a-lactose monohydrate stability can change, which affects its hygroscopicity. The P-anhydrous crystals, which can be produced slowly at temperatures above 93.5 °C, have greater hygroscopicity and a much higher solubility than the a-lactose monohydrate crystals. The crystalline form of a- lactose monohydrate is more stable than the P-anhydrous crystals which, in high moisture conditions, can rapidly turn into the a-lactose monohydrate form.

[0032] Drugs are typically composed of active pharmaceutical ingredients (APIs), commonly considered to be the most important substances in the formulation because of their pharmaceutical action, and excipients, the presence of which is usually critical for drug bioavailability. Lactose is one of the most commonly used excipients in the pharmaceutical industry, owing to its physical and chemical properties, such as chemical inertia, stability, and non-toxicity, and further owing to its moderate price. Considering the organoleptic characteristics of lactose powder, i.e., it being white and odorless and having a sweet taste, its wide acceptance as a component in pharmaceutical formulations is notable.

[0033] There are many ways to use lactose as an excipient, and different pharmaceutical forms contain lactose. For example, lactose can be used as a diluent in tablets, lozenges, capsules, and powder for intravenous injections. Tablets are the most common pharmaceutical form on the market containing lactose. In order to ensure adequate product processability throughout manufacturing, a precise volume of powder is needed to create tablets. Therefore, when the quantity of the API is low, diluents, such as lactose, are necessary to adjust the mass of the solid dose. Sometimes diluents, also referred to as fillers, may constitute up to 90% of the total dosage weight. Lactose can act as a soluble diluent in formulations due to its excellent flowability and compressibility as well as its rheological properties. In tablets, lactose powder can be used in different sizes of granulation and crystal forms in order to modulate its properties in the formulation. Moreover, lactose particles can produce granules of relatively high porosity, which improves the drug’s dissolution after being compressed. In this context, P-lactose crystals are characterized by more favorable compressibility and tensile-strength responses than a-lactose crystals. A prominent use case of a-lactose monohydrate includes inhaler formulations, whereas the anhydrous P-lactose is most often used in tabulated formulations.Docket No.: TP387690WO1

[0034] Some of the conventional methods adopted for determining the a-lactose / p- lactose ratio and analyzing the crystallization behavior include thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). However, these methods may present at least some of the following challenges: destruction of the sample, being relatively timeconsuming, lacking sufficient accuracy, and being relatively expensive. These and other related problems in the state of the art can beneficially be addressed using at least some embodiments disclosed herein below. At least some examples of the disclosed Raman methodology advantageously overcome at least some of the above-mentioned challenges and offer a rapid, reliable, and in some cases inline-integrable or contactless way for identification and quantification of the above-described lactose forms in solid samples.

[0035] Raman spectroscopy is a spectroscopic technique used to measure the intensity and wavelengths of light inelastically scattered from analytes. A source of monochromatic light, usually a laser emitting in the visible, near infrared, or near ultraviolet spectral range, is used to illuminate the sample. The laser light interacts with molecular vibrations, phonons, and / or other excitations in the sample, resulting in the energy of the laser photons being shifted up or down. The shifts in energy are measured with a spectrometer to obtain a Raman spectrum of the sample. The Raman spectrum can then be analyzed, e.g., to determine certain characteristics of the sample.

[0036] In traditional Raman, the spectral range from about 200 cm1to about 1800 cm1is used and often referred to as the “chemical fingerprint” region because most Raman-active intramolecular vibrations have frequencies in this frequency range. In a complementary way, low-frequency Raman (LFR) can be used to measure Raman spectra in the spectral range from about 5 cm1to about 200which is often referred to as the “structural fingerprint” region because many inter-molecular vibrations and / or lattice / phonon modes of materials manifest themselves in this spectral region. The wavenumber range of 5 cm1to 200 cm1is equivalent to the frequency range of 0.15 THz to 6.0 THz. Hence, LFR is sometimes referred to as terahertz (or THz) Raman.

[0037] Some benefits of LFR may include, but are not limited to, fast and unambiguous differentiation of polymorphs, unsolvated versus solvated crystalline forms, synthetic pathways, raw materials, and contaminants. By capturing both the low- frequency (low- wavenumber) Stokes and low-frequency anti-Stokes signals, LFRDocket No.: TP387690WO1 systems tend to boost the overall Raman intensity and improve the signal-to-noise ratio (SNR). In addition, the symmetrical nature of Stokes / anti-Stokes signals can be used to confirm Raman peak frequencies as well as provide an inherent self-calibration capability, thereby beneficially improving the overall reliability of measurements.

