Systems and methods for spectrometer calibration

Abstract

Scientific instrument support systems and related methods, computing devices, and computer readable media are disclosed herein. For example, in some embodiments, a method of supporting spectral calibration may include generating a base calibration model using data from a plurality of base spectral instruments, and trimming the base calibration model using data from a target spectral instrument to generate a target calibration model for use with the target spectral instrument. In some embodiments, the number of wavelengths used to generate the base calibration model and / or the target calibration model may be less than the total number of wavelengths represented in the outputs of the spectrum instruments.

Description

[0001] Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 394,553 filed on August 2, 2022, the entire contents of which are incorporated herein by reference. Background Art

[0003] Many scientific instruments require calibration, i.e., a correlation between the output of a scientific instrument and a known state or property. For example, a spectroscopic instrument may output an intensity as a function of a property of a sample, and the calibration of such a spectroscopic instrument may specify the relationship between the output intensity and the sample property. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] The embodiments will be easily understood by the following detailed description in conjunction with the accompanying drawings. For ease of description, the same reference numerals designate the same structural elements. The embodiments are illustrated in the various figures of the accompanying drawings by way of example and not by way of limitation.

[0005] Figure 1 is a flow chart 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.

[0006] Figure 2 Shown are the full spectrum of intensities that can be used in the calibration model and a sampling of the spectrum according to various embodiments.

[0007] Figure 3 and Figure 4 Example neural network model architectures that may be used for base models and target models are shown according to various embodiments.

[0008] Figure 5A and Figure 5B Example performance results are shown for calibrating a spectroscopic instrument to identify the amount of sulfur in a metal sample using a base model and a fine-tuned target model, respectively, according to various embodiments.

[0009] Figure 6 is a block diagram of an example scientific instrument support module for performing support operations according to various embodiments.

[0010] Figure 7 is a flow diagram of an example method of performing support operations according to various embodiments.

[0011] Figure 8 is an example of a graphical user interface that may be used to perform some or all of the supporting methods disclosed herein, according to various embodiments.

[0012] Fig. 9is a block diagram of an example computing device that may perform some or all of the scientific instrument support methods disclosed herein, according to various embodiments.

[0013] Fig.10 is a block diagram of an example scientific instrument support system in which some or all of the scientific instrument support methods disclosed herein may be performed, according to various embodiments. DETAILED DESCRIPTION

[0014] Scientific instrument support systems and related methods, computing devices, and computer-readable media are disclosed herein. For example, in some embodiments, a method for supporting spectral calibration may include: receiving first calibration data, wherein the first calibration data includes the amount of a chemical element in a first sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by a first spectroscopic instrument performing a spectral analysis on the first sample; receiving second calibration data, wherein the second calibration data includes the amount of the chemical element in a second sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by a second spectroscopic instrument performing a spectral analysis on the second sample, and wherein the second spectroscopic instrument is different from the first spectroscopic instrument; and generating a calibration model based on training a machine learning model using the first calibration data and the second calibration data, the calibration model associating a plurality of spectral intensities at the corresponding plurality of wavelengths with the amount of the chemical element in the sample. In some such embodiments, the number of the plurality of wavelengths associated with the corresponding plurality of 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 the spectral analysis of the first sample. In some such embodiments, the calibration model is a first calibration model, and the method further includes: receiving third calibration data, wherein the third calibration data includes the amount of the chemical element in a third calibration sample and a plurality of spectral intensities associated with corresponding multiple wavelengths, the plurality of spectral intensities being generated by spectrally analyzing the third sample using a third spectroscopic instrument, and wherein the third spectroscopic instrument is different from the first spectroscopic instrument and the second spectroscopic instrument; and generating a second calibration model by training a machine learning model based on the first calibration model using the third calibration data, the second calibration model associating the plurality of spectral intensities at corresponding multiple wavelengths with the amount of the chemical element in the sample.

[0015] The scientific instrument support embodiments disclosed herein can achieve improved performance relative to conventional methods. For example, conventional calibration of spectral instruments usually requires measuring dozens or hundreds of samples, and then fitting the resulting intensity data to the amount of different chemical elements or other components of the sample (for example, using linear, quadratic or cubic functions). The interaction between different chemical elements in the intensity spectrum of the sample may cause constructive interference, destructive interference or other (sometimes nonlinear) effects, so conventional calibration requires the expertise of well-trained technicians to properly compensate for these effects, thereby further increasing the complexity and time of the calibration process. In addition, the traditional calibration process may need to be implemented in different ways, and / or may produce different results for each instrument, thereby requiring a lot of time and effort. Thus, the embodiments disclosed herein provide improvements to scientific instrument technology (for example, improvements in computer technology supporting such scientific instruments, and other improvements).

[0016] Relative to conventional methods, the embodiments disclosed herein can achieve faster, more accurate, and less labor-intensive calibration of spectroscopic instruments. For example, as discussed further below, conventional calibration methods typically require multiple days of hands-on effort by a well-trained technician to achieve a successful calibration. These methods suffer from a number of technical problems and limitations, including the inability to transfer the use of a spectroscopic instrument from one use case to another without another full calibration, resulting in instrument downtime.

[0017] Each embodiment in the embodiments disclosed herein can improve the traditional method to achieve the technical advantages of faster, more accurate and more labor-saving calibration by utilizing specific data from the calibration of similar spectroscopic instruments. Such technical advantages cannot be achieved by routine and conventional methods, and all users of the system including such embodiments can benefit from these advantages (for example, by helping users perform technical tasks, such as calibrating spectroscopic instruments through guided human-computer interaction processes). Therefore, the technical features of the embodiments disclosed herein are obviously unconventional in the field of spectroscopy, as are the combinations of the features of the embodiments disclosed herein. The calculation and user interface features disclosed herein not only involve the collection and comparison of information, but also apply new analysis and technical techniques to change the operation of the instrument by improving the calibration of the spectroscopic instrument (without the calibration, the instrument cannot perform its most basic functions). Therefore, the present disclosure introduces functions that both conventional computing devices and humans cannot perform.

[0018] Thus, embodiments of the present disclosure may serve any of a number of technical purposes, such as controlling a particular technical system or process (e.g., a spectroscopic instrument system and associated analytical processes); determining how to control a machine based on measurement results; reducing the amount of calibration data to be processed; and / or providing more efficient processing of existing calibration data. In particular, the present disclosure provides technical solutions to technical problems, including but not limited to properly calibrating spectroscopic instruments to achieve accurate analytical results.

[0019] Thus, embodiments disclosed herein provide improvements in analytical instrument technology (eg, improvements in computer technology that supports analytical instrumentation, among other improvements).

