Systems and methods for spectroscopic instrument calibration

Abstract

Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a method for supporting spectroscopic calibration may include generating a base calibration model using data from a plurality of base spectroscopic instruments, and fine-tuning the base calibration model using data from the target spectroscopic instrument to generate a target calibration model for use with the target spectroscopic instrument. In some embodiments, the number of wavelengths used in generating the base calibration model and / or the target calibration model may be less than the total number of wavelengths represented in the output of the spectroscopic instrument.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Patent Application No. 63 / 394,553, filed August 2, 2022, the entire contents of which are incorporated herein by reference in their entirety. [Background technology]

[0002] Many scientific instruments require calibration, i.e., the correlation between the output of the scientific instrument and a known state or property. A spectroscopic instrument, for example, may output an intensity that is a function of a property of a sample, and calibration of such a spectroscopic instrument may define the relationship between the output intensity and the sample property. [Brief explanation of the drawings]

[0003]

[0013] The embodiments will be readily understood by the following detailed description taken in conjunction with the accompanying drawings, in which:

[0014] To facilitate this description, like reference numerals refer to like structural elements;

[0015] The embodiments are illustrated in the figures of the accompanying drawings, by way of example, and not by way of limitation. [Figure 1] FIG. 1 is a flow diagram of a process for training a base model and creating a fine-tuned target model that can be used for calibration of a spectroscopic instrument, according to various embodiments. [Figure 2] 1 illustrates a full spectrum of intensities and a sampling of that spectrum that may be used in a calibration model, according to various embodiments. [Figure 3] 1 illustrates an example neural network model architecture that may be used for the base model and the target model, according to various embodiments. [Figure 4] 1 illustrates an example neural network model architecture that may be used for the base model and the target model, according to various embodiments. [Figure 5A]10A-10C illustrate exemplary performance results for the use of a base model and a fine-tuned target model, respectively, to calibrate a spectroscopic instrument to recognize the amount of sulfur in a metal sample, according to various embodiments. [Figure 5B] 10A-10C illustrate exemplary performance results for the use of a base model and a fine-tuned target model, respectively, to calibrate a spectroscopic instrument to recognize the amount of sulfur in a metal sample, according to various embodiments. [Figure 6] FIG. 1 is a block diagram of an exemplary scientific instrument support module for performing support operations, according to various embodiments. [Figure 7] FIG. 1 is a flow diagram of an exemplary method for performing an assistive operation, according to various embodiments. [Figure 8] 1 is an example of a graphical user interface that may be used in implementing some or all of the assistance methods disclosed herein, according to various embodiments. [Figure 9] FIG. 1 is a block diagram of an exemplary computing device that may implement some or all of the scientific instrumentation methods disclosed herein, according to various embodiments. [Figure 10] FIG. 1 is a block diagram of an exemplary scientific instrument support system in which some or all of the scientific instrument support methods disclosed herein may be implemented, according to various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0004]

[0009] Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a method for supporting spectroscopic calibration may include receiving first calibration data, the first calibration data including an amount of a chemical element in a first sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths generated by spectroscopic analysis of the first sample with a first spectroscopic instrument; receiving second calibration data, the second calibration data including an amount of the chemical element in a second sample and a plurality of spectral intensities associated with the corresponding plurality of wavelengths generated by spectroscopic analysis of the second sample with a second spectroscopic instrument, the second spectroscopic instrument being different from the first spectroscopic instrument; and generating a calibration model relating the plurality of spectral intensities at the corresponding plurality of wavelengths to the amount of the chemical element in the sample based on training a machine learning model using the first calibration data and the second calibration data. In some such embodiments, the number of wavelengths associated with corresponding spectral intensities in the first calibration data is less than the total number of wavelengths having associated spectral intensities output by the first spectroscopic instrument during spectroscopic analysis of the first sample. In some such embodiments, the calibration model is the first calibration model, and the method further includes receiving third calibration data including amounts of chemical elements in the third calibration sample and multiple spectral intensities associated with the corresponding multiple wavelengths produced by spectroscopic analysis of the third sample by the third spectroscopic instrument, the third spectroscopic instrument being different from the first spectroscopic instrument and the second spectroscopic instrument; and generating a second calibration model relating the multiple spectral intensities at the corresponding multiple wavelengths to the amounts of the chemical elements in the sample by training a machine learning model based on the first calibration model using the third calibration data.

[0005] Embodiments of scientific instrument support disclosed herein may achieve improved performance compared to conventional approaches. For example, conventional calibration of spectroscopic instruments typically requires measuring tens or hundreds of samples and then fitting the resulting intensity data to the amounts of different chemical elements or other constituents of the sample (e.g., using linear, quadratic, or cubic functions). Interactions between different chemical elements in the sample's intensity spectrum can result in constructive interference, destructive interference, or other (sometimes nonlinear) effects; therefore, conventional calibration requires the expertise of highly trained technicians to properly compensate for these effects, making the calibration process even more complex and time-consuming. In addition, conventional calibration processes may need to be performed differently and / or may produce different results for each instrument, requiring significant time and effort. Therefore, embodiments disclosed herein provide improvements to scientific instrument technology (e.g., improvements to the computer technology supporting such scientific instruments, among other things).

[0006] The embodiments disclosed herein may achieve faster, more accurate, and less labor-intensive calibration of spectroscopic instruments compared to conventional approaches. For example, as discussed further below, conventional approaches to calibration typically require multiple days of hands-on work by highly trained technicians to successfully achieve calibration. These approaches suffer from several technical challenges and limitations, including the inability to switch the use of the spectroscopic instrument from one use case to another without another full calibration, resulting in instrument downtime.

[0007] Various of the embodiments disclosed herein may improve upon conventional approaches to achieve the technical advantages of faster, more accurate, and less labor-intensive calibration by utilizing specific data from calibrations of similar spectroscopic instruments. Such technical advantages are unattainable through routine conventional approaches, and all users of systems incorporating such embodiments benefit from these advantages (e.g., by assisting users in performing technical tasks such as calibrating a spectroscopic instrument through a guided human-machine interaction process). Thus, the technical features of the embodiments disclosed herein, as well as combinations of features of the embodiments disclosed herein, are clearly unconventional in the field of spectroscopy. The computational and user interface features disclosed herein not only involve the collection and comparison of information, but also apply new analytical and technical techniques to modify the operation of the spectroscopic instrument (without which the instrument could not perform its most basic functions) by improving the calibration of the spectroscopic instrument. Thus, the present disclosure introduces functionality that neither conventional computing devices nor humans could perform.

[0008] Accordingly, embodiments of the present disclosure may serve any of several technical purposes, such as controlling a particular technical system or process (e.g., a spectroscopic instrument system and associated analytical process), determining how to control a machine from measurements, reducing the amount of calibration data to be processed, and / or providing more efficient processing of existing calibration data, etc. In particular, the present disclosure provides technical solutions to technical problems, including, but not limited to, properly calibrating a spectroscopic instrument to achieve accurate analytical results.

[0009] Thus, the embodiments disclosed herein provide improvements to analytical instrument technology (eg, improvements in computer technology supporting analytical instruments, among other things).