[0038] FIG. 2 is a block diagram illustrating a Raman instrument 200 with which various embodiments can be practiced. The Raman instrument 200 includes a laser 210 configured to generate an optical output beam 212 which is then directed to a sample S via a turning prism 214, an optical filter 216, a dichroic beamsplitter filter 236, and an objective lens 230. In one example, the laser 210 is a single mode 785-nm wavelength- stabilized diode laser. In other examples, the laser 210 can be configured to emit at another suitable wavelength, such as 488 nm, 514 nm, 532 nm, 633 nm, or 830 nm. Each of the filters 216 and 236 is an ultra-narrowband volumetric holographic grating (VHG) filter that is spectrally matched to the output wavelength of the laser 210 and is configured to remove (e.g., block or reject) the amplified spontaneous emission (ASE) that is typically present as a component in the optical output beam 212. In some examples, the ASE can be on the same order of magnitude or larger than the optical signals of interest. If not significantly attenuated or removed, the ASE can disadvantageously reduce the SNR or swamp the LFR signals. The objective lens 230 operates to focus the laser light onto the sample S, collect the back-scattered light, and direct the collected light back toward the dichroic beamsplitter filter 236. In one example, the dichroic beamsplitter filter 236 is a 90 / 10 beamsplitter at the laser wavelength and, as such, operates to reflect about 90% of the Rayleigh scatter back towards the laser 210 while transmitting substantially all of the Raman-shifted light.

[0039] The Raman instrument 200 further includes two additional ultra-narrowband VHG notch filters 240, 246. In one example, each of the VHG notch filters 240, 246 has an optical density greater than 4.0 at the laser wavelength and operates to further attenuate the collected Rayleigh scattered light while transmitting the Raman-shifted light with relatively high (e.g., >80%) transmission efficiency. A coupling lens 250 operates to couple the light transmitted by the VHG notch filters 240, 246 into an optical fiber 254 connected to a spectrometer 260. In one example, the spectrometer 260 is a high- resolution, high-throughput single stage spectrometer. The spectrometer 260 disperses the received Raman-shifted light in wavelength, and the dispersed light is detected by aDocket No.: TP387690WO1 pixelated CCD detector 262. In one example, the spectrometer 260 and the CCD detector 262 are configured to provide a spectral resolution of approximately 1.25 cm1in the LFR range. An electrical readout signal 264 (representing a Raman spectrum of the sample S) is directed from the CCD detector 262 via a communication channel, link, or connection to an electronic controller 270 for processing and analysis. Various examples of such processing and analysis are described in more detail below in reference to FIGS. 3-4.

[0040] FIG. 3 graphically illustrates Raman spectra of a-lactose monohydrate and anhydrous P-lactose acquired with the Raman instrument 200 according to some examples. More specifically, a spectrum 302 is the spectrum of substantially pure a- lactose monohydrate crystalline powder, and a spectrum 304 is the spectrum of substantially pure anhydrous P-lactose crystalline powder. Both of the spectra 302, 304 include respective LFR portions (which are located in the spectral range between -200 cm1and +200 cm '). Negative wavenumber values correspond to the anti-Stokes portions of the Raman spectra, whereas positive wavenumber values correspond to the Stokes portions of the Raman spectra. The peak spectrally located at 0 cm1is a manifestation of the residual excitation laser light that has leaked through the filters of the Raman instrument 200. Both of the spectra 302, 304 have been obtained under substantially identical acquisition conditions.

[0041] Visual comparison of the spectra 302, 304 readily reveals their differences in intensity and peak composition. For example, the spectrum 302 has a generally higher intensity in the LFR region than the spectrum 304. The Stokes LFR portion of the spectrum 302 includes relatively strong Raman peaks A, B, and C spectrally located at 18 cm ', 31 cm ', and 46 cm respectively. In contrast, the Stokes LFR portion of the spectrum 304 only includes Raman peaks corresponding to the Raman peaks A and C, whereas there is no Raman peak corresponding to the Raman peak B. There are also some additional differences in the Raman peak compositions of the spectra 302, 304 in the spectral range between 50 cm1and 200 cm A person of ordinary skill in the pertinent art will readily appreciate that the above-mentioned spectral signatures and spectral differences manifested in the spectra 302, 304 are sufficient for individual identification of a-lactose monohydrate and anhydrous P-lactose in solid samples and can further be used to determine the proportion of these two solid forms of lactose in theDocket No.: TP387690WO1 sample S containing a mixture thereof. An example of the corresponding analytical method is described below in reference to FIG. 4.

[0042] FIG 4 is a flowchart illustrating a method 400 performed via a computing device for providing support to the Raman instrument 200 according to some examples. In different examples, the method 400 can be configured to use different respective multivariate chemometric models, e.g., as described in more detail below. An example computing device that can be used to carry out the method 400 is described in more detail below in reference to FIG. 5.