[0020] In the following detailed description, reference is made to the accompanying drawings, which form a part of the detailed description, wherein the same reference numerals designate the same parts throughout, and in the accompanying drawings, practicable embodiments are shown by way of example. It should 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 should not be considered to have a limiting meaning.

[0021] The various operations may be described as a plurality of discrete actions or operations in a manner that is most helpful for understanding the subject matter disclosed herein. However, the described order should not be interpreted as implying that these operations must rely on the order. Specifically, these operations may not be performed in the order presented. The described operations may be performed in an order different from the described embodiments. Various additional operations may be performed, and / or the described operations may be omitted in additional embodiments.

[0022] 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 appropriate element may be represented by multiple instances of the element, and vice versa. For example, a set of operations described as being performed by a processing device may be implemented by different ones of the operations being performed by different processing devices.

[0023] This specification uses the phrases "embodiment," "various embodiments," and "some embodiments," each of which may refer to one or more of the same or different embodiments. In addition, the terms "including," "comprising," "having," and the like as used with respect to the embodiments of the present disclosure are synonymous. When used to describe a range of sizes, the phrase "between X and Y" means a range that includes X and Y. As used herein, "device" may refer to any individual device, collection of devices, part of a device, or collection of parts of a device. The drawings are not necessarily drawn to scale.

[0024] As described above, embodiments disclosed herein can reduce the time and complexity of calibrating new or uncalibrated (or miscalibrated) spectroscopic instruments. Various embodiments include using a base model and / or a model transfer via fine tuning. As further discussed herein, a base model can be a machine learning model trained based on calibration data of multiple spectroscopic instruments (referred to herein as "base" spectroscopic instruments for ease of explanation). This base model has been trained based on specific data to read the spectrum of an instrument of a related type and to take into account variations between instruments. Embodiments including model transfer can use a small amount of data from another instrument to fine tune the base model to "fit" the base model to the other instrument (referred to herein as "target" instrument for ease of explanation). Therefore, a finely tuned model can be trained to read the spectrum of a target instrument, and the target instrument can be used to provide an accurate measurement result of an expected value. This finely tuned model can be used as an instrument calibration model, and will be used by users of spectroscopic instruments to perform measurements. The fine tuning process can provide one or more benefits for the calibration process. For example, various embodiments of the embodiments disclosed herein can reduce the amount of data from an instrument required to calibrate the instrument, requiring only a relatively small amount of data from the instrument to teach the base model the changes between the previous instrument and the target instrument. In another example, various embodiments of the embodiments disclosed herein can automate the model learning and fine-tuning process, thereby allowing a less skilled operator to successfully calibrate and operate an instrument that would normally require a highly skilled operator who can manually make decisions regarding the construction of a calibration curve.

[0025] Figure 11 is a flow chart 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 the associated target model 112 can receive as input the spectral intensity at multiple wavelengths of a given sample, and can generate as output the amount of one or more chemical elements or other components in the sample. For example, a sample can be provided to the target instrument 108, a resulting output of intensities at one or more wavelengths can be generated, and those intensities / wavelengths can be provided to the target model 112, which can output the amounts of various chemical elements in the sample (e.g., 70.27% iron, 18.09% chromium, 9.21% nickel, 0.99% niobium, 0.788% manganese, etc.).

[0026] When the target instrument 108 is a new instrument from an existing product line of base instruments 102, a large amount of base calibration data 104 from the calibration of the base instrument 102 may be available. This base calibration data 104 may reflect complex patterns for proper understanding of the spectral data from the base instrument 102, and thus this base calibration data 104 may be used to train the base model 106 to read and provide measurements of the data collected by the base instrument 102.

[0027] One problem with this large set of base calibration data 104 data is that it often includes variations from 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. When all historical calibration data are merged into the base calibration data 104, these variations increase the difficulty of learning how to process the spectral data. In some embodiments of the systems and methods disclosed herein, the problem of inter-instrument variation can be solved by refining the spectral data into simpler features, which themselves constitute the base calibration data 104, and these features are informed by the physical processes behind the measurements. These refined spectral features can remove or at least reduce the inter-instrument variability in the base calibration data 104, which can simplify the ML learning process and thus can (1) reduce the amount of base calibration data 104 required to train the base model 106, (2) reduce the amount of target calibration data 110 required to train the target model 112, and / or (3) produce a more accurate target model 112 for calibration.

[0028] In some embodiments, the "distilled" features that can be used as base calibration data 104 to train base model 106 can correspond to a sampling of the available full spectrum. For example, when the full set of available calibration data includes intensities associated with each of N wavelengths, the base calibration data set 104 can include intensities associated with M wavelengths, where M is less than N. In various embodiments, M can be less than 50% of N, less than 25% of N, less than 10% of N, or less than 5% of N. The specific wavelengths selected for base calibration data 104 can be selected to correspond to those wavelengths at which line elements of the chemical element (or other component) of interest appear. As an example, Figure 2 The intensity measurements associated with a particular sample across the full spectrum of available wavelengths are shown as darker lines, and the subset of wavelengths (along with associated intensities) used to train the base model 106 as part of the base calibration data 104 are also shown as lighter lines. Figure 2 In the example of FIG. 1 , the number of wavelengths used in the base calibration data 104 is 120, representing less than 1% of the total number of available wavelengths in the full spectrum.

[0029] 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 allow the base model 106 and the target model 112 to be adapted to train nonlinearities, correlations, anti-correlations, and other relationships in the calibration data, which may be desirable for calibrating the target instrument 108. It should be noted that the base model 106 and the target model 112 may have the same or similar architecture to facilitate transfer learning between models.

[0030] Figure 3 An example neural network model architecture for a base model 106 / target model 112 is shown. 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 (comprising data representing the spectral intensity of the sample at various wavelengths), the output of the convolutional layer 116 may be provided as an input to the first dense layer 118, the output of the first dense layer 118 may be provided as an input to the second dense layer 120, and the output of the second dense layer 120 may be provided to the output layer 122 (comprising data representing the estimated amount of various chemical elements or other components in the sample). In a specific 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 two, the first dense layer 118 may have 28 nodes, the second dense layer 120 may have six nodes, and the output layer 122 may have one node (corresponding to the estimated amount of the associated chemical element or other component). Figure 3The model architecture can be a convolutional neural network with fully connected layers using any suitable activation function, such as the rectified linear (ReLU) activation function. Figure 3 The specific number and arrangement of layers in are merely illustrative, and other numbers and arrangements of layers may be used for base model 106 / target model 112. Using convolutional layers such as convolutional layer 116 may be particularly helpful when input 114 is substantially a full spectrum (rather than a sparsely sampled set of wavelengths as discussed elsewhere herein). Figure 3 The specific number and arrangement of layers in are merely exemplary, and other numbers and arrangements of layers may be used for base model 106 / target model 112 .