[0010] In the following detailed description, reference is made to the accompanying drawings that form a part hereof, where like numerals refer to like parts throughout and which show, by way of illustration, embodiments that may be practiced. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. Therefore, the following detailed description is not to be taken in a limiting sense.

[0011] Various operations may be described as multiple separate actions or operations, in the order most helpful in understanding the subject matter disclosed herein. However, the order of description should not be construed as implying that these operations are necessarily order dependent. In particular, these operations may not be performed in the order presented. The operations described may be performed in a different order than in the described embodiment. Various additional operations may be performed and / or described operations may be omitted in additional embodiments.

[0012] For purposes of this disclosure, the phrases “A and / or B” and “A or B” mean (A), (B), or (A and B). For purposes of this disclosure, the phrases “A, B, and / or C” and “A, B, or C” mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). Although some elements may be referred to in the singular (e.g., “processing device”), any suitable element may be represented by multiple instances of that element, and vice versa. For example, a set of operations described as being performed by a processing device may be implemented with different ones of the operations performed by different processing devices.

[0013] This description uses the phrases "one embodiment," "various embodiments," and "some embodiments," each of which may refer to one or more of the same or different embodiments. Furthermore, terms such as "comprising," "including," and "having," when used with respect to embodiments of the present disclosure, are synonymous. When used to describe a range of dimensions, the phrase "between X and Y" represents a range that includes X and Y. As used herein, an "apparatus" may refer to any individual device, a collection of devices, a portion of a device, or a collection of portions of devices. The drawings are not necessarily drawn to scale.

[0014] As described above, embodiments disclosed herein may reduce the time and complexity of calibrating a new or otherwise uncalibrated (or miscalibrated) spectroscopic instrument. Various embodiments include the use of a base model and / or model transfer via fine-tuning. As discussed further herein, a base model may be a machine learning model trained with calibration data from multiple spectroscopic instruments (referred to herein as a “base” spectroscopic instrument for ease of illustration). Such a base model is trained with specific data to read the spectra of an associated type of instrument and account for instrument-to-instrument variation. Embodiments involving model transfer may fine-tune the base model using a small amount of data from another instrument to “fit” the base model to that other instrument (referred to herein as a “target” instrument for ease of illustration). Thus, a fine-tuned model may be trained to read the spectrum of the target instrument and provide accurate measurements of desired values ​​at the target instrument. Such a fine-tuned model may serve as an instrument calibration model and be used by a user of the spectroscopic instrument to make measurements. The fine-tuning process may provide one or more benefits to the calibration process. For example, various of the embodiments disclosed herein may reduce the amount of data from an instrument that is needed to calibrate that instrument, with only a relatively small amount of data from the instrument needed to teach the base model the variations between the previous instrument and the target instrument. In another example, various of the embodiments disclosed herein may automate the model learning and fine-tuning process, allowing less skilled operators to successfully calibrate and operate instruments that previously required highly skilled operators who could manually make decisions regarding the construction of calibration curves.

[0015] 1 is a flow diagram of a process 100 for training a base model 106 based on base calibration data 104 from one or more base instruments 102 and fine-tuning the base model 106 using target calibration data 110 from a target instrument 108 to create a “fine-tuned” target model 112 that can be used to calibrate the target instrument 108. In particular, the base model 106 and associated target model 112 may receive as input spectral intensities at multiple wavelengths for a given sample and may generate as output the amounts of one or more chemical elements or other constituents in the sample. For example, a sample may be provided to the target instrument 108, a resultant output of intensities at one or more wavelengths may be generated, and those intensities / wavelengths may be provided to the target model 112, which may output the amounts of various chemical elements in the sample (e.g., iron 70.27%, chromium 18.09%, nickel 9.21%, niobium 0.99%, manganese 0.788%, etc.).

[0016] When the target device 108 is a new device from an existing product line of the base device 102, a large amount of base calibration data 104 may be available from the calibration of the base device 102. This base calibration data 104 may reflect complex patterns used to properly understand the spectral data coming from the base device 102, and thus this base calibration data 104 may be used to train the base model 106 to interpret and provide measurements of the data collected by the base device 102.

[0017] One problem with this large set of base calibration data 104 data is that it often contains variations from either intentional or unintentional differences between one or more of the base instruments 102. For example, a particular pixel in the output of one spectroscopic instrument may correspond to a particular wavelength, while the same pixel in the output of another spectroscopic instrument may correspond to a different wavelength. These variations increase the difficulty of learning how to process the spectral data when all past calibration data is combined into the base calibration data 104. In some embodiments of the systems and methods disclosed herein, the problem of inter-instrument variation may be addressed by distilling the spectral data into simpler features that themselves make up the base calibration data 104, features that are informed by the physical processes behind the measurements. These distilled spectral features may eliminate or at least reduce instrument-to-instrument variability in the base calibration data 104, which may simplify the ML learning process and thus (1) reduce the amount of base calibration data 104 needed to train the base model 106, (2) reduce the amount of target calibration data 110 needed to train the target model 112, and / or (3) result in a more accurate target model 112 for use in calibration.

[0018] In some embodiments, the “distilled” features that may be used as base calibration data 104 to train base model 106 may correspond to a sampling of the available full spectrum. For example, if the full set of available calibration data includes intensities associated with each of N wavelengths, base calibration data 104 may include intensities associated with M wavelengths, where M is less than N. In various embodiments, M may be less than 50% of N, less than 25% of N, less than 10% of N, or less than 5% of N. The particular wavelengths selected for use in base calibration data 104 may be selected to correspond to wavelengths at which line components of a chemical element (or other constituent) of interest appear. As an example, FIG. 2 illustrates, with darker lines, intensity measurements associated with a particular sample across the full spectrum of available wavelengths, and, with lighter lines, a subset of wavelengths used (with associated intensities) as part of base calibration data 104 to train base model 106. In the example of FIG. 2, the number of wavelengths used in the base calibration data 104 is 120, representing less than 1% of the total available number of wavelengths in the full spectrum.

[0019] In some embodiments, the base model 106 and the target model 112 may have the architecture of a deep learning neural network. Such an architecture may enable the base model 106 and the target model 112 to adapt to nonlinearities, correlations, anti-correlations, and other relationships in the training calibration data, which may be desirable for calibrating the target device 108. Note that the base model 106 and the target model 112 may have the same or similar architecture to facilitate transfer learning between the models.

[0020] 3 illustrates an exemplary neural network model architecture for the base model 106 / target model 112. The model architecture may include a convolutional layer 116, a first dense layer 118, and a second dense layer 120. The convolutional layer 116 may receive an input 114 (including data representing spectral intensities at various wavelengths for a sample), and the output of the convolutional layer 116 may be provided as an input to the first dense layer 118, the output of which may be provided as an input to the second dense layer 120, the output of which may be provided to an output layer 122 (including data representing estimated amounts of various chemical elements or other constituents in the sample). In a particular example, the input may be a vector of 52,284 intensity values ​​collected using three different integration methods corresponding to 17,428 wavelengths, the convolutional layer 116 may have two filters with a kernel size of 2, the first dense layer 118 may have 28 nodes, the second dense layer 120 may have 6 nodes, and the output layer 122 may have one node (corresponding to an estimated quantity of the associated chemical element or other constituent). The model architecture of FIG. 3 may be a convolutional neural network with fully connected layers using any suitable activation function (e.g., a rectified linear (ReLU) activation function). The particular number and arrangement of layers in FIG. 3 are merely illustrative, and other numbers and arrangements of layers for the base model 106 / target model 112 may be used. The use of a convolutional layer such as the convolutional layer 116 may be particularly useful when the input 114 is substantially full spectrum (rather than a sparsely sampled set of wavelengths, as discussed elsewhere herein). The particular number and arrangement of layers in FIG. 3 is merely illustrative, and other numbers and arrangements of layers for the base model 106 / target model 112 may be used.

