JP7362502B2 - Evaluation systems, methods and programs for photosynthetic samples - Google Patents

Description

One aspect of the present disclosure relates to a photosynthetic sample evaluation system, method, and program.

As one of the bioassays for evaluating the influence of environmental factors on organisms, a method has been known that evaluates changes over time in delayed luminescence emitted by photosynthetic samples such as algae. Patent Document 1 discloses a technique for evaluating photosynthetic samples by using function fitting.

Patent No. 6599173

The purpose of this study is to appropriately evaluate temporal changes in delayed luminescence emitted by photosynthetic samples using a method different from conventional methods.

A photosynthetic sample evaluation system according to one aspect of the present disclosure includes measurements that indicate changes over time in delayed luminescence emitted by a photosynthetic sample for each of an exposed sample exposed to environmental factors and a control sample not exposed to environmental factors. The data is accepted, and scores of multiple principal components are calculated for each of the exposed sample and the control sample based on the evaluation model obtained by principal component analysis and the measured data, and the scores of multiple principal components of the exposed sample are calculated. and at least one processor configured to calculate a score difference between the control sample and the plurality of principal component scores of the control sample.

A method for evaluating a photosynthetic sample according to one aspect of the present disclosure is performed by a computer, and evaluates delayed luminescence emitted by the photosynthetic sample for each of an exposed sample exposed to an environmental factor and a control sample not exposed to an environmental factor. a step of accepting measurement data showing changes over time; a step of calculating scores of a plurality of principal components for each of the exposed sample and the control sample based on the measurement data and an evaluation model obtained by principal component analysis; calculating a score difference between the score of the plurality of principal components of the exposed sample and the score of the plurality of principal components of the control sample.

A photosynthetic sample evaluation program according to one aspect of the present disclosure includes a measurement showing changes over time in delayed luminescence emitted by a photosynthetic sample for each of an exposed sample exposed to environmental factors and a control sample not exposed to environmental factors. a step of receiving data; a step of calculating scores of multiple principal components for each of the exposed sample and the control sample based on the evaluation model obtained by principal component analysis and the measured data; The computer is caused to perform a step of calculating a score difference between the principal component score and the plurality of principal component scores of the control sample.

In this aspect, scores for a plurality of principal components can be obtained for each of the exposed sample and the control sample by using the evaluation model and measurement data showing changes over time in delayed luminescence emitted by the photosynthetic sample. Then, by calculating the difference between the score of the exposed sample and the score of the control sample, the amount of change in score due to exposure can be obtained. Since the amount of change in the score reflects the influence of environmental factors contained in the exposed sample, by using this amount of change, it is possible to appropriately evaluate the temporal change in delayed luminescence emitted by the photosynthetic sample.

According to one aspect of the present disclosure, it is possible to appropriately evaluate changes over time in delayed luminescence emitted by a photosynthetic sample.

It is a graph showing an example of attenuation of delayed luminescence of a photosynthetic sample. FIG. 3 is a diagram showing the mechanism of delayed light emission. It is a figure showing an example of application of an evaluation system. FIG. 1 is a diagram showing an example of the configuration of a measuring device. 1 is a diagram illustrating an example of a hardware configuration related to an evaluation system. It is a diagram showing an example of a functional configuration related to the evaluation system. It is a flowchart showing an example of the operation of the evaluation system. It is a flowchart showing an example of the operation of the evaluation system. 3 is a diagram showing attenuation curves of chemical substances in Example 1. FIG. 3 is a diagram showing eigenvectors in Example 1. FIG. 3 is a diagram showing scores for each principal component of an attenuation curve in Example 1. FIG. 3 is a diagram showing the amount of change in score due to exposure in Example 1. FIG. 3 is a diagram showing the results of cluster analysis in Example 1. FIG. FIG. 3 is a diagram showing attenuation curves of chemical substances in Example 2. FIG. 7 is a diagram showing the amount of change in score due to exposure in Example 2. FIG. 7 is a diagram showing the results of cluster analysis in Example 2.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant description will be omitted.

[System overview]
A photosynthetic sample evaluation system (also simply referred to as an "evaluation system" in the present disclosure) 1 according to an embodiment evaluates a photosynthetic sample using measurement data (attenuation curve) indicating changes over time in delayed luminescence emitted by the photosynthetic sample. This is a system for In this specification, "evaluating a photosynthetic sample" means obtaining objective results by performing calculations on the measurement data.

Evaluation system 1 can be used for various purposes. For example, the evaluation system 1 is used for purposes such as investigating the influence of environmental factors on photosynthetic samples, diagnosing the growth of photosynthetic samples, analyzing or developing chemical substances, researching photosynthesis, investigating environmental factors, and developing cultivars. Good too. In this disclosure, an environmental factor refers to an element that can have some influence on the photosynthetic function of a photosynthetic sample. Examples of environmental factors include, but are not limited to, chemicals, water, temperature, and air.

A photosynthetic sample refers to an object that has a photosynthetic function and can emit delayed luminescence. The photosynthesis sample used for evaluation is not limited at all. For example, photosynthetic samples include algae, phytoplankton, cyanobacteria, photosynthetic bacteria, plant leaves or their fragments, cultured plant cells such as callus, photosynthetic organelles or thylakoid membranes extracted from plants, or artificially It may also be a synthesized membrane or protein complex with a photosynthesis-like function.

FIG. 1 is a graph showing an example of attenuation of delayed luminescence of a photosynthetic sample. As shown in Figure 1, the attenuation of delayed luminescence shown in the measurement data is a non-exponential heavy-tailed distribution when viewed from two axes: the luminescence amount (signal intensity) axis and the time axis. -tailed distribution). The vertical axis of the graph in FIG. 1 shows the amount of light emission (signal intensity), and the horizontal axis shows the time after excitation. The post-excitation time is the elapsed time from the point in time when irradiation of the excitation light ended.

FIG. 2 is a diagram showing the mechanism of delayed light emission. Photosynthetic electron transfer, which is the theoretical background of the photosynthetic sample evaluation method according to the present disclosure, will be explained using FIG. 2.

In general, when a photosynthetic sample having a photosynthetic function absorbs a large amount of light energy, individual reactions in the photosynthetic electron transport system become rate-limiting, making it difficult for photosynthetic electron transport to proceed smoothly. In other words, the redox state of the photosynthetic sample becomes different from normal. Such a state can be obtained by changing the light conditions applied to the photosynthetic sample. The light conditions are, for example, the amount of light, wavelength, pulse width, and irradiation time of light given to the photosynthetic sample. In this specification, a state in which photosynthetic electron transfer does not proceed smoothly (a state in which the redox state of a photosynthetic sample is different from normal) is referred to as "saturation" of the photosynthetic electron transport system.

As shown in Figure 2, the photosynthetic reaction of the photosynthetic sample mainly involves the photosystem II complex (PSII), cytochrome b6f complex ( _cytb6f ), and photosystem I complex (PSI) present in the chloroplast thylakoid membrane. ), a reducing substance (NADPH) produced by ATP synthase, and a carbon dioxide fixation reaction using ATP synthase.