[0031] As described above, in some embodiments, the base model 106 and the target model 112 may be trained to estimate the amount of multiple chemical elements or other components in a sample based on the spectral intensity 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 amount of different chemical elements in the sample. Therefore, in such embodiments, the determination of the amount of different chemical elements in a sample may include inputting the spectral intensity at multiple wavelengths into different base models 106 / target models 112, wherein each model generates an estimated amount of the associated chemical element in the sample. When a particular user is only interested in a small number of chemical elements, creating different models for different chemical elements allows for a reduction in the total amount of computation at the expense of managing multiple chemical element specific models (rather than one multi-chemical element model).

[0032] Figure 4 An example neural network model architecture for a base model 106 / target model 112 is shown in an embodiment in which each chemical element is associated with a different base model 106 / target model 112 (and thus identifying the amounts of multiple chemical elements or other components in a sample requires the use of multiple associated base models 106 / target models 112). Figure 4In an example of , a first layer 126 may receive an input 124 (comprising data representing the spectral intensity of a sample at various wavelengths), an output of the first layer 126 may be provided as an input to a second layer 128, an output of the second layer 128 may be provided as an input to a third layer 130, an output of the third layer 130 may be provided to a fourth layer 132, and an output of the fourth layer 132 may be provided to an output layer 134 (comprising data representing an estimated amount of a chemical element or other constituent 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 one node (corresponding to the estimated amount of the associated chemical element or other constituent). Figure 4 The model architecture can be a fully connected network using any suitable activation function, such as the rectified linear (ReLU) activation function. Figure 4 The specific number and arrangement of layers in are merely exemplary, and other numbers and arrangements of layers may be used for base model 106 / target model 112 .

[0033] 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 a loss function during training, and training may be stopped early when the error stops improving (using a 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.

[0034] After the base model 106 is trained, it can be stored on a central server (e.g., a remote computing device 5040 and / or a service local computing device 5030, as described below with reference to Fig.10 108 (e.g., a "new" instrument being calibrated), the operator can retrieve the base model 106 and fine-tune it using the target calibration data 110 to generate a target model 112 for the target instrument 108.

[0035] As described above, in order to "transfer" the base model 106 to the target instrument 108, data from the target instrument 108 can be collected and used to fine-tune the base model 106 to the target instrument 108, thereby generating the target model 112. This fine-tuning process can change the base model 107 to account for the differences between the base instrument 102 and the target instrument 108, which is usually solved by creating a brand new calibration. Unlike traditional calibration methods, for the transfer learning process discussed herein, the operator does not need to understand and identify specific changes because the fine-tuning process will determine these changes by itself. The deep learning model of the base model 106 and the target model 112 can easily adapt to several types of changes, including spectral shifts, intensity shifts, intensity correlations, nonlinear effects, and / or others. Fine-tuning the base model 106 to generate the target model 112 can be performed according to any suitable technique known in the art, such as the techniques described in "Deep calibration transfer: Transferring deep learning models between infrared spectroscopy instruments" by Puneet Mishra and Dário Passos (Infrared Physics & Technology, Vol. 117, 2021, 103863).

[0036] The amount of target calibration data 110 used to fine-tune the base model 106 may be significantly less than the amount of base calibration data 104 used to train the base model 106, and may also be significantly less than the amount of data required to perform a full routine calibration of the target instrument 108. For example, for some spectroscopy instruments (such as some optical emission spectroscopy (OES) instruments, e.g., spark source spectroscopy instruments), only 10% to 20% of the data generated during a routine 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 has been fine-tuned to generate the target model 112, the target calibration data 110 used in the fine-tuning may be added to the set of base calibration data 104 and used to update the base model 106 as needed.

[0037] Figure 5A and Figure 5B Example performance results are shown for calibrating a spectroscopic instrument to identify the amount of sulfur in a metal sample (a particularly difficult spectroscopic task) using a base model 106 and a fine-tuned target model 112, respectively. In this particular example, base calibration data 104 from 46 base instruments 102 are used to train the base model 106 to generate a predicted amount of sulfur in a sample based on the base instrument 102 measurements of the sample, as shown in FIG. Figure 5AThe base calibration data 104 used in this example represents data collected over the course of several years of intensive calibration efforts. Figure 5B 1 represents the performance (also strong performance) of a fine-tuned target model 112 after retraining the base model 106 using target calibration data 110 from the target instrument 108, where the amount of target calibration data 110 is equal to about 20% of the amount of data typically required to calibrate a spectroscopic instrument.

[0038] In some embodiments, the initial target model 112 may be generated by the manufacturer or seller of the target instrument 108, and the target model 112 may be retrained or otherwise updated by the purchaser or other user of the target instrument 108 to implement recalibration. Such recalibration may be performed to compensate for drift or other small changes in the performance of the target instrument 108 since the initial calibration, and / or may be performed to enable the target instrument 108 to measure different use cases. For example, the target instrument 108 may be calibrated at the factory with the initial target model 112 for a specific use case, such as measuring iron-based metals in a sample. Although the target instrument 108 has all the components required for a different use case, such as measuring aluminum-based metals, the target instrument 108 may not be initially calibrated for that use case. Different use cases generally require different calibrations due to different interactions between elements of a plasma, such as a spectroscopic instrument. If a user wishes to use the target instrument 108 for a use case different from the use case for which it was originally calibrated, conventional calibration requires that the target instrument 108 be sent back to the factory for recalibration, or a technician be sent to the location of the target instrument 108 to perform a manual calibration. This results in significant downtime and cost to the user and must be repeated every time usage changes.

[0039] However, using the systems and methods disclosed herein, a fine-tuning process can be utilized to allow a user to quickly and easily perform a new calibration of a target instrument 108. In some such embodiments, the user will use the target instrument 108 to measure a selected number of reference samples for a new use case (e.g., provided by the manufacturer of the target instrument 108), with the results serving as the target calibration data 110 for fine-tuning the base model 106 trained on the base calibration data 104 for the same use case. The resulting target model 112 can be used as a new calibration for the target instrument 108 for the new use case. In some embodiments, the base calibration data 104, the target calibration data 110, and the spectral data provided to the target model 112 during operation of the target instrument 108 may have been pre-processed using conventional methods to, for example, map detector pixels to wavelengths, compensate for temperature-induced drift, etc.

[0040] The total reduction in calibration complexity and time achieved by the systems and methods disclosed herein can be significant relative to conventional methods. For example, for conventional calibration of some OES instruments, approximately 100 samples may be required to calibrate the instrument and a technician manually create a calibration curve for the instrument. For each individual instrument, this calibration may take 1 to 2 days to perform. Using the systems and methods disclosed herein, the number of samples required to fine-tune the model for a particular instrument, the amount of input required by the technician, and the technician's technical knowledge can be reduced.

