JP6599173B2 - Photosynthesis sample evaluation system, photosynthesis sample evaluation method, and photosynthesis sample evaluation program - Google Patents

Description

  One aspect of the present invention relates to a system, method, and program for evaluating photosynthetic samples.

  As one of bioassays for evaluating the influence of chemical substances on living organisms, a technique for evaluating the tendency of delayed luminescence emitted from a photosynthetic sample such as algae has been conventionally known. Here, delayed light emission is a phenomenon in which a photosynthetic pigment emits light when energy is applied to an organism (photosynthesis sample) having a photosynthesis function.

  As one of the methods for evaluating delayed emission, there is a function fitting that applies attenuation of delayed emission to a model function. For example, in Patent Document 1 below, coefficient values of a plurality of functions are derived so that the change over time of the measured amount of delayed emission is fitted as a sum of a plurality of preset functions, and evaluation is performed based on the coefficient values. A method for deriving a value is described.

JP 2008-116401 A

  The function fitting described in Patent Document 1 is performed on the entire measured data, and a sum of a plurality of functions is used to accurately perform the fitting. However, if the sum of a plurality of functions is used, the number of coefficients to be controlled increases, and information is shared between the coefficients. In order to perform this function fitting accurately, it is necessary to adjust many kinds of coefficients. However, the adjustment needs to be performed according to human experience depending on the type of photosynthesis sample and measurement conditions, and it is difficult to automate it. . It would be convenient if the photosynthetic sample could be evaluated accurately and more easily.

  An evaluation system for a photosynthetic sample according to an aspect of the present invention evaluates a photosynthetic sample by receiving a measurement data indicating a temporal change in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light. And a determination unit that determines a model function indicating a distribution with a heavy tail, and the determination unit includes measurement data received by the reception unit, including a first time point when the delayed light emission is first measured. The first data in the time zone and the second data in the second time zone after the first time zone are divided, and the first data is weighted more than the second data. The model function to be fitted to the first data is determined by executing the function fitting.

  A photosynthetic sample evaluation method according to an aspect of the present invention is a photosynthetic sample evaluation method executed by an evaluation system including a processor, wherein delayed light emission generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light Including a reception step for receiving measurement data indicating a change in time of the image, and a determination step for determining a model function indicating a heavy tail distribution used for evaluating the photosynthetic sample. The determination step is received in the reception step. The measurement data is divided into first data in a first time zone including a time point when delayed luminescence is first measured, and second data in a second time zone after the first time zone. And performing a function fitting weighted to the first data rather than the second data, And determining a model function for fitting to over data.

  An evaluation program for a photosynthetic sample according to one aspect of the present invention evaluates a photosynthetic sample by receiving a measurement data indicating a temporal change in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light. And a determination step for determining a model function indicating a distribution with a heavy tail is executed by the computer, and the determination step includes the measurement data received in the reception step and the time point when the delayed light emission is first measured. Dividing the first data in the first time zone and the second data in the second time zone after the first time zone; and the first data over the second data Determining a model function to be fitted to the first data by performing a function fitting weighted towards Including.

  In such an aspect, the measurement data is divided into first data indicating the initial stage (initial attenuation) of the delayed light emission attenuation and second data indicating the attenuation after the initial attenuation (late attenuation). Is done. Then, by function fitting that places importance on the initial attenuation rather than the late attenuation, a model function indicating a distribution with a heavy tail that accurately represents the initial attenuation is determined. The present inventor has found that the photosynthetic sample can be accurately evaluated by neglecting or ignoring the late decay and obtaining its model function fitting to the early decay. In addition, since it is not necessary to use a plurality of model functions, the coefficient to be adjusted is smaller than in the case of using the sum of a plurality of functions, and as a result, function fitting is simplified. For these reasons, photosynthetic samples can be evaluated accurately and more easily.

  According to one aspect of the present invention, photosynthetic samples can be accurately and more easily evaluated.

It is a figure which shows the structure of the evaluation system which concerns on embodiment. It is a graph which shows attenuation | damping of the delayed light emission of a photosynthetic sample. It is a flowchart which shows operation | movement of the evaluation system (evaluation apparatus) which concerns on embodiment. It is a figure which shows the structure of the evaluation program which concerns on embodiment. It is a figure which shows the hardware constitutions of the computer with which the evaluation program which concerns on embodiment is performed. 6 is a graph showing the result of function fitting in Example 1. 3 is a graph showing a difference spectrum in Example 1. 10 is a graph showing the result of function fitting in Comparative Example 1. 6 is a graph showing a difference spectrum in Comparative Example 1. It is a figure which shows the mechanism of delayed light emission. It is a graph which shows the control group ratio by DCMU exposure. It is a graph which shows the control section ratio in DBMIB exposure. It is a graph which shows the control group ratio by AntA exposure. It is a graph which shows the control section ratio in CCCP exposure.

  Hereinafter, embodiments of the present invention 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 is omitted.

  First, the function and configuration of the photosynthetic sample evaluation system 1 (also simply referred to as “evaluation system” in this specification) 1 according to the present embodiment will be described with reference to FIGS. FIG. 1 is a diagram showing a configuration of the evaluation system 1. FIG. 2 is a graph showing the decay of delayed emission of the photosynthetic sample.

  The evaluation system 1 is a mechanism for evaluating a photosynthetic sample using measurement data indicating a temporal change in delayed luminescence emitted from the photosynthetic sample. “Evaluating the photosynthetic sample” in this specification means obtaining an objective result by executing an operation on the measurement data. Evaluation of the photosynthetic sample includes function fitting to the measured data. The model function itself obtained by function fitting can be the evaluation result of the photosynthetic sample. Alternatively, the result of further processing based on the obtained model function can be the evaluation result of the photosynthetic sample.

  The evaluation system 1 can be used for various purposes. For example, the evaluation system 1 is used to investigate the effects of chemical substances on photosynthetic samples, to diagnose growth of photosynthetic samples, to analyze or develop chemical substances, to study photosynthesis, to investigate environmental factors (for example, water, temperature, air, etc.) May be used.

  The photosynthetic sample used for evaluation is not particularly limited as long as it has a photosynthetic function and can emit delayed light emission. For example, photosynthetic samples can be algae, phytoplankton, cyanobacteria, photosynthetic bacteria, plant leaves or fragments thereof, plant cultured cells such as callus, photosynthetic organelles or thylakoid membranes extracted from plants, or artificially It may be a synthesized membrane or protein complex having a photosynthesis-like function.

  As shown in FIG. 1, the evaluation system 1 includes a control device 10, a measurement device 20, and an evaluation device 30. The control device 10 and the evaluation device 30 are connected by wire or wireless, and can transmit and receive data to and from each other. The evaluation device 30 and the measurement device 20 are connected by wire or wireless, and can transmit and receive data to and from each other. The control device 10 and the measurement device 20 can transmit and receive data to and from each other via the evaluation device 30.

  The control device 10 includes hardware such as a processor (for example, a CPU) and a memory, and controls the measurement device 20 by transmitting a control signal to the measurement device 20. The control device 10 may further control the evaluation device 30.

  The measuring device 20 is a device that measures the amount of delayed light emission of the photosynthetic sample. The measuring device 20 measures the light emission amount of delayed light emission at predetermined time intervals, and outputs data indicating the light emission amount to the evaluation device 30 each time.