First, PSII and PSI absorb light and perform an electron transfer reaction. Specifically, upon irradiation with light, water molecules (H ₂ O) are decomposed in PSII and electrons (e ⁻ ) are extracted. This electron is transferred from the PSII reaction center (P680) to the oxidized plastoquinone (pQ _OX ) bound to the Q _B electron acceptor (Q _B ) site via the Q _A electron acceptor (Q _A ). . This then produces reduced plastoquinone (pQ _red ).

After the reduced plastoquinone is released from the Q _B site and moves to the plastoquinone pool (pQpool), it is reoxidized by cytb ₆ f and returns to oxidized plastoquinone. At this time, hydrogen ions (H ⁺ ) flow into the thylakoid membrane via cytb ₆ f, forming a proton gradient. The hydrogen ions that form the proton gradient are used for ATP synthesis by ATP synthase. On the other hand, electrons extracted during reoxidation of reduced plastoquinone are transferred to the PSI reaction center (P700). This electron is transferred from P700 to ferredoxin (Fd) within the PSI and contributes to the production of NADPH. Excess electrons (reducing power) after PSI are transferred to cytb ₆ f via Fd by cyclic electron transfer and reduce oxidized plastoquinone. This results in reduced plastoquinone (cyclic electron transfer).

Delayed luminescence is said to be luminescence that occurs mainly due to chemical re-excitation of the reaction center chlorophyll of PSII as a result of the reverse reaction of the photosynthetic electron transfer reaction described above, but PSI is also involved in luminescence as well. There are also reports that it does. Furthermore, it is understood that electrons distributed inside PSII, pQpool, and after PSI are the main components contributing to re-excitation of chlorophyll. Therefore, when the above-mentioned photosynthetic electron transfer reaction changes due to the influence of environmental factors (for example, harmful substances), a change appears in delayed luminescence.

Generally, the electron transfer ability of each of pQpool, cytb ₆ f, and PSI is lower than that of PSII. Therefore, when PSII is excessively reduced by light irradiation, pQpool is excessively reduced due to electron transfer from PSII, and the oxidized plastoquinone that receives electrons from PSII decreases, resulting in the efficiency of photosynthetic electron transfer from PSII. is known to decrease. Such a phenomenon is one of saturation of photosynthetic electron transport. More specifically, when PSII absorbs light excessively, reoxidation of reduced plastoquinone by _cytb6f becomes rate-limiting, leading to a shortage of oxidized plastoquinone that can accept electrons from PSII. . Therefore, electron transfer from PSII to cytb ₆ f is stagnated. As a result, excessive electrons accumulate in PSII, which has various effects on photosynthetic reactions, such as destruction of the reaction center of PSII. This kind of effect is called photoinhibition.

In order to avoid such photoinhibition, it is known that various metabolic changes are caused, such as the light-harvesting pigment protein complex (LHC2), which normally supplies electrons to PSII, moves to PSI (state transition). It is being Such metabolic changes caused by photoinhibition are gradually recovered by stopping excessive light irradiation.

[System configuration]
FIG. 3 is a diagram showing an example of application of the evaluation system according to the embodiment. In this embodiment, the evaluation system 1 includes a server 10. The server 10 is a computer that evaluates measurement data showing changes over time in delayed luminescence emitted by photosynthetic samples. The server 10 is connected to at least one terminal 20 via a communication network N. Although FIG. 3 shows two terminals 20, the number of terminals 20 is not limited at all. Furthermore, the server 10 may be connected to the database 30 via the communication network N. The configuration of the communication network N is not limited. For example, the communication network N may include the Internet or may include an intranet.

The terminal 20 is a computer used by a user who performs work (eg, testing, research, development, evaluation, etc.) using bioassays to evaluate the effects of environmental factors on living organisms. The terminal 20 has a function of transmitting to the server 10 measurement data indicating changes over time in delayed luminescence emitted by the photosynthetic sample. The terminal 20 may be equipped with a measurement device for acquiring measurement data, or may acquire measurement data from another computer or the like without being equipped with a measurement device. The type and configuration of the terminal 20 are not limited. For example, the terminal 20 may be a mobile terminal such as a high-performance mobile phone (smartphone), a tablet terminal, a wearable terminal (for example, a head mounted display (HMD), smart glasses, etc.), a laptop personal computer, or a mobile phone. Alternatively, the terminal 20 may be a stationary terminal such as a desktop personal computer.

FIG. 4 is a diagram showing an example of the configuration of the measuring device. The measurement device 200 includes a control unit 210, a communication unit 220, and a measurement unit 230. The control unit 210 and the communication unit 220 are connected by wire or wirelessly, and can send and receive data to and from each other. The communication unit 220 and the measurement unit 230 are connected by wire or wirelessly and can send and receive data to and from each other. The control unit 210 and the measurement unit 230 can exchange data with each other via the communication unit 220.

The control unit 210 is a device that includes hardware such as at least one processor (for example, CPU: Central Processing Unit) and a memory, and controls the measurement unit 230 by transmitting a control signal to the measurement unit 230. Control unit 210 may further control communication unit 220.

The measurement unit 230 is a device that measures the amount of delayed luminescence of the photosynthetic sample. The measurement unit 230 measures the amount of delayed light emission at predetermined time intervals, and outputs data indicating the amount of light emission to the communication unit 220 each time.

FIG. 4 also shows a cross section of the measurement unit 230. The measurement unit 230 includes a housing 231 , an installation section 232 , a light source 233 , a photodetector 234 , a filter 235 , a condensing optical system 236 , and a shutter 237 arranged inside the housing 231 .

The casing 231 is formed of a light-blocking member that blocks light, or is formed of a member coated with a paint that blocks light, so that light does not enter the inside. The casing 231 includes a main body portion 231a having an introduction port 238 formed at one end thereof, and a lid portion 231b capable of closing the introduction port 238. The opening and closing of the lid portion 231b is monitored and controlled by the control unit 210. When the shutter 237 is open, the lid 231b is locked so that light from outside the housing 231 does not enter the optical sensor 234a.

The installation section 232 is a section for placing a container containing a photosynthesis sample to be measured. The container may contain environmental factors in addition to the photosynthetic sample. For example, a container contains a solution containing a photosynthetic sample and a chemical. The installation part 232 is provided at a position where a container can be installed from the introduction port 238. The installation part 232 may have a fixing claw for fixing the container.

The light source 233 is a device that irradiates light of a predetermined wavelength (for example, 280 nm to 800 nm) to the photosynthetic sample in the container installed in the installation section 232 in order to generate delayed light emission. The light emitted from the light source 233 is excitation light. The light source 233 may be a monochromatic light source or a combination of multiple light sources. Irradiation from the light source 233 may be continuous irradiation for a predetermined period of time, or may be pulsed lighting in an arbitrary pattern. Further, a plurality of light sources having the same or different wavelength characteristics may be caused to emit light in sequence, or a plurality of light sources may be caused to emit light at the same time. Illumination from light source 233 is controlled by control unit 210.