[0041] Figure 6 1 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 logical components of the scientific instrument support module 1000 may be included in a single computing device, or may be distributed across multiple computing devices that communicate with each other as appropriate. Fig. 9 The computing device 4000 of the present invention discusses an example of a computing device that can implement the scientific instrument support module 1000 alone or in combination, and the present invention is referred to herein. Fig.10 The scientific instrument support system 5000 of FIG. 5 discusses an example of a system of interconnected computing devices, wherein the scientific instrument support module 1000 may be implemented across one or more of these computing devices.

[0042] The scientific instrument support module 1000 may include a first logic component 1002, a second logic component 1004, and a third logic component 1006. As used herein, the term "logic component" may include a device that performs a set of operations associated with the logic component. For example, any of the logic elements included in the support module 1000 may be implemented by one or more computing devices, which are programmed with instructions to cause one or more processing devices of the computing device to perform an associated set of operations. In a specific embodiment, the logic element may include one or more non-transient computer-readable media, which have instructions on the one or more non-transient computer-readable media, which, when executed by one or more processing devices in one or more computing devices, cause the one or more computing devices to perform an associated set of operations. As used herein, the term "module" may refer to a collection of one or more logic elements, which together perform functions associated with the module. Different logic elements in the logic elements in the module may take the same form or may take different forms. For example, some logic components in the module may be implemented by a programmed general-purpose processing device, while other logic components in the module may be implemented by an application-specific integrated circuit (ASIC). As 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 the associated figures; for example, when the module is to perform a subset of the operations discussed herein with reference to the module, the module may include a subset of the logic elements depicted in the associated figures.

[0043] According to any of the embodiments disclosed herein, the first logic component 1002 may receive calibration data from one or more spectroscopic instruments. 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).

[0044] According to any of the embodiments disclosed herein, the second logic component 1004 may train and deploy a base machine learning model trained using calibration data received by the first logic component 1002 (such as the base calibration model 106 trained using the base calibration data 104 ).

[0045] According to any of the embodiments disclosed herein, the third logic component 1006 may train and deploy a target machine learning model trained using calibration data received by the first logic component 1002 (such as the target calibration model 112 trained using the target calibration data 110 ).

[0046] Figure 7is a flow chart of a method 2000 of performing support operations according to various embodiments. Although reference may be made to specific embodiments disclosed herein (e.g., Figure 6 The scientific instrument support module 1000 discussed in this article is referenced Figure 8 GUI 3000 discussed in this article Fig. 9 The computing device 4000 discussed and / or referenced herein Fig.10 The operation of method 2000 is illustrated using the scientific instrument support system 5000 discussed above, but method 2000 can be used in any suitable setting to perform any suitable support operation. Figure 7 The operations are each illustrated once in a particular order, but the operations may be reordered and / or repeated as needed and appropriate (eg, different operations may be performed in parallel where appropriate).

[0047] At 2002, a first operation may be performed. For example, the first logic component 1002 of the support module 1000 may perform the operation of 2002. According to any implementation of the embodiments disclosed herein, the first operation may include receiving calibration data from one or more spectroscopic instruments. The calibration data may include basic calibration data obtained from a base spectroscopic instrument (such as the basic calibration data 104 of the base spectroscopic instrument 102), and / or target calibration data (such as the target calibration data 110 of the target spectroscopic instrument 108).

[0048] At 2004, a second operation may be performed. For example, the second logic component 1004 of the support module 1000 may perform the operation of 2004. According to any of the embodiments disclosed herein, the second operation may include training and deploying a base machine learning model trained using the calibration data received at 2002 (such as the base calibration model 106 trained using the base calibration data 104).

[0049] At 2006, a third operation may be performed. For example, the third logic component 1006 of the support module 1000 may perform the operation of 2006. According to any of the embodiments disclosed herein, the third operation may include training and deploying a target machine learning model trained using the calibration data received at 2002 (such as the target calibration model 112 trained using the target calibration data 110).

[0050] The scientific instrument support methods disclosed herein may include interaction with a human user (e.g., via the referenced Fig.10 These interactions may include providing information to the user (e.g., about scientific instruments such as Fig.10information about the operation of a scientific instrument 5010), information about samples 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 the user with the option of inputting commands (e.g., controlling a scientific instrument such as Fig.10 In some embodiments, these interactions may be performed through a graphical user interface (GUI) that includes a display device (e.g., a graphical user interface such as a display device ... Fig. 9 A visual display on a display device 4010 discussed herein) that provides output to a user and / or prompts a user to provide input (e.g., via a display device 4010 discussed herein). Fig. 9 Other I / O devices 4012 discussed herein include one or more input devices such as a keyboard, mouse, trackpad, or touch screen). The scientific instrument support system disclosed herein may include any suitable GUI for interacting with a user.

[0051] Figure 8 An example GUI 3000 is depicted that can be used to perform some or all of the supporting methods disclosed herein according to various embodiments. As described above, the GUI 3000 can be provided in a scientific instrument support system (e.g., as described herein with reference to Fig.10 A computing device (e.g., a scientific instrument support system 5000 as discussed herein) Fig. 9 The computing device 4000 discussed herein) may include a display device (eg, Fig. 9 The user may use any suitable input device (e.g., the display device 4010 discussed herein) to input the content of the display device 4010. Fig. 9 Any of the input devices included in the other I / O devices 4012 discussed) and input techniques (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button actuation, etc.) interact with the GUI 3000.

[0052] GUI 3000 may include a data display area 3002 , a data analysis area 3004 , a scientific instrument control area 3006 , and a settings area 3008 . Figure 8 The specific number and arrangement of regions depicted in GUI 3000 are merely exemplary, and any number and arrangement of regions (including any desired features) may be included in GUI 3000.

[0053] The data display area 3002 may display data generated by a scientific instrument (e.g., Fig.10 For example, the data display area 3002 may display the intensities associated with different wavelengths, as is known in the art.

[0054] The data analysis region 3004 can display the results of the data analysis (e.g., the results of analyzing the data illustrated in the data display region 3002 and / or other data). For example, the data analysis region 3004 can display the amounts of chemical elements or other components in the sample generated based on the intensities associated with different wavelengths and a calibration model, as discussed herein. In some embodiments, the data display region 3002 and the data analysis region 3004 can be combined in the GUI 3000 (e.g., to include the data output from the scientific instrument and some analysis of the data in a common graph or region).

[0055] The scientific instrument control area 3006 may include a user-friendly interface that allows the user to control scientific instruments (e.g., Fig.10 For example, the scientific instrument control area 3006 may include an option to initiate calibration (which may result in a prompt to the user on how to perform the calibration).