  FIG. 1 shows a cross section of the measuring device 20. As shown in this figure, the measuring device 20 includes a housing 21, an installation unit 22, a light source 23, a photodetector 24, a filter 25, a condensing optical system 26, and a shutter 27 disposed in the housing 21. Is provided. The casing 21 is formed of a light shielding member that blocks light or a member coated with a paint that blocks light so that light does not enter inside. The casing 21 includes a main body portion 21 a having an introduction port 28 formed at one end thereof, and a lid portion 21 b that can close the introduction port 28. The opening and closing of the lid 21b is monitored and controlled by the control device 10. When the shutter 27 is open, the lid 21b is locked so that light from the outside of the housing 21 does not enter the optical sensor 24a.

  The installation part 22 is a part 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, the container contains a solution containing a photosynthetic sample and a chemical substance. The installation unit 22 is provided at a position where the container can be installed from the introduction port 28. The installation part 22 may have a fixing claw for fixing the container.

  The light source 23 is a device that irradiates light of a predetermined wavelength (for example, 280 to 800 nm) to a photosynthetic sample in a container installed in the installation unit 22 in order to generate delayed light emission. The light emitted from the light source 23 is excitation light. The light source 23 may be a monochromatic light source or a combination of a plurality of light sources. The irradiation from the light source 23 may be continuous irradiation for a predetermined time, or may be pulse lighting with an arbitrary pattern. In addition, a plurality of light sources having the same or different wavelength characteristics may be sequentially emitted, or a plurality of light sources may be simultaneously emitted. Irradiation from the light source 23 is controlled by the control device 10.

  The photodetector 24 is a device that detects the amount of delayed light emission generated from the photosynthetic sample when light is emitted from the light source 23. The photodetector 24 includes an optical sensor 24a that detects delayed light emission, and a calculation unit 24b that calculates a light emission amount of delayed light emission based on a signal output from the optical sensor 24a. Specifically, the photodetector 24 is configured by a photomultiplier tube, a photon counter, or the like. The photodetector 24 outputs data indicating the calculated light emission amount to the evaluation device 30. The light detector 24 executes delayed light emission detection, light emission amount calculation, and output at predetermined time intervals (for example, at intervals of 0.1 seconds).

  A filter 25, a condensing optical system 26, and a shutter 27 are provided in this order in a region from the installation unit 22 to the photodetector 24. The filter 25 is a member that transmits delayed light emission. The condensing optical system 26 is an instrument that collects, reflects, and transmits weak delayed light emission and inputs it to the photodetector 24. The shutter 27 is configured to be openable and closable so that delayed light emission is detected by the photodetector 24 only when necessary. When the shutter 27 is closed, delayed light emission is blocked. Opening and closing of the shutter 27 is controlled by the control device 10.

  The evaluation device 30 is a device that receives measurement data input from the measurement device 20 and evaluates a photosynthetic sample based on the measurement data. The evaluation device 30 is composed of one or more computers. FIG. 1 shows a functional configuration of the evaluation device 30. As shown in this figure, the evaluation device 30 includes a reception unit 31, a storage unit 32, a determination unit 33, and an evaluation unit 34 as functional components.

  The accepting unit 31 is a functional element that accepts measurement data indicating a temporal change in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light. Since the light detector 24 of the measuring device 20 outputs light emission amount data at predetermined time intervals, the receiving unit 31 sequentially receives the data. The measurement data indicating the time-dependent change in delayed light emission is a set of a plurality of data received by the evaluation device 30 during a predetermined time (for example, 60 seconds). The accepting unit 31 stores the measurement data in the storage unit 32.

  The storage unit 32 is a functional element that stores the measurement data received by the receiving unit 31. The stored measurement data is referred to by the determination unit 33 or the evaluation unit 34.

  The determination unit 33 is a functional element that determines a model function to be fitted to measurement data. The determination unit 33 reads the measurement data from the storage unit 32 in order to execute this process, and the measurement data is received by the reception unit 31. First, the determination unit 33 sets the measurement data to the first data in the first time zone including the time point when the delayed light emission is first measured, and the second data in the second time zone after the first time zone. The data is divided into two pieces of data. Here, the first time zone and the second time zone do not overlap. Subsequently, the determination unit 33 determines a model function to be fitted to the first data by performing function fitting in which the first data is weighted more than the second data.

  As shown in FIG. 2, the attenuation of delayed light emission indicated by the measurement data is a distribution with a heavy non-exponential tail (Heavy) when captured on two axes, the light emission amount (signal intensity) axis and the time axis. -Tailed distribution). The vertical axis of the graph in FIG. 2 represents the amount of light emission (signal intensity), and the horizontal axis represents the time after excitation. The post-excitation time is the elapsed time from the point in time when the irradiation of excitation light is terminated.

As an example, the measurement data is set of n measured values _{V 1 ~V n, V 1,} V 2 these measurements with time, _..., and obtained in the order of V _n. In this case, the first data is composed of m (where m <n) measurement values V _{1 to} V _m that are continuous along the time axis. The first measurement values of V ₁ was delayed luminescence, i.e., the value at the time the delayed luminescence is measured first. Hereinafter, the first measured value of delayed light emission is also referred to as “initial value” of delayed light emission. The time zone in which the measured values V _{1 to} V _m are obtained is the first time zone, and this first time zone is a time zone corresponding to the initial stage of the decay of delayed light emission. In this specification, the initial stage of decay of delayed light emission is referred to as “initial decay”. The first data is data indicating initial attenuation among the measurement data. On the other hand, the second data consists of measured values _{V m} + 1 ~V _n obtained after the measured value _{V m.} The time zone in which the measured value after the measured value V _m is obtained is the second time zone, and this second time zone is the time zone corresponding to the later stage of the decay of the delayed light emission. In this specification, the latter stage of decay of delayed luminescence is referred to as “late decay”. The second data is data indicating late decay in the measurement data. It can be said that the initial attenuation is attenuation in a past time zone rather than a predetermined boundary region on the time axis, and the late attenuation is attenuation in a future time zone than the boundary region. The boundary area may be indicated by a point or a time range. In the above example in which the measurement data is a set of _n measurement values V _{1 to} V _n , the boundary area exists between the time when the measurement value V _m is obtained and the time when the measurement value V _{m + 1} is obtained. To do.

  The determination unit 33 selects a part of the measurement data as the first data, thereby dividing the measurement data into the first data and the second data. This process can be rephrased as a process for determining the first time zone, or can be rephrased as a process for determining the position of the boundary area. The determination unit 33 may select the first data based on the light emission amount (signal intensity) of delayed light emission, or may select the first data based on the elapsed time. The first data is a set of consecutive measurements including the first measurement. That is, the determination unit 33 selects the first data without excluding the measurement value indicating the initial attenuation.

The determination unit 33 may select the first data based on the degree of attenuation from the initial value of delayed light emission. Specifically, the setting determination unit 33 from the measurement data, 1 / e n of the light-emitting amount is the initial value ^(although, e is Napier's constant, n ≧ 1) the time range to decay to a first time period Then, the portion corresponding to the first time zone in the measurement data may be selected as the first data. For example, when n = 1, the determination unit 33 obtains a time range in which the light emission amount attenuates to about 36.8% (= 1 / e) of the initial value as the first time zone. When n = 2, the determination unit 33 obtains a time range in which the light emission amount is attenuated to about 13.5% (= 1 / e ² ) of the initial value as the first time zone.

When the light emission amount is set a time range which decays to 1 / e ^a of the initial value as the first time zone, in order the corresponding number of data (number of measurements) is small (e.g., number of data = 2) , Function fitting may not be performed properly. In this case, the determination unit 33 once expands the time range to a time range in which the light emission amount attenuates to 1 / e ^b (a <b) of the initial value, and the coefficient obtained by the function fitting in the extended time range. The model function may be determined by executing function fitting using a numerical value as an initial value in a time range before expansion. For example, it is assumed that the number of data (number of measured values) in a time range (initial time range) in which the light emission amount attenuates to 1 / e of the initial value is equal to or less than a predetermined number. In this case, the determination unit 33 performs function fitting in a time range (extended time range) in which the light emission amount attenuates to 1 / e ^{2 of the} initial value. Then, the determination unit 33 determines the model function by executing function fitting with the coefficient value obtained by the function fitting as an initial value for the initial time range.