The photodetector 234 is a device that detects the amount of delayed light emission generated from the photosynthetic sample by being irradiated with light from the light source 233. The photodetector 234 includes an optical sensor 234a that detects delayed light emission, and a calculation unit 234b that calculates the amount of delayed light emission based on a signal output by the optical sensor 234a. Specifically, the photodetector 234 is composed of a photomultiplier tube, a photon counter, or the like. Photodetector 234 outputs data indicating the calculated amount of light emission to communication unit 220. The photodetector 234 detects delayed light emission and calculates and outputs the amount of light emission at predetermined time intervals (for example, at 0.1 second intervals).

A filter 235, a condensing optical system 236, and a shutter 237 are provided in this order in a region extending from the installation section 232 to the photodetector 234. The filter 235 is a member that transmits delayed light emission. The condensing optical system 236 is a device that condenses, reflects, and transmits weak delayed light emission to input the light to the photodetector 234. The shutter 237 is configured to be openable and closable so that the delayed light emission is detected by the photodetector 234 only when necessary. When the shutter 237 is closed, delayed light emission is blocked. The opening and closing of the shutter 237 is controlled by the control unit 210.

The communication unit 220 is a device that receives measurement data input from the measurement unit 230 and transmits the measurement data to the terminal 20. The communication unit 220 receives measurement data indicating changes over time in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light. Since the photodetector 234 of the measurement unit 230 outputs data on the amount of light emitted at predetermined time intervals, the communication unit 220 sequentially receives the data. The measurement data indicating the temporal change in delayed light emission is a set of a plurality of pieces of data received by the communication unit 220 during a predetermined period of time (for example, 60 seconds). Communication unit 220 transmits the measurement data to terminal 20. When the measuring device 200 and the terminal 20 are integrally formed, the communication unit 220 may be configured to transmit measurement data to the server 10.

Returning to FIG. 3, the database 30 is a device that can store at least one of data input to the evaluation system 1 and output data from the evaluation system 1. The installation location of the database 30 is not limited. For example, the database 30 may be provided in a computer system separate from the evaluation system 1, or may be a component of the evaluation system 1.

FIG. 5 is a diagram showing an example of a hardware configuration related to the evaluation system 1, and specifically shows a server computer 100 functioning as the server 10.

Server computer 100 includes a processor 101, a main storage section 102, an auxiliary storage section 103, and a communication section 104 as examples of hardware components.

Processor 101 is a computing device that executes an operating system and application programs. Examples of processors include a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), but the type of processor 101 is not limited to these. For example, processor 101 may be a combination of sensors and dedicated circuitry. The dedicated circuit may be a programmable circuit such as an FPGA (Field-Programmable Gate Array), or may be another type of circuit.

The main storage unit 102 is a device that stores programs for implementing the server 10, calculation results output from the processor 101, and the like. The main storage unit 102 is configured of, for example, at least one of ROM (Read Only Memory) and RAM (Random Access Memory).

The auxiliary storage unit 103 is generally a device that can store a larger amount of data than the main storage unit 102. The auxiliary storage unit 103 is configured by a nonvolatile storage medium such as a hard disk or flash memory. The auxiliary storage unit 103 stores a server program P1 and various data for causing the server computer 100 to function as the server 10. For example, the auxiliary storage unit 103 may store data related to at least one of the data stored in the database 30. In this embodiment, the evaluation program is implemented as a server program P1.

The communication unit 104 is a device that performs data communication with other computers via the communication network N. The communication unit 104 is configured by, for example, a network card or a wireless communication module.

Each functional element of the server 10 is realized by loading a server program P1 onto the processor 101 or the main storage unit 102 and causing the processor 101 to execute the program. The server program P1 includes codes for realizing each functional element of the server 10. The processor 101 operates the communication unit 104 according to the server program P1 to read and write data in the main storage unit 102 or the auxiliary storage unit 103. Each functional element of the server 10 is realized through such processing.

Server 10 may be composed of one or more computers. When a plurality of computers are used, one logical server 10 is configured by connecting these computers to each other via a communication network.

The server program P1 may be provided after being permanently recorded on a non-temporary recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the server program P1 may be provided via a communication network as a data signal superimposed on a carrier wave.

FIG. 6 is a diagram showing an example of a functional configuration related to the evaluation system 1. The server 10 includes a reception section 11, an evaluation section 12, a standardization section 13, a change amount calculation section 14, and a similarity determination section 15 as functional components.

The receiving unit 11 is a functional element that receives measurement data indicating changes over time in delayed luminescence emitted by the photosynthetic sample. In one example, the measurement data may include at least one of measurement data of the exposed sample and measurement data of the control sample. An exposed sample is a sample exposed to environmental factors (photosynthetic sample). A control sample is a sample used for comparison with an exposed sample, and more specifically refers to a sample (photosynthetic sample) that has not been exposed to environmental factors. The measurement data of the exposed sample may be a data set measured according to the degree of exposure to an environmental factor. In one example, the data set is a collection of data obtained for each of a plurality of exposure concentrations.

The evaluation unit 12 is a functional element that constructs an evaluation model from measurement data. The evaluation model refers to a mathematical model used to evaluate changes in delayed luminescence over time. In one example, the evaluation model is obtained by performing principal component analysis on the measurement data. The evaluation unit 12 is also a functional element that calculates scores of a plurality of principal components based on the evaluation model and measurement data. Principal component analysis is a statistical data analysis method that summarizes a large number of variables into fewer variables (these variables are called "principal components"). The scores of a plurality of principal components refer to scores calculated for each of a plurality of principal components based on an evaluation model and measurement data (attenuation curve).

The standardization unit 13 is a functional element that standardizes the scores of the plurality of principal components calculated by the evaluation unit 12. Standardization refers to the process of matching the scale of scores among multiple principal components. This standardization makes it possible to evaluate multiple principal components using the same scale.

The change amount calculation unit 14 is a functional element that calculates the amount of change in score due to exposure. The change calculation unit 14 calculates the score difference between the score of the plurality of principal components of the exposed sample and the score of the plurality of principal components of the control sample corresponding to the exposed sample. The score difference is the amount of change in score due to exposure.

The similarity determination unit 15 separates exposed samples with known environmental factors (also referred to as "known exposed samples" in this disclosure) and exposed samples with unknown environmental factors (in this disclosure, this is referred to as "unknown exposed samples"). This is a functional element that determines the similarity between samples (also referred to as "sample"). In this disclosure, similarity refers to the degree to which an unknown exposed sample resembles a known exposed sample; in other words, it refers to how similar an unknown environmental factor is to a known environmental factor. It refers to degree. The similarity determining unit 15 determines similarity by comparing the score difference regarding the known exposed sample and the score difference regarding the unknown exposed sample. The similarity determining unit 15 also determines at least one of the degree of influence of the environmental factor and the type of the environmental factor, based on which known exposed sample the unknown exposed sample has a high degree of similarity. can be evaluated.

