Analysis device, analysis system, analysis method, and program

Abstract

Provided are an analysis device, an analysis system, an analysis method, and a program that exhibit improved prediction accuracy.　The analysis device comprises: a first acquisition unit that acquires first data relating to an optical characteristic of which behavior changes in response to interaction with a subject; a second acquisition unit that, by using a method different from that of the first acquisition unit, acquires second data relating to a characteristic of the subject; and an analysis unit that, on the basis of the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit in relation to the subject, analyzes the subject by using machine learning.

Description

Analysis device, analysis system, analysis method, and program

[0001] The present disclosure relates to an analysis device, an analysis system, an analysis method, and a program.

[0002] Methods such as discriminating the type of a subject, detecting an abnormality of the subject, and estimating the performance of the subject using various analysis methods have been widely developed. In such analysis methods, data acquired in advance from a large number of samples is machine-learned to construct a learned model. Based on the constructed learned model, the actually acquired data is analyzed to derive a determination result or an estimation result.

[0003] Generally, the larger the number of sample data used for analysis, the more accurately determination or estimation can be performed. However, actual data often has variations and may include values (outliers) that deviate significantly from the normal range. To address this problem, methods for improving the prediction accuracy of analysis by removing outliers have been proposed (Patent Documents 1 to 3).

[0004] In Patent Documents 1 and 2, a prediction formula is generated from the acquired data by discriminant analysis, and outliers are detected as abnormalities of the specimen. In Patent Document 3, by updating the accumulated learning data group, even if the sensor detects an outlier, it can be immediately deleted.

[0005] Japanese Patent Application Laid-Open No. 2021-89484 Japanese Patent Application Laid-Open No. 2023-71993 Japanese Patent Application Laid-Open No. 2015-35118

[0006] However, these methods of removing outliers significantly narrow the analysis range as the number of outliers increases, and the prediction accuracy is likely to decrease. For example, in a data acquisition device, it is difficult to perform accurate prediction when it is used in an unexpected environment, fails, deteriorates, etc.

[0007] The problem to be solved by the present disclosure is to provide an analysis device etc. with improved prediction accuracy.

[0008] To solve the above problems, the analysis device of this disclosure includes: a first acquisition unit that acquires first data about optical properties whose behavior changes in interaction with a subject; a second acquisition unit that acquires second data about the characteristics of the subject by a method different from that of the first acquisition unit; and an analysis unit that analyzes the subject by machine learning based on the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit.

[0009] The analysis system of this disclosure comprises: an analysis device; a first measuring device for measuring first data about optical properties whose behavior changes in interaction with a subject; and a second measuring device for measuring second data about the characteristics of the subject by a method different from that of the first measuring device.

[0010] The analysis method of this disclosure comprises: a first acquisition step of acquiring first data about optical properties whose behavior changes in interaction with a subject; a second acquisition step of acquiring second data about the characteristics of the subject by a method different from that of the first acquisition step; and an analysis step of analyzing the subject by machine learning based on the first data acquired in the first acquisition step and the second data acquired in the second acquisition step.

[0011] The program of this disclosure causes the computer of an analysis apparatus, which includes a first acquisition unit that acquires first data about optical properties whose behavior changes in interaction with a subject, and a second acquisition unit that acquires second data about the characteristics of the subject by a method different from that of the first acquisition unit, to function as an analysis unit that analyzes the subject by machine learning based on the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit for the subject.

[0012] According to this disclosure, the prediction accuracy of the analysis can be improved.

[0013] This is a block diagram illustrating the schematic configuration of the analysis system. This is a schematic cross-sectional diagram of a laminate. This is a schematic cross-sectional diagram of a laminate coated with the test material. This is a schematic cross-sectional diagram of a laminate coated with a light-emitting probe. This is a schematic cross-sectional diagram of an organic EL element. This is a diagram illustrating the mechanism by which the target substance and light-emitting dye molecules interact to produce light. This is a diagram illustrating the synthesis method of the light-emitting dye molecules. This shows a flowchart of the data acquisition method. This shows a flowchart of the analysis method. This shows human sensory evaluation of 16 types of milk. This shows prediction of sensory evaluation by multiscale measurement of 16 types of milk.

[0014] Hereinafter, one or more embodiments of this disclosure will be described with reference to the drawings. However, the scope of this disclosure is not limited to the disclosed embodiments.

[0015] [Analysis System] Figure 1 is a block diagram showing the schematic configuration of the analysis system 100 of this embodiment. The analysis system 100 comprises a first measuring device 10, a second measuring device 20, and an analysis device 30. The first measuring device 10 and the analysis device 30, and the second measuring device 20 and the analysis device 30 may communicate with each other. The first measuring device 10, the second measuring device 20, and the analysis device 30 may also be integrated. The first measuring device 10 comprises a first measuring unit 11, a first communication unit 12, and a first control unit 13. The second measuring device 20 comprises a second measuring unit 21, a second communication unit 22, and a second control unit 23. The analysis device 30 comprises a display unit 31, an operation unit 32, a communication unit 33, and a control unit 34. Details of each part will be described below.

[0016] The first acquisition unit and the second acquisition unit each acquire data on the characteristics of the subject using different methods. In this embodiment, the data obtained by the first acquisition unit is also called the first data, and the data obtained by the second acquisition unit is also called the second data.

[0017] As mentioned above, the data actually obtained from subjects often contains variability. There are two factors that cause this variability: first, variability in the subjects themselves, and second, variability in the measuring device itself. Variability caused by the first factor is important in terms of predictive accuracy and reproducibility of the subject analysis and must be taken into consideration. On the other hand, variability caused by the second factor significantly reduces the predictive accuracy and reproducibility of the subject analysis, and is therefore preferable to have none.

[0018] In this embodiment, the first acquisition unit acquires data on optical properties whose behavior changes in interaction with the subject. Generally, devices that acquire data on the behavior of light have good sensitivity and can acquire complex data, but on the other hand, the data is easily altered by even slight factors. Therefore, variations in the acquired data are likely to occur due to individual differences that occur during the manufacturing of the device, minute changes in the luminescent probe contained in the device, etc.

[0019] In this embodiment, a second data acquisition unit is further provided, and the second data acquisition unit acquires data in a different manner than the first data acquisition unit. This allows for the acquisition of data from multiple perspectives on the subject. As a result, the influence of variability in the first measuring device 10 itself, which includes the first measuring unit 11, can be reduced, and the predictive accuracy of the analysis of the subject can be improved.

[0020] The number of data points acquired by the first acquisition unit and the second acquisition unit are not particularly limited, but the more data points there are, the better the prediction accuracy of the analysis. The number of data points is appropriately selected depending on the data acquisition method, etc. Preferably, the number of data points is equal to or greater than the number of explanatory variables. If a large number of data points can be acquired, preferably the number of data points is 10 times or more the number of explanatory variables, and more preferably 100 times or more.

[0021] It is preferable that the number of data points in the first and second data sets be the same. For example, it is preferable that the number of data points (measurements) in the first data set and the number of data points (measurements) in the second data set are the same. By having the same number of data points, all acquired data can be effectively utilized, and the efficiency of the analysis can be improved.

[0022] The details of each device are described below.

[0023] [Measuring device] (First measuring unit) The first measuring device 10 is not particularly limited as long as it is a device that acquires data on optical properties whose behavior changes in interaction with the subject. One method of acquiring such data is to irradiate the sample with light from a light source and acquire spectral data of the light obtained from the sample. Examples of light irradiated from a light source include X-rays, ultraviolet rays, visible light, infrared rays, etc.