  Alternatively, the determination unit 33 may set the attenuation level as a fixed value without using the Napier number (e), and obtain a time range in which the light emission amount attenuates from the initial value to that level as the first time zone. For example, the determination unit 33 obtains a time range in which the light emission amount attenuates to 30% of the initial value as the first time zone, or sets a time range in which the light emission amount attenuates to 20% of the initial value as the first time zone. You may ask for it.

  The determination unit 33 may select the first data based on the elapsed time from the time when the initial value of the delayed light emission is measured or the elapsed time from the time when the delayed light emission is terminated (time after excitation). Good. For example, the determination unit 33 sets the first predetermined ratio of the total measurement time corresponding to the entire measurement data as the first time zone, and selects the portion corresponding to that time zone as the first data May be. When the total measurement time is 60 seconds and the predetermined ratio is set to 1%, the determination unit 33 sets a set of measurement values obtained in the first 0.6 seconds out of the total measurement time. Select as 1 data. Alternatively, the determination unit 33 sets a time (for example, the first 0.5 seconds) indicated by a predetermined fixed value as the first time zone regardless of the total measurement time length, and corresponds to the time zone. The portion may be selected as the first data.

  The determination unit 33 may set the first time zone for each piece of measurement data, regardless of the degree of attenuation of the light emission amount or the passage of time. Alternatively, the determination unit 33 may determine one measurement data as reference data, and apply the length of the first time zone set for the reference data to other measurement data as it is. For example, the reference data may be data obtained by measuring delayed luminescence from a photosynthetic sample that is not exposed to an environmental factor to be evaluated (for example, a chemical substance). Of course, other types of measurement data may be adopted as the reference data.

  As described above, various methods are conceivable as processing for determining the first time zone or processing for selecting the first data. In any case, since the second data is data other than the first data among the measurement data, the determination unit 33 determines the first time zone or the first data, thereby obtaining the measurement data. The first data indicating the initial attenuation and the second data indicating the late attenuation can be divided.

  The determination unit 33 may perform a smoothing process using a moving average method or the like on the measurement data before dividing the measurement data into the first data and the second data. Although the signal indicating the detection of delayed light emission may fluctuate, the fluctuation is reduced by this smoothing process, so that the initial attenuation range can be obtained more accurately.

  When the measurement data is divided into the first data and the second data, the determination unit 33 executes the function fitting in which the first data is weighted more than the second data. Determine the model function to fit to the data. “Function fitting in which the first data is weighted more than the second data” refers to fitting the model function to the first data rather than fitting the model function to the second data (late decay). This function fitting gives priority to fitting to (initial attenuation). The function fitting is a process of adjusting the coefficient value of the model function in order to apply the model function to the measurement data (actual data). Considering this definition, “function fitting weighted to the first data rather than the second data” indicates how much the difference between the model function and the measurement data contributes to the adjustment of the model function. For the contribution, this means that the contribution in the first data is made larger than the contribution in the second data. By setting the contributions of the two types of data in this way, it becomes possible to obtain a model function that fits well to the first data.

  The model function used in the present embodiment is a model function indicating a distribution with a heavy tail. That is, the determination unit 33 fits a model function indicating a heavy tail distribution to the measurement data. Hereinafter, the “model function indicating a heavy distribution” is also simply referred to as a “model function”. For example, as shown in FIG. 2, the relationship between the logarithm of the light emission amount of delayed light emission and time is represented by a distribution with a heavy tail. The specific type of model function indicating a heavy tail distribution is not limited, and examples include Stretched-Exponential function (hereinafter referred to as “StrExp function”) and Lorentz function (Cauchy distribution).

ここで、変数Ｘは励起後時間であり、変数ＹはＸにおける遅延発光の発光量である。変数Ｉは測定された遅延発光の初期値であり、変数τは減衰速度（遅延発光の発光量が初期値の１／ｅまで減衰するまでの時間）である。変数ｈは裾の重さ（ｔａｉｌ ｈｅａｖｉｎｅｓｓ）であり、ｈ＞０である。ｈ＝０のときは、式（１）は単一指数減衰を表す。減衰が単一指数減衰よりも遅くなる場合に「裾が重い」と表現する。一般の蛍光信号は単一指数減衰であり、ｈ＞０は、遅延発光の特徴を表す指標の一つと考えられる。なお、（１＋ｈ）をｈｅｔｅｒｏｇｅｎｅｉｔｙ（異質度または不等質度という意）ということもある。また、１／（１＋ｈ）をβと表す場合もある。 In the present embodiment, the StrExp function is used as an example. The StrExp function is represented by the following formula (1).

Here, the variable X is the time after excitation, and the variable Y is the light emission amount of delayed luminescence at X. The variable I is the measured initial value of delayed light emission, and the variable τ is the decay rate (time until the light emission amount of delayed light emission is attenuated to 1 / e of the initial value). The variable h is the tail weight and h> 0. When h = 0, equation (1) represents single exponential decay. When the attenuation is slower than the single exponential attenuation, it is expressed as “heavy tail”. A general fluorescence signal has a single exponential decay, and h> 0 is considered to be one of indices indicating the characteristics of delayed emission. Note that (1 + h) may also be referred to as heterogeneity (meaning heterogeneity or inequality). In some cases, 1 / (1 + h) is represented as β.

  The method for calculating the error between the model function and the measurement data is not limited. Examples of errors include mean square error square root (RMSE), mean absolute error (MAE), mean square error square root of logarithmic transformation value, χ criterion, χ square criterion, relative error, mean square root of relative error, absolute criterion, and A relative square criterion is mentioned.

  In function fitting, an error component is acquired between measurement data at the same time point and model data obtained from a model function. Then, the sum of the plurality of error components obtained at each time point is calculated, or the sum is divided by the number of data to obtain an average value, thereby evaluating the error. It can also be said that the contribution degree of the first data and the second data in the function fitting is an index indicating how much each error component contributes (reflects) to the calculation of the sum.

  A method for setting the contributions of the first data and the second data for weighting the first data more than the second data is not limited.

  As an example, the determination unit 33 may use the error component obtained by calculation for the first data as it is, but may not use the error component for the second data. Assuming that the contribution is in the range of 0 to 1, in this case, the contribution of the first data (initial attenuation) is 1, and the contribution of the second data (late attenuation) is 0. For example, the determination unit 33 may not use the error component related to the second data by multiplying the error component by 0 for the second data (the contribution of the second data is set to 0). Or the determination part 33 does not need to use the component of the error regarding 2nd data by not referring 2nd data.

Alternatively, the determination unit 33 uses the error component obtained by the calculation as it is for the first data, while multiplying the error component by a sufficiently small value for the second data, so that the second data is more than the second data. The first data may be weighted. The setting method of “a sufficiently small value” is not limited. For example, the “sufficiently small value” is a value such that the sum of error components of the second data (late decay) is sufficiently smaller than the sum of error components of the first data (initial decay). It may be. For example, it is assumed that measurement data V ₁ , V ₂ ,..., V _{n including n} measurement values exist, and the first data (initial attenuation) is V _{1 to} V _m (where m <n). It is assumed that the second data (late decay) is V _{m + 1 to} V _n . In this case, the determination unit 33 may calculate the sum S of the second data V _{m + 1 to} V _n and multiply each error component of the second data by the reciprocal (1 / S) of the sum. In this case, the “sufficiently small value” is (1 / S). Alternatively determination unit _33, the sum S of _{V m} + 1 ~V _n, calculates a value A obtained by dividing the number of measurement values constituting a second data (n-m), the inverse of the value (1 / A) May be multiplied by each error component of the second data. In this case, the “sufficiently small value” is the reciprocal (1 / A) (or 1 / {S / (nm)}). Alternatively, the contribution of the second data may be 1/10 or less of the contribution of the first data, 1/100 or less, or 1/1000 or less. It may be smaller than these.