[0021] As noted above, in some embodiments, the base model 106 and the target model 112 may be trained to estimate the amounts of multiple chemical elements or other components in a sample based on spectral intensities at multiple wavelengths. In other embodiments, the base model 106 and the associated target model 112 may be trained to estimate the amount of a single chemical element or other component in a sample, and different base models 106 / target models 112 may be created to estimate the amounts of different chemical elements in the sample. Thus, in such embodiments, determining the amounts of different chemical elements in a sample may involve inputting spectral intensities at multiple wavelengths into different base models 106 / target models 112, with each model generating an estimate of the associated chemical element in the sample. Creating different models for different chemical elements may reduce the overall amount of computation when only a few chemical elements are of interest to a particular user, at the expense of managing multiple chemical-element-specific models (instead of a single multi-chemical-element model).

[0022] 4 illustrates an exemplary neural network model architecture for the base model 106 / target model 112 in an embodiment in which each chemical element is associated with a different base model 106 / target model 112 (thus, identifying the amount of multiple chemical elements or other constituents in a sample requires the use of multiple associated base models 106 / target models 112). In the example of FIG. 4, a first layer 126 may receive input 124 (including data representing spectral intensities at various wavelengths for the sample), the output of the first layer 126 may be provided as input to a second layer 128, the output of the second layer 128 may be provided as input to a third layer 130, the output of the third layer 130 may be provided to a fourth layer 132, and the output of the fourth layer 132 may be provided to an output layer 134 (including data representing the estimated amounts of chemical elements or other constituents in the sample). In some embodiments, the input may be a vector of 176 intensity values ​​corresponding to 176 selected wavelengths, the first layer 126 may have 32 nodes, the second layer 128 may have 28 nodes, the third layer 130 may have 6 nodes, the fourth layer 132 may have 3 nodes, and the output layer 134 may have 1 node (corresponding to the estimated quantity of the associated chemical element or other constituent). The model architecture of Figure 4 may be a fully connected network using any suitable activation function (e.g., a rectified linear (ReLU) activation function). The particular number and arrangement of layers in Figure 4 are merely illustrative, and other numbers and arrangements of layers for the base model 106 / target model 112 may be used.

[0023] Any suitable technique may be used to train the base model 106 / target model 112 disclosed herein. For example, mean absolute error may be used as the loss function during training, and training may be stopped early when the error stops improving (using some number of epochs, such as 10, to provide a "patience" parameter that delays early stopping). In some embodiments, an exponential decay schedule may be used to reduce the learning rate over time.

[0024] After the base model 106 has been trained, it may be stored on a central server (e.g., a remote computing device 5040 and / or a service local computing device 5030, as discussed below with reference to FIG. 10 ) that can be accessed by operators engaged in calibrating multiple different spectroscopic instruments. For each target instrument 108 (e.g., a “new” instrument being calibrated), the operator may retrieve the base model 106 and use the target calibration data 110 to fine-tune the base model 106 to generate a target model 112 for that target instrument 108.

[0025] As discussed above, to “transfer” the base model 106 to the target device 108, data from the target device 108 is collected and used to fine-tune the base model 106 to the target device 108, resulting in the target model 112. This fine-tuning process may modify the base model 107 to account for differences between the base device 102 and the target device 108, which are traditionally addressed by creating an entirely new calibration. Unlike traditional calibration approaches, with the transfer learning process discussed herein, an operator does not need to understand and identify specific variances because the fine-tuning process itself determines them. The deep learning models of the base model 106 and the target model 112 can easily adapt to several types of variations, including spectral shifts, intensity shifts, intensity correlations, nonlinear effects, and / or others. Fine-tuning the base model 106 to generate the target model 112 may be performed according to any suitable technique known in the art, such as the technique described in Puneet Mishra, Dario Passos, "Deep calibration transfer: Transferring deep learning models between infrared spectroscopy instruments," Infrared Physics & Technology, Volume 117, 2021, 103863.

[0026] The amount of target calibration data 110 used to fine-tune the base model 106 can be significantly less than the amount of base calibration data 104 used to train the base model 106, and can be significantly less than the amount of data required to perform a full conventional calibration of the target instrument 108. For example, for some spectroscopic instruments (such as some optical emission spectroscopy (OES) instruments, e.g., spark source spectroscopy instruments), only 10% to 20% of the data generated during conventional calibration may be required to fine-tune the base model 106 to achieve a satisfactory target model 112. In some embodiments, once the base model 106 is fine-tuned to generate the target model 112, the target calibration data 110 used in the fine-tuning can be added to the set of base calibration data 104 and used to update the base model 106, if desired.

[0027] 5A and 5B illustrate example performance results for the use of a base model 106 and a fine-tuned target model 112, respectively, to calibrate a spectroscopic instrument to recognize the amount of sulfur in a metal sample, a particularly challenging spectroscopic task. In this particular example, base calibration data 104 from 46 base instruments 102 was used to train the base model 106 to generate predicted amounts of sulfur in a sample based on measurements of the sample by the base instruments 102, as represented by the strong results in FIG. 5A. The base calibration data 104 used in this example represents data collected over several years of intensive calibration work. FIG. 5B illustrates the (also strong) performance of the fine-tuned target model 112 after the base model 106 was retrained using target calibration data 110 from target instruments 108, an amount of target calibration data 110 equal to approximately 20% of the amount of data traditionally required to calibrate a spectroscopic instrument.

[0028] In some embodiments, the initial target model 112 may be generated by the manufacturer or distributor of the target instrument 108, and the target model 112 may be retrained or otherwise updated by a purchaser or other user of the target instrument 108 to achieve recalibration. Such recalibration may be performed to compensate for drift or other small variations in the performance of the target instrument 108 since the initial calibration and / or to enable the target instrument 108 to measure a different use case. For example, the target instrument 108 may be calibrated at the factory using the initial target model 112 for a specific use case, such as for measuring iron-based metals in a sample. The target instrument 108 may have all of the components necessary for a different use case, such as for measuring aluminum-based metals, but the target instrument 108 may not have been initially calibrated for that use case. Different use cases have typically required different calibrations due, for example, to different interactions between elements of the spectroscopic instrument's plasma. If a user wants to use the target device 108 for a different use case than the one it was originally calibrated for, traditional calibration requires that the target device 108 be sent back to the factory for recalibration or that a technician be dispatched to the location of the target device 108 to perform a manual calibration, which results in a lot of downtime and cost for the user and must be repeated each time the use case changes.