[0024] In this embodiment, it is preferable to use a luminescent probe to acquire spectral data of light obtained from a sample. In this embodiment, a "luminescent probe" refers to a substance that has the property of binding to or associating with the target substance and emits light when returning from an excited state to a ground state. By using a luminescent probe, accurate data about the target substance can be obtained, and the predictive accuracy of the analysis can be improved.

[0025] If the sample contains multiple substances, the luminescent probe may bind to or associate only with specific target substances within the sample. The luminescent probe may be a molecule, a molecular aggregate, or a particle containing molecules.

[0026] The method for acquiring spectral data of light obtained from a sample using a light-emitting probe is not particularly limited. For example, one method is to use an organic electroluminescent (EL) element. Another method is to use a light-emitting dye molecule having a main chain of a specific structure as the light-emitting probe.

[0027] (Organic EL Elements) The following configurations are possible for organic electroluminescent (EL) elements: (i) Transparent substrate / anode / light-emitting layer (detection region) / cathode (ii) Transparent substrate / anode / light-emitting layer (detection region / electron transport layer) / cathode (iii) Transparent substrate / anode / light-emitting layer (hole transport layer / detection region) / cathode (iv) Transparent substrate / anode / light-emitting layer (hole transport layer / detection region / electron transport layer) / cathode (v) Transparent substrate / anode / light-emitting layer (hole transport layer / detection region / electron transport layer / electron injection layer) / cathode (vi) Transparent substrate / anode / light-emitting layer (hole injection layer / hole transport layer / electron blocking layer / detection region / hole blocking layer / electron transport layer) / cathode

[0028] The above-described organic EL element has a light-emitting layer between its electrodes, and the light-emitting layer is the detection region. The organic EL element can emit light when the test subject and the light-emitting probe interact within the light-emitting layer. Various data can be obtained by acquiring the light emission information and current density of the light-emitting probe. A method for acquiring data using the organic EL element will be explained with reference to Figures 2 to 5.

[0029] In this method, first, a laminate comprising a transparent substrate 111, a transparent electrode 112 (e.g., an ITO film), a hole transport layer 113 (e.g., a polyaromatic diamine), and a receiving layer 114 (e.g., polystyrene) is prepared (Figure 2). The transparent substrate 111, transparent electrode 112, hole transport layer 113, etc., can be the same as those used in known organic EL devices. The receiving layer 114 only needs to be able to receive the subject and the light-emitting probe. The receiving layer 114 may also serve as the host layer.

[0030] Next, the sample layer 121 is applied to a desired area of ​​the receiving layer 114 of the laminate using any method (Figure 3).

[0031] Next, the luminescent probe is applied in a pattern to desired locations on the receiving layer 114 coated with the subject layer 121 using an inkjet method or the like (Figure 4). As a result, the areas coated with the luminescent probe become the luminescent layer 122. For example, by applying a component that dissolves the receiving layer 114 together with the luminescent probe, the applied luminescent probe, a part of the subject layer 121, and a part of the receiving layer 114 are mixed, and an integrated luminescent layer 122 is formed. In the luminescent layer 122, the subject and the luminescent probe interact. Although not shown in the figure, the luminescent probe may also be applied to areas where the subject layer 121 is not coated to obtain luminescence information from the luminescent probe alone.

[0032] Finally, a counter electrode layer 115, which is paired with the transparent electrode 112, is placed on the light-emitting layer 122 to fabricate an organic EL element (Figure 5).

[0033] In the fabricated organic EL element, the test subject and the light-emitting probe in the light-emitting layer 122 are excited by a conventional method, and emission spectral data is acquired.

[0034] When acquiring a light spectrum using an organic EL element, i.e., when using an organic EL element as a sensing device, fluorescent compounds, delayed-fluorescence compounds, and phosphorescent compounds can be used as light-emitting probes. Furthermore, different phosphorescent compounds may be used in combination as light-emitting probes, or phosphorescent compounds and fluorescent compounds may be used in combination. This allows for the acquisition of any desired emission color. Additionally, multiple light-emitting compounds with different emission colors may be combined to produce white light.

[0035] In this specification, "fluorescent compound" refers to a compound that emits fluorescence other than delayed fluorescence. "Fluorescence" refers to the light emitted when returning from a singlet excited state to the ground state. "Fluorescence other than delayed fluorescence" refers to fluorescence excluding the "delayed fluorescence" exemplified below. Examples of "delayed fluorescence" include "thermally activated delayed fluorescence (TADF)". Examples of "delayed fluorescence" include "triplet-triplet annihilation (TTA) delayed fluorescence".

[0036] In other words, in this specification, "fluorescent compound" does not include "delayed fluorescent compounds" such as "thermally activated delayed fluorescent compounds" and "triplet-triplet annihilation delayed fluorescent compounds." "Fluorescent compound" refers to a compound that does not undergo upconversion by reverse intersystem crossing from the lowest excited triplet energy level to the lowest excited singlet energy level.

[0037] Fluorescent compounds do not necessarily need to be heavy metal complexes like phosphorescent compounds. So-called organic compounds, composed of common combinations of elements such as carbon, oxygen, nitrogen, and hydrogen, can be used as fluorescent compounds. Other nonmetallic elements such as phosphorus, sulfur, and silicon may also be used as fluorescent compounds. Complexes of typical metals such as aluminum and zinc may also be used as fluorescent compounds. Known fluorescent compounds used in the light-emitting layers of organic EL devices may also be used as fluorescent compounds.

[0038] In this specification, "phosphorescent compound" refers to a compound that emits phosphorescence. Specifically, a "phosphorescent compound" refers to a compound that emits phosphorescence at room temperature (25°C) and has a phosphorescence quantum yield of 0.01 or higher at 25°C. A phosphorescence quantum yield of 0.1 or higher is preferred.

[0039] "Phosphorescence" refers to the light emitted when returning from a triplet excited state to the ground state. Known phosphorescent compounds used in the light-emitting layers of organic EL devices can be used as the phosphorescent compound.

[0040] In this specification, "delayed fluorescence compound" refers to a compound that emits delayed fluorescence. "Delayed fluorescence" refers to the light emitted when a singlet excited state returns to the ground state as a result of upconversion by reverse intersystem crossing from the lowest excited triplet energy level to the lowest excited singlet energy level. Known delayed fluorescence compounds used in the light-emitting layer of organic EL elements can be used as the delayed fluorescence compound.

[0041] When using an organic EL element as a sensing device, for blue, green, and red with different emission colors, a plurality of data may be taken at intervals of several nanometers. Also, the current density may be measured every 1 V. As a result, a large number of data can be obtained.

[0042] (Luminescent dye molecule) In the method using the luminescent dye molecule described below, a microwell capable of forming a plurality of reaction fields on a plane is used. Different types of luminescent dye molecules are prepared in advance on each well, and the sampled subject is injected into each well to obtain emission spectrum data. From this plurality of emission spectrum data, the state of the subject is quantified.

[0043] In this method, a plate in which a plurality of wells are regularly arranged is used. In a plate having such wells, the wells (reaction fields) are physically separated from each other by partitions. Therefore, it is difficult for the subject and the luminescent dye molecule to mix in adjacent reaction fields, and it is easy to analyze accurately.

[0044] The luminescent dye molecule exhibits two or more types of emission selected from fluorescence, phosphorescence, excimer emission, exciplex emission, thermally activated delayed fluorescence, excited-state intramolecular proton emission, triplet-triplet annihilation delayed fluorescence, twisted intramolecular charge transfer emission, and aggregated organic emission for a single excitation light.