  Note that the determination unit 33 may multiply the error component of the first data by a value smaller than 1 assuming that the contribution is in the range of 0 to 1. However, the determination unit 33 sets the contribution degree of the first data to be larger than the contribution degree of the second data.

  In order to realize the determination unit 33, the determination module P13 of the evaluation program P1 to be described later includes an algorithm related to a model function, an error calculation algorithm, an algorithm related to definition or determination of contribution, and the like. The determination unit 33 determines a model function to be fitted to the first data by performing function fitting in which the first data is weighted more than the second data. Determining the model function means determining a coefficient value of the model function that is estimated to be best fitted to the first data. In the present embodiment, the determination unit 33 does not fit measurement data to the sum of a plurality of model functions, but performs function fitting using only one model function (for example, the function represented by the above formula (1)). To do. That is, the determination unit 33 determines a single model function indicating a heavy distribution. Since only one model function is used, the number of coefficients to be considered is reduced, so that it is possible to automatically (simplely) evaluate photosynthetic samples without depending on human experience.

  The determination unit 33 outputs information on the determined model function (for example, an expression indicating the model function and the determined coefficient value) to the evaluation unit 34. Therefore, the determination unit 33 also has a function as an output unit. The determined model function is used to evaluate the photosynthetic sample.

  The evaluation unit 34 is a functional element that evaluates the photosynthetic sample using the determined model function. In order to evaluate the photosynthetic sample, the evaluation unit 34 refers to measurement data stored in the storage unit 32 (that is, measurement data received by the reception unit 31) as necessary.

  The evaluation method of a photosynthetic sample is not limited at all. For example, the evaluation unit 34 may use the determined model function itself (for example, the coefficient value of the model function) as the evaluation result. At least one of the damping rate τ and the hem weight h represented by the above formula (1) may be used as the evaluation result. Since the initial attenuation can be evaluated by the model function itself showing the distribution with a heavy tail, which is fitted to the first data, it can be said that the photosynthetic sample is evaluated only by obtaining the model function.

  Alternatively, the evaluation unit 34 may obtain a difference spectrum that is a difference between the model data indicated by the model function and the measurement data (actual data). The calculation of the difference spectrum is an example of the evaluation of the photosynthetic sample. The evaluation unit 34 may evaluate the photosynthetic sample using at least one of the peak height, peak position, and integrated value (model difference sum) of the difference spectrum. The peak height is the maximum value of the difference spectrum, the peak position is the time when the peak height appears, and the model difference sum is the integrated value of the difference between the model data and the measured data. Alternatively, the evaluation unit 34 may evaluate the photosynthetic sample by using a further arbitrary function fitting for the difference spectrum. By analyzing the difference spectrum, it becomes possible to capture a phenomenon of delayed light emission that cannot be explained only by a model function showing a heavy tail distribution.

  By obtaining the model function by function fitting in which the first data is weighted more than the second data, it becomes easy to obtain a peak-shaped (peak-shaped) difference spectrum. The peak-like difference spectrum clearly reflects the decay characteristics of delayed luminescence, making it easier to evaluate photosynthetic samples. The function fitting in this embodiment also contributes to the derivation of a characteristic difference spectrum that leads to an accurate evaluation of the photosynthetic sample.

  The evaluation unit 34 may evaluate the influence of environmental factors on the photosynthetic sample using at least one of the determined model function and the difference spectrum. The “environmental factor” in the present specification is an element that may have some influence on the photosynthetic function, for example, a chemical substance. This evaluation may be indicated by a numerical value or a determination result of harmful or harmless. Alternatively, the evaluation may be a comparison result with reference data that is a result of evaluating a photosynthetic sample that has not been exposed to environmental factors.

  Although the evaluation part 34 outputs an evaluation result, the output method is not limited. For example, the evaluation unit 34 may output the evaluation result to a monitor or a printer, may output it to another device through a communication line, or may store it in a storage device such as a database.

  Next, the operation of the evaluation system 1 (evaluation apparatus 30) will be described with reference to FIG. 3, and the evaluation method for the photosynthetic sample according to the present embodiment will be described. FIG. 3 is a flowchart showing the operation of the evaluation system 1 (evaluation apparatus 30).

  First, the receiving unit 31 receives measurement data indicating a temporal change in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light (step S11, receiving step). In the present embodiment, the reception unit 31 receives the measurement data from the photodetector 24 of the measurement device 20.

  Subsequently, the determination unit 33 divides the measurement data into first data indicating initial attenuation and second data indicating late attenuation (step S12, determination step). As described above, the determination unit 33 may select the first data based on the degree of attenuation from the initial value of the delayed emission, or the first data based on the elapsed time such as the time after excitation. You may choose. Thereafter, the determination unit 33 determines a model function indicating a heavy tail distribution to be fitted to the first data by performing function fitting in which the first data is weighted more than the second data. (Step S13, determination step). As described above, in this process, the type of model function and the type of error used in function fitting are not limited. Various methods can be considered for determining the contribution degree of the first data and the contribution degree of the second data.

  Subsequently, the evaluation unit 34 evaluates the photosynthetic sample using the determined model function indicating the heavy tail distribution (step S14), and outputs the evaluation result (step S15). As described above, various methods are conceivable for the photosynthesis sample evaluation method and the evaluation result output method.

  The evaluation system 1 (evaluation apparatus 30) evaluates various photosynthetic samples or photosynthetic samples under various environments by executing the processing of steps S11 to S15 on each measurement data, and the evaluation results are obtained. Can be output.

  Next, a photosynthesis sample evaluation program P1 that causes a computer to function as the evaluation device 30 will be described with reference to FIGS. FIG. 4 is a diagram showing the configuration of the evaluation program P1. FIG. 5 is a diagram illustrating a hardware configuration of the computer 100 on which the evaluation program P1 is executed.

  As shown in FIG. 4, the evaluation program P1 includes a main module P10, a reception module P11, a storage module P12, a determination module P13, and an evaluation module P14.

  The main module P10 is a part that comprehensively controls the function of evaluating photosynthetic samples. The functions realized by the reception module P11, the storage module P12, the determination module P13, and the evaluation module P14 are the same as the functions of the reception unit 31, the storage unit 32, the determination unit 33, and the evaluation unit 34, respectively.

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

  As shown in FIG. 5, a computer 100 includes a processor 101 that executes an operating system, application programs, and the like, a main storage unit 102 that includes ROM and RAM, and auxiliary storage that includes a hard disk, a flash memory, and the like. Unit 103, a communication control unit 104 including a network card or a wireless communication module, an input device 105 such as a keyboard and a mouse, and an output device 106 such as a display.

  Each functional element of the evaluation device 30 reads predetermined software on the processor 101 or the main storage unit 102, operates the communication control unit 104, the input device 105, the output device 106, and the like under the control of the processor 101, This is realized by reading and writing data in the main storage unit 102 or the auxiliary storage unit 103. Data and a database necessary for processing are stored in the main storage unit 102 or the auxiliary storage unit 103.