[0043] The method 400 includes the computing device receiving from the detector 262 of the Raman instrument 200 one or more electrical readout signals 264 (in a block 402). As already indicated above, each of the readout signals 264 represents a respective Raman spectrum acquired with the Raman instrument 200 from the sample S. In various examples, the set of acquisition parameters with which the Raman instrument 200 performs the measurements corresponding to the received readout signals 264 may be the same as or different from the set of acquisition parameters used to acquire the training / calibration data for the multivariate chemometric model employed in the method 400. In some examples, the set of acquisition parameters includes: (i) the output wavelength of the laser 210; (ii) the output power of the laser 210; (iii) exposure time of the detector 262 per acquired spectrum; (iv) the number of acquired spectra for averaging; and (v) inter-acquisition delay time.

[0044] The method 400 also includes the computing device applying one or more preprocessing operations (in a block 404) to the readout signal(s) received in the block 402. In various examples, the preprocessing operations of the block 404 may include one or more of the operations selected from the following nonexclusive list: (i) averaging two or more readout signals; (ii) baseline removal; (iii) normalization; (iv) selection of one or more spectral regions that are narrower than the full spectral range covered in the signal acquisition; (v) signal filtering; (vi) computing a derivative; and (vii) mean centering. A result of the preprocessing operations performed in the block 404 is hereafter referred to as a preprocessed spectrum. In various examples, the preprocessed spectrum generated in the block 404 has a format and / or a set of attributes conforming to the input format accepted by the corresponding multivariate chemometric model.Docket No.: TP387690WO1

[0045] The method 400 also includes the computing device determining whether the preprocessed spectrum is an outlier for the chemometric model (in a decision block 406). In one example implementation of the decision block 406, the preprocessed spectrum is projected onto the model space, and the projection is checked for an outlier status using the Q residuals versus Hotelling’s T2(Q-v-T) plot. More specifically, if the projection falls within the delineated boundaries on the Q-v-T plot, then the spectrum is judged not to be an outlier. On the other hand, if the projection falls outside such boundaries on the Q-v-T plot, then the spectrum is judged to be an outlier. In other example implementations of the decision block 406, other suitable outlier-determination criteria can also be used in the decision block 406.

[0046] When it is determined that the preprocessed spectrum is an outlier (“Yes” at the decision block 406), the spectrum is discarded and the processing of the method 400 is terminated. When it is determined that the preprocessed spectrum is not an outlier (“No” at the decision block 406), the processing of the method 400 advances onto a block 408.

[0047] The method 400 also includes the computing device calculating one or more predicted values (in the block 408). The calculations of the block 408 are performed using the preprocessed spectrum and the selected multivariate chemometric model. In some examples, the calculated predicted value(s) include the percentages of a-lactose monohydrate and anhydrous P-lactose in the total lactose present in the sample S. In some cases, the predicted percentage for one of the lactose forms can be zero. In one example, operations of the block 408 include: (i) transforming the preprocessed spectrum using the latent variables of the chemometric model to calculate the corresponding score vector and (ii) multiplying the calculated score vector and the regression coefficient vector of the chemometric model to obtain the predicted value(s).

[0048] The method 400 also includes the computing device performing or initiating one or more responsive actions (in a block 410). One example of such responsive action includes an information action, such as the computing device displaying a predicted value obtained in the block 408 on a display device. Another example of such responsive action includes the computing device adding the predicted value obtained in the block 408 to a graph or plot shown on a graphical user interface (GUI). In some cases, the displayed plot shows the percentages of a-lactose monohydrate and anhydrous P-lactoseDocket No.: TP387690WO1 as a function of time. Yet another example of such responsive action includes the computing device providing an input, based on the predicted value obtained in the block 408, to a corresponding electronic controller of a component of the equipment used to store or manufacture the product for which the sample S is used as a control sample. The electronic controller may then initiate or cause a suitable in-process control action and / or equipment control action directed at adjusting the process parameter(s) and / or equipment configuration(s) to address undesired deviations or trends manifested by the predicted value. Upon completion of the operations of the block 410, the method 400 is terminated.

[0049] In one example, a multivariate chemometric model used in the method 400 is constructed based on a set of calibration data obtained with the Raman instrument 200 using a plurality of calibration samples S having different, a priori known amounts of a- lactose monohydrate and anhydrous P-lactose. Depending on the product formulation that is going to be evaluated with the method 200, the calibration samples may also include additional pertinent components, such as one or more APIs used in the product formulation. The Raman spectra acquired with the Raman instrument 200 or an equivalent thereof for the set of calibration samples may typically be subjected to the same preprocessing operations as those described above in reference to the block 404 of the method 400.