[0056] The settings area 3008 may include options that allow the user to control the features and functions of the GUI 3000 (and / or other GUIs) and / or perform common computing operations with respect to the data display area 3002 and the data analysis area 3004 (e.g., saving data to a storage device (such as a storage device referred to herein)). Fig. 9 4004, as discussed herein), sending data to another user, tagging data, etc.). For example, settings area 3008 may include an option to change usage (which may trigger recalibration, as discussed herein).

[0057] As noted above, the scientific instrument support module 1000 may be implemented by one or more computing devices. Fig. 9 is a block diagram of a computing device 4000 that can perform some or all of the scientific instrument support methods disclosed herein, according to various embodiments. In some embodiments, the scientific instrument support module 1000 can be implemented by a single computing device 4000 or by multiple computing devices 4000. In addition, as discussed below, the computing device 4000 (or multiple computing devices 4000) that implements the scientific instrument support module 1000 can be Fig.10 Part of one or more of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040.

[0058] Fig. 9The computing device 4000 is illustrated as having multiple components, but any one or more of these components may be omitted or repeated depending on suitability for the application and setting. In some embodiments, some or all of the components included in the computing device 4000 may be attached to one or more motherboards and encapsulated in a housing (e.g., including plastic, metal, and / or other materials). In some embodiments, some of these components may be manufactured on a single system on a chip (SoC) (e.g., the SoC may include one or more processing devices 4002 and one or more storage devices 4004). Additionally, in various embodiments, the computing device 4000 may not include Fig. 9 One or more of the illustrated components, 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., a connector and driver circuitry) to which display device 4010 may be coupled.

[0059] The 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 to convert the electronic data into other electronic data that can be stored in registers and / or memory. The processing device 4002 may include one or more digital signal processors (DSPs), application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (specialized processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing devices.

[0060] The computing device 4000 may include a storage device 4004 (e.g., one or more storage devices). The storage device 4004 may include one or more memory devices, such as a random access memory (RAM) (e.g., a static RAM (SRAM) device, a magnetic RAM (MRAM) device, a dynamic RAM (DRAM) device, a resistive RAM (RRAM) device, or a conductive bridge RAM (CBRAM) device), a hard drive-based memory device, a solid-state memory device, a network drive, a cloud drive, or any combination of memory devices. In some embodiments, the storage device 4004 may include a memory that shares a die with the processing device 4002. In such an embodiment, the memory may be used as a cache memory and may include, for example, an embedded dynamic random access memory (eDRAM) or a spin transfer torque magnetic random access memory (STT-MRAM). In some embodiments, the storage device 4004 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processing devices (e.g., processing device 4002), cause the computing device 4000 to perform any appropriate method or portion of the methods disclosed herein.

[0061] 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 manage communications between the computing device 4000 and other computing devices. For example, the interface device 4006 may include circuits for managing wireless communications for transmitting data to and from the computing device 4000. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that can communicate data through a non-solid medium using modulated electromagnetic radiation. The term does not imply that the associated device does not contain any wires, although in some embodiments it may not contain any wires. The circuitry included in the interface device 4006 for managing wireless communications may implement any of a plurality of wireless standards or protocols, including but not limited to Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendments), Long Term Evolution (LTE) projects, and any amendments, updates, and / or revisions (e.g., Advanced LTE projects, Ultra Mobile Broadband (UMB) projects (also known as "3GPP2"), etc.). In some embodiments, the circuitry included in the interface device 4006 for managing wireless communications may operate according to a Global System for Mobile Communications (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, the circuitry included in the interface device 4006 for managing wireless communications may operate in accordance with Enhanced Data 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 its derivatives, and any other wireless protocols designated as 3G, 4G, 5G, and higher versions, etc. In some embodiments, the interface device 4006 may include one or more antennas (e.g., one or more antenna arrays) to receive and / or send wireless communications.

[0062] In some embodiments, interface device 4006 may include a circuit for managing wired communication, such as electrical communication protocol, optical communication protocol or any other suitable communication protocol. For example, interface device 4006 may include a circuit supporting communication according to Ethernet technology. In some embodiments, interface device 4006 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 group of circuits of interface device 4006 may be dedicated to shorter range wireless communication such as Wi-Fi or Bluetooth, and a second group of circuits of interface device 4006 may be dedicated to longer range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In some embodiments, a first group of circuits of interface device 4006 may be dedicated to wireless communication, and a second group of circuits of interface device 4006 may be dedicated to wired communication.

[0063] The computing device 4000 may include a battery / power circuit 4008. The battery / power circuit 4008 may include one or more energy storage devices (e.g., batteries or capacitors), and / or circuits for coupling components of the computing device 4000 to an energy source separate from the computing device 4000 (e.g., AC line power).

[0064] Computing device 4000 may include display device 4010 (eg, multiple display devices). Display device 4010 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touch screen display, a liquid crystal display (LCD), a light emitting diode display, or a flat panel display.

[0065] 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, headphones, earbuds, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), a positioning 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, a sensor (e.g., a thermocouple or other temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, an accelerometer, a gyroscope, etc.), an image capture device such as a camera, a keyboard, a cursor control device such as a mouse, a stylus, a trackball, or a touchpad, a barcode reader, a quick response (QR) code reader, or a radio frequency identification (RFID) reader.

[0066] The computing device 4000 may have any suitable form factor suitable for its applications and settings, such as a handheld or mobile computing device (e.g., a cellular phone, a smart phone, a mobile Internet device, a tablet computer, a laptop computer, a netbook computer, an ultrabook computer, a personal digital assistant (PDA), an ultra-mobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0067] 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. Fig.10 is a block diagram of an example scientific instrument support system 5000 in which some or all of the scientific instrument support methods disclosed herein may be performed, according to various embodiments. The scientific instrument support modules and methods disclosed herein (e.g., Figure 6 The Scientific Instrument Support Module 1000 and Figure 7 The method 2000) may be implemented by one or more of the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, or the remote computing device 5040 of the scientific instrument support system 5000.

[0068] 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 the Fig. 9 Any of the embodiments of the computing device 4000 discussed herein, 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 adopt the embodiments described herein. Fig. 9 Any suitable form of implementation of the computing device 4000 discussed herein.

[0069] 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 suitable form described herein. Fig. 9 The processing device 5002 discussed above may be in the form of any processing device in the processing device 4002, and the processing device 5002 included in different devices 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 those described herein. Fig. 9The storage device 5004 discussed above may be in the form of any storage device, and the storage device 5004 included in different devices 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 those described herein. Fig. 9 The form of any of the interface devices 4006 discussed, and the interface devices 5006 included in different devices 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.