  EXAMPLES Hereinafter, although this invention is demonstrated concretely based on an Example, this invention is not limited to them at all.

Example 1
Green algae (Pseudokirchneriella subcapitata) was used as a photosynthesis sample. The photosynthesis sample was irradiated with excitation light having a wavelength of 700 nm for 1 second, and the amount of delayed light emission generated after the irradiation was completed was measured at intervals of 0.1 second between 0 and 60 seconds after excitation. And 600 pieces of data obtained by this measurement were used as measurement data.

The StrExp function was fitted to the delayed emission decay curve indicated by the measurement data. First, the maximum value of measurement data was set to 1000, and the measurement data was normalized as an integer from 0 to 1000. The initial values of the coefficients I, τ, and h of the StrExp function were set as I = 1000, τ = 0.5, and h = 1.2, and function fitting was performed on the 1 / e initial attenuation interval. In this example, the measurement data corresponding to the 1 / e initial decay consisted of 8 measurements obtained between 0.0 and 0.7 seconds after excitation. In function fitting, mean square error (RMSE) was used as the error, and GRG nonlinear programming was used as the solution. The initial value of the solver function of Microsoft Excel (registered trademark) 2010 was used for the initial setting of GRG nonlinear programming. When the coefficient value of the StrExp function does not converge, by performing function fitting on the 1 / e ² initial attenuation interval, the converged coefficient values I, τ, and h are obtained, and the obtained coefficient values are Function fitting with initial values was performed for 1 / e initial decay.

In order to return the data normalized with the maximum value of 1000 to the level of the actual data, the coefficient value I is converted into the correction value I ₀ using the following formula, and the coefficient values I ₀ , τ, and h are fitted to the actual data. Got as.
I ₀ = (maximum value of attenuation curve) × I / 1000

By the above processing, results of I ₀ = 15864, τ = 0.755, h = 0.95 were obtained. FIG. 6 is a graph showing the result of this function fitting. The vertical axis of the graph is the light emission amount (RLU), and the horizontal axis is the time after excitation (seconds). 600 measurements were obtained between 0.1 and 60 seconds after excitation, but the measurements were plotted in the range of 0 to 59.9 seconds in the graph. Circles in the graph indicate measured data, and among these, data indicated by hatched circles corresponds to the 1 / e attenuation interval. The curve in the graph represents the StrExp function obtained by function fitting.

  A difference spectrum indicating the transition of the difference between the actual data and the model data (StrExp function) in FIG. 6 was obtained. FIG. 7 is a graph showing the difference spectrum. The vertical axis of the graph is the light emission amount (RLU), and the horizontal axis is the time after excitation (seconds). In this graph, 600 differences were plotted in the range of 0 to 59.9 seconds. As is apparent from the graph, a difference spectrum having a peak shape (mountain shape) was obtained in this example. The peak height was 940 (RLU), the peak position was 4.3 seconds, and the total model difference was 144755.

Using the coefficient values I (I ₀ ), τ, h obtained by function fitting, the values related to the difference spectrum, and the like, the characteristics of the delayed light emission attenuation can be evaluated.

(Comparative Example 1)
In Comparative Example 1, function fitting was performed on the entire measurement data obtained in Example 1 (that is, all 600 measurement values). Function fitting that covers the whole area of measurement data is a conventional function with the same contribution (index indicating how much the difference between the model function and the measurement data contributes to the adjustment of the model function) for all measurement values. It is fitting. The result is shown in FIG. Moreover, the difference spectrum obtained from the result of the function fitting is shown in FIG. The difference from the first embodiment is only the section to be subjected to function fitting. Regarding the coefficient value of the StrExp function, the following results were obtained: I ₀ = 16035, τ = 0.817, h = 1.39. Further, the peak height of the difference spectrum was 509 (RLU), the peak position was 0.1 second, and the total model difference was −256.

  As the model difference sum is −256, if the entire measurement data is subject to function fitting, the difference spectrum does not have a peak shape. Therefore, function fitting for the entire measurement data is not suitable for calculating the peak height and position and the model difference sum (that is, evaluation based on the difference spectrum cannot be performed). On the other hand, if only the initial attenuation of the measurement data is used as a function fitting target, a peak-shaped (mountain-shaped) difference spectrum can be obtained, so that evaluation based on the difference spectrum becomes possible.

(Example 2)
Using the method shown in Example 1, the effect of chemical substances on the photosynthetic reaction of green algae was evaluated. Chemical substances include 3- (3,4-dichlorophenyl) -1,1-dimethyl-urea (DCMU), 2,5-dibromo-6-isopropyl-3-methyl-1,4-benzoquinone (DBMIB), AntimycinA ( AntA) and Carbonylcyanide m-chlorophenyl hydrazone (CCCP) were prepared. DCMU causes photosystem II electron transport inhibition, and DBMIB causes electron transport inhibition in the cytochrome complex. AntA is an inhibitor of the circulating electron transfer pathway from the photosystem I to the plastoquinone pool, and CCCP is an inhibitor (uncoupler) for coupling the photosynthetic electron transfer reaction and the carbon dioxide fixation reaction.

  For each of the four types of chemical substances, six types of experiments were conducted: no exposure (exposure concentration 0 μg / L) and five levels of exposure concentrations. In each experiment, after photosynthetic samples (green algae) were cultured for 1 hour under each of them, the photosynthetic sample was irradiated with excitation light having a wavelength of 700 nm for 1 second, and the amount of delayed luminescence generated after irradiation was excited Measurements were made at intervals of 0.1 second between 0 and 60 seconds. Using 600 pieces of data obtained by this measurement as measurement data, function fitting using the StrExp function was executed by the same method as in Example 1 to obtain a difference spectrum.

Tables 1-4 show the evaluation results for DCMU, DBMIB, AntA, and CCCP, respectively. Each table shows the following values: The top row of each table is the exposure concentration (μg / L).
-Coefficient values I ₀ , τ, h of StrExp model (StrExp function)
-Difference spectrum peak height, peak position, and model difference total-RMSE as fitting-related values and number of data corresponding to initial attenuation (number of data used for function fitting)
_-Integrated value DFI _1.0-59.9s of emission amount between 1.0 and 59.9 seconds after excitation, which is an example of a conventional method

  As can be seen from Tables 1 to 4, the function fitting shown in Example 1 was able to obtain the StrExp function and difference spectrum results for all drugs to be evaluated and all exposure concentrations.

In addition, each of the coefficient value (I ₀ , τ, h) of the StrExp function obtained by function fitting, the peak height of the difference spectrum, the peak position, and the model difference sum shows a change according to the exposure concentration. I understood. Therefore, by verifying the coefficient value of the StrExp function and the value relating to the difference spectrum, it is possible to identify or estimate the influence of environmental factors (for example, chemical substances) on the photosynthetic sample.

As an example, with reference to Table 1 and FIG. 10, the effect of DCMU, which is a chemical substance with a well-known mechanism of photosynthesis inhibition, on green algae will be examined. FIG. 10 is a diagram illustrating a mechanism of delayed light emission that can be imagined. The meanings of the abbreviations in the figure are as follows.
・ PSII: Photochemical II
P680: Photosystem II reaction center Q _B : Q _B quinone binding site PQP: Plastoquinone pool pQ _ox : Plastoquinone (oxidized type)
PQ _red : plastoquinone (reduced type; plastosemiquinone or plus and quinol)
CB6F: cytochrome complex Pc: plastocyanin PSI: photosystem I
-P700: Photosystem I reaction center-Fd: Ferredoxin-CET: Circulating electron transfer pathway-DL: Delayed luminescence-Solid arrow: Electron transfer reaction-Dashed arrow: Reverse reaction of imagined electron transfer

  The following reference describes evaluation by the StrExp model of simulation data of emission decay assuming one recombination center and one trap state in a crystal. According to the evaluation result, the emission decay of the crystal shows StrExp type decay when the electron re-trap is sufficiently larger than the recombination, and as a result, β = 1 / (1 + h) in the above equation (1) is large. (That is, h becomes smaller).