[System operation]
The operation of the evaluation system 1 will be described with reference to FIG. 7, and the method for evaluating photosynthetic samples according to this embodiment will be described. FIG. 7 is a flowchart showing an example of the operation of the evaluation system 1.

Step S11: The reception unit 11 receives measurement data indicating changes over time in delayed luminescence emitted by the photosynthetic sample for each of the exposed sample exposed to environmental factors and the control sample not exposed to environmental factors. The reception unit 11 may receive measurement data from the terminal 20 (or the measurement device 200), or may acquire measurement data stored in the database 30 by the terminal 20.

Step S12: The evaluation unit 12 selects processing related to the evaluation model. Specifically, the evaluation unit 12 determines whether to construct an evaluation model from the measurement data ("construct" in step S12) or to calculate scores of a plurality of principal components based on the evaluation model and the measurement data (step S12). The processing is distributed according to the "evaluation"). The evaluation unit 12 may distribute processing based on whether or not an evaluation model has been constructed. Specifically, the evaluation unit 12 selects the construction of an evaluation model when the evaluation model has not been constructed yet, and selects the calculation of the score when the evaluation model has been constructed. Alternatively, the evaluation unit 12 may choose to construct an evaluation model when updating the evaluation model using new measurement data.

Step S13 (“construction” in step S12): The evaluation unit 12 performs principal component analysis on each of the measurement data of the exposed sample and the measurement data of the control sample. The measurement data for each sample includes a plurality of measurement values obtained at predetermined time intervals. In one example, measurement data for a group of one or more exposed samples and one or more control samples has a variance of i×α, where i is the number of measured values for each sample, and α is the number of samples to be measured. It can be expressed as a covariance matrix Y. In one example, the evaluation unit 12 extracts a plurality of principal components by performing principal component analysis on the variance-covariance matrix Y, and obtains an eigenvector matrix F and a score matrix Z. When the number of principal components to be extracted is k, the eigenvector matrix F is an i×k matrix and is composed of i×k eigenvectors f _ik . The score matrix Z is an α×k matrix, and is composed of α×k scores z _αk . The score z _αk can be said to be a weighting coefficient, and can also be said to be an evaluation value expressing the characteristics of the attenuation curve. Further, the characteristics of each attenuation curve indicated by the measurement data are expressed by ZF ^T using the score matrix Z and the transposed matrix F ^T (k×i matrix) of the eigenvector matrix F. Specifically, the difference between the average value M _i of delayed luminescence amount at time point i of each attenuation curve (in other words, the average value of α delayed luminescence quantities located at the i-th position) is determined by the score z _αk . It is expressed by the following equation (1) using the eigenvector f _ki of the transposed matrix F ^T . Here, x^ _αi represents the characteristic of the amount of delayed light emission at time i of sample α (the difference between the amount of delayed light emission and the average value).

In one example, in the variance-covariance matrix Y, the average value M _i of the delayed light emission amount at time i is subtracted from each delayed light emission amount at time i. Therefore, when calculating the variance-covariance matrix Y from the eigenvector matrix F and the score matrix Z, it is necessary to add the average value M _i at each time point i. In this example, the relationship among the variance-covariance matrix Y, the eigenvector matrix F (transposed matrix F ^T ), the score matrix Z, and the matrix M indicating the mean value M _i at each time point can be expressed as Y=M+ZF ^T I can do it.

Step S14: The evaluation unit 12 constructs the eigenvector matrix F obtained by principal component analysis as an evaluation model. The evaluation unit 12 stores this evaluation model (eigenvector matrix F) in a predetermined storage unit. The evaluation system 1 can construct an evaluation model in advance through steps S11 to S14.

Step S15: The evaluation unit 12 calculates scores of a plurality of principal components based on the evaluation model and the measurement data. For example, the evaluation unit 12 calculates the unknown score matrix Z (scores of multiple principal components) by Z=YF using the variance-covariance matrix Y (measurement data) and the eigenvector matrix F (evaluation model). can do. For each of the at least one exposed sample, the evaluation unit 12 calculates scores of the plurality of principal components of the exposed sample and scores of the plurality of principal components of the control sample based on the evaluation model and the measurement data of the exposed sample. do. Assuming that the number of measured values for each sample is 300, the score of each principal component is calculated, for example, by the following equation (2).

Here, x _i is the i-th luminescence amount of the attenuation curve, and M _i is the average value of the i-th luminescence amount of the attenuation curve for α samples to be measured.

Step S16: The standardization unit 13 standardizes the scores of the plurality of principal components calculated by the evaluation unit 12. In one example, the standardization unit 13 standardizes the scores of each principal component of all measurement data applied to the evaluation model by unifying the average value to 0 and standard deviation to 1. By standardizing the scores of multiple principal components, the scores of each principal component can be evaluated uniformly. The standardization unit 13 can standardize the scores of a plurality of principal components using, for example, the following equation (3).

Here, μ _k is the average value of the score z _k of the k-th principal component of the attenuation curve, and σ is the standard deviation of the score z _k of the k-th principal component of the attenuation curve. _z'k is a standardized score.

Step S17: The change amount calculation unit 14 calculates the amount of change in the score due to exposure. In one example, the change calculation unit 14 calculates, for each of the one or more exposed samples, a score between a standardized score for the plurality of principal components of the exposed sample and a standardized score for the plurality of principal components of the control sample. Calculate the difference. For example, if the number of principal components is 3, the change amount calculation unit 14 can calculate the score difference Δz′ _k using the following equation (4).

Here, z' _ke is the score of the k-th principal component of the exposed sample e, and z' _kc is the score of the k-th principal component of the control sample c.

Next, with reference to FIG. 8, the operation of the evaluation system 1 will be explained, and an application example of the photosynthesis sample evaluation method according to this embodiment will be explained. FIG. 8 is a flowchart showing an example of the operation of the evaluation system 1. In FIG. 8, it is assumed that the evaluation model has been constructed in advance.

Steps S21a, S21b: The receiving unit 11 receives measurement data for each of the known exposed sample and the control sample (step S21a). The receiving unit 11 also receives measurement data for each of the unknown exposed sample and the control sample (step S21b). The measurement data acquisition method may be the same or different between step S21a and step S21b.

Steps S22a, S22b: The evaluation unit 12 calculates scores of a plurality of principal components based on the evaluation model and the measurement data. The evaluation unit 12 calculates the scores of the plurality of principal components of the known exposed sample and the score of the plurality of principal components of the control sample based on the evaluation model, the measured data of the known exposed sample, and the measured data of the control sample. is calculated (step S22a). The evaluation unit 12 calculates the score of the plurality of principal components of the unknown exposed sample and the score of the plurality of principal components of the control sample based on the evaluation model, the measured data of the unknown exposed sample, and the measured data of the control sample. is calculated (step S22b).

Steps S23a and S23b: The standardization unit 13 standardizes the scores of the plurality of principal components calculated by the evaluation unit 12. The standardization unit 13 standardizes the scores of each principal component for the known exposure sample and the control sample (step S23a). The standardization unit 13 standardizes the scores of each principal component for the unknown exposure sample and the control sample (step S23b).