[0029] However, using the systems and methods disclosed herein, a fine-tuning process can be utilized to enable a user to quickly and easily perform a new calibration of a target device 108. In some such embodiments, a user would use the target device 108 to measure a select number of reference samples (e.g., provided by the manufacturer of the target device 108) for a new use case, and the results would serve as target calibration data 110 for use in fine-tuning a base model 106 trained with base calibration data 104 for the same use case. The resulting target model 112 could serve as a new calibration for the target device 108 for the new use case. In some embodiments, the base calibration data 104, the target calibration data 110, and the spectroscopic data provided to the target model 112 during operation of the target device 108 may have been pre-processed using conventional methods, e.g., to map detector pixels to wavelengths, compensate for temperature-induced drift, etc.

[0030] Compared to conventional approaches, the overall reduction in calibration complexity and time achieved by the systems and methods disclosed herein can be significant. For example, conventional calibration of some OES instruments can require approximately 100 samples to calibrate the instrument, with a skilled technician manually creating the instrument's calibration curve. Such a calibration can require 1-2 days to perform for each individual instrument. Using the systems and methods disclosed herein, the number of samples needed to fine-tune a model for a particular instrument, the amount of input required from a technician, and that technician's technical knowledge can all be reduced.

[0031] FIG. 6 is a block diagram of a scientific instrument support module 1000 for performing support operations, according to various embodiments. The scientific instrument support module 1000 may be implemented by circuitry (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the scientific instrument support module 1000 may be contained in a single computing device or may be distributed across multiple computing devices that communicate with each other as needed. An example of a computing device that may implement the scientific instrument support module 1000, alone or in combination, is discussed herein with reference to the computing device 4000 of FIG. 9 , and an example of a system of interconnected computing devices in which the scientific instrument support module 1000 may be implemented across one or more of the computing devices is discussed herein with reference to the scientific instrument support system 5000 of FIG. 10 .

[0032] The scientific instrument support module 1000 may include first logic 1002, second logic 1004, and third logic 1006. As used herein, the term “logic” may include an apparatus that performs a set of operations associated with the logic. For example, any of the logic elements included in the support module 1000 may be implemented by one or more computing devices programmed with instructions to cause one or more processing devices of the computing devices to perform the associated set of operations. In particular embodiments, a logic element may include one or more non-transitory computer-readable media having instructions that, when executed by one or more processing devices of the one or more computing devices, cause the one or more computing devices to perform the associated set of operations. As used herein, the term “module” may refer to a collection of one or more logic elements that together perform a function associated with the module. Different logic elements within a module may take the same form or different forms. For example, some logic within a module may be implemented by a programmed general-purpose processing device, while other logic within the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different ones of the logic elements in a module may be associated with different sets of instructions executed by one or more processing devices. A module may not include all of the logic elements depicted in an associated figure; for example, a module may include a subset of the logic elements depicted in an associated figure when the module performs a subset of the operations discussed herein with reference to that module.

[0033] The first logic 1002 may receive calibration data from one or more spectroscopic instruments according to any of the embodiments disclosed herein. The calibration data may include base calibration data obtained from the base spectroscopic instruments, such as base calibration data 104 for the base spectroscopic instrument 102, and / or target calibration data, such as target calibration data 110 for the target spectroscopic instrument 108.

[0034] The second logic 1004 may train and deploy a base machine learning model (such as a base calibration model 106 trained using base calibration data 104) trained using the calibration data received by the first logic 1002 according to any of the embodiments disclosed herein.

[0035] The third logic 1006 may train and deploy a target machine learning model (such as the target calibration model 112 trained using the target calibration data 110) trained using the calibration data received by the first logic 1002 according to any of the embodiments disclosed herein.

[0036] 7 is a flow diagram of a method 2000 for performing support operations, according to various embodiments. The operations of method 2000 may be illustrated with reference to particular embodiments disclosed herein (e.g., the scientific instrument support module 1000 discussed herein with reference to FIG. 6, the GUI 3000 discussed herein with reference to FIG. 8, the computing device 4000 discussed herein with reference to FIG. 9, and / or the scientific instrument support system 5000 discussed herein with reference to FIG. 10), but method 2000 may be used in any suitable setting to perform any suitable support operations. Although the operations are illustrated in FIG. 7 once each and in a particular order, the operations may be reordered and / or repeated as desired or appropriate (e.g., different operations performed may be performed in parallel, if suitable).

[0037] In 2002, a first operation may be performed. For example, first logic 1002 of assistance module 1000 may perform the operation of 2002. The first operation may include receiving calibration data from one or more spectroscopic instruments according to any of the embodiments disclosed herein. The calibration data may include base calibration data obtained from a base spectroscopic instrument, such as base calibration data 104 of base spectroscopic instrument 102, and / or target calibration data, such as target calibration data 110 of target spectroscopic instrument 108.

[0038] A second operation may be performed in 2004. For example, second logic 1004 of assistance module 1000 may perform the operation of 2004. The second operation may include training and deploying a base machine learning model (such as base calibration model 106 trained using base calibration data 104) trained using the calibration data received in 2002, according to any of the embodiments disclosed herein.

[0039] A third operation may be performed in 2006. For example, third logic 1006 of assistance module 1000 may perform the operation of 2006. The third operation may include training and deploying a target machine learning model trained using the calibration data received in 2002 (such as target calibration model 112 trained using target calibration data 110) according to any of the embodiments disclosed herein.

[0040] The scientific instrument assistance methods disclosed herein may involve interactions with a human user (e.g., via a user local computing device 5020 discussed herein with reference to Figure 10). These interactions may include providing the user with information (e.g., information about the operation of the scientific instrument, such as the scientific instrument 5010 of Figure 10, information about a sample being analyzed or other tests or measurements performed by the scientific instrument, information retrieved from a local or remote database, or other information) or providing options for the user to enter commands (e.g., to control the operation of the scientific instrument, such as the scientific instrument 5010 of Figure 10, or to control the analysis of data generated by the scientific instrument), queries (e.g., to a local or remote database), or other information. In some embodiments, these interactions may be implemented through a graphical user interface (GUI), which includes a visual display on a display device (e.g., display device 4010 discussed herein with reference to FIG. 9) that provides output to the user and / or prompts the user to provide input (e.g., via one or more input devices, such as a keyboard, mouse, trackpad, or touchscreen included in other I / O devices 4012 discussed herein with reference to FIG. 9). The scientific instrument support systems disclosed herein may include any suitable GUI for interaction with a user.

[0041] 8 depicts an exemplary GUI 3000 that may be used in implementing some or all of the assistance methods disclosed herein, according to various embodiments. As noted above, the GUI 3000 may be provided on a display device (e.g., the display device 4010 discussed herein with reference to FIG. 9 ) of a computing device (e.g., the computing device 4000 discussed herein with reference to FIG. 9 ) of a scientific instrument assistance system (e.g., the scientific instrument assistance system 5000 discussed herein with reference to FIG. 10 ), and a user may interact with the GUI 3000 using any suitable input device (e.g., any of the input devices included in the other I / O devices 4012 discussed herein with reference to FIG. 9 ) and input technique (e.g., cursor movement, motion capture, face recognition, gesture detection, voice recognition, button activation, etc.).