  Reference: Reuven Chen, "Apparent stretched-exponential luminescence decay in crystalline solids," Journal of Luminescence, 102-103 (2003) 510-518,

  The inventor has invented a method for applying the light emission decay behavior in a simple crystal as shown in the above-mentioned reference to the evaluation of a much more complicated photosynthetic electron transfer reaction. Assume that the recombination center corresponds to P680, which is a source of delayed luminescence, and the electron trap state corresponds to a molecule (such as quinone) that receives electrons from P680. Here, the case where the recombination becomes larger than the re-trap is a state where the photosynthetic electron transfer reaction is inhibited, for example, a state where the photosynthetic sample is exposed to a photosynthesis inhibitor such as DCMU.

DCMU inhibits electron transfer at _{Q B} quinone binding site in photosystem II. Q _B quinone binds to be susceptible D1 proteolysis by photosynthesis inhibition. Between the Q _B site (Q _B ) and the photosystem II reaction center (P680), there is a primary electron acceptor pheophytin and a secondary electron acceptor Q _A quinone (not shown). DCMU is known to inhibit the electron transfer by binding to Q _B site in place of the Q _B quinone.

That is, by DCMU exposure, electron transfer is While likely to be limited between the Q _B quinone binding site photosystem reaction center (P680), electron transfer after Q _B is reduced. Also, exposure to DCMU causes electrons to accumulate in the electron acceptor molecule from P680 (this means a reduced state). At this time, the recombination in P680 increases and the electron transfer is reduced. That is, it can be considered that the recombination in the above reference is larger than the re-trap. According to the reference, the light emission decay of the crystal becomes a simple exponential decay in such a case and approaches the state of β = 1 (that is, h = 0).

As can be seen from Table 1, the DCMU exposure increases the coefficient value I ₀ of the StrExp model and decreases the coefficient values τ and h, the peak height of the difference spectrum increases, the peak appears earlier, and the model difference total Can be recognized as smaller.

  The coefficient value h was h = 0.953 in the control group (when the exposure concentration was 0 μg / L), but greatly decreased by DCMU exposure, and reached 0.117 at the maximum exposure concentration (93.2 μg / L). Declined. This result agrees with the analysis result of the emission decay of the crystal shown in the above reference.

In this example, information not described in the above-mentioned reference was also obtained. For example, the fact that the coefficient value τ indicating the lifetime of light emission decay (the time until the light emission decreases to 1 / e of the initial value) decreases due to exposure to DCMU as well as the coefficient value h is information that cannot be obtained from the references. is there. The coefficient value I ₀ increased with exposure to DCMU, which is thought to be due to an increase in charge recombination at P680 since the first electron acceptor molecule Q _A quinone was reduced from P680. Thus, the coefficient value of the StrExp function according to the present invention can be considered useful for estimating the state of photosynthetic electron transfer. That is, it is considered that the coefficient I ₀ is an index of the magnitude of charge recombination at P680, the coefficient τ is an index of the emission lifetime, and h is an index of the size of the electron trap in the electron transfer reaction. Here, the electron trap refers to temporary holding state of the electrons is considered mainly corresponds to Q _A quinone.

Evaluation results of difference spectra not described in the above references are also useful for estimating the state of photosynthesis. As shown in Table 1, since the difference spectrum has a substantially peak shape, delayed luminescence can be evaluated based on the peak height, peak position, and model difference sum. In DCMU exposure data, the peak height increases according to the exposure concentration, the peak position appears earlier, and the model difference sum decreases. Such a change in the difference spectrum can be evaluated in association with the state of the photosynthetic electron transfer reaction. As described above, StrExp function is mainly thought to reflect the state of the electron transfer reaction from P680 to _{Q A} quinone. The difference spectrum can be considered to reflect a light emitting component which can not be explained by the electron transfer reaction from P680 to Q _A quinone. The present inventors from various studies, it was considered to be electron transfer reaction of Q _B quinone binding sites later.

Q _B quinones which bind to Q _B site is one-electron reduction to become a plast semiquinone, it becomes two-electron reduced by plus quinol, released into the thylakoid membranes. Photosystem reaction center about 10 molecules per one plastoquinone are present in the thylakoid membrane (this is referred to as plastoquinone pool (PQP)), coupled to the Q _B site was replaced with positive Toki Nord that any is liberated To do.

Here, two-electron excited plus Toki exchange plastoquinone and Nord, so that positive Toki Nord liberated in PQP was reoxidized by cytochrome complex, the state of the Q _B site is affected by the state of the PQP. It can be considered that the state of PQP involves a reoxidation reaction by a cytochrome complex, an electron transfer reaction of photosystem I, and a cyclic electron transfer reaction from photosystem I to PQP. It is known that there are at least two systems in the circulation type electron transfer reaction, that is, the NDH pathway and the PGR5 pathway. In addition to this circulating electron transfer pathway, the existence of a Q cycle for returning electrons from the cytochrome complex to the plastoquinone pool is assumed and supported by various studies.

Consider that the peak height of the difference spectrum increases with DCMU exposure, the peak position appears earlier, and the model difference sum becomes smaller. Since the height of the difference spectrum is increased, and there is a possibility that electrons are trapped somewhere after Q _B site by DCMU exposure. Since the peak position hardly changes from around 1 second even when the exposure concentration increases, it is considered that the physical and chemical relationship between the recombination site (P680) and the trap site does not change. The total model difference does not change much when the exposure concentration is low, and tends to decrease as the exposure concentration increases. The model difference sum can be considered to reflect the amount of electrons trapped in the electron trap site indicated by the peak position. Changes in peak height and peak position, as well as the coefficient values I ₀ , τ, h of the StrExp function, greatly changed from the lowest exposure concentration (5.8 μg / L), and thus directly reflected the effects of DCMU exposure. You can think that you are. On the other hand, the total model difference may reflect a different change. For example, as one of them, a route of heat dissipation by the deactivated photosystem II can be considered.

Next, for each of the coefficient value of the StrExp function shown in Tables 1 to 4, the value related to the difference spectrum, and DFI _1.0-59.9s , the ratio (%) of the exposure group to the control group was determined, and the results of the four types of chemical substances Compared. Hereinafter, this ratio is referred to as “control ratio”. The control group refers to the case where the exposure concentration is 0 μg / L, and the exposure group refers to the other cases. Tables 5-8 show the percentages for DCMU, DBMIB, AntA, and CCCP, respectively. The top row of each table is the exposure concentration (μg / L).

_Further , graphs of the coefficient value τ, the model difference sum, and the conventional method (DFI _{1.0-59.9 s} ) shown in Tables 5 to 8 are shown in FIGS. The vertical axis of each graph is the control ratio (%), and the horizontal axis is the exposure concentration (μg / L).

As shown in FIG. 11, DFI _1.0-59.9s decreases according to the exposure concentration of DCMU, but the coefficient value τ decreases more rapidly than DFI _1.0-59.9s . Thus, it can be seen that the coefficient value τ is more sensitive to _DCMU exposure than DFI _1.0-59.9s . On the other hand, the model difference total increases once at the lowest exposure concentration (5.8 μg / L) and then decreases at an exposure concentration of 11.7 μg / L, but the decrease is _slower than DFI _1.0-59.9s. It is. Therefore, it can be seen that the model difference sum is less sensitive than DFI _1.0-59.9s . These results show that the conventional method (DFI _1.0-59.9s ) is a mixture of the change described by the StrExp model including the coefficient value τ and the change described by the difference spectrum including the model difference sum, This suggests a decrease in sensitivity. Therefore, it can be said that the present invention can detect the influence of DCMU with higher sensitivity than the conventional method.