Steps S24a and S24b: The change amount calculation unit 14 calculates the amount of change in the score due to exposure. The change amount calculation unit 14 calculates the score difference between the score of the plurality of principal components of the known exposed sample and the score of the plurality of principal components of the control sample (step S24a). The change amount calculating unit 14 also calculates the score difference between the score of the plurality of principal components of the unknown exposed sample and the score of the plurality of principal components of the control sample (step S24b).

Since steps S21a to S24a and steps S21b to S24b are independent processes, they may be executed in parallel or sequentially. Furthermore, steps S21a to S24a may be performed in advance to prepare score differences for known exposed samples, and then steps S21b to S24b may be performed, for example, on another day. Although it is assumed in FIG. 8 that the evaluation model has been constructed in advance, the evaluation unit 12 may construct the evaluation model based on the measurement data obtained in step S21a.

Step S25: The similarity determining unit 15 determines the similarity between the known exposed sample and the unknown exposed sample. The similarity determining unit 15 determines the similarity based on the score difference regarding the known exposed sample and the score difference regarding the unknown exposed sample. In one example, the similarity determination unit 15 can determine whether the score differences between the exposed samples have a high degree of similarity by performing cluster analysis. The cluster analysis may be, for example, a hierarchical cluster analysis. The method for calculating the distance between clusters may be a group average method, a shortest distance method, a longest distance method, a Ward method, a variable method, or the like. The similarity coefficient used for cluster analysis may be a cosine coefficient (cosine similarity) of the difference between both scores. The cosine coefficient represents the closeness of the angle between two vectors, and it can be determined that the closer the cosine coefficient is to 1, the higher the degree of similarity between the two vectors. The similarity determination unit 15 can classify unknown environmental factors into clusters of known environmental factors by cluster analysis.

Step S26: The similarity determining unit 15 determines the degree of influence of the unknown environmental factor and the type of the unknown environmental factor based on which known exposed sample the unknown exposed sample has a high degree of similarity. Evaluate at least one of the following. In one example, when an unknown environmental factor is classified into a cluster of known environmental factors by cluster analysis, the similarity determination unit 15 determines that the unknown environmental factor belongs to the same or similar type as the known environmental factor. evaluate. The similarity determination unit 15 also evaluates the degree of influence of the unknown environmental factor by comparing the exposure concentrations of the unknown environmental factor and the known environmental factor, which belong to the same cluster, for example. In one example, the similarity determination unit 15 can evaluate the magnitude of each score change due to exposure. In this way, the similarity determination unit 15 can estimate the mechanism of action of the unknown environmental factor based on the similarity determination result. Furthermore, the length of the vector used in cluster analysis represents the magnitude of the effect. The length of the vector is expected to be shorter for samples exposed to non-toxic or less active environmental factors. However, if the attenuation curve changes due to, for example, a temperature change during measurement, the length of the vector may also change. Therefore, the similarity determining unit 15 may estimate the mechanism of action of the unknown environmental factor by determining the toxicity of the environmental factor based on the length of the vector. For example, the similarity determination unit 15 determines that the environmental factor is toxic if the length of the vector exceeds a predetermined threshold, and determines that the environmental factor is toxic if the length of the vector does not exceed the threshold. It may be determined that it does not exist. The predetermined threshold value may vary depending on, for example, the exposure concentration, the type of environmental factor, and the like.

Examples will be specifically described below, but the present disclosure is not limited thereto.

(Example 1)
[Measurement preparation]
Green algae (Raphidocelis subcapitata) was used as a photosynthetic sample. As chemical substances (environmental factors), there are four types of photosynthesis inhibitors with different mechanisms of action (3-(3,4-dichlorophenyl)-1,1-dimethyl urea (DCMU), 2,5-dibromo-3-methyl- 6-isopropyl-p-benzoquinone (DBMIB), Antimycin A (Ant-A), and carbonyl cyanide m-chlorophenylhydrazone (CCCP)) were used. Exponential phase algal suspensions were prepared as photosynthetic samples by pre-incubating frozen algae for 1 hour.

Prepare 12 exposed solutions prepared with each of the 4 types of chemical substances at 3 concentrations (low, medium, high) and 1 non-exposed solution with no chemical added as sample solutions. did. DMSO (Dimethyl sulfoxide) was used as an auxiliary agent when preparing the sample solution for the exposed area. In Example 1, regarding the exposure area, the relationship between the exposure concentration and the amount of change in delayed luminescence amount due to exposure (ΔDF) was investigated in advance for four types of chemicals, and the exposure was adjusted so that ΔDF was about the same for the four types of chemicals. The concentration was determined. ΔDF was calculated using the following equation (5). Equation (5) indicates that ΔDF is calculated from measurement data of delayed light emission for 30 seconds.

Here, c ₁ is the delayed luminescence amount from 0.1 to 1 second (1 second) after excitation in the non-exposed area, t _i is the delayed luminescence amount i seconds after excitation in the exposed area, and c _i is the delayed luminescence amount in the non-exposed area at i seconds after excitation. The amount of delayed luminescence at i seconds after excitation is shown. Using equation (5), for each of the four types of chemical substances, the exposure concentrations at which ΔDF≒1, ΔDF≒1.5, and ΔDF≒2 were defined as low concentration, medium concentration, and high concentration, respectively. An example of exposure concentration is shown in Table 1 below.

By mixing 2 mL of algae suspension with each of the 13 sample solutions, 13 measurement samples (12 exposed samples and 1 control sample) with a cell density of 20 × 104 cells/mL and a total volume of 10 mL were prepared. sample) was prepared. Samples were prepared in a random order and culture was started. Each sample was incubated for 1 hour by orbital shaking at 24±1° C. and continuously irradiated with a fluorescent lamp (50 μmol/m ² /s) for 1 hour.

[measurement]
Delayed luminescence caused by excitation of photosynthetic samples by light irradiation was measured. The measurement time was set to 30 seconds during which the attenuation change in the amount of delayed light emission accumulated at 0.1 second intervals was clear. For each of the 13 measurement samples, the measurement sample was placed in a dark place for 60 seconds, the measurement sample was irradiated with white LED light (500 μmol/m ² /s) for 30 seconds, and the measurement sample was placed in a dark place for 5 seconds. After placing it, the measurement sample was irradiated with 700 nm LED light (20 μmol/m ² /s) for 1 second to measure delayed luminescence. The relative delayed luminescence amount was recorded at 0.1 second intervals from 0.1 seconds to 30 seconds after the excitation light was turned off. Measurements were carried out in the same procedure on different days (2 repetitions), and a total of 26 decay curves were obtained as measurement data.