[0045] The luminescent dye molecule can be used for analyzing the structure, state, etc. of a specific target substance. Specifically, when the luminescent dye molecule and the target substance interact with each other, the structure, electronic state, etc. of the chromophore or luminophore in the luminescent dye molecule change, and a complex emission behavior different from that of the luminescent dye molecule alone is obtained.

[0046] For example, as shown in FIG. 6 for a single excitation light, a luminescent dye molecule that exhibits three different types of emission, fluorescence, phosphorescence, and excimer emission, is allowed to interact with a target substance. Due to the interaction between the target substance and the luminescent dye molecule, the processes by which fluorescence, phosphorescence, and excimer emission occur change respectively, and the wavelength or lifetime of each light changes. As a result, depending on the structure, state, etc. of the target substance, a complex and large number of data combined from these lights can be obtained, and from this data, the structure, state, etc. of the target substance can be grasped in great detail.

[0047] The specific structure of the luminescent dye molecule according to this embodiment will be described below.

[0048] The main chain of the luminescent dye molecule may have one or more structural units, each containing a sugar structure derived from a pentose or hexose and a phosphate ester bond attached to the sugar structure. The main chain may contain only one of these structural units, or it may contain multiple such units. That is, it may be a structure having one sugar structure and one phosphate ester bond attached to the sugar structure, or it may be a structure containing alternating sugar structures and phosphate ester bonds.

[0049] The main chain of a luminescent dye molecule has sugar structures at both ends, with one more sugar structure than the number of phosphate ester bonds. Furthermore, if the main chain contains multiple structural units, these units may be identical or different.

[0050] The number of structural units contained in the main chain of the luminescent dye molecule is appropriately selected according to the type of target substance, and is preferably in the range of 2 to 6. If the amount of structural units is large, the luminescent dye molecule is more likely to act specifically on the target substance. In this embodiment, it is preferable to obtain a large amount of data by having the luminescent dye molecule interact with various positions on the target substance. For this reason, it is preferable that the luminescent dye molecule and the target substance have a moderate degree of specificity, not excessively so, and the number of structural units is preferably 6 or less.

[0051] The main chain of the luminescent dye molecule may include structures other than the above-mentioned structural units, to the extent that it does not impair the purpose and effect of this embodiment. The structures at both ends of the main chain are not particularly limited and may include, for example, hydroxyl groups, alkoxy groups, etc.

[0052] Examples of pentoses include ribose, deoxyribose, and xylose. Examples of hexoses include allose, glucose, and mannose. Among these, it is particularly preferable that the sugar structure is derived from ribose or deoxyribose. This allows the main chain of the luminescent pigment molecule to have a structure similar to that of DNA or RNA, and the luminescent pigment molecule to readily interact with DNA or RNA.

[0053] When the above structural unit includes a structure derived from ribose or deoxyribose, the phosphate ester bond is preferably bonded to the carbon at position 3 and position 5 of ribose or deoxyribose. The chromophore or luminescent phore is preferably bonded to the carbon at position 1 of ribose or deoxyribose. In other words, the luminescent dye molecule according to this embodiment preferably includes a structure represented by the following general formula (1a) or (1b).

[0054]

[0055] In the general formulas (1a) and (1b), Y represents a chromophore or luminescent phose as described below.

[0056] The chromophore or luminescent phore of the luminescent dye molecule may have a structure that emits a predetermined type of light independently in response to a single excitation light, or emits a predetermined light through the action of multiple chromophores or luminescent phores. Preferably, the chromophore or luminescent phore is bonded to the sugar structure of the main chain such that the sugar structure is in its β-form.

[0057] In this embodiment, "chromophore" refers to a structure that absorbs light with a wavelength of 300 nm or more, and "luminescent phore" refers to a structure that absorbs light with a wavelength of 300 nm or more and emits light.

[0058] The number of chromophores or luminescent groups in a luminescent dye molecule may be as few as one, provided that the luminescent dye molecule is capable of exhibiting multiple types of luminescence. However, from the viewpoint of making it easier for the luminescent dye molecule to exhibit multiple types of luminescence, the number of chromophores or luminescent groups is preferably two or more, and more preferably in the range of three to six. When a luminescent dye molecule has multiple chromophores or luminescent groups, there may be only one type, or there may be two or more types.

[0059] In a luminescent dye molecule, it is preferable that one chromophore or luminescent phore is bound to one sugar structure in the main chain. Therefore, if a luminescent dye molecule has two or more chromophores or luminescent phore, it is preferable that there are also two or more sugar structures in the main chain. In other words, it is preferable that the number of chromophores or luminescent phore in a luminescent dye molecule is equal to or less than the number of sugar structures in the main chain.

[0060] If the number of chromophores or luminescent groups in a luminescent dye molecule is less than the number of sugar structures in the main chain, some sugar structures will not have chromophores or luminescent groups attached. Sugar structures that do not have chromophores or luminescent groups attached do not need to have other atomic groups attached, but they may have native bases attached to them to the extent that it does not impair the purpose and effect of this embodiment. In this specification, "native bases" refers to adenine, guanine, cytosine, thymine, and uracil.

[0061] The total number of native bases bound to the sugar structure is preferably 50% or less, and more preferably 25% or less, of the total number of sugar structures in the main chain. Having 50% or less native bases suppresses the association of luminescent dye molecules, making the interaction between the test subject and the luminescent dye molecules more dominant. Furthermore, it is preferable that the native and non-native bases are bound in such a way that the sugar structure is in the β-form.

[0062] Examples of fluorescent chromophores or luminescent phoses include structures derived from fluorescein, rhodamine, boron dipyromethene, etc. Examples of phosphorescent chromophores or luminescent phoses include structures derived from iridium complexes, platinum complexes, etc. Examples of excimer-luminescent chromophores or luminescent phoses include structures derived from pyrene, anthracene, perylene, etc. Examples of exciplex-luminescent chromophores or luminescent phoses include structures derived from pyrene-dimethylaniline, etc.

[0063] Examples of chromophores or luminescent molecules that emit thermally activated delayed fluorescence include structures derived from 4CzIPN, DABNA, etc. Examples of chromophores or luminescent molecules that emit excited-state intramolecular proton emission include structures derived from hydroxyphenylbenzoxazole, etc. Examples of chromophores or luminescent molecules that emit triplet-triplet annihilation delayed fluorescence include structures derived from 9,10-diphenylanthracene, rubrene, etc. Examples of chromophores or luminescent molecules that emit twisted intramolecular charge transfer emission include structures derived from diaminoanthracene, diaminonaphthalene, etc. Examples of chromophores or luminescent molecules that emit aggregated organic emission include structures derived from tetraphenylethene, hexaphenylsilole, etc.

[0064] In particular, the chromophore or luminescent phore preferably contains at least one structure selected from a structure that emits fluorescence, a structure that emits excimer emission, and a structure that emits exciplex emission. It is especially preferable that the chromophore or luminescent phore contains a structure that emits fluorescence. The fluorescence emitted by the luminescent dye molecule makes it easier to analyze with various measuring devices.

[0065] It is preferable that the luminescent dye molecule exhibits multiple types of emission when irradiated with light in the wavelength range of 300 to 400 nm. Because the luminescent dye molecule exhibits multiple types of emission, a special light source is not required when analyzing a subject, and the analysis is less likely to damage the subject.

[0066] The molecular weight of the luminescent dye molecule is appropriately selected depending on the type of chromophore or luminescent phose, the length of the main chain, etc. The molecular weight is preferably in the range of 500 to 10,000, and more preferably in the range of 100 to 4,000. A molecular weight of 10,000 or less results in moderately low specificity to the test subject, allowing the luminescent dye molecule to react non-specifically with multiple locations in the target substance within the test subject.