[0070] The scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, and the remote computing device 5040 can communicate with other elements of the scientific instrument support system 5000 via a communication path 5008. The communication path 5008 can communicatively couple the interface device 5006 (as shown) of different elements of the scientific instrument support system 5000, and can be a wired or wireless communication path (e.g., according to the present reference Fig. 9 any communication technology discussed in the communication technologies of the interface device 4006 of the computing device 4000). Fig.10 The specific scientific instrument support system 5000 depicted in FIG. 5 includes a communication path between each pair of devices in the scientific instrument 5010, the user local computing device 5020, the service local computing device 5030, and the remote computing device 5040, but this specific implementation of "fully connected" is merely illustrative, and in various embodiments, various communication paths in the communication paths 5008 may not exist. 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 may communicate with the scientific instrument 5010 via the communication path 5008 between the service local computing device 5030 and the user local computing device 5020 and the communication path 5008 between the user local computing device 5020 and the scientific instrument 5010.

[0071] Scientific instrument 5010 may include any suitable scientific instrument, such as a spectroscopic instrument (e.g., an OES instrument).

[0072] The user-local computing device 5020 can 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 can also be local to the scientific instrument 5010, but this is not necessarily the case; for example, a user-local computing device 5020 in a user's home or office can be remote from the scientific instrument 5010, but in communication with the scientific instrument so that the user can use the user-local computing device 5020 to control and / or access data from the scientific instrument 5010. In some embodiments, the user-local computing device 5020 can be a laptop, a smart phone, or a tablet device. In some embodiments, the user-local computing device 5020 can be a portable computing device.

[0073] The service local computing device 5030 can be a computing device local to the entity serving the scientific instrument 5010 (e.g., according to any of the embodiments of the computing device 4000 discussed herein). For example, the service local computing device 5030 can be a device local to the manufacturer of the scientific instrument 5010 or a third-party service company. In some embodiments, the service local computing device 5030 can 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 described above) to receive data about the operation of the scientific instrument 5010, the user local computing device 5020, and / or the remote computing device 5040 (e.g., self-test results of the scientific instrument 5010, calibration coefficients used by the scientific instrument 5010, measurement results 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 via multiple "indirect" communication paths 5008, as described above) to send data to the scientific instrument 5010, the user local computing device 5020 and / or the remote computing device 5040 (e.g., thereby updating programmed instructions (such as firmware) in the scientific instrument 5010 to initiate execution of a test or calibration sequence in the scientific instrument 5010, updating 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 utilize the scientific instrument 5010 or the user's local computing device 5020 to communicate with the service local computing device 5030 to report problems with the scientific instrument 5010 or the user's local computing device 5020, thereby requesting a technician visit to improve the operation of the scientific instrument 5010, order consumables or replacement parts associated with the scientific instrument 5010, or for other purposes.

[0074] The remote computing device 5040 can be a computing device remote from the scientific instrument 5010 and / or the user local computing device 5020 (e.g., according to any of the embodiments of the computing device 4000 discussed herein). In some embodiments, the remote computing device 5040 can be included in a data center or other large-scale server environment. In some embodiments, the remote computing device 5040 can include a network attached storage device (e.g., as part of the storage device 5004). The remote computing device 5040 can store data generated by the scientific instrument 5010, perform analysis of data generated by the scientific instrument 5010 (e.g., according to programmed instructions), facilitate communication between the user local computing device 5020 and the scientific instrument 5010, and / or facilitate communication between the service local computing device 5030 and the scientific instrument 5010.

[0075] In some embodiments, Fig.10 One or more of the elements of the scientific instrument support system 5000 illustrated in FIG. 5 may not be present. In addition, in some embodiments, Fig.10 Multiple of the various elements of the elements of the scientific instrument support system 5000 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 located in different locations). As another example, the scientific instrument support system 5000 may include multiple scientific instruments 5010, all of which communicate with the service local computing device 5030 and / or the remote computing device 5040; in such an embodiment, the service local computing device 5030 may monitor these multiple scientific instruments 5010, and the service local computing device 5030 may cause updates or other information to be "broadcasted" to multiple scientific instruments 5010 simultaneously. Different scientific instruments 5010 in the scientific instrument support system 5000 may be close to each other (e.g., in the same room) or far away from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the scientific instrument 5010 may be connected to an Internet of Things (IoT) stack that allows the scientific instrument 5010 to be commanded and controlled 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 instrument 5010 through an intermediary remote computing device 5040. In some embodiments, the scientific instrument 5010 may be sold by a manufacturer along with one or more associated user-local computing devices 5020 as part of a local scientific instrument computing unit 5012.

[0076] The following paragraphs include examples of various embodiments among the embodiments disclosed herein.

[0077] Embodiment 1 is a method for supporting spectral calibration, the method comprising: receiving first calibration data, wherein the first calibration data comprises the amount of a chemical element in a first sample and a plurality of spectral intensities associated with corresponding multiple wavelengths, the plurality of spectral intensities being generated by performing spectral analysis on the first sample by a first spectral instrument; receiving second calibration data, wherein the second calibration data comprises the amount of the chemical element in a second sample and a plurality of spectral intensities associated with corresponding multiple wavelengths, the plurality of spectral intensities being generated by performing spectral analysis on the second sample by a second spectral instrument, and wherein the second spectral instrument is different from the first spectral instrument; based on training a machine learning model using the first calibration data and the second calibration data The method comprises the steps of: generating a calibration model by using a machine learning model based on the first calibration model, wherein the calibration model associates multiple spectral intensities at corresponding multiple wavelengths with the amount of the chemical element in the sample; receiving third calibration data, wherein the third calibration data includes the amount of the chemical element in the third calibration sample and multiple spectral intensities associated with the corresponding multiple wavelengths, the multiple spectral intensities are generated by spectrally analyzing the third sample with a third spectroscopic instrument, and wherein the third spectroscopic instrument is different from the first spectroscopic instrument and the second spectroscopic instrument; and generating a second calibration model by training a machine learning model based on the first calibration model using the third calibration data, wherein the second calibration model associates multiple spectral intensities at corresponding multiple wavelengths with the amount of the chemical element in the sample.

[0078] Embodiment 2 includes the subject matter of embodiment 1, and further specifies that the first sample has the same material composition as the second sample.

[0079] Embodiment 3 includes the subject matter of embodiment 1, and further specifies that the first sample has a different material composition than the second sample.

[0080] Embodiment 4 includes the subject matter of any one of Embodiments 1 to 3, and further specifies that the number of the multiple wavelengths associated with the corresponding multiple 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 spectral analysis of the first sample.

[0081] Embodiment 5 includes the subject matter of any one of Embodiments 1 to 4, and further specifies that the number of the multiple wavelengths associated with the corresponding multiple 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 spectral analysis of the second sample.