As shown in FIG. 12, DFI _1.0-59.9s decreases according to the exposure concentration of DBMIB, but the coefficient value τ decreases more rapidly than DFI _1.0-59.9s . Thus, it can be seen that the coefficient value τ is more sensitive to _DBMIB exposure than DFI _{1.0-59.9 s} . On the other hand, the model difference sum is higher than the control group except for the highest exposure concentration (2576 μg / L), and the sensitivity to the exposure concentration of DBMIB is low. These results show that the conventional method (DFI _1.0-59.9s ) is a mixture of the change described by the StrExp model including the coefficient value τ and the change described by the difference spectrum including the model difference sum, This suggests a decrease in sensitivity. Therefore, it can be said that the present invention can detect the influence of DBMIB with higher sensitivity than the conventional method.

As shown in FIG. 13, DFI _1.0-59.9s hardly decreases as the exposure concentration of AntA increases. On the other hand, the coefficient value τ is significantly lower than DFI _{1.0-59.9 s, indicating} that the coefficient value τ is more sensitive than DFI _{1.0-59.9 s} to _AntA exposure. On the other hand, the model difference total hardly changed between the exposure concentrations of 13.7 to 55 μg / L, and increased slightly compared with the control group at the exposure concentrations of 110 μg / L and 220 μg / L, and the change according to the exposure concentration unacceptable. These results show that the conventional method (DFI _1.0-59.9s ) is a mixture of the change described by the StrExp model including the coefficient value τ and the change described by the difference spectrum including the model difference sum, This suggests a decrease in sensitivity. Therefore, it can be said that the present invention can detect the influence of AntA with higher sensitivity than the conventional method.

As shown in FIG. 14, DFI _1.0-59.9s decreases with the exposure concentration of CCCP. The model difference total also decreases according to the exposure concentration in the same way as DFI _1.0-59.9s , but overall, the model difference total decreases slightly according to the exposure concentration and is slightly higher than DFI _1.0-59.9s. It turns out that it is highly sensitive. On the other hand, the coefficient value [tau] shows little change depending on the exposure concentration at a low exposure concentration (51.2 to 204.6 [mu] g / L), and the exposure concentration at a high exposure concentration (409.2 to 818.5 [mu] g / L). Decreased according to However, the degree of decline was more gradual than DFI _1.0-59.9s and the model difference sum, and thus was less sensitive. These results show that the conventional method (DFI _1.0-59.9s ) is a mixture of the change described by the StrExp model including the coefficient value τ and the change described by the difference spectrum including the model difference sum, This suggests a decrease in sensitivity. Therefore, it can be said that the present invention can detect the influence of AntA with higher sensitivity than the conventional method.

From the above results, it is understood that the influence of chemical substances can be evaluated with higher sensitivity than the conventional method (DFI _{1.0-59.9 s} ) by using the function fitting and the difference spectrum according to the present invention. The change of each coefficient value was different for each chemical substance, which is considered to reflect the difference in the influence of the chemical substance on the photosynthetic electron transfer reaction. Therefore, it is considered that the kind of chemical substance that affects the photosynthetic sample can be estimated using the difference.

  As described above, the evaluation system for a photosynthetic sample according to one aspect of the present invention includes a receiving unit that receives measurement data indicating temporal changes in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light. A determination unit that determines a model function indicating a heavy-tailed distribution used for evaluating the photosynthetic sample, and the determination unit is configured to measure the measurement data received by the reception unit, and the delayed emission is first measured. The first data in the first time zone including the time point is divided into the second data in the second time zone after the first time zone, and the first data is divided into the second data. The model function to be fitted to the first data is determined by executing function fitting with the weighting on the first data.

  A photosynthetic sample evaluation method according to an aspect of the present invention is a photosynthetic sample evaluation method executed by an evaluation system including a processor, wherein delayed light emission generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light Including a reception step for receiving measurement data indicating a change in time of the image, and a determination step for determining a model function indicating a heavy tail distribution used for evaluating the photosynthetic sample. The determination step is received in the reception step. The measurement data is divided into first data in a first time zone including a time point when delayed luminescence is first measured, and second data in a second time zone after the first time zone. And performing a function fitting weighted to the first data rather than the second data, And determining a model function for fitting to over data.

  An evaluation program for a photosynthetic sample according to one aspect of the present invention evaluates a photosynthetic sample by receiving a measurement data indicating a temporal change in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light. And a determination step for determining a model function indicating a distribution with a heavy tail is executed by the computer, and the determination step includes the measurement data received in the reception step and the time point when the delayed light emission is first measured. Dividing the first data in the first time zone and the second data in the second time zone after the first time zone; and the first data over the second data Determining a model function to be fitted to the first data by performing a function fitting weighted towards Including.

  In such an aspect, the measurement data is divided into first data indicating the initial stage (initial attenuation) of the delayed light emission attenuation and second data indicating the attenuation after the initial attenuation (late attenuation). Is done. Then, by function fitting that places importance on the initial attenuation rather than the late attenuation, a model function indicating a distribution with a heavy tail that accurately represents the initial attenuation is determined. The present inventor has found that the photosynthetic sample can be accurately evaluated by neglecting or ignoring the late decay and obtaining its model function fitting to the early decay. In addition, since it is not necessary to use a plurality of model functions, the coefficient to be adjusted is smaller than in the case of using the sum of a plurality of functions, and as a result, function fitting is simplified. By simplifying function fitting, it is possible to automatically perform function fitting for various types of photosynthetic samples and environmental factors without depending on human experience. For these reasons, photosynthetic samples can be evaluated accurately and more easily.

  In the photosynthetic sample evaluation system according to another aspect, the determination unit may determine a single model function.

  In this case, it is sufficient to determine one model function indicating a heavy tail distribution, so that the coefficient to be adjusted is smaller than when using the sum of a plurality of functions, and as a result, function fitting is simplified. .

  In the photosynthetic sample evaluation system according to another aspect, the determination unit may select the first data based on a light emission amount or an elapsed time of delayed light emission.

  Considering one of these two factors related to the decay of delayed light emission, it is possible to select the first data necessary and sufficient for accurate function fitting.

In the evaluation system of the photosynthetic sample according to another aspect, determining unit, a first data by setting the time range for light emission amount of delayed luminescence is attenuated to 1 / e ⁿ of the initial value as the first time period Where n ≧ 1.

  In this case, the first time zone (initial time zone) is dynamically set according to the degree of attenuation of delayed light emission, so it is necessary for accurate function fitting regardless of photosynthetic samples and measurement conditions. It is possible to select sufficient first data.

If the rating system photosynthetic sample according to another aspect, the determination unit, setting the initial time range luminescence amount of delayed luminescence is attenuated to 1 / e ^a of the initial value as the first time period, said first of if the number of measurements of delayed luminescence corresponding to the time period is equal to or less than a predetermined number, perform the function fitting in the extended time range luminescence amount of delayed luminescence is attenuated to 1 / e ^b of the initial value, where a <B, and the model function may be determined by executing function fitting with the coefficient value obtained by function fitting in the extended time range as an initial value for the initial time range.

  In this case, when the number of first data is insufficient, function fitting is performed after the time range corresponding to the first time zone is expanded to increase the number of data, and the result is used. Thus, function fitting is performed in the first time zone (initial time range) of the original length. By this processing, function fitting can be performed accurately even when the number of data (number of measurement values) in the first time zone is small.