[0067] Luminescent dye molecules can be synthesized by the following procedure: Prepare monomers by bonding a chromophore or luminescent ester and a phosphate ester to a pentose or hexose. Polymerize these monomers in the desired sequence using the phosphoramidide method with a DNA / RNA synthesizer or the like.

[0068] In this synthesis method, as shown in Figure 7, for example, multiple types of monomers with different chromophores or luminescent phoses are prepared, and the desired number of monomers can be combined by changing the sequence of these monomers. In the example shown in Figure 7, three types of monomers with chromophores or luminescent phoses A, B, and C, respectively, are prepared, and the three monomers can be combined by changing the sequence of these monomers.

[0069] In other words, a wide variety of luminescent dye molecules can be synthesized from multiple types of monomers with different chromophores or luminescent phores. In the example shown in Figure 7, 27 different luminescent dye molecules can be synthesized. By changing the type of monomer used or the number of monomer bonds, a very large number of luminescent dye molecules can be synthesized.

[0070] The luminescent dye molecules according to this embodiment have a main chain structure similar to naturally occurring substances such as DNA and RNA, and can easily interact with various target substances contained in the sample. Therefore, by using luminescent dye molecules, data about the target substances can be easily and in detail. Furthermore, the luminescent dye molecules exhibit multiple types of emission when irradiated with light at a specific wavelength. Therefore, complex and large amounts of data can be obtained depending on the state of the target substance.

[0071] The following describes an example of a method for acquiring signal data using luminescent dye molecules, but the method of data acquisition is not limited to this method.

[0072] Figure 8 shows a flowchart of the data acquisition method. A microwell plate is prepared for the interaction of the luminescent dye molecule and the subject, and either the luminescent dye molecule or the subject is placed in the reaction field (well) (first component placement step: step S101). The placed luminescent dye molecule or subject is referred to as the first component.

[0073] The method for placing the first component in each reaction site is not particularly limited and can be appropriately selected depending on the type and physical properties of the first component. Examples of methods for placing the first component include coating by inkjet, coating by dispenser, placement of a carrier supporting the first component, and direct fixation of the first component to the reaction site. Among these, the inkjet method is preferred. By using the inkjet method, liquid first components can be efficiently placed in multiple reaction sites, and a large amount of data can be acquired.

[0074] If the first component is a luminescent dye molecule and the plate has multiple reaction fields, the same type of first component may be placed in all of the reaction fields, or multiple types of first components may be placed in the same reaction field. Alternatively, different types of first components may be placed in two or more reaction fields. By placing different types of first components in different reaction fields, multiple types of interactions occur between the luminescent dye molecule and the target substance, allowing for a more detailed analysis of the target substance.

[0075] First signal data is acquired from the plate on which the first component is placed (first signal data acquisition step: step S102).

[0076] A luminescent dye molecule or the subject, as the other component of the first component, is placed in the reaction field of the plate from which the first signal data was acquired (second component placement step: step S103). The placed luminescent dye molecule or subject is referred to as the second component.

[0077] Second signal data is acquired from the plate on which the second component is placed (second signal data acquisition step: step S104).

[0078] (Second Measurement Unit) The second measurement device 20 is not particularly limited as long as it measures data different from the data measured by the first measurement device 10. In particular, from the viewpoint of being able to acquire and analyze optical spectral data, just like the first measurement unit 11, it is preferable that the second measurement unit 21 also measures data on optical properties whose behavior changes in interaction with the subject. In particular, it is preferable to measure the optical spectral data obtained from the sample using an luminescent probe.

[0079] However, in this case, the second measurement unit 21 uses a second light-emitting probe that is different from the first light-emitting probe used in the first measurement unit 11. In this embodiment, the light-emitting probe used in the first measurement unit 11 is also called the first light-emitting probe, and the light-emitting probe used in the second measurement unit 21 is also called the second light-emitting probe. For example, the first measurement unit 11 acquires light spectral data using an organic EL element, and the second measurement unit 21 acquires light spectral data using the light-emitting dye molecule.

[0080] The second measurement unit 21 may also measure data on characteristics caused by electromagnetic waves or ultrasound that interact with the subject and change their behavior. This method does not use a light-emitting probe, which is prone to minute changes, thus reducing data variability caused by the device.

[0081] The method for acquiring data using ultrasound involves irradiating a subject with ultrasound waves, receiving the transmitted or reflected waves from the subject with an ultrasound probe such as a piezoelectric element, and obtaining a received signal. When acquiring data over a wide area of ​​the subject, if the subject moves autonomously, the position or direction of the ultrasound probe is fixed while irradiating with ultrasound. If the subject is stationary, the ultrasound may be irradiated while moving the position or direction relative to the subject.

[0082] If necessary, an ultrasound image may be generated based on the strength information, phase information, etc., of the received signal. The ultrasound image may include an ultrasound tomographic image representing the cross-sectional information of the subject. When ultrasound propagates through the subject, there may be discontinuous or non-uniform areas due to the structure or composition of the subject. Changes in strength information, phase information, etc., of the reflected or transmitted components that occur at this time can be imaged in two-dimensional or three-dimensional space along with positional information, and the resulting image is called a "tomographic image". The ultrasound image may be a still image or a moving image. In addition, quantitative data such as feature quantities may be extracted from the ultrasound image. "Feature quantities" refer to information extracted by applying statistical analysis such as frequency analysis and principal component analysis to image information, for example.

[0083] The method for acquiring electromagnetic wave data involves first creating a circuit board containing the test subject by setting an antenna pattern, electrodes, etc., composed of microstrips, on a dielectric substrate. Next, the frequency characteristics, including resonance characteristics and attenuation characteristics, are measured for the fabricated circuit board. From the measurement results, data on the conductivity and dielectric properties of the test subject are acquired. When acquiring data over a wide area of ​​the test subject, the position or direction of the antenna, etc., may be moved while acquiring the data.

[0084] The second measurement unit 21 may also acquire data using other known analytical methods, such as gas chromatography / mass spectrometry (GC / MS).

[0085] (Communication Unit) The first communication unit 12 transmits the first data measured by the first measurement unit 11 to the communication unit 35 of the analysis device 30. The second communication unit 22 transmits the second data measured by the second measurement unit 21 to the communication unit 35 of the analysis device 30. The first communication unit 12 and the second communication unit 22 may use wireless communication or wired communication.

[0086] (Control Units) The first control unit 13 and the second control unit 23 are processors that comprehensively control the operation of the first measuring device 10 and the second measuring device 20, respectively. The first control unit 13 and the second control unit 23 are equipped with a CPU (Central Processing Unit) that performs various calculations, and RAM (Random Access Memory) that provides the CPU with a working memory space and stores temporary data.

[0087] [Analysis Device] (Display Unit) The display unit 31 displays various information on its screen based on display control signals received from the control unit 34. The display unit 31 is equipped with a display device. Examples of display devices include a display, a projector, etc. The display unit 31 displays the results of the analysis performed by the control unit 34 based on the first data and the second data, and notifies the user.

[0088] (Operation Unit) The operation unit 32 accepts various inputs through user operation. The operation unit 32 is equipped with input devices. Examples of input devices include keyboards, mice, various switches, touchscreens, touchpads, etc.

[0089] (Communication Unit) The communication unit 33 receives data transmitted from the first communication unit 12 of the first measuring device 10 and the second communication unit 22 of the second measuring device 20. The communication unit 33 may use either wireless communication or wired communication.