[0082] Embodiment 6 includes the subject matter described in any one of Embodiments 1 to 5, and further includes: using the calibration model to output the amount of the chemical element in the tested sample based on multiple spectral intensities associated with corresponding multiple wavelengths, wherein the multiple spectral intensities are generated by performing spectral analysis on the tested sample.

[0083] Embodiment 7 includes the subject matter of any one of embodiments 1 to 6, and further specifies that the third sample has the same material composition as the second sample.

[0084] Embodiment 8 includes the subject matter of any one of embodiments 1-6, and further specifies that the third sample has a different material composition than the second sample.

[0085] Embodiment 9 includes the subject matter of any one of Embodiments 1 to 8, and further specifies that the number of the multiple wavelengths associated with the corresponding multiple 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 spectral analysis of the third sample.

[0086] Embodiment 10 includes the subject matter described in any one of Embodiments 1 to 9, and further includes: using the second calibration model to output the amount of the chemical element in the tested sample based on multiple spectral intensities associated with the corresponding multiple wavelengths, and the multiple spectral intensities are generated by performing spectral analysis on the tested sample by the third spectroscopic instrument.

[0087] Embodiment 11 includes the subject matter of any one of Embodiments 1 to 10, and further specifies 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.

[0088] Embodiment 12 is a method for supporting spectral calibration, the method comprising: receiving calibration data, wherein the calibration data comprises the amount of a chemical element in a sample and a plurality of spectral intensities associated with corresponding multiple wavelengths, the plurality of spectral intensities being generated by a spectral instrument performing spectral analysis on the sample, and wherein the number of the plurality of wavelengths associated with the corresponding multiple spectral intensities in the calibration data is less than the total number of wavelengths having associated spectral intensities output by the spectral instrument during spectral analysis of the sample; and generating a calibration model based on training a machine learning model using the calibration data, the calibration model associating the plurality of spectral intensities at the corresponding multiple wavelengths with the amount of the chemical element in the sample; and receiving second calibration data, wherein the second calibration data comprises the amount of the chemical element in a second sample and a plurality of spectral intensities associated with corresponding multiple wavelengths, the plurality of spectral intensities being generated by a second spectral instrument performing spectral analysis on the second sample, wherein the number of the plurality of wavelengths associated with the corresponding multiple spectral intensities in the second calibration data is less than the total number of wavelengths having associated spectral intensities output by the second spectral instrument during spectral analysis of the sample, and wherein the second spectral instrument is different from the first spectral instrument; wherein the calibration model is generated based on training the machine learning model using the second calibration data.

[0089] Embodiment 13 includes the subject matter of embodiment 12, and further specifies that the first sample has the same material composition as the second sample.

[0090] Embodiment 14 includes the subject matter of embodiment 12, and further specifies that the first sample has a different material composition than the second sample.

[0091] Embodiment 15 includes the subject matter described in any one of Embodiments 12 to 14, and further includes: using the calibration model to output the amount of the chemical element in the tested sample based on multiple spectral intensities associated with corresponding multiple wavelengths, wherein the multiple spectral intensities are generated by performing spectral analysis on the tested sample.

[0092] Embodiment 16 includes the subject matter according to any one of Embodiments 12 to 15, and further specifies that 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 further includes: receiving second calibration data, wherein the second calibration data includes the amount of the chemical element in a second calibration sample and a plurality of spectral intensities associated with corresponding multiple wavelengths, the plurality of spectral intensities being generated by performing spectral analysis on the second sample by a second spectroscopic instrument, and wherein the second spectroscopic instrument is different from the first spectroscopic instrument; and generating a second calibration model by training a machine learning model based on the first calibration model using the second calibration data, the second calibration model associating the plurality of spectral intensities at corresponding multiple wavelengths with the amount of the chemical element in the sample.

[0093] Embodiment 17 includes the subject matter of embodiment 16, and further specifies that the second sample has the same material composition as the first sample.

[0094] Embodiment 18 includes the subject matter of embodiment 16, and further specifies that the second sample has a different material composition than the first sample.

[0095] Embodiment 19 includes the subject matter of any one of Embodiments 16 to 18, and further specifies that the number of the multiple wavelengths associated with the corresponding multiple 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 spectral analysis of the second sample.

[0096] Embodiment 20 includes the subject matter described in any one of Embodiments 16 to 19, and further includes: using the second calibration model to output the amount of the chemical element in the tested sample based on multiple spectral intensities associated with corresponding multiple wavelengths, wherein the multiple spectral intensities are generated by performing spectral analysis on the tested sample by the second spectroscopic instrument.

[0097] Embodiment 21 includes the subject matter of any one of Embodiments 16 to 20, and further specifies that the amount of the second calibration data used to generate the second calibration model is less than the amount of the first calibration data used to generate the first calibration model.

[0098] Embodiment 22 is a method for supporting spectral calibration, the method comprising: receiving calibration data, wherein the calibration data comprises the amount of chemical elements in a sample and a plurality of spectral intensities associated with corresponding multiple wavelengths, the plurality of spectral intensities being generated by performing spectral analysis of the sample by a spectroscopic instrument, and wherein the number of the plurality of wavelengths associated with the corresponding multiple spectral intensities in the calibration data is less than the total number of wavelengths having associated spectral intensities output by the spectroscopic instrument during spectral analysis of the sample; and generating a target calibration model for the spectroscopic instrument by training a machine learning model based on a basic machine learning model using the calibration data of the spectroscopic instrument, the target calibration model associating the plurality of spectral intensities at the corresponding multiple wavelengths with the amount of chemical elements in the sample, wherein the basic machine learning model is trained based on calibration data from one or more spectroscopic instruments different from the spectroscopic instrument.

[0099] Embodiment 23 includes the subject matter described in Embodiment 22, and further includes: using the target calibration model to output the amount of the chemical element in the tested sample based on multiple spectral intensities associated with corresponding multiple wavelengths, and the multiple spectral intensities are generated by spectral analysis of the tested sample by the spectroscopic instrument.

[0100] Embodiment 24 is a method for supporting spectral calibration, the method comprising: receiving calibration data, wherein the calibration data comprises the amount of chemical elements in a sample and a plurality of spectral intensities associated with corresponding multiple wavelengths, the plurality of spectral intensities being generated by performing spectral analysis on the sample by a spectral instrument; and generating a target calibration model for the spectral instrument by training a machine learning model based on a basic machine learning model using the calibration data of the spectral instrument, the target calibration model associating the plurality of spectral intensities at the corresponding multiple wavelengths with the amount of chemical elements in the sample, wherein the basic machine learning model is trained based on calibration data from a plurality of spectral instruments different from the spectral instrument.