[0042] GUI 3000 may include a data display region 3002, a data analysis region 3004, a scientific instrument control region 3006, and a settings region 3008. The particular number and arrangement of regions depicted in Figure 8 is merely illustrative, and GUI 3000 may include any number and arrangement of regions, including any desired features.

[0043] The data display area 3002 may display data generated by a scientific instrument (e.g., the scientific instrument 5010 discussed herein with reference to FIG. 10 ). For example, the data display area 3002 may display intensities associated with different wavelengths, as is known in the art.

[0044] The data analysis area 3004 may display the results of a data analysis (e.g., the results of analyzing the data illustrated in the data display area 3002 and / or other data). For example, the data analysis area 3004 may display the amount of a chemical element or other constituent in a sample generated based on intensities associated with different wavelengths and a calibration model, as discussed herein. In some embodiments, the data display area 3002 and the data analysis area 3004 may be combined in the GUI 3000 (e.g., to include data output from a scientific instrument and some analysis of the data in a common graph or area).

[0045] The scientific instrument control area 3006 may include options that allow a user to control scientific instruments (e.g., scientific instruments 5010 discussed herein with reference to FIG. 10 ). For example, the scientific instrument control area 3006 may include an option to initiate a calibration (which may prompt the user on how to perform the calibration).

[0046] Settings area 3008 may include options that allow a user to control features and functionality of GUI 3000 (and / or other GUIs) and / or perform common computing operations with respect to data display area 3002 and data analysis area 3004 (e.g., saving data on a storage device, such as storage device 4004 discussed herein with reference to FIG. 9 , sending data to another user, labeling data, etc.). For example, settings area 3008 may include options for changing use cases (which may trigger a recalibration, as discussed herein).

[0047] As noted above, the scientific instrument support module 1000 may be implemented by one or more computing devices. Figure 9 is a block diagram of a computing device 4000 that may implement some or all of the scientific instrument support methods disclosed herein, according to various embodiments. In some embodiments, the scientific instrument support module 1000 may be implemented by a single computing device 4000 or by multiple computing devices 4000. Furthermore, as discussed below, the computing device 4000 (or multiple computing devices 4000) that implements the scientific instrument support module 1000 may be part of one or more of the scientific instrument 5010, user local computing device 5020, service local computing device 5030, or remote computing device 5040 of Figure 10.

[0048] 9 is illustrated as having several components, any one or more of which may be omitted or duplicated as appropriate for the application and configuration. In some embodiments, some or all of the components included in computing device 4000 may be mounted on one or more motherboards and encased in a housing (e.g., comprising plastic, metal, and / or other materials). In some embodiments, some of these components may be fabricated on a single system-on-a-chip (SoC) (e.g., an SoC may include one or more processing devices 4002 and one or more storage devices 4004). 9, but may include interface circuitry (not shown) for coupling to 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 suitable interface). For example, computing device 4000 may not include display device 4010, but may include display device interface circuitry (e.g., connectors and driver circuits) to which display device 4010 may be coupled.

[0049] Computing device 4000 may include a processing device 4002 (e.g., one or more processing devices). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from registers and / or memory and converts the electronic data into other electronic data that may be stored in registers and / or memory. Processing device 4002 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), or other processors. integrated circuit (ASIC), central processing unit (CPU) The processing device may include a CPU, a graphics processing unit (GPU), a cryptographic processor (a dedicated processor that executes cryptographic algorithms in hardware), a server processor, or any other suitable processing device.

[0050] The computing device 4000 may include a storage device 4004 (e.g., one or more storage devices). The storage device 4004 may include random access memory (RAM) (e.g., static RAM). The storage device 4004 may include one or more memory devices, such as static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging RAM (CBRAM) devices, hard drive-type memory devices, solid-state memory devices, networked drives, cloud drives, or any combination of memory devices. In some embodiments, the storage device 4004 may include memory that shares a die with the processing device 4002. In such embodiments, the memory may be used as cache memory and may include, for example, embedded dynamic random access memory (eDRAM) or spin transfer torque magnetic random access memory (STT-MRAM). In some embodiments, storage device 4004 may include a non-transitory computer-readable medium having instructions that, when executed by one or more processing devices (e.g., processing device 4002), cause computing device 4000 to perform any suitable of the methods or portions of those methods disclosed herein.

[0051] The computing device 4000 may include an interface device 4006 (e.g., one or more interface devices 4006). The interface device 4006 may include one or more communication chips, connectors, and / or other hardware and software to govern communications between the computing device 4000 and other computing devices. For example, the interface device 4006 may include circuitry for managing wireless communications for data transfer to and from the computing device 4000. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc. that may communicate data through the use of modulated electromagnetic radiation over a non-solid medium. This term does not imply that the associated device does not include any wiring, although in some embodiments it may not. The circuitry included in interface device 4006 for managing wireless communications may implement any of several wireless standards or protocols, including, but not limited to, Wi-Fi (IEEE 802.11 family), the IEEE 802.16 standard (e.g., the IEEE 802.16-2005 amendment), Institute for Electrical and Electronic Engineers (IEEE) standards including the Long-Term Evolution (LTE) project with any amendments, updates, and / or revisions (e.g., the Advanced LTE project, the Ultra Mobile Broadband (UMB) project (also referred to as "3GPP®2"), etc.).In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications is compatible with Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA, and the like. In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with a GSM HSPA, E-HSPA, or LTE network. In some embodiments, the circuitry included in the interface device 4006 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, the circuitry included in the interface device 4006 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 protocols designated as 3G, 4G, 5G, and beyond. In some embodiments, the interface device 4006 may include one or more antennas (eg, one or more antenna arrays) for receiving and / or transmitting wireless communications.

[0052] In some embodiments, the interface device 4006 may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communications protocol. For example, the interface device 4006 may include circuitry to support communications according to Ethernet technology. In some embodiments, the interface device 4006 may support both wireless and wired communications, and / or may support multiple wired and / or wireless communications protocols. For example, a first set of circuits in the interface device 4006 may be dedicated to short-range wireless communications, such as Wi-Fi or Bluetooth, and a second set of circuits in the interface device 4006 may be dedicated to long-range wireless communications, such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In some embodiments, the first set of circuits in the interface device 4006 may be dedicated to wireless communications, and the second set of circuits in the interface device 4006 may be dedicated to wired communications.

[0053] Computing device 4000 may include battery / power circuitry 4008. Battery / power circuitry 4008 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 4000 to an energy source separate from computing device 4000 (e.g., AC line power).

[0054] The computing device 4000 may include a display device 4010 (e.g., multiple display devices). The display device 4010 may include any visual indicator, 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.

[0055] The computing device 4000 may include other input / output (I / O) devices 4012. The other I / O devices 4012 may include, for example, one or more audio output devices (e.g., speakers, headsets, earphones, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), a location device (e.g., a GPS device that communicates with a satellite-based system to receive the location of the computing device 4000, as is known in the art), an audio codec, a video codec, a printer, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, accelerometers, gyroscopes, etc.), an image capture device such as a camera, a keyboard, a cursor control device such as a mouse, stylus, trackball, or touchpad, a barcode reader, a Quick Response (QR) code reader, or a radio frequency identification (RFID) device. The device may include a radio frequency identification (RFID) reader.