[0090] (Control Unit) The control unit 34 is a processor that provides overall control over the operation of the analysis device 30. The control unit 34 includes a CPU (Central Processing Unit) that performs various calculations, and RAM (Random Access Memory) that provides the CPU with working memory space and stores temporary data.

[0091] The control unit 34 acquires first data measured by the first measurement unit 11 of the first measuring device 10. At this time, the control unit 34 functions as a first acquisition unit. The control unit 34 also acquires second data measured by the second measurement unit 21 of the second measuring device 20. At this time, the control unit 34 functions as a second acquisition unit. Based on the data acquired by the first and second acquisition units, the control unit 34 analyzes the subject using machine learning. At this time, the control unit 34 functions as an analysis unit. Specifically, the CPU reads the stored program, loads it into RAM, and performs the analysis in cooperation with the program loaded into RAM.

[0092] (Analysis using machine learning) In machine learning, multiple predictive models are created based on the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit. By combining the results of the multiple predictive models created, a trained model capable of predicting information about the subject is constructed.

[0093] Information about the subject includes the structure and content of compounds contained in the subject. If the subject is food, information about the subject includes sensory evaluations such as flavor, texture, and smell. In this embodiment, the first acquisition unit acquires complex and large amounts of data. Furthermore, the second acquisition unit acquires data using a different method than the first acquisition unit, thus reducing data variability caused by the device. As a result, even sensory evaluations, which are difficult to predict, can be predicted more accurately.

[0094] If the structure and content of the target substance in the sample are known in advance, a predictive model can be constructed by performing machine learning with the characteristics of the analytical data (i.e., the first and second data sets) as explanatory variables and the structure and content of the target substance as the dependent variable.

[0095] As explanatory variables, numerical values ​​representing the characteristics of the first and second data sets, and numerical values ​​calculated from them, can be used. If the first or second data set is a spectral distribution, the intensity of light at each wavelength can be used as an explanatory variable. The dependent variable can be appropriately selected according to the purpose of the analysis. It is not limited to the structure or content of the target substance; any other variable related to the target substance may be used, or it may be a sensory evaluation of the target substance.

[0096] Machine learning can be either supervised or unsupervised. Supervised learning is a learning method that learns the relationship between input and output from training data that has correct labels. Unsupervised learning is a learning method that learns the structure of a data set from training data that does not have correct labels.

[0097] Machine learning may be reinforcement learning, deep learning, or deep reinforcement learning. "Reinforcement learning" refers to a learning method that learns the "optimal sequence of actions" through trial and error. "Deep learning" refers to a learning method that learns the features contained in a large amount of data in a stepwise, deeper way. "Deep reinforcement learning" refers to a learning method that combines reinforcement learning and deep learning. Analysis in machine learning can be performed using the statistical analysis software "JMP16.2" or "JMPpro16.2" manufactured by SAS Institute Japan Co., Ltd. Examples of prediction algorithms used in machine learning include principal component analysis (PCA), cluster analysis (hierarchical method, k-means method, normal mixed method), linear discriminant analysis (LDA), and partial least squares regression (PLS regression). These algorithms may also be used in combination.

[0098] Other algorithms used in machine learning include decision trees, random forests, bootstrap forests, neural networks, K-nearest neighbors, simple Bayes, support vector machines (SVM), nominal logistic regression (multiple logits), and generalized regression (Ridge, Lasso).

[0099] When the sample size is approximately 50 or less, the explanatory variables are 100 or more, and the variables are spectral data, the algorithm is preferably linear regression from the viewpoint of suppressing overfitting and ensuring robustness (reproducibility) of the results. In particular, PCA or PLS regression using dimensionality-reduced principal components is preferred, and PLS regression is especially preferred. Similarly, from the viewpoint of suppressing overfitting and ensuring robustness of the results, the algorithm is preferably LDA (Long-Range Data Method in JMP) using dimensionality-reduced principal components.

[0100] Linear Discriminant Analysis (LDA) in "JMP16.2" offers two methods: the stepwise method, which allows for arbitrary selection of explanatory variables, and the wide-format data method, which reduces dimensionality to the principal components. Both methods are applicable. Of these, the wide-format data method is preferred, as it reduces computation time and suppresses overfitting.

[0101] For evaluating discrimination performance, for example, the entire sample data is divided into training data and validation data in an arbitrary proportion, linear discriminant analysis (LDA) is performed, and the discrimination accuracy (correctness), misclassification rate, and entropy R-squared are calculated. For calculating validation results using validation data, it is preferable to use the hold-out method, which uses an arbitrary or random set of validation data. Alternatively, for calculating validation results using validation data, it is preferable to use k-fold cross-validation, which divides the entire sample data into k sets and performs cross-validation on combinations of these k sets.

[0102] In calculating validation results using k-fold cross-validation, it is preferable to display the k average values ​​and the one with the best statistical performance among the k discrimination models. Here, "best statistical performance" means that the error from the average value is small. In addition, in calculating validation results using k-fold cross-validation, it is preferable to display the one with the smallest difference in discrimination accuracy between training and validation, or the difference in entropy R-squared.

[0103] (Subject) The type of subject according to this embodiment is not particularly limited and may be a substance whose structure is known or a substance whose structure is unknown. The subject may be a mixture of various compounds, etc. The subject may belong to any field, such as the medical field, the industrial field, or the food field. The acquisition unit may acquire data for the entire subject, or it may acquire data for a specific target substance within the subject.

[0104] Examples of target substances belonging to the medical field include proteins, antibodies, antibody-containing beads, and tumor markers. Examples of subjects containing these target substances include sweat and blood. Examples of target substances belonging to the industrial field include metal ions, metal nanoparticles, carbon nanotubes, magnetic fluids, nanosilica, and crystalline zirconia. Examples of subjects containing these target substances include rainwater, rivers, small ponds, tropical fish breeding water, soil, and wastewater. In other words, the analytical device of this embodiment can be applied to water quality management. Examples of target substances belonging to the food field include proteins, lipids, and carbohydrates. Examples of subjects containing these target substances include food products in general.

[0105] [Analysis Method] Figure 9 shows a flowchart of the analysis method of this embodiment. The analysis method of this embodiment has a first acquisition step (step S201), a second acquisition step (step S202), and an analysis step (step S203). The first acquisition step S201 is a step of measuring the subject with the first measurement unit 11 and acquiring the obtained first data. The second acquisition step S202 is a step of measuring the subject with the second measurement unit 21 and acquiring the obtained second data. The analysis step S203 is a step of analyzing the subject using the acquired first data and second data. Details of each step are as described in the sections on the first measurement unit 11, the second measurement unit 21, and the control unit 34 (analysis unit).

[0106] In this embodiment, the system includes a first acquisition unit (control unit 34) that acquires first data about optical properties whose behavior changes in interaction with the subject, a second acquisition unit (control unit 34) that acquires second data about the characteristics of the subject using a method different from that of the first acquisition unit, and an analysis unit (control unit 34) that analyzes the subject using machine learning based on the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit. This improves the prediction accuracy of the analysis.

[0107] In this embodiment, it is preferable that the first acquisition unit includes a first luminescent probe whose luminescence behavior changes upon interaction with the subject. This allows for accurate data on the target substance contained in the subject, thereby improving the predictive accuracy of the analysis.

[0108] In this embodiment, it is preferable that the first acquisition unit has a light-emitting layer 122 containing a first light-emitting probe between the electrodes (between the transparent electrode 112 and the counter electrode layer 115). This allows various data about the target substance contained in the sample to be obtained, improving the predictive accuracy of the analysis.