Since 26 attenuation curves having 30 seconds of data accumulated at 0.1 second intervals were obtained, the measured data can be expressed as a variance-covariance matrix Y of i(300)×α(26). In Example 1, the first to third principal components are extracted by principal component analysis of the variance-covariance matrix Y, and an eigenvector matrix F (300×3) and a score matrix Z (26×3) are obtained. Ta. The eigenvector matrix F is composed of eigenvectors f _ik (i=1, 2,..., 300; k=1, 2, 3). The score matrix Z is composed of scores z _αk (α=1, 2,..., 26; k=1, 2, 3). In Example 1, the eigenvector matrix F obtained by principal component analysis was constructed as an evaluation model.

For each sample, three principal component scores (z ₁ , z ₂ , z ₃ ) were calculated using the above-mentioned formula (2) based on the evaluation model and the measured data (attenuation curve).

In Example 1, in order to uniformly evaluate the scores of the three principal components (z ₁ , z ₂ , z ₃ ), the average value of the scores of each principal component was unified to 0, and the standard deviation was unified to 1. Specifically, the score (z ₁ , z ₂ , z ₃ ) was converted into (z' ₁ , z' ₂ , z' ₃ ) by standardizing it using Equation (3) described above.

In order to focus on the amount of change in z' _k due to exposure for each attenuation curve, the score difference Δz' _k between z' _k in each exposed area and z _{' k} in the corresponding non-exposed area is calculated using the above equation (4). was calculated.

[Similarity judgment]
In order to evaluate the similarity of Δz′ _k among the 24 exposed samples, a hierarchical cluster analysis was performed on Δz′ _k (Δz′ ₁ , Δz′ ₂ , Δz′ ₃ ) of each exposed sample. As the similarity coefficient used for cluster analysis, a cosine coefficient indicating the angle between two vectors was selected, taking into consideration the spatial distribution of Δz' _k (Δz _{' 1} , Δz' ₂ , Δz' ₃ ) to be analyzed. . In Example 1, the group average method in which the correlation coefficient between the similarity coefficient matrix and the Coffen matrix was the largest was used for estimation.

[result]
The evaluation results of the photosynthetic sample according to Example 1 will be explained with reference to FIGS. 9 to 13.

FIG. 9 is a diagram showing attenuation curves of chemical substances in Example 1. In any of the graphs (a) to (d) in FIG. 9, the vertical axis indicates the amount of light emission, and the horizontal axis indicates the time after excitation. Graphs (a) to (d) all show the average values of two replicates for each of the non-exposed area, low-concentration exposure area, medium-concentration exposure area, and high-concentration exposure area. In all samples, the amount of delayed luminescence reached its maximum immediately after excitation and then attenuated. The decay curve changed with increasing exposure concentration, and the tendency of the change was different for each chemical. In DCMU and DBMIB, the amount of luminescence immediately after excitation increased significantly compared to Ant-A and CCCP. The amount of delayed luminescence caused by exposure to DBMIB attenuated more slowly than the amount of delayed luminescence caused by exposure to DCMU. The amount of delayed luminescence due to exposure of Ant-A did not increase as much immediately after excitation as DCMU and DBMIB, and decay became faster after 10 seconds after excitation. The amount of delayed luminescence due to CCCP exposure decreased in all time ranges. Further, in DCMU, DBMIB, and Ant-A, a point where four attenuation curves intersect (hereinafter, this point is referred to as a "cross point") was observed. In DCMU, a cross point was observed around 1 second after excitation. In DBMIB, a cross point was observed around 5 seconds after excitation. In Ant-A, a cross point was observed around 5 seconds after excitation.

FIG. 10 is a diagram showing eigenvectors in Example 1. In any of the graphs (a) to (c) in FIG. 10, the vertical axis indicates the eigenvector, and the horizontal axis indicates the time after excitation. Graphs (a), (b), and (c) respectively show the first principal component (PC1), the second principal component (PC2), and the third principal component (PC3). The contribution rates of the eigenvalues of the first principal component (PC1), second principal component (PC2), and third principal component (PC3) were 96.6%, 2.1%, and 0.8%, respectively. The cumulative contribution rate of PC1 to PC3 was 99.5%. The eigenvector of PC1 rapidly decreased for 1 second after excitation. The eigenvector of PC2 was negative immediately after excitation, increased from there, turned positive at 0.4 seconds, reached a maximum at 0.8 seconds, and then decreased. The eigenvector of PC3 decreased immediately after excitation, turned negative, reached a minimum at 1.1 seconds, increased from there, turned positive, reached a maximum at 13.5 seconds, and then slowly decreased again.

FIG. 11 is a diagram showing scores for each principal component of the attenuation curve in Example 1. Graphs (a), (b), and (c) in FIG. 11 show the first principal component (PC1), second principal component (PC2), and third principal component (PC3), respectively. Each graph shows the average value of two replicates for each of the four chemical substances in the non-exposed area (N), low-concentration exposure area (L), medium-concentration exposure area (M), and high-concentration exposure area (H). shows. Error bars indicate Student's LSD (least significant difference). In PC1, the scores of DCMU, DBMIB, and Ant-A increased as the exposure concentration increased, and the increase in the score of DBMIB was the largest. On the other hand, only the CCCP score decreased. In PC2, the scores of four types of chemical substances decreased as the exposure concentration increased, and the decrease in the score of CCCP was the largest. In PC3, the scores of the four chemical substances decreased as the exposure concentration increased, and the decrease in the score of Ant-A was the largest.

FIG. 12 is a diagram showing the amount of change in score due to exposure in Example 1. Specifically, FIG. 12 is a graph with the non-exposed area as the origin and three coordinate axes corresponding to the three principal components. Mean values of replicates are shown. FIG. 12 also shows the 95% confidence interval for each concentration of each chemical substance in the exposure area as a cylindrical region based on the results of variance analysis. The amount of change in the scores of the four types of chemical substances was distributed in different directions for each chemical substance from the origin as the exposure concentration increased.

FIG. 13 is a diagram showing the results of cluster analysis in Example 1. The dendrogram in FIG. 13 shows the results of hierarchical cluster analysis on the amount of change in score (average value of two replicates) of four types of chemical substances. "_L", "_M", and "_H" in each chemical substance name indicate a low concentration exposure area, a medium concentration exposure area, and a high concentration exposure area, respectively. When clusters were divided around a similarity of 0.900, each of the four chemical substances was clearly classified into four clusters.

(Example 2)
In Example 2, four solutions of unknown chemical substances A and B at two concentrations (low, high) in the exposed area and one solution in the non-exposed area were prepared as sample solutions. Then, two repetitions of the exposure test were conducted using the method shown in Example 1, and a total of 10 decay curves were obtained. In Example 2, the attenuation curve was evaluated using the evaluation model constructed in Example 1. The evaluation was performed by determining the amount of change for each of the decay curves of chemical substances A and B using equations (2) to (4) above, and calculating the amount of change for the four known chemical substances shown in Example 1. compared with. Furthermore, by performing cluster analysis on the amount of change in chemical substances A and B, we analyzed whether it was possible to estimate unknown chemical substances.

The evaluation results of the photosynthetic sample according to Example 2 will be explained with reference to FIGS. 14 to 16.