[0050] In some examples, a best fit of the calibration data is obtained by feeding the preprocessed calibration data into the Eigen Vector SOLO software configured under the partial least squares (PLS) regression option. In other examples, other suitable commercially available software and / or other statistical-method options, such as principal component regression (PCR), least absolute shrinkage model and selection operator (LASSO), and elastic net regression, can alternatively be used. The optimized model parameters obtained during the fit are used to construct the corresponding multivariate chemometric model. The model is then cross-validated using the leave-out-one method (e.g., with a contiguous block of 10). In other examples, other suitable cross validation strategies, such as K-fold validation, random subsets, Venetian blinds, and others, can be used. In some examples, the number of latent variables used in the multivariate chemometric model can be optimized by comparing the relative performance and computational complexity of the corresponding model variants. In most use cases, an optimal number of latent variables is in the range from two to four.Docket No.: TP387690WO1

[0051] In various examples, the software used to implement the method 400 may be provided with multiple multivariate chemometric models corresponding to different respective products expected to be evaluated with the method 400. Based on the specific product at hand, a corresponding suitable multivariate chemometric model can be selected by the user from the plurality of available chemometric models. In some cases, two or more different chemometric models may be available for the same product. Such models may differ, for example, in the spectral range included in the analysis. In one example, a first chemometric model may include only the Stokes LFR range whereas a second chemometric model may include both Stokes and anti-Stokes LFR ranges.

[0052] In one example, the method 400 is performed via a computing device for supporting a Raman instrument in quality control applications. The method begins with a user collecting a batch sample containing lactose or a real-time sample collection. The method 400 continues via an operation of receiving a set of electrical readout signals from the Raman instrument, which represent a low-frequency Raman (LFR) spectrum of a solid sample containing lactose. The corresponding computing device then processes these signals to estimate the percentages of a-lactose monohydrate and anhydrous P- lactose present in the solid sample. This estimation is achieved using a selected multivariate chemometric model that interprets the LFR spectrum. Based on the estimated percentages, the computing device initiates one or more quality control actions. These actions may include adjusting manufacturing parameters or storage conditions to ensure the product maintains its desired quality and consistency. This method thus provides a robust framework for real-time quality control, leveraging advanced spectroscopic analysis to optimize industrial processes involving lactose.

[0053] FIG. 5 is a block diagram illustrating a computing device 500 one or more instances of which can be used with or coupled to the Raman instrument 200 according to some examples. In various examples, one or more instances of the computing device 500 can be used to carry out the method 400. In some examples, an instance of the computing device is used to implement the electronic controller 270.

[0054] The computing device 500 of FIG. 5 is illustrated as having a number of components, but any one or more of these components may be omitted or duplicated, as suitable for the application and setting. In some embodiments, some or all of the components included in the computing device 500 may be attached to one or moreDocket No.: TP387690WO1 motherboards and enclosed in a housing. In some embodiments, some of those components may be fabricated onto a single system-on-a-chip (SoC) (e.g., the SoC may include one or more electronic processing devices 502 and one or more storage devices 504). Additionally, in various embodiments, the computing device 500 may not include one or more of the components illustrated in FIG. 5, but may include interface circuitry for coupling to the one or more components using any suitable interface (e.g., a Universal Serial Bus (USB) interface, a High-Definition Multimedia Interface (HDMI) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI) interface, an Ethernet interface, a wireless interface, or any other appropriate interface). For example, the computing device 500 may not include a display device 510, but may include display device interface circuitry (e.g., a connector and driver circuitry) to which an external display device 510 may be coupled.

[0055] The computing device 500 includes a processing device 502 (e.g., one or more processing devices). As used herein, the terms “electronic processor device” and “processing device” interchangeably refer to any device or portion of a device that processes electronic data from registers and / or memory to transform that electronic data into other electronic data that may be stored in registers and / or memory. In various embodiments, the processing device 502 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), server processors, or any other suitable processing devices.

[0056] The computing device 500 also includes a storage device 504 (e.g., one or more storage devices). In various embodiments, the storage device 504 may include one or more memory devices, such as random-access memory (RAM) devices (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging RAM (CBRAM) devices), hard drive-based memory devices, solid-state memory devices, networked drives, cloud drives, or any combination of memory devices. In some embodiments, the storage device 504 may include memory that shares a die with the processing device 502. In such an embodiment, the memory may be used as cache memory and include embedded dynamic random-access memory (eDRAM) or spin transfer torque magnetic random-access memory (STT-MRAM), for example. In some embodiments, the storageDocket No.: TP387690WO1 device 504 may include non-transitory computer readable media having instructions thereon that, when executed by one or more processing devices (e.g., the processing device 502), cause the computing device 500 to perform any appropriate ones of the methods disclosed herein below or portions of such methods.