[0109] In this embodiment, it is preferable that the second acquisition unit includes a second light-emitting probe whose light emission behavior changes in interaction with the subject. This allows for the acquisition of optical spectral data, similar to the first acquisition unit, making analysis easier and improving the prediction accuracy of the analysis.

[0110] In this embodiment, it is preferable that the first or second luminescent probe has a main chain having one or more structural units including a sugar structure derived from a pentose or hexose and a phosphate ester bond attached to the sugar structure, and one or more chromophores or luminescent phosphodiphores attached to the sugar structure. This allows the luminescent probe to easily interact with target substances having a main chain structure similar to that of DNA, RNA, etc. As a result, data about the target substance can be easily and in detail, and the predictive accuracy of the analysis can be improved.

[0111] In this embodiment, it is preferable that the second acquisition unit acquires second data regarding the characteristics caused by electromagnetic waves or ultrasound that change behavior in interaction with the subject. This reduces data variability caused by the data acquisition device and improves the prediction accuracy of the analysis.

[0112] In this embodiment, it is preferable that the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit have the same number of data points. This allows all acquired data to be effectively utilized, improving the efficiency of the analysis.

[0113] In this embodiment, the machine learning prediction algorithm is preferably linear regression. This helps to suppress overfitting and improve the robustness (reproducibility) of the prediction results.

[0114] In this embodiment, the machine learning prediction algorithm is preferably a partial least squares regression. This suppresses overfitting and improves the robustness (reproducibility) of the prediction results.

[0115] In this embodiment, the analysis system 100 includes an analysis device 30, a first measuring device 10 that measures first data about optical properties whose behavior changes in interaction with the subject, and a second measuring device 20 that measures second data about the characteristics of the subject using a method different from that of the first measuring device 10.

[0116] In this embodiment, the analysis method includes a first acquisition step (step S201) for acquiring first data about optical properties whose behavior changes in interaction with the subject, a second acquisition step (step S202) for acquiring second data about the characteristics of the subject by a method different from that of the first acquisition step (step S201), and an analysis step (step S203) for analyzing the subject by machine learning based on the first data acquired in the first acquisition step (step S201) and the second data acquired in the second acquisition step (step S202).

[0117] In this embodiment, the program causes the computer of an analysis device, which includes a first acquisition unit that acquires first data about optical properties whose behavior changes in interaction with the subject, and a second acquisition unit that acquires second data about the characteristics of the subject in a different manner from the first acquisition unit, to function as an analysis unit (control unit 34) that analyzes the subject by machine learning based on the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit 3.

[0118] Furthermore, the detailed configuration and operation of each device constituting the analysis apparatus may also be modified as appropriate, without departing from the spirit of this disclosure.

[0119] The following will provide specific examples, but this disclosure is not limited to these.

[0120] <Data Acquisition> Twenty-four types of commercially available milk (Milk 1 to Milk 24) were used as subjects, and data was acquired using organic EL elements (OLED sensors), luminescent dye molecules (luminescent DNA sensors), and mass spectrometry (MS sensors).

[0121] [Data acquisition using an OLED sensor] (1) Fabrication of a blue OLED sensor A substrate was prepared on a 30 mm x 30 mm x 0.7 mm glass substrate with a 100 nm thick indium tin oxide (ITO) film deposited on it. After patterning the substrate, the substrate was ultrasonically cleaned with isopropyl alcohol and dried with dry nitrogen gas. The substrate was then cleaned with UV ozone for 5 minutes to obtain a transparent support substrate with an ITO transparent electrode (anode) attached.

[0122] An insulating polymer solution was obtained by diluting polystyrene (manufactured by ACROS ORGANICS Co., Ltd., molecular weight = 260,000) with n-propyl acetate as the solvent to 1.0% by mass. The insulating polymer solution was applied to the anode by spin coating at 500 rpm for 30 seconds. The coating film was then dried at 120°C for 30 minutes to obtain a laminate with a receiving layer 50 nm thick.

[0123] Commercially available milk 1 was used as the test subject. The laminate obtained above was immersed in a petri dish containing milk 1 for 1 minute, then the liquid was removed with an air gun and the laminate was dried.

[0124] n-propyl acetate was used as the solvent, and a luminescent probe (blue phosphorescent compound Ep1, described below) was mixed with the solvent at a concentration of 10 mg / mL. The resulting mixture was heated with ultrasound for 30 minutes, and then filtered through a 0.2 μm filter to remove aggregated components, thereby preparing a luminescent ink.

[0125]

[0126] The obtained luminescent ink was dropped onto the receiving layer of the laminate using an inkjet method under the following conditions to form a luminescent layer (detection region). The laminate was dried at 120°C for 30 minutes to evaporate the solvent.

[0127] The conditions for the inkjet method were as follows: The inkjet device used was the "IJCS-1" (manufactured by Konica Minolta, Inc.), and the inkjet head used was the "KM512" (manufactured by Konica Minolta, Inc.). The number of ejection shots was 2, the distance between the ejection nozzles from the head was 140 μm pitch, and printing was performed at a head scan speed of 90 mm / sec.

[0128] The laminate with the detection region formed was attached to the vacuum deposition apparatus. The vacuum chamber was 4 x 10 －４ The pressure was reduced to Pa, and an electron injection layer and a cathode were formed on the detection region of the laminate under the following conditions to obtain a blue OLED sensor A. The electron injection layer was formed by depositing potassium fluoride at a deposition rate of 0.1 Å / sec to a thickness of 2.0 nm. The cathode was formed by depositing aluminum at a deposition rate of 4 Å / sec to a thickness of 100 nm.

[0129] The resulting blue OLED sensor had four detection areas (2 x 2 mm) within a 30 x 30 mm area, with the detection area (inkjet area) being the same size as the electrode. The polystyrene receiving layer is insoluble in the milk sample. Therefore, when the laminate was immersed in the sample and dried, trace amounts of solid components of the milk sample were present on the surface of the polystyrene receiving layer. Subsequently, when the luminescent ink was applied to the receiving layer using an inkjet method, the solvent of the luminescent ink dissolved the polystyrene. The dissolved polystyrene, luminescent probe, and sample reached the lower electrode while forming a dispersed state. As a result, a detection area was formed in which the luminescent probe and sample were dispersed at the molecular level.

[0130] In this embodiment, 24 blue OLED sensors were prepared for each type of sample (Milk 1 to Milk 24), resulting in 4 x 24 = 96 measurement points for each sample. The anode and cathode were configured to allow voltage application via wiring.

[0131] (2) Preparation of green and red OLED sensors A green OLED sensor and a red OLED sensor were prepared using the same procedure as for the preparation of the blue OLED sensor, except that the light-emitting probe was changed to compound 1 (Green) or compound 2 (RED).

[0132]

[0133] (3) Data acquisition For the three types of OLED sensors (blue, green, and red) that were prepared, the spectral radiance spectrum [W・sr －１ ・m －２ nm －１ ] and the current density [mA / cm²] for each drive voltage ２ The following measurements were taken: The driving voltage was set in 1V increments from 1V to 10V, and the current at each voltage was measured. A spectroradiometer "CS-2000" (manufactured by Konica Minolta, Inc.) was used to measure brightness. A "6243 DC VOLTAGE CURRENT SOURCE / MONITOR" (manufactured by ADCMT Corporation) was used to measure current.

[0134] Using OLED sensors containing B (blue dopant), G (green dopant), and R (red dopant), 51 spectral data points were recorded at 5 nm intervals from 450 to 700 nm. The spectral data was normalized by setting the value at the wavelength with the highest radiance to 1.