[0101] Embodiment 25 includes the subject matter of embodiment 24, and further specifies that the number of said multiple wavelengths associated with the corresponding said multiple spectral intensities in said calibration data is less than the total number of wavelengths having associated spectral intensities output by said spectroscopic instrument during spectral analysis of said sample.

[0102] Embodiment 26 includes the subject matter described in any one of Embodiments 24 to 25, and further includes: using the target calibration model to output the amount of the chemical element in the tested sample based on multiple spectral intensities associated with corresponding multiple wavelengths, wherein the multiple spectral intensities are generated by performing spectral analysis on the tested sample by the spectroscopic instrument.

[0103] Embodiment 27 is a computer-implemented method, which includes any of the methods disclosed herein (eg, any of the methods according to Embodiments 1 to 26).

[0104] Embodiment 28 is a computer-implemented method, which includes any of the methods disclosed herein (eg, any of the methods according to Embodiments 1 to 26).

[0105] Embodiment 29 is a data processing apparatus, device or system, which includes components for implementing any of the methods disclosed herein (for example, any of the methods according to Embodiments 1 to 26).

[0106] Embodiment 30 is a data processing apparatus, device or system, which includes a processor suitable for or configured to perform any of the methods disclosed herein (for example, any of the methods described in Embodiments 1 to 26).

[0107] Embodiment 31 is a computer-readable medium comprising instructions that, when executed by a computer, cause the computer to implement any of the methods disclosed herein (eg, any of the methods according to Embodiments 1 to 26).

[0108] Embodiment 32 is a computer program product, which includes instructions, and when the program is executed by a computer, the instructions cause the computer to implement any of the methods disclosed herein (for example, any of the methods according to Embodiments 1 to 26).

[0109] Embodiment 33 includes any of the scientific instrument support modules disclosed herein.

[0110] Embodiment 34 includes any of the methods disclosed herein.

[0111] Embodiment 35 includes any GUI among the GUIs disclosed in this article.

[0112] Embodiment 36 includes any of the scientific instrument support computing devices and systems disclosed herein.

Claims

1. A method for supporting spectral calibration, the method comprising: receiving first calibration data, wherein the first calibration data includes an amount of a chemical element in a first sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by performing spectral analysis on the first sample by a first spectral instrument; receiving second calibration data, wherein the second calibration data comprises an amount of the chemical element in a second sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectroscopically analyzing the second sample using a second spectroscopic instrument, and wherein the second spectroscopic instrument is different from the first spectroscopic instrument; generating a calibration model based on training a machine learning model using the first calibration data and the second calibration data, the calibration model correlating a plurality of spectral intensities at a corresponding plurality of wavelengths with an amount of the chemical element in a sample; receiving third calibration data, wherein the third calibration data comprises an amount of the chemical element in a third calibration sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectroscopically analyzing the third sample by a third spectroscopic instrument, and wherein the third spectroscopic instrument is different from the first spectroscopic instrument and the second spectroscopic instrument; as well as A second calibration model is generated by training a machine learning model based on the first calibration model using the third calibration data, the second calibration model correlating a plurality of spectral intensities at corresponding plurality of wavelengths with the amount of the chemical element in the sample.

2. A method according to claim 1, wherein the number of the multiple wavelengths associated with the corresponding multiple 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 spectral analysis of the first sample.

3. A method according to claim 1, wherein the number of the multiple wavelengths associated with the corresponding multiple 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 spectral analysis of the second sample.

4. The method according to claim 1, further comprising: The calibration model is used to output the amount of the chemical element in the tested sample based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectrally analyzing the tested sample. The method of claim 1 , wherein the third sample has the same material composition as the second sample. The method of claim 1 , wherein the third sample has a different material composition than the second sample.

7. A method according to claim 1, wherein the number of the multiple wavelengths associated with the corresponding multiple 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 spectral analysis of the third sample.

8. The method according to claim 1, further comprising: The second calibration model is used to output the amount of the chemical element in the tested sample based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectrally analyzing the tested sample by the third spectral instrument. 9 . 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. A method for supporting spectral calibration, the method comprising: receiving calibration data, wherein the calibration data includes an amount of a chemical element 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, and wherein a number of the plurality 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; as well as generating a calibration model based on training a machine learning model using the calibration data, the calibration model correlating a plurality of spectral intensities at a corresponding plurality of wavelengths with an amount of the chemical element in the sample; as well as receiving second calibration data, wherein the second calibration data comprises an amount of the chemical element in a second sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectroscopic analysis of the second sample by a second spectroscopic instrument, wherein a number of the plurality 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 wherein the second spectroscopic instrument is different from the first spectroscopic instrument; Wherein the calibration model is generated based on training the machine learning model using the second calibration data. 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. The method according to claim 10, further comprising: The calibration model is used to output the amount of the chemical element in the tested sample based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectrally analyzing the tested sample.

14. The method according to claim 13, wherein the calibration model is a first calibration model, the spectroscopic instrument is a first spectroscopic instrument, the calibration data is a first calibration data, and the method further comprises: receiving second calibration data, wherein the second calibration data comprises an amount of the chemical element in a second calibration sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectroscopically analyzing the second sample using a second spectroscopic instrument, and wherein the second spectroscopic instrument is different from the first spectroscopic instrument; as well as A second calibration model is generated by training a machine learning model based on the first calibration model using the second calibration data, the second calibration model correlating a plurality of spectral intensities at corresponding plurality of wavelengths with the amount of the chemical element in the sample. The method of claim 14 , wherein the second sample has the same material composition as the first sample. The method of claim 14 , wherein the second sample has a different material composition than the first sample.

17. A method according to claim 14, wherein the number of the multiple wavelengths associated with the corresponding multiple 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 spectral analysis of the second sample.

18. A method for supporting spectral calibration, the method comprising: receiving calibration data, wherein the calibration data includes an amount of a chemical element in a sample and a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectrally analyzing the sample with a spectral instrument; as well as A target calibration model for the spectroscopic instrument is generated by training a machine learning model based on a basic machine learning model using the calibration data of the spectroscopic instrument, wherein the target calibration model associates multiple spectral intensities at corresponding multiple wavelengths with the amount of chemical elements in the sample, wherein the basic machine learning model is trained based on calibration data from multiple spectroscopic instruments different from the spectroscopic instrument.

19. The method of claim 18, wherein the number of the plurality 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 spectral analysis of the sample.

20. The method according to claim 18, further comprising: The target calibration model is used to output the amount of the chemical element in the tested sample based on a plurality of spectral intensities associated with a corresponding plurality of wavelengths, the plurality of spectral intensities being generated by spectrally analyzing the tested sample by the spectral instrument.

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