FIG. 14 is a diagram showing attenuation curves of chemical substances in Example 2. In both graphs (a) and (b) of FIG. 14, the vertical axis indicates the amount of light emission, and the horizontal axis indicates the time after excitation. Graphs (a) and (b) both show the average values of two replicates for the non-exposed area, the low-concentration exposure area, and the high-concentration exposure area. For chemical substance A, the amount of delayed luminescence increased immediately after excitation, a cross point was observed around 1 second after excitation, and the amount of delayed luminescence decreased around 10 seconds after excitation. For chemical substance B, the amount of delayed luminescence increased slightly immediately after excitation, a cross point was observed before 1 second after excitation, and the amount of delayed luminescence decreased in the subsequent time range. The characteristics of changes in the attenuation curves due to exposure to chemical substances A and B were similar to those of DCMU and CCCP, respectively, among the four types of chemical substances shown in FIG.

FIG. 15 is a diagram showing the amount of change in score due to exposure in Example 2. Specifically, FIG. 15 is a graph having three coordinate axes corresponding to the three principal components with the non-exposed area as the origin, and shows the amount of change in the score of the exposed sample for each of chemical substances A and B, and The average value of two repetitions is shown for the amount of change in the score of the exposure area for four known chemical substances. FIG. 15 also shows the 95% confidence interval for each concentration of each chemical substance in the exposure area as a cylindrical region based on the results of variance analysis. In addition, the areas exposed to low concentrations of chemical substance A are indicated by white squares, the areas exposed to high concentrations of chemical substance A are indicated by white triangles, the areas exposed to low concentrations of chemical substance B are indicated by black squares, and the areas exposed to high concentrations of chemical substance B are indicated by black triangles. show. The area exposed to low concentrations of chemical substance A (white square) was plotted near the area exposed to DCMU. On the other hand, the area exposed to a high concentration of chemical substance A (white triangle) was plotted at a position where _Δz'1 becomes large with respect to the distribution direction of the area exposed to DCMU. The area exposed to low concentrations of chemical substance B (black square) was plotted near the area exposed to CCCP. On the other hand, the areas exposed to high concentrations of chemical substance B (black triangles) were plotted at positions where Δz _{' 3} becomes large with respect to the distribution direction of the CCCP exposed areas.

FIG. 16 is a diagram showing the results of cluster analysis in Example 2. The dendrograms (a) and (b) in FIG. 16 show the changes in scores Δz′ _k (k=1 , 2, 3), the results obtained by cluster analysis of the average values (14 samples) of two replicates are shown. Dendrograms (a) and (b) in FIG. 16 each represent the degree of similarity in the amount of change in score between the 14 samples. The dendrogram (a) in FIG. 16 shows the classification of the chemical substances A and B in the low concentration exposure area, and the dendrogram (b) in FIG. 16 shows the classification in the high concentration exposure area of the chemical substances A and B. "_L", "_M", and "_H" in each chemical substance name indicate a low concentration exposure area, a medium concentration exposure area, and a high concentration exposure area, respectively.

The cluster shown in the dendrogram (a) of FIG. 16 was divided into four clusters around a similarity of 0.900. The area with low exposure to chemical substance A (A_L) was classified into the DCMU cluster. The area with low exposure to chemical substance B (B_L) was classified into the CCCP cluster. The cluster shown in the dendrogram (b) of FIG. 16 was divided into five clusters around the similarity of 0.900. The area with high exposure to chemical substance A (A_H) was classified into the DCMU cluster. The high-concentration exposure zone (B_H) for chemical substance B was classified outside the CCCP cluster, but it had the highest degree of similarity to CCCP among the four types of chemicals.

The actual chemical substance A is the triazine herbicide cymetrine (0,0.023,0.094 μmol/L), which exhibits the same effect as the urea herbicide DCMU. That is, it was statistically determined that among the four types of chemical substances, chemical substance A has the highest degree of similarity in the amount of change in score with DCMU, which has the same mechanism of action. Further, the actual chemical substance B is 3,5-dichlorophenol (0, 2, 37 μmol/L), which is the same uncoupling agent although it does not have as strong an effect as CCCP. That is, it was statistically determined that chemical substance B had the highest degree of similarity in score change with CCCP, which has the same mechanism of action, among the four types of chemical substances.

From the above results, by using the score difference (amount of change in score) between the score of multiple principal components of the exposed sample and the score of multiple principal components of the control sample, we can estimate the temporal change in delayed luminescence emitted by the photosynthetic sample. Can be evaluated appropriately. Furthermore, the mechanism of action of the unknown environmental factor can be estimated based on the result of determining the similarity between the score difference for the known exposed sample and the score difference for the unknown exposed sample.

The technology of the present disclosure can be used in various fields such as wastewater management, pesticide development, and breeding. For example, when an abnormality occurs in wastewater, the type of causative substance (action mechanism) can be estimated by evaluating a wastewater sample using the technology of the present disclosure. For example, when developing a new herbicide based on a known herbicide with a certain mechanism of action, it is necessary to determine to what extent the mechanism of action is the same or different between the new herbicide and the known herbicide. be able to.

The technology of the present disclosure also enables users who perform work (e.g., testing, research, development, evaluation, etc.) using bioassays to evaluate the impact of environmental factors on living organisms by constructing an evaluation model in advance. It can be a system that provides analysis services. A system that provides analysis services accepts measurement data via any network and processes the measurement data, which reduces the burden of analysis on the user side. Furthermore, since the evaluation model can be updated with new measurement data, it is possible to construct evaluation models that are more flexible and complete than stand-alone analysis services.

[effect]
As explained above, the photosynthetic sample evaluation system according to one aspect of the present disclosure evaluates the delayed luminescence emitted by the photosynthetic sample for each of the exposed sample exposed to environmental factors and the control sample not exposed to environmental factors. It accepts measured data showing changes over time, calculates multiple principal component scores for each of the exposed sample and control sample based on the measurement data and an evaluation model obtained by principal component analysis, and At least one processor configured to calculate a score difference between the plurality of principal component scores and the plurality of principal component scores of the control sample.

A method for evaluating a photosynthetic sample according to one aspect of the present disclosure is performed by a computer, and evaluates delayed luminescence emitted by the photosynthetic sample for each of an exposed sample exposed to an environmental factor and a control sample not exposed to an environmental factor. a step of accepting measurement data showing changes over time; a step of calculating scores of a plurality of principal components for each of the exposed sample and the control sample based on the measurement data and an evaluation model obtained by principal component analysis; calculating a score difference between the score of the plurality of principal components of the exposed sample and the score of the plurality of principal components of the control sample.

A photosynthetic sample evaluation program according to one aspect of the present disclosure includes a measurement showing changes over time in delayed luminescence emitted by a photosynthetic sample for each of an exposed sample exposed to environmental factors and a control sample not exposed to environmental factors. a step of receiving data; a step of calculating scores of multiple principal components for each of the exposed sample and the control sample based on the evaluation model obtained by principal component analysis and the measured data; The computer is caused to perform a step of calculating a score difference between the principal component score and the plurality of principal component scores of the control sample.