[0056] The computing device 4000 may have any form factor suitable for its use and configuration, such as a handheld or mobile computing device (e.g., a mobile phone, smartphone, mobile internet device, tablet computer, laptop computer, netbook computer, ultrabook computer, personal digital assistant (PDA), ultra-mobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0057] One or more computing devices implementing any of the scientific instrument support modules or methods disclosed herein may be part of a scientific instrument support system. Figure 10 is a block diagram of an exemplary scientific instrument support system 5000 in which some or all of the scientific instrument support methods disclosed herein may be implemented, according to various embodiments. The scientific instrument support modules and methods disclosed herein (e.g., scientific instrument support module 1000 of Figure 6 and method 2000 of Figure 7) may be implemented by one or more of the scientific instrument 5010, user local computing device 5020, service local computing device 5030, or remote computing device 5040 of the scientific instrument support system 5000.

[0058] Any of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may include any of the embodiments of the computing device 4000 discussed herein with reference to FIG. 9, and any of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may take the form of any suitable of the embodiments of the computing device 4000 discussed herein with reference to FIG. 9.

[0059] The scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may each include a processing device 5002, a storage device 5004, and an interface device 5006. The processing device 5002 may take any suitable form, including any of the forms of the processing devices 4002 discussed herein with reference to Figure 9, and the processing devices 5002 included in different ones of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may take the same form or different forms. The storage device 5004 may take any suitable form, including any of the forms of the storage devices 5004 discussed herein with reference to Figure 9, and the storage devices 5004 included in different ones of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may take the same form or different forms. The interface device 5006 may take any suitable form, including any of the forms of the interface devices 4006 discussed herein with reference to FIG. 9, and the interface devices 5006 included in different ones of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 may take the same or different forms.

[0060] The scientific instruments 5010, the user local computing device 5020, the service local computing device 5030, and the remote computing device 5040 may communicate with other elements of the scientific instrument support system 5000 via communication paths 5008. The communication paths 5008 may communicatively couple the interface devices 5006 of the different elements of the scientific instrument support system 5000, as shown, and may be wired or wireless communication paths (e.g., according to any of the communication techniques discussed herein with reference to the interface device 4006 of the computing device 4000 of FIG. 9 ). While the particular scientific instrument support system 5000 depicted in FIG. 10 includes communication paths between each pair of the scientific instruments 5010, the user local computing device 5020, the service local computing device 5030, and the remote computing device 5040, this “fully connected” implementation is merely illustrative, and in various embodiments, various ones of the communication paths 5008 may not be present. For example, in some embodiments, the service local computing device 5030 may not have a direct communication path 5008 between its interface device 5006 and the interface device 5006 of the scientific instrument 5010, but instead may communicate with the scientific instrument 5010 via a communication path 5008 between the service local computing device 5030 and the user local computing device 5020, and a communication path 5008 between the user local computing device 5020 and the scientific instrument 5010.

[0061] Scientific instrument 5010 may include any suitable scientific instrument, such as a spectroscopic instrument (eg, an OES instrument).

[0062] The user local computing device 5020 may be a computing device local to a user of the scientific instrument 5010 (e.g., according to any of the embodiments of the computing device 4000 discussed herein). In some embodiments, the user local computing device 5020 may be local to the scientific instrument 5010, but need not be; for example, a user local computing device 5020 in the user's home or office may be remote from the scientific instrument 5010 but may communicate with it so that the user can control and / or access data from the scientific instrument 5010 using the user local computing device 5020. In some embodiments, the user local computing device 5020 may be a laptop, smartphone, or tablet device. In some embodiments, the user local computing device 5020 may be a portable computing device.

[0063] The servicing local computing device 5030 may be a computing device local to an entity that provides services to the scientific instrument 5010 (e.g., according to any of the embodiments of the computing device 4000 discussed herein). For example, the servicing local computing device 5030 may be local to the manufacturer of the scientific instrument 5010 or a third-party service company. In some embodiments, the servicing local computing device 5030 may communicate with the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., via a direct communication path 5008 or via multiple "indirect" communication paths 5008, as discussed above) to receive data regarding the operation of the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., results of self-diagnostic tests of the scientific instrument 5010, calibration coefficients used by the scientific instrument 5010, measurements of sensors associated with the scientific instrument 5010, etc.). In some embodiments, the service local computing device 5030 may communicate with the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., via a direct communication path 5008 or multiple "indirect" communication paths 5008, as discussed above) to transmit data to the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., to update programmed instructions such as firmware in the scientific instrument 5010, to initiate the performance of a test or calibration sequence in the scientific instrument 5010, to update programmed instructions such as software in the user local computing device 5020 or the remote computing device 5040, etc.).A user of the scientific instrument 5010 may use the scientific instrument 5010 or the user local computing device 5020 to communicate with the service local computing device 5030 to report a problem with the scientific instrument 5010 or the user local computing device 5020, to request a technician visit to improve the operation of the scientific instrument 5010, to order consumable or replacement parts associated with the scientific instrument 5010, or for other purposes.

[0064] The remote computing device 5040 may be a computing device that is remote from the scientific instrument 5010 and / or remote from the user local computing device 5020 (e.g., according to any of the embodiments of computing device 4000 discussed herein). In some embodiments, the remote computing device 5040 may be included in a data center or other large-scale server environment. In some embodiments, the remote computing device 5040 may include network-attached storage (e.g., as part of the storage device 5004). The remote computing device 5040 may store data generated by the scientific instrument 5010, perform analysis of the data generated by the scientific instrument 5010 (e.g., according to programmed instructions), facilitate communications between the user local computing device 5020 and the scientific instrument 5010, and / or facilitate communications between the service local computing device 5030 and the scientific instrument 5010.

[0065] In some embodiments, one or more of the elements of the scientific instrument support system 5000 illustrated in Figure 10 may not be present. Further, in some embodiments, more than one of various of the elements of the scientific instrument support system 5000 of Figure 10 may be present. For example, the scientific instrument support system 5000 may include multiple user local computing devices 5020 (e.g., different user local computing devices 5020 associated with different users or at different locations). In another example, the scientific instrument support system 5000 may include multiple scientific instruments 5010 that all communicate with a servicing local computing device 5030 and / or a remote computing device 5040; in such an embodiment, the servicing local computing device 5030 may monitor these multiple scientific instruments 5010, and the servicing local computing device 5030 may trigger updates or other information to be "broadcast" to the multiple scientific instruments 5010 simultaneously. Different scientific instruments 5010 in the scientific instrument support system 5000 may be located near each other (e.g., in the same room) or far from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the scientific instruments 5010 may be connected to an Internet-of-Things (IoT) stack that enables command and control of the scientific instruments 5010 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications may be accessed by a user operating a user local computing device 5020 that communicates with the scientific instruments 5010 through an intervening remote computing device 5040. In some embodiments, the scientific instruments 5010 may be sold by a manufacturer as part of a local scientific instrument computing unit 5012, along with one or more associated user local computing devices 5020.

[0066] [Example] The following paragraphs contain examples of various embodiments disclosed herein.