[0135] [Data acquisition using a luminescent DNA sensor] (1) Synthesis of luminescent dye molecules 1 to 16: Unless otherwise specified, all reactions were carried out in oven-dried glassware under a nitrogen atmosphere. Chemical products not otherwise specified were purchased from Aldrich Co., Ltd., Tokyo Chemical Industry Co., Ltd., or Kanto Chemical Co., Ltd., and used as is without purification.

[0136] (1.1) Monomer 1 having a main chain containing a phosphate ester and a luminescent phosphodiol bonded to the main chain was synthesized via intermediates 1 to 6 of the synthesis of monomer 1.

[0137] Synthesizing intermediate 1: Thymidine (15.0 g, 61.9 mmol) and imidazole (16.9 g, 248 mmol) were dissolved in dimethylformamide (124 mL). To the resulting solution, tert-butyldimethylsilyl chloride (19.6 g, 130 mmol) was added and the mixture was stirred at room temperature for 17 hours. Water was added to the resulting reaction mixture, and liquid-liquid extraction was performed with ethyl acetate. The resulting organic phase was dried over magnesium sulfate, and the solvent was removed by distillation to obtain the target intermediate 1 as a colorless solid (28.3 g, yield 97%).

[0138] - Synthesis of intermediate 2: Intermediate 1 (28.3 g, 60.1 mmol) and ammonium sulfate (12.7 g, 96.2 mmol) were dissolved in hexamethyldisilazane (314 mL, 1.50 mol). The resulting solution was heated under reflux for 3 hours. Water was added to the resulting reaction mixture, and liquid-liquid extraction was performed with ethyl acetate. The resulting organic phase was dried over magnesium sulfate, and the solvent was removed by distillation. The resulting crude product was purified by silica gel column chromatography to obtain the target intermediate 2 as a brown liquid (13.2 g, yield 64%).

[0139] - Synthesis of Intermediate 4: A mixture of intermediate 2 (10.1 g, 29.3 mmol), 1-bromopyrene (8.24 g, 29.3 mmol), tris(dibenzylideneacetone)dipalladium(0) (671 mg, 733 μmol), tritert-butylphosphonium tetrafluoroborate (850 mg, 2.93 mmol), dicyclohexylmethylamine (9.35 mL, 44.0 mmol), and 1,4-dioxane (100 mL) was heated at 90°C for 1 hour. Water was added to the heated mixture to stop the reaction, and liquid-liquid extraction was performed with ethyl acetate. The resulting organic phase was dried over magnesium sulfate, and the solvent was removed by distillation. The crude product containing the obtained intermediate 3 was used directly in the next reaction.

[0140] To the crude product containing intermediate 3, tetrahydrofuran (100 mL), 1 M tetrabutylammonium fluoride / tetrahydrofuran solution (117 mL, 117 mmol), and acetic acid (6.74 mL, 117 mmol) were added, and the mixture was stirred at 40°C for 2 hours. Water was added to the resulting reaction product to stop the reaction, and liquid-liquid extraction was performed with ethyl acetate. The resulting organic phase was dried over magnesium sulfate, and the solvent was removed by distillation. The resulting crude product was purified by silica gel column chromatography to obtain the target intermediate 4 as a light brown solid (5.87 g, yield 63%).

[0141] - Synthesis of Intermediate 5: Sodium triacetylborate (11.8 g, 55.8 mmol) and acetic acid (7.87 mL, 138 mmol) were dissolved in acetonitrile (93 mL). The resulting solution was cooled to 0°C, and a solution of intermediate 4 (5.87 g, 18.6 mmol) dissolved in tetrahydrofuran (62 mL) was added dropwise. After the addition was complete, the resulting reaction mixture was heated to room temperature, stirred for 15 minutes, and then water was added to stop the reaction. Liquid-liquid extraction was performed with ethyl acetate. The resulting organic phase was dried over magnesium sulfate, and the solvent was removed by distillation. The resulting crude product was purified by silica gel column chromatography and reverse-phase chromatography to obtain the target intermediate 5 as a colorless solid (3.44 g, yield 58%).

[0142] - Synthesis of intermediate 6: Intermediate 5 (3.44 g, 10.8 mmol), 4,4'-dimethoxytrityl chloride (4.40 g, 13.0 mmol), ethyl diisopropylamine (2.82 mL, 16.2 mmol), and anhydrous pyridine (54 mL) were mixed. The resulting mixture was stirred at room temperature for 4 hours. Methanol was added to the resulting reaction solution to stop the reaction, and the solvent was removed by distillation. The resulting crude product was purified by silica gel column chromatography to obtain the target intermediate 6 as a colorless viscous solid (5.71 g, yield 85%).

[0143] Synthetic intermediate 6 of monomer 1 (5.71 g, 9.20 mmol) was mixed with ethyl diisopropylamine (6.42 mL, 36.8 mmol) and anhydrous dichloromethane (92 mL). 2-Cyanoethyldiisopropyl chlorophosphoramidito (3.08 mL, 13.8 mmol) was added dropwise to the resulting mixture at 0°C. The resulting reaction mixture was heated to room temperature and stirred for 3 hours, after which the solvent was removed by distillation. The crude product was purified by silica gel column chromatography to obtain the target monomer 1 as a colorless solid (4.64 g, yield 61%).

[0144] (1.2) Preparation of Monomer 2 Monomer 2 was purchased from Glen Research Co., Ltd. (Sterling, Virginia) as a reagent with the structure shown below.

[0145] (1.3) Synthesis of Luminescent Dye Molecules 1-16 Following the standard method, 16 oligonucleotides (Seq1-16) with mixed sequences of monomer 1 and monomer 2 were synthesized as shown in Table I below. DNA synthesis reagents were purchased from Glen Research, Inc. (Sterling, Virginia). All oligonucleotides were synthesized using the DNA / RNA synthesizer "NTS T-series" (manufactured by Nippon Techno Service Co., Ltd.) using the standard protocol for phosphoramidite-based coupling methods.

[0146] Each luminescent dye molecule carrier obtained by automated synthesis was reacted with ammonium water at room temperature for 2 hours. The particulate carrier was excised, and the solvent was dried using a centrifugal dryer. Ultrapure water was added to the dried particulate carrier to obtain first components 1 to 16 containing each luminescent dye molecule 1 to 16. It was confirmed that these luminescent dye molecules 1 to 16 emit fluorescence and excimer emission when exposed to specific excitation light (light with a wavelength of 350 nm).

[0147]

[0148] (2) Data Acquisition (2.1) Arrangement of Luminescent Dye Molecules A 96-well microplate was prepared in which 12 rows of 8 wells with an opening diameter of 7 mm were arranged at intervals of 9 mm. 100 μL each of the above-mentioned luminescent dye molecules 1 to 16 were placed in the 96-well microplate to form multiple reaction fields. An automated dispensing device, "NichiMart CUBE" (manufactured by NICHIRYO Co., Ltd.), was used to arrange the luminescent dye molecules.

[0149] (2.2) Acquisition of first signal data A 96-well microplate containing the above-mentioned luminescent dye molecules was irradiated with excitation light (wavelength 350 nm), and the resulting fluorescence spectra were acquired as first signal data.

[0150] (2.3) Placement of the subjects After acquiring the first signal data described above, 20 μL of milk 1 was placed in each of the reaction fields where the luminescent dye molecules 1 to 16 were placed in the 96-well microplate. For milk 2 to milk 24, the reaction fields were formed in the same procedure as for milk 1, except that the type of milk was changed. An automated dispensing device "NichiMart CUBE" (manufactured by NICHIRYO Co., Ltd.) was used to place the milk.