In this aspect, scores for a plurality of principal components can be obtained for each of the exposed sample and the control sample by using the evaluation model and measurement data showing changes over time in delayed luminescence emitted by the photosynthetic sample. Then, by calculating the difference between the score of the exposed sample and the score of the control sample, the amount of change in score due to exposure can be obtained. Since the amount of change in the score reflects the influence of environmental factors contained in the exposed sample, by using this amount of change, it is possible to appropriately evaluate the temporal change in delayed luminescence emitted by the photosynthetic sample.

In the photosynthetic sample evaluation system according to other aspects, the at least one processor may be further configured to standardize the multiple principal component scores of the exposed sample and the multiple principal component scores of the control sample. .

In this case, the scales are matched between the scores of the multiple principal components of the exposed sample and the scores of the multiple principal components of the control sample, so the scores of each principal component can be evaluated uniformly.

In the photosynthetic sample evaluation system according to another aspect, the exposed samples include a known exposed sample and an unknown exposed sample, and the at least one processor calculates a score difference between the unknown exposed sample and the known exposed sample. The score difference may be further configured to determine the similarity between the scores.

In this case, by using the score difference for the known exposed sample and the score difference for the unknown exposed sample, the degree of similarity between the two can be obtained. Therefore, it is possible to determine which known exposed samples the unknown exposed sample is similar to.

In the photosynthetic sample evaluation system according to another aspect, the at least one processor performs evaluation of the unknown environment based on a high degree of similarity between the score difference of the unknown exposed sample and the score difference of the known exposed sample. The method may be further configured to evaluate at least one of the degree of influence of a factor and the type of unknown environmental factor.

When the score difference of the unknown exposed sample and the score difference of the known exposed sample are highly similar, it can be said that the effects of both on the mechanism of delayed luminescence are also similar. Therefore, the mechanism of action of environmental factors contained in unknown exposed samples can be estimated.

In another aspect of the photosynthetic sample evaluation system, determining the similarity is performed by hierarchical cluster analysis using a cosine coefficient between the score difference of the unknown exposure sample and the score difference of the known exposure sample. , similarity may also be determined.

In this case, similarity can be determined more clearly by combining cosine coefficients and hierarchical cluster analysis.

In the photosynthetic sample evaluation system according to another aspect, the evaluation model may be represented by an eigenvector.

In this case, the evaluation model can be constructed from the eigenvectors obtained by principal component analysis of the measured data, making it easy to construct the evaluation model.

[Modified example]
The detailed description has been made above based on the embodiments of the present disclosure. However, the present disclosure is not limited to the above embodiments. The present disclosure can be modified in various ways without departing from the gist thereof.

The server 10 may be configured without at least one of the standardization section 13 and the similarity determination section 15. That is, the scores of the plurality of principal components calculated by the evaluation unit 12 may or may not be standardized by the standardization unit 13. The process of determining the similarity between the score difference of the unknown exposure sample and the score difference of the known exposure sample may not be performed.

The expression "at least one processor executes the first process, executes the second process, ... executes the n-th process", or an expression corresponding to this, means that from the first process to This concept includes the case where the executing entity (that is, the processor) of the n processes up to the n-th process changes midway through. That is, this expression is a concept that includes both a case in which all of the n processes are executed by the same processor, and a case in which the processors in the n processes are changed according to an arbitrary policy.

The processing steps of the method executed by at least one processor are not limited to the examples in the above embodiments. For example, some of the steps (processes) described above may be omitted, or each step may be executed in a different order. Furthermore, any two or more of the steps described above may be combined, or some of the steps may be modified or deleted. Alternatively, other steps may be performed in addition to each of the above steps. Also, some or all of each step may be performed by other devices or computer systems.

DESCRIPTION OF SYMBOLS 1... Evaluation system, 10... Server, 11... Reception part, 12... Evaluation part, 13... Standardization part, 14... Change amount calculation part, 15... Similarity determination part, 20... Terminal, 30... Database, 100... Server computer , 101... Processor, 102... Main memory, 103... Auxiliary memory, 104... Communication unit, 200... Measuring device, 210... Control unit, 220... Communication unit, 230... Measuring unit, 231... Housing, 231a... Main body Part, 231b...Lid part, 232...Installation part, 233...Light source, 234...Photodetector, 234a...Photosensor, 234b...Calculation part, 235...Filter, 236...Condensing optical system, 237...Shutter, 238...Introduction Mouth, P1...server program.

Claims (8)

Accepting measurement data showing changes over time in delayed luminescence emitted by photosynthetic samples for each of an exposed sample exposed to environmental factors and a control sample not exposed to environmental factors,
Based on the evaluation model obtained by principal component analysis and the measurement data, calculate scores of a plurality of principal components for each of the exposed sample and the control sample,
A photosynthetic sample evaluation system comprising at least one processor configured to calculate a score difference between a score of the plurality of principal components of the exposed sample and a score of the plurality of principal components of the control sample. 2. The photosynthetic sample of claim 1, wherein the at least one processor is further configured to standardize the plurality of principal component scores of the exposed sample and the plurality of principal component scores of the control sample. rating system. The exposed sample includes a known exposed sample and an unknown exposed sample,
the at least one processor is further configured to determine similarity between the unknown exposure sample score difference and the known exposure sample score difference;
The photosynthetic sample evaluation system according to claim 1 or 2. The at least one processor determines the degree of influence of the unknown environmental factor and the unknown based on the degree of similarity between the score difference of the unknown exposed sample and the score difference of the known exposed sample. 4. The photosynthetic sample evaluation system according to claim 3, further configured to evaluate at least one of the types of environmental factors. Determining the similarity is determining the similarity by hierarchical cluster analysis using a cosine coefficient between the score difference of the unknown exposed sample and the score difference of the known exposed sample. , The photosynthetic sample evaluation system according to claim 3 or 4. The photosynthetic sample evaluation system according to any one of claims 1 to 5, wherein the evaluation model is represented by an eigenvector. 1. A computer-implemented method for evaluating photosynthetic samples, the method comprising:
receiving measurement data indicating changes over time in delayed luminescence emitted by the photosynthetic sample for each of an exposed sample exposed to environmental factors and a control sample not exposed to environmental factors;
Calculating scores of a plurality of principal components for each of the exposed sample and the control sample based on the evaluation model obtained by principal component analysis and the measurement data;
A method for evaluating a photosynthetic sample, comprising the step of calculating a score difference between the score of the plurality of principal components of the exposed sample and the score of the plurality of principal components of the control sample. receiving measurement data indicating changes over time in delayed luminescence emitted by the photosynthetic sample for each of an exposed sample exposed to environmental factors and a control sample not exposed to environmental factors;
Calculating scores of a plurality of principal components for each of the exposed sample and the control sample based on the evaluation model obtained by principal component analysis and the measurement data;
A photosynthesis sample evaluation program that causes a computer to execute a step of calculating a score difference between a score of the plurality of principal components of the exposed sample and a score of the plurality of principal components of the control sample.

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