[0067] Example 1 is a method for assisting in spectroscopic calibration, comprising: receiving first calibration data, the first calibration data including amounts of chemical elements in a first sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths generated by spectroscopic analysis of the first sample with a first spectroscopic instrument; receiving second calibration data, the second calibration data including amounts of chemical elements in a second sample and a plurality of spectral intensities associated with the corresponding plurality of wavelengths generated by spectroscopic analysis of the second sample with a second spectroscopic instrument, the second spectroscopic instrument being different from the first spectroscopic instrument; and training a machine learning model using the first calibration data and the second calibration data. generating a calibration model relating a plurality of spectral intensities at a corresponding plurality of wavelengths to an amount of the chemical element in the sample; receiving third calibration data including an amount of the chemical element in a third calibration sample and a plurality of spectral intensities associated with the corresponding plurality of wavelengths generated by spectroscopic analysis of the third sample by a third spectroscopic instrument, the third spectroscopic instrument being different from the first spectroscopic instrument and the second spectroscopic instrument; and generating a second calibration model relating a plurality of spectral intensities at a corresponding plurality of wavelengths to an amount of the chemical element in the sample by using the third calibration data to train a machine learning model based on the first calibration model.

[0068] Example 2 includes the subject matter of Example 1, further specifying that the first sample has the same material composition as the second sample.

[0069] Example 3 includes the subject matter of Example 1, further specifying that the first sample has a different material composition than the second sample.

[0070] Example 4 includes the subject matter of any of Examples 1-3, further providing that the number of wavelengths associated with corresponding spectral intensities in the first calibration data is less than a total number of wavelengths having associated spectral intensities output by the first spectroscopic device during spectroscopic analysis of the first sample.

[0071] Example 5 includes the subject matter of any of Examples 1-4, further providing that the number of wavelengths associated with corresponding spectral intensities in the second calibration data is less than the total number of wavelengths having associated spectral intensities output by the second spectroscopic device during spectroscopic analysis of the second sample.

[0072] Example 6 includes the subject matter of any of Examples 1-5, further including using the calibration model to output the amounts of the chemical elements in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test.

[0073] Example 7 includes the subject matter of any of Examples 1-6, further specifying that the third sample has the same material composition as the second sample.

[0074] Example 8 includes the subject matter of any of Examples 1-6, further specifying that the third sample has a different material composition than the second sample.

[0075] Example 9 includes the subject matter of any of Examples 1-8, further providing that the number of wavelengths associated with corresponding spectral intensities in the third calibration data is less than a total number of wavelengths having associated spectral intensities output by the third spectroscopic device during spectroscopic analysis of the third sample.

[0076] Example 10 includes the subject matter of any of Examples 1-9, further including using the second calibration model to output the amounts of the chemical elements in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test with the third spectroscopic instrument.

[0077] Example 11 includes the subject matter of any of Examples 1-10, further providing that an amount of the third calibration data used to generate the second calibration model is less than an amount of the first calibration data used to generate the first calibration model.

[0078] Example 12 is a method for assisting in spectroscopic calibration, comprising receiving calibration data including amounts of chemical elements in a sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample with a spectroscopic instrument, wherein the number of wavelengths associated with the corresponding plurality of spectral intensities in the calibration data is less than a total number of wavelengths having associated spectral intensities output by the spectroscopic instrument during spectroscopic analysis of the sample; and generating a calibration model relating the plurality of spectral intensities at the corresponding plurality of wavelengths to the amounts of the chemical elements in the sample based on receiving the calibration data and training a machine learning model using the calibration data. and receiving second calibration data including amounts of chemical elements in a second sample and a plurality of spectral intensities associated with the corresponding plurality of wavelengths produced by spectroscopic analysis of the second sample by a second spectroscopic instrument, wherein the number of wavelengths associated with the corresponding plurality of spectral intensities in the second calibration data is less than a total number of wavelengths having associated spectral intensities output by the second spectroscopic instrument during spectroscopic analysis of the sample, and the second spectroscopic instrument is different from the first spectroscopic instrument; and wherein a calibration model is generated based on training a machine learning model using the second calibration data.

[0079] Example 13 includes the subject matter of Example 12, further specifying that the first sample has the same material composition as the second sample.

[0080] Example 14 includes the subject matter of Example 12, further specifying that the first sample has a different material composition than the second sample.

[0081] Example 15 includes the subject matter of any of Examples 12-14, further including using the calibration model to output the amount of the chemical element in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test.

[0082] Example 16 includes the subject matter of any of Examples 12-15, further providing that the calibration model is a first calibration model, the spectroscopic instrument is a first spectroscopic instrument, the calibration data is the first calibration data, and the method further includes receiving second calibration data including amounts of chemical elements in a second calibration sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the second sample by the second spectroscopic instrument, the second spectroscopic instrument being different from the first spectroscopic instrument; and generating a second calibration model relating the plurality of spectral intensities at the corresponding plurality of wavelengths to the amounts of the chemical elements in the sample by using the second calibration data to train a machine learning model based on the first calibration model.

[0083] Example 17 includes the subject matter of Example 16, further providing that the second sample has the same material composition as the first sample.

[0084] Example 18 includes the subject matter of Example 16, further specifying that the second sample has a different material composition than the first sample.

[0085] Example 19 includes the subject matter of any of Examples 16-18, further providing that the number of wavelengths associated with the corresponding plurality of spectral intensities in the second calibration data is less than the total number of wavelengths having associated spectral intensities output by the second spectroscopic device during spectroscopic analysis of the second sample.

[0086] Example 20 includes the subject matter of any of Examples 16-19, further including using the second calibration model to output the amounts of the chemical elements in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test with the second spectroscopic instrument.

[0087] Example 21 includes the subject matter of any of Examples 16-20, further specifying that an amount of the second calibration data used to generate the second calibration model is less than an amount of the first calibration data used to generate the first calibration model.

[0088] Example 22 is a method for assisting in spectroscopic calibration, the method including: receiving calibration data including amounts of chemical elements in a sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths generated by spectroscopic analysis of the sample by a spectroscopic instrument, wherein the number of wavelengths associated with the corresponding plurality of spectral intensities in the calibration data is less than the total number of wavelengths having associated spectral intensities output by the spectroscopic instrument during spectroscopic analysis of the sample; and generating a target calibration model for the spectroscopic instrument relating the plurality of spectral intensities at the corresponding plurality of wavelengths to the amounts of the chemical elements in the sample based on training a machine learning model based on a base machine learning model using the calibration data for the spectroscopic instrument, wherein the base machine learning model is trained with calibration data from one or more spectroscopic instruments different from the spectroscopic instrument.

[0089] Example 23 includes the subject matter of example 22, further including using the target calibration model to output amounts of the chemical elements in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test with the spectroscopic instrument.

[0090] Example 24 is a method for assisting in spectroscopic calibration, the method including: receiving calibration data including amounts of chemical elements in a sample and a plurality of spectral intensities associated with a plurality of corresponding wavelengths generated by spectroscopic analysis of the sample by a spectroscopic instrument; and generating a target calibration model for the spectroscopic instrument relating the plurality of spectral intensities at the corresponding plurality of wavelengths to the amounts of the chemical elements in the sample based on training a machine learning model based on a base machine learning model using the calibration data for the spectroscopic instrument, wherein the base machine learning model is trained with calibration data from a plurality of spectroscopic instruments different from the spectroscopic instrument.