[0057] The computing device 500 further includes an interface device 506 (e.g., one or more interface devices 506). In various embodiments, the interface device 506 may include one or more communication chips, connectors, and / or other hardware and software to govern communications between the computing device 500 and other computing devices. For example, the interface device 506 may include circuitry for managing wireless communications for the transfer of data to and from the computing device 500. The term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data via modulated electromagnetic radiation through a nonsolid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they might not. Circuitry included in the interface device 506 for managing wireless communications may implement any of a number of wireless standards or protocols, including but not limited to Institute for Electrical and Electronic Engineers (IEEE) standards including Wi-Fi (IEEE 802.11 family), IEEE 802.16 standards, Long-Term Evolution (LTE) project along with any amendments, updates, and / or revisions (e.g., advanced LTE project, ultramobile broadband (UMB) project (also referred to as “3GPP2”), etc.). In some embodiments, circuitry included in the interface device 506 for managing wireless communications may operate in accordance with a Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE network. In some embodiments, circuitry included in the interface device 506 for managing wireless communications may operate in accordance with Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some embodiments, circuitry included in the interface device 506 for managing wireless communications may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution- Data Optimized (EV-DO), and derivatives thereof, as well as any other wireless protocolsDocket No.: TP387690WO1 that are designated as 3G, 4G, 5G, and beyond. In some embodiments, the interface device 506 may include one or more antennas (e.g., one or more antenna arrays) configured to receive and / or transmit wireless signals.

[0058] In some embodiments, the interface device 506 may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communication protocols. For example, the interface device 506 may include circuitry to support communications in accordance with Ethernet technologies. In some embodiments, the interface device 506 may support both wireless and wired communication, and / or may support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first set of circuitry of the interface device 506 may be dedicated to shorter-range wireless communications such as Wi-Fi or Bluetooth, and a second set of circuitry of the interface device 506 may be dedicated to longer-range wireless communications such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, or others. In some other embodiments, a first set of circuitry of the interface device 506 may be dedicated to wireless communications, and a second set of circuitry of the interface device 506 may be dedicated to wired communications.

[0059] The computing device 500 also includes battery / power circuitry 508. In various embodiments, the battery / power circuitry 508 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of the computing device 500 to an energy source separate from the computing device 500 (e.g., to AC line power).

[0060] The computing device 500 also includes a display device 510 (e.g., one or multiple individual display devices). In various embodiments, the display device 510 may include any visual indicators, such as a heads-up display, a computer monitor, a projector, a touchscreen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0061] The computing device 500 also includes additional input / output (I / O) devices 512. In various embodiments, the I / O devices 512 may include one or more data / signal transfer interfaces, audio I / O devices (e.g., microphones or microphone arrays, speakers, headsets, earbuds, alarms, etc.), audio codecs, video codecs, printers, sensors (e.g.,Docket No.: TP387690WO1 thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, etc.), image capture devices (e.g., one or more cameras), human interface devices (e.g., keyboards, cursor control devices, such as a mouse, a stylus, a trackball, or a touchpad), etc.

[0062] Depending on the specific embodiment, various components of the interface devices 506 and / or I / O devices 512 can be configured to output suitable control signals, receive suitable control / telemetry signals, and receive and transmit data streams. In some examples, the interface devices 506 and / or I / O devices 512 include one or more analog- to-digital converters (ADCs) for transforming received analog signals into a digital form suitable for operations performed by the processing device 502 and / or the storage device 504. In some additional examples, the interface devices 506 and / or I / O devices 512 include one or more digital-to-analog converters (DACs) for transforming digital signals provided by the processing device 502 and / or the storage device 504 into an analog form suitable for being transmitted through a communication channel.

[0063] According to an example embodiment disclosed above, e.g., in the summary section and / or in reference to any one or any combination of some or all of FIGS. 1-5, provided is an apparatus comprising: a Raman instrument; and a computing device configured to: receive from the Raman instrument a set of electrical readout signals representing a low-frequency Raman (LFR) spectrum of a solid sample including lactose; and estimate percentages of a-lactose monohydrate and anhydrous P-lactose in the lactose of the solid sample using a selected multivariate chemometric model and further using the LFR spectrum.

[0064] In some embodiments of the above apparatus, the solid sample is a drug formulation further including one or more active pharmaceutical ingredients.

[0065] In some embodiments of any of the above apparatus, the computing device is further configured to apply a set of preprocessing operations to the LFR spectrum to obtain a corresponding preprocessed Raman spectrum conforming to an input format of the selected multivariate chemometric model.

[0066] In some embodiments of any of the above apparatus, the set of preprocessing operations includes one or more operations selected from the group consisting of normalization of the LFR spectrum, averaging two or more of the readout signals,Docket No.: TP387690WO1 baseline removal, spectrum smoothing, computing a derivative of a smoothed spectrum, exclusion of one or more wavenumber ranges, and mean centering.