[0151] (2.4) Acquisition of second signal data A 96-well microplate containing the above-mentioned luminescent dye molecules and milk as the test subject was irradiated with excitation light (wavelength 350 nm), and the resulting fluorescence spectra were acquired as second signal data.

[0152] [Data acquisition using MS sensor] Data on dimethyl disulfide, an aroma component found in milk, was acquired using the following gas chromatography / mass spectrometry (GC / MS) method.

[0153] (1) Extraction of aroma component (dimethyl sulfide) 10 g of each milk was dispensed into a 20 mL vial. Dry nitrogen was continuously sprayed onto each dispensed milk at a rate of 40 mL / min for 10 minutes at 25°C, and the volatile aroma component was adsorbed onto a "TDU tube" (manufactured by GERSTEL Corporation). The "TDU tube" was heat-treated at 230°C to desorb the aroma component, and then cooled to -50°C in a cooling unit to concentrate the aroma component.

[0154] (2) Data Acquisition The extracted aroma components were introduced into a gas chromatography / mass spectrometer (GC / MS) and analytical data was acquired. The GC / MS used was the "HP5975C (GC)" and the "HP7890A (MS)" (both manufactured by Agilent Technologies, Inc.).

[0155] For the GC column used for analysis, we used "DB-WAX" (30 m x 0.25 mm inner diameter, 0.25 μm film thickness, manufactured by Agilent Technologies, Inc.). The column was set to be held at 40°C for 2.5 minutes, then heated to 240°C at a rate of 5°C / min, and held at 240°C for 10 minutes. For the detection of the target component by MS, the total ion chromatogram and mass spectrum results were compared with the library, and dimethyl sulfide, an aroma component, was measured. The peak area of ​​the main fragment ion of dimethyl sulfide was used as the detection amount.

[0156] <Human Sensory Evaluation> 100 panelists conducted a sensory evaluation of Milk 1 through Milk 24. The sensory evaluation consisted of seven items: sweetness, aftertaste intensity, aroma pleasantness, taste pleasantness, texture, aroma intensity, and taste richness, each rated on a 5-point scale. The score was expressed as the arithmetic mean, calculated by dividing the sum of each panelist's score by the number of panelists.

[0157] <Machine Learning> One-third of the obtained data, i.e., data from eight types of milk (milk 17-24), was used as training data, and the remaining two-thirds of the obtained data, i.e., data from sixteen types of milk (milk 1-16), was used as validation data.

[0158] The data acquired from each of the above sensors was combined in groups of one to three to form the following data sets: Data set A: Data acquired by the MS sensor Data set B: Data acquired by the OLED sensor Data set C: Data acquired by the OLED sensor and the MS sensor Data set D: Data acquired by the luminescent DNA sensor Data set E: Data acquired by the luminescent DNA sensor and the MS sensor Data set F: Data acquired by the luminescent DNA sensor and the OLED sensor Data set G: Data acquired by the luminescent DNA sensor, the OLED sensor and the MS sensor (multiscale measurement)

[0159] For the training data, the above data sets for eight types of milk (milk 17-24) were used as explanatory variables, and the results of sensory evaluation were used as the dependent variable. Predictive models were created using the following five algorithms: partial least squares regression (PLS regression), support vector machine, bootstrap Mori, and generalized regression (Lasso).

[0160] <Verification> (1) Verification 1 Verification 1 was performed using verification data from 16 types of milk (Milk 1 to 16). In the prediction model of each algorithm, the entropy R-squared value was calculated when the data acquired by each of the above sensors was combined with each of the above data groups. Note that data groups A, B and D are comparative examples, and data groups C, E, F and G are examples of this embodiment.

[0161] Generally, the R-squared values ​​in data group C (Example) were larger than those in data group A (Comparative Example) or data group B (Comparative Example), indicating higher prediction accuracy. Similarly, the R-squared values ​​in data groups E to G (Example) were larger than those in data group D (Comparative Example), indicating higher prediction accuracy. From this, it was found that predicting using two or more types of sensor data in combination improves prediction accuracy compared to predicting using only one type of sensor data.

[0162] (2) Verification 2 Verification 2 was conducted using verification data from 16 types of milk (Milk 1 to 16). Figure 10 shows the results of sensory evaluations performed by humans on the verification data for the 16 types of milk (Milk 1 to 16).

[0163] The data acquired from each of the above sensors was combined into data group G (multiscale measurement), and the prediction accuracy of a prediction model created using the Bootstrap Mori algorithm was verified. The sensory evaluation predicted by this multiscale measurement is shown in Figure 11.

[0164] The predictions shown in Figure 11 closely match the actual results shown in Figure 10, demonstrating that predictions using multiscale measurements are sufficiently accurate.

[0165] This disclosure can improve the predictive accuracy of analysis in the subject.

[0166] 10 First measuring device 11 First measuring unit 12 First communication unit 13 First control unit 20 Second measuring device 21 Second measuring unit 22 Second communication unit 23 Second control unit 30 Analysis device 31 Display unit 32 Operation unit 33 Communication unit 34 Control unit 100 Analysis system 111 Transparent substrate 112 Transparent electrode 113 Hole transport layer 114 Receiving layer 115 Counter electrode layer 121 Subject layer 122 Light-emitting layer

Claims

1. An analysis device comprising: a first acquisition unit that acquires first data about optical properties whose behavior changes in interaction with a subject; a second acquisition unit that acquires second data about the characteristics of the subject by a method different from that of the first acquisition unit; and an analysis unit that analyzes the subject by machine learning based on the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit.

2. The analysis apparatus according to claim 1, wherein the first acquisition unit includes a first light-emitting probe whose light-emitting behavior changes upon interaction with the subject.

3. The analysis apparatus according to claim 2, wherein the first acquisition unit has a light-emitting layer including the first light-emitting probe between electrodes.

4. The analysis apparatus according to claim 2, wherein the second acquisition unit includes a second light-emitting probe whose light-emitting behavior changes upon interaction with the subject.

5. The analytical apparatus according to claim 4, wherein the first light-emitting probe or the second light-emitting probe has a main chain having one or more structural units including a sugar structure derived from a pentose or hexose and a phosphate ester bond attached to the sugar structure, and one or more chromophores or light-emitting phosphodiphores attached to the sugar structure.

6. The analysis apparatus according to claim 1, wherein the second acquisition unit acquires the second data regarding the characteristics of electromagnetic waves or ultrasound that change in behavior in interaction with the subject.

7. The analysis apparatus according to claim 1, wherein the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit have the same number of data points.

8. The analysis apparatus according to claim 1, wherein the machine learning prediction algorithm is linear regression.

9. The analysis apparatus according to claim 1, wherein the machine learning prediction algorithm is partial least squares regression.

10. An analysis system comprising: an analysis apparatus according to claim 1; a first measuring apparatus for measuring first data about optical properties whose behavior changes in interaction with a subject; and a second measuring apparatus for measuring second data about the characteristics of the subject by a method different from that of the first measuring apparatus.

11. An analysis method comprising: a first acquisition step of acquiring first data about optical properties whose behavior changes in interaction with a subject; a second acquisition step of acquiring second data about the characteristics of the subject by a method different from that of the first acquisition step; and an analysis step of analyzing the subject by machine learning based on the first data acquired in the first acquisition step and the second data acquired in the second acquisition step.

12. A program that causes the computer of an analysis apparatus, which comprises a first acquisition unit that acquires first data about optical properties whose behavior changes in interaction with a subject, and a second acquisition unit that acquires second data about the characteristics of the subject by a method different from that of the first acquisition unit, to function as an analysis unit that analyzes the subject by machine learning based on the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit for the subject.

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