[0091] Example 25 includes the subject matter of example 24, further specifying that the number of wavelengths associated with corresponding spectral intensities in the calibration data is less than the total number of wavelengths having associated spectral intensities output by the spectroscopic device during spectroscopic analysis of the sample.

[0092] Example 26 includes the subject matter of any of Examples 24-25, further including using the target calibration model to output the amounts of the chemical elements in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test with the spectroscopic instrument.

[0093] Example 27 is a computer-implemented method that includes any of the methods disclosed herein (eg, any of the methods of Examples 1-26).

[0094] Example 28 is a computer-implemented method that includes any of the methods disclosed herein (eg, any of the methods of Examples 1-26).

[0095] Example 29 is a data processing apparatus, device, or system comprising means for carrying out any of the methods disclosed herein (eg, any of the methods of Examples 1-26).

[0096] Example 30 is a data processing apparatus, device, or system comprising a processor adapted or configured to perform any of the methods disclosed herein (e.g., any of the methods of Examples 1 to 26).

[0097] Example 31 is a computer-readable medium containing instructions that, when executed by a computer, cause the computer to perform any of the methods disclosed herein (e.g., any of the methods of Examples 1-26).

[0098] Example 32 is a computer program product including instructions that, when executed by a computer, cause the computer to perform any of the methods disclosed herein (e.g., any of the methods of Examples 1 to 26).

[0099] Example 33 includes any of the scientific instrument support modules disclosed herein.

[0100] Example 34 includes any of the methods disclosed herein.

[0101] Example 35 includes any of the GUIs disclosed herein.

[0102] Example 36 includes any of the scientific instrument-assisted computing devices and systems disclosed herein.

Claims

1. 1. A method for assisting spectroscopic calibration, comprising: receiving first calibration data, the first calibration data including amounts of chemical elements in a first sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the first sample with a first spectroscopic instrument; receiving second calibration data, the second calibration data including amounts of the chemical elements in a second sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the second sample with a second spectroscopic instrument, the second spectroscopic instrument being different from the first spectroscopic instrument; generating a calibration model relating a plurality of spectral intensities at a plurality of corresponding wavelengths to amounts of the chemical element in the sample based on training a machine learning model using the first calibration data and the second calibration data; receiving third calibration data, the third calibration data including amounts of the chemical elements in a third sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the third sample by a third spectroscopic instrument, the third spectroscopic instrument being different from the first spectroscopic instrument and the second spectroscopic instrument; and generating a second calibration model relating a plurality of spectral intensities at a corresponding plurality of wavelengths to the amount of the chemical element in the sample by using the third calibration data to train a machine learning model based on the first calibration model.

2. 2. The method of claim 1, wherein the number of wavelengths associated with the corresponding plurality of spectral intensities in the first calibration data is less than a total number of wavelengths having associated spectral intensities output by the first spectroscopic instrument during spectroscopic analysis of the first sample.

3. 2. The method of claim 1, wherein the number of wavelengths associated with the corresponding plurality of spectral intensities in the second calibration data is less than the total number of wavelengths having associated spectral intensities output by the second spectroscopic instrument during spectroscopic analysis of the second sample.

4. 10. The method of claim 1, further comprising using the calibration model to output the amount of the chemical element in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test.

5. The method of claim 1 , wherein the third sample has the same material composition as the second sample.

6. The method of claim 1 , wherein the third sample has a different material composition than the second sample.

7. 2. The method of claim 1, wherein the number of wavelengths associated with the corresponding plurality of spectral intensities in the third calibration data is less than the total number of wavelengths having associated spectral intensities output by the third spectroscopic instrument during spectroscopic analysis of the third sample.

8. 10. The method of claim 1, further comprising using the second calibration model to output the amount of the chemical element in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test by the third spectroscopic instrument.

9. 2. The method of claim 1, wherein an amount of the third calibration data used to generate the second calibration model is less than an amount of the first calibration data used to generate the first calibration model.

10. 1. A method for assisting spectroscopic calibration, comprising: receiving calibration data including amounts of chemical elements in a sample and a plurality of spectral intensities associated with corresponding wavelengths produced by spectroscopic analysis of the sample with a spectroscopic instrument, wherein the number of wavelengths associated with the corresponding spectral intensities in the calibration data is less than a total number of wavelengths having associated spectral intensities output by the spectroscopic instrument during spectroscopic analysis of the sample; generating a calibration model relating a plurality of spectral intensities at a plurality of corresponding wavelengths to amounts of the chemical elements in the sample based on training a machine learning model using the calibration data; receiving second calibration data including amounts of the chemical elements in a second sample and a plurality of spectral intensities associated with corresponding wavelengths produced by spectroscopic analysis of the second sample by a second spectroscopic instrument, wherein the number of wavelengths associated with the corresponding spectral intensities in the second calibration data is less than a total number of wavelengths having associated spectral intensities output by the second spectroscopic instrument during spectroscopic analysis of the sample, and the second spectroscopic instrument is different from the spectroscopic instrument; The method, wherein the calibration model is generated based on training the machine learning model using the second calibration data.

11. The method of claim 10 , wherein the first sample has the same material composition as the second sample.

12. The method of claim 10 , wherein the first sample has a different material composition than the second sample.

13. 11. The method of claim 10, further comprising using the calibration model to output the amount of the chemical element in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test.

14. the calibration model is a first calibration model, the spectroscopic instrument is a first spectroscopic instrument, the calibration data is first calibration data, and the method comprises: receiving second calibration data, the second calibration data including amounts of the chemical elements in a second sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the second sample with a second spectroscopic instrument, the second spectroscopic instrument being different from the first spectroscopic instrument; 14. The method of claim 13, further comprising: using the second calibration data to train a machine learning model based on the first calibration model to generate a second calibration model relating a plurality of spectral intensities at corresponding wavelengths to amounts of the chemical element in the sample.

15. The method of claim 14 , wherein the second sample has the same material composition as the first sample.

16. The method of claim 14 , wherein the second sample has a different material composition than the first sample.

17. 15. The method of claim 14, wherein the number of wavelengths associated with the corresponding plurality of spectral intensities in the second calibration data is less than the total number of wavelengths having associated spectral intensities output by the second spectroscopic instrument during spectroscopic analysis of the second sample.

18. 1. A method for assisting spectroscopic calibration, comprising: receiving calibration data including amounts of chemical elements in a sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample with a spectroscopic instrument; generating a target calibration model for the spectroscopic instrument relating a plurality of spectroscopic intensities at a plurality of corresponding wavelengths to amounts of chemical elements in a sample based on training a machine learning model based on a base machine learning model using the calibration data for the spectroscopic instrument, wherein the base machine learning model is trained with calibration data from a plurality of spectroscopic instruments different from the spectroscopic instrument.

19. 20. The method of claim 18, wherein the number of wavelengths associated with the corresponding spectral intensities in the calibration data is less than the total number of wavelengths having associated spectral intensities output by the spectroscopic instrument during spectroscopic analysis of the sample.

20. 20. The method of claim 18, further comprising using the target calibration model to output the amount of the chemical element in the sample under test based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths produced by spectroscopic analysis of the sample under test with the spectroscopic instrument.

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