[0067] In some embodiments of any of the above apparatus, the exclusion operation comprises removing from consideration an anti-Stokes portion of the LFR spectrum.

[0068] In some embodiments of any of the above apparatus, the selected multivariate chemometric model is constructed using calibration data and a statistical method selected from the group consisting of partial least squares (PLS) regression, principal component regression (PCR), least absolute shrinkage model and selection operator (LASSO), and elastic net regression.

[0069] In some embodiments of any of the above apparatus, the selected multivariate chemometric model is configured to perform the estimation based on both Stokes and anti-Stokes portions of the LFR spectrum.

[0070] In some embodiments of any of the above apparatus, the selected multivariate chemometric model is trained with calibration data corresponding to a plurality of calibration samples including a first active pharmaceutical ingredient (API); and wherein the solid sample includes a different second API.

[0071] In some embodiments of any of the above apparatus, the computing device is further configured to perform or initiate a responsive action based on the estimated percentages.

[0072] According to another example embodiment disclosed above, e.g., in the summary section and / or in reference to any one or any combination of some or all of FIGS. 1-5, provided is a method performed via a computing device for providing support to a Raman instrument, the method comprising: receiving from the Raman instrument a set of electrical readout signals representing a low-frequency Raman (LFR) spectrum of a solid sample including lactose; and estimating percentages of a-lactose monohydrate and anhydrous P-lactose in the lactose of the solid sample using a selected multivariate chemometric model and further using the LFR spectrum.

[0073] In some embodiments of the above method, the solid sample is a drug formulation further including one or more active pharmaceutical ingredients.Docket No.: TP387690WO1

[0074] In some embodiments of any of the above methods, the method further comprises applying a set of preprocessing operations to the LFR spectrum to obtain a corresponding preprocessed Raman spectrum conforming to an input format of the selected multivariate chemometric model.

[0075] In some embodiments of any of the above methods, the set of preprocessing operations includes one or more operations selected from the group consisting of normalization of the LFR spectrum, averaging two or more of the readout signals, baseline removal, spectrum smoothing, computing a derivative of a smoothed spectrum, exclusion of one or more wavenumber ranges, and mean centering.

[0076] In some embodiments of any of the above methods, the exclusion operation comprises removing from consideration an anti-Stokes portion of the LFR spectrum.

[0077] In some embodiments of any of the above methods, the selected multivariate chemometric model is constructed using calibration data and a statistical method selected from the group consisting of partial least squares (PLS) regression, principal component regression (PCR), least absolute shrinkage model and selection operator (LASSO), and elastic net regression.

[0078] In some embodiments of any of the above methods, the selected multivariate chemometric model is configured to perform the estimation based on both Stokes and anti-Stokes portions of the LFR spectrum.

[0079] In some embodiments of any of the above methods, the selected multivariate chemometric model is trained with calibration data corresponding to a plurality of calibration samples including a first active pharmaceutical ingredient (API); and wherein the solid sample includes a different second API.

[0080] In some embodiments of any of the above methods, the method further comprises performing or initiating a responsive action based on the estimated percentages.

[0081] A non-transitory computer-readable medium storing instructions that, when executed by the computing device, cause the computing device to perform operations comprising any one of the above methods.Docket No.: TP387690WO1

[0082] With regard to the processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments and should in no way be construed so as to limit the claims.

[0083] Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent upon reading the above description. The scope should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the technologies discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the application is capable of modification and variation.

[0084] All terms used in the claims are intended to be given their broadest reasonable constructions and their ordinary meanings as understood by those knowledgeable in the technologies described herein unless an explicit indication to the contrary is made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.

[0085] The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments incorporate more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in fewer than all features of aDocket No.: TP387690WO1 single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.

[0086] While this disclosure includes references to illustrative embodiments, this specification is not intended to be construed in a limiting sense. Various modifications of the described embodiments, as well as other embodiments within the scope of the disclosure, which are apparent to persons skilled in the art to which the disclosure pertains are deemed to lie within the principle and scope of the disclosure, e.g., as expressed in the following claims.

[0087] Some embodiments may be implemented as circuit-based processes, including possible implementation on a single integrated circuit.

[0088] Some embodiments can be embodied in the form of methods and apparatuses for practicing those methods. Some embodiments can also be embodied in the form of program code recorded in tangible media, such as magnetic recording media, optical recording media, solid state memory, floppy diskettes, CD-ROMs, hard drives, or any other non-transitory machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the patented invention(s). Some embodiments can also be embodied in the form of program code, for example, stored in a non-transitory machine- readable storage medium including being loaded into and / or executed by a machine, wherein, when the program code is loaded into and executed by a machine, such as a computer or a processor, the machine becomes an apparatus for practicing the patented invention(s). When implemented on a general-purpose processor, the program code segments combine with the processor to provide a unique device that operates analogously to specific logic circuits.

[0089] Unless explicitly stated otherwise, each numerical value and range should be interpreted as being approximate as if the word “about” or “approximately” preceded the value or range.

[0090] The use of figure numbers and / or figure reference labels in the claims is intended to identify one or more possible embodiments of the claimed subject matter in order to facilitate the interpretation of the claims. Such use is not to be construed asDocket No.: TP387690WO1 necessarily limiting the scope of those claims to the embodiments shown in the corresponding figures.

[0091] Although the elements in the following method claims, if any, are recited in a particular sequence with corresponding labeling, unless the claim recitations otherwise imply a particular sequence for implementing some or all of those elements, those elements are not necessarily intended to be limited to being implemented in that particular sequence.

[0092] Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments necessarily mutually exclusive of other embodiments. The same applies to the term “implementation.”

[0093] Unless otherwise specified herein, the use of the ordinal adjectives “first,” “second,” “third,” etc., to refer to an object of a plurality of like objects merely indicates that different instances of such like objects are being referred to, and is not intended to imply that the like objects so referred-to have to be in a corresponding order or sequence, either temporally, spatially, in ranking, or in any other manner.

[0094] Unless otherwise specified herein, in addition to its plain meaning, the conjunction “if’ may also or alternatively be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” which construal may depend on the corresponding specific context. For example, the phrase “if it is determined” or “if [a stated condition] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event].”

[0095] Also, for purposes of this description, the terms “couple,” “coupling,” “coupled,” “connect,” “connecting,” or “connected” refer to any manner known in the art or later developed in which energy is allowed to be transferred between two or more elements, and the interposition of one or more additional elements is contemplated,Docket No.: TP387690WO1 although not required. Conversely, the terms “directly coupled,” “directly connected,” etc., imply the absence of such additional elements.

[0096] As used herein in reference to an element and a standard, the term compatible means that the element communicates with other elements in a manner wholly or partially specified by the standard and would be recognized by other elements as sufficiently capable of communicating with the other elements in the manner specified by the standard. The compatible element does not need to operate internally in a manner specified by the standard.

[0097] The functions of the various elements shown in the figures, including any functional blocks labeled as “processors” and / or “controllers,” may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and nonvolatile storage. Other hardware, conventional and / or custom, may also be included. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.

[0098] As used in this application, the terms “circuit,” “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry); (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions); and (c) hardware circuit(s) and or processor(s), such as a microprocessor s) orDocket No.: TP387690WO1 a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.” This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0099] It should be appreciated by those of ordinary skill in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[0100] Any numerical range recited herein includes all values from the lower value to the upper value. For example, if a range is stated as 1% to 50%, it is intended that the narrower ranges thereof, such as 2% to 40%, 10% to 30%, 1% to 3%, etc., are expressly enumerated by said statement. These specific examples represent only a limited subset of what is intended to be covered, and all possible combinations of numerical values between and including the lowest value and the highest value of the enumerated range are to be considered to be expressly stated in this application. Concentration ranges, pH ranges, and other ranges of specific parameters are intended to be interpreted in a manner similar to the “%” example.

[0101] The modifier “about” or “approximately” used in connection with a quantity is inclusive of the stated value and has the meaning dictated by the context (for example, it includes at least the degree of error associated with the measurement of the particular quantity). The modifier “about” or “approximately” should also be considered as disclosing the range defined by the absolute values of the two endpoints. For example, the expression “from about 2 to about 4” also discloses the range “from 2 to 4.” The term “about” may refer to plus or minus 10% of the indicated number. For example, “aboutDocket No.: TP387690WO110%” may indicate a range of 9% to 11%, and “about 1” may mean from 0.9-1.1. Other meanings of “about” may be apparent from the context, such as rounding off, so that, for example, “about 1” may also mean from 0.5 to 1.4.

[0102] For purposes of this disclosure, the chemical elements are identified in accordance with the Periodic Table of the Elements, CAS version, Handbook of Chemistry and Physics, 75th Ed., inside cover, and specific functional groups are generally defined as described therein. Additionally, the present disclosure relies on general principles of organic chemistry, inorganic chemistry, and material science, as accepted in the pertinent arts. For example, specific functional moieties and reactivity in accordance with some of such principles are described in Organic Chemistry, Thomas Sorrell, University Science Books, Sausalito, 1999; Smith and March, March's Advanced Organic Chemistry, 5th Edition, John Wiley & Sons, Inc., New York, 2001; Larock, Comprehensive Organic Transformations, VCH Publishers, Inc., New York, 1989; Carruthers, Some Modern Methods of Organic Synthesis, 3rd Edition, Cambridge University Press, Cambridge, 1987, the entire contents of each of which are incorporated herein by reference.

[0103] “BRIEF SUMMARY OF SOME SPECIFIC EMBODIMENTS” in this specification is intended to introduce some example embodiments, with additional embodiments being described in “DETAILED DESCRIPTION” and / or in reference to one or more drawings. “BRIEF SUMMARY OF SOME SPECIFIC EMBODIMENTS” is not intended to identify essential elements or features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter.

Claims

Docket No.: TP387690WO1CLAIMS1. A method performed via a computing device for providing support to a Raman instrument, the method comprising: receiving from the Raman instrument a set of electrical readout signals representing a low-frequency Raman (LFR) spectrum of a solid sample including lactose; and estimating percentages of a-lactose monohydrate and anhydrous P-lactose in the lactose of the solid sample using a selected multivariate chemometric model and further using the LFR spectrum.

2. The method of claim 1, wherein the solid sample is a drug formulation further including one or more active pharmaceutical ingredients.

3. The method of claim 1, further comprising applying a set of preprocessing operations to the LFR spectrum to obtain a corresponding preprocessed Raman spectrum conforming to an input format of the selected multivariate chemometric model.

4. The method of claim 3, wherein the set of preprocessing operations includes one or more operations selected from the group consisting of: normalization of the LFR spectrum; averaging two or more of the readout signals; baseline removal; spectrum smoothing; computing a derivative of a smoothed spectrum; exclusion of one or more wavenumber ranges; and mean centering.

5. The method of claim 4, wherein the exclusion operation comprises removing from consideration an anti-Stokes portion of the LFR spectrum.

6. The method of claim 1, wherein the selected multivariate chemometric model is constructed using calibration data and a statistical method selected from the group consisting of:Docket No.: TP387690WO1 partial least squares (PLS) regression; principal component regression (PCR); least absolute shrinkage model and selection operator (LASSO); and elastic net regression.

7. The method of claim 1, wherein the selected multivariate chemometric model is configured to perform the estimation based on both Stokes and anti-Stokes portions of the LFR spectrum.

8. The method of claim 1, wherein the selected multivariate chemometric model is trained with calibration data corresponding to a plurality of calibration samples including a first active pharmaceutical ingredient (API); and wherein the solid sample includes a different second API.

9. The method of claim 1, further comprising performing or initiating a responsive action based on the estimated percentages.

10. The method of claim 9, wherein the responsive action is selected from the group consisting of: an information action; an in-process control action; and an equipment control action.

11. The method of claim 1, further comprising initiating one or more quality control actions.

12. A non-transitory computer-readable medium storing instructions that, when executed by the computing device, cause the computing device to perform operations comprising the method of any one of claims 1-11.

13. An apparatus, comprising: a Raman instrument; and a computing device configured to:Docket No.: TP387690WO1 receive from the Raman instrument a set of electrical readout signals representing a low-frequency Raman (LFR) spectrum of a solid sample including lactose; and estimate percentages of a-lactose monohydrate and anhydrous P-lactose in the lactose of the solid sample using a selected multivariate chemometric model and further using the LFR spectrum.

14. The apparatus of claim 13, wherein the solid sample is a drug formulation further including one or more active pharmaceutical ingredients.

15. The apparatus of claim 13, wherein the computing device is further configured to apply a set of preprocessing operations to the LFR spectrum to obtain a corresponding preprocessed Raman spectrum conforming to an input format of the selected multivariate chemometric model.

16. The apparatus of claim 15, wherein the set of preprocessing operations includes one or more operations selected from the group consisting of: normalization of the LFR spectrum; averaging two or more of the readout signals; baseline removal; spectrum smoothing; computing a derivative of a smoothed spectrum; exclusion of one or more wavenumber ranges; and mean centering.

17. The apparatus of claim 16, wherein the exclusion operation comprises removing from consideration an anti-Stokes portion of the LFR spectrum.

18. The apparatus of claim 13, wherein the selected multivariate chemometric model is constructed using calibration data and a statistical method selected from the group consisting of: partial least squares regression (PLS); principal component regression (PCR); least absolute shrinkage model and selection operator (LASSO); andDocket No.: TP387690WO1 elastic net regression.

19. The apparatus of claim 13, wherein the selected multivariate chemometric model is configured to perform the estimation based on both Stokes and anti-Stokes portions of the LFR spectrum.

20. The apparatus of claim 13, wherein the selected multivariate chemometric model is trained with calibration data corresponding to a plurality of calibration samples including a first active pharmaceutical ingredient (API); and wherein the solid sample includes a different second API.

21. The apparatus of claim 13, wherein the computing device is further configured to perform or initiate a responsive action based on the estimated percentages.

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