  In the photosynthetic sample evaluation system according to another aspect, the function fitting in which the first data is weighted more than the second data makes the contribution in the first data more than the contribution in the second data. The function fitting to be increased, and the contribution may be a degree that contributes to the adjustment of the model function by the difference between the model data obtained from the model function and the measurement data.

  Model function showing a heavy tail distribution that fits to the first data by placing importance on the first data over the second data with respect to the degree to which the difference between the model data and the measured data contributes to the model function Can be determined more accurately.

  In the photosynthetic sample evaluation system according to another aspect, the function fitting in which the first data is weighted more than the second data is a function using only the first data without using the second data. It may be a fitting.

  In this case, since the function fitting is executed with a small number of data (that is, only the first data), it is possible to determine a model function showing a heavy distribution at a higher speed.

  The photosynthesis sample evaluation system according to another aspect may further include an evaluation unit that evaluates the photosynthesis sample using the model function determined by the determination unit.

  In the photosynthetic sample evaluation system according to another aspect, the model function includes an initial value of the measured delayed emission, an attenuation rate indicating a time until the amount of delayed emission is attenuated to 1 / e, A Stretched-Exponential function expressed using “Sato”, and the evaluation unit may evaluate the photosynthetic sample using at least one of the attenuation rate and the weight of the skirt.

  Since the decay speed and the weight of the skirt constituting the StrExp function can express the characteristics of delayed emission attenuation well, the photosynthetic sample can be accurately evaluated by using at least one of these elements.

  In the photosynthetic sample evaluation system according to another aspect, the evaluation unit may evaluate the photosynthetic sample using a difference spectrum between model data and measurement data indicated by the model function determined by the determination unit.

  Since this difference spectrum can well represent the characteristics of delayed emission decay, the photosynthesis sample can be accurately evaluated by using this difference spectrum.

  In the photosynthetic sample evaluation system according to another aspect, the evaluation unit may evaluate the photosynthetic sample using at least one of a peak height, a peak position, and an integrated value of the difference spectrum.

  Since the difference spectrum peak height, peak position, and integrated value of the difference can well represent the characteristics of delayed emission decay, it is possible to accurately evaluate photosynthetic samples by using at least one of these elements. .

  The present invention has been described in detail above based on the embodiments and examples. However, the present invention is not limited to the above-described embodiments and examples. The present invention can be variously modified without departing from the gist thereof.

  The configuration of the evaluation system is not limited. For example, one device may have the functions of the control device 10 and the evaluation device 30, one device may have the functions of the control device 10 and the measuring device 20, or one device may be the control device. 10, the functions of the measuring device 20 and the evaluation device 30 may be provided. If the evaluation system can acquire measurement data from other devices or computer systems, the evaluation system may include only devices or functions corresponding to the evaluation device 30. In this case, the receiving unit of the evaluation device receives the measurement data by receiving the measurement data from another device or a computer system, or by accessing the other device and reading the measurement data.

  In the above embodiment, the evaluation device 30 includes the evaluation unit 34. However, the function corresponding to the evaluation unit 34 may be implemented in a computer system other than the evaluation system. In this case, the processes of steps S14 and S15 shown in FIG. 3 are executed by the other computer system. In any case, the model function determined by the evaluation system according to the present invention is used to evaluate the photosynthetic sample.

  The measurement data is divided into first data in a first time zone including a time point when delayed luminescence is first measured, and second data in a second time zone after the first time zone. This is a concept including a case where measurement data is divided into three or more data. For example, the measurement data includes data D1 in a time zone T1 including a time point at which delayed light emission is first measured, data D2 in a time zone T2 after the time zone T1, and a time zone T3 after the time zone T2. Is divided into data D3. In this case, the first time is the time zone T1, and the first data is data D1. The second time zone may be either the time zone T2 or the time zone T3, or may be a combination of the time zones T2 and T3. Therefore, the second data may be one of data D2 and data D3, or may be a combination of data D2 and data D3.

  In the above embodiment, the determination unit 33 determines a single model function. However, the determination unit may determine a plurality of model functions to be fitted to the first data for one measurement data. For example, the determination unit may determine the plurality of model functions such that a sum of a plurality of model functions each having a heavy tail distribution fits to the first data.

  DESCRIPTION OF SYMBOLS 1 ... Evaluation system, 10 ... Control apparatus, 20 ... Measuring apparatus, 30 ... Evaluation apparatus, 31 ... Reception part, 32 ... Memory | storage part, 33 ... Determination part, 34 ... Evaluation part, P1 ... Evaluation program, P10 ... Main module, P11: reception module, P12: storage module, P13: determination module, P14: evaluation module.

Claims (12)

A receiving unit that receives measurement data indicating a temporal change in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light;
Determining a single, heavy tail model function used to evaluate the photosynthetic sample; and
The determination unit is
The measurement data received by the receiving unit includes first data in a first time zone including a time point at which the delayed light emission is first measured, and a second time zone after the first time zone. Divided into second data at
Determining the model function to be fitted to the first data by performing a function fitting weighted to the first data rather than the second data;
Evaluation system for photosynthetic samples. The determining unit selects the first data based on a light emission amount or an elapsed time of the delayed light emission;
The photosynthesis sample evaluation system according to claim 1 . The determination unit, the light emission amount of the delayed luminescence selects the first data time range to decay to 1 / e ⁿ by setting as the first time period of the initial value, wherein n ≧ 1 Is,
The photosynthesis sample evaluation system according to claim 2 . The determination unit is
When the light emission amount of the delayed luminescence sets the initial time range to decay to 1 / e ^a of the initial value as the first time period, the number of measurements of the delayed luminescence corresponding to the time zone of the first There is equal to or less than a predetermined number, perform the function fitting in the extended time range luminescence amount of the delayed luminescence is attenuated to 1 / e ^b of the initial value, a where a <b,
The model function is determined by performing function fitting on the initial time range with a coefficient value obtained by function fitting in the extended time range as an initial value.
The photosynthesis sample evaluation system according to claim 3 . The function fitting in which the first data is weighted more than the second data is a function fitting that makes the contribution in the first data larger than the contribution in the second data. Here, the degree of contribution is a degree that contributes to the adjustment of the model function by the difference between the model data obtained from the model function and the measurement data.
The evaluation system of the photosynthetic sample as described in any one of Claims 1-4 . The function fitting weighted toward the first data rather than the second data is a function fitting using only the first data without using the second data.
The evaluation system of the photosynthetic sample as described in any one of Claims 1-5 . The photosynthesis sample evaluation system according to any one of claims 1 to 6 , further comprising an evaluation unit that evaluates the photosynthesis sample using the model function determined by the determination unit. The model function is a function represented by using the measured initial value of delayed light emission, the decay rate indicating the time until the light emission amount of delayed light emission is attenuated to 1 / e, and the weight of the tail,
The evaluation unit evaluates the photosynthetic sample using at least one of the decay rate and the weight of the skirt;
The photosynthesis sample evaluation system according to claim 7 . The evaluation unit evaluates the photosynthetic sample using a difference spectrum between the model data indicated by the model function determined by the determination unit and the measurement data;
The photosynthesis sample evaluation system according to claim 7 or 8 . The evaluation unit evaluates the photosynthetic sample using at least one of a peak height, a peak position, and an integrated value of the difference spectrum;
The photosynthesis sample evaluation system according to claim 9 . A method for evaluating a photosynthetic sample executed by an evaluation system comprising a processor,
Receiving a measurement data indicating a time-dependent change in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light; and
Determining a model function indicative of a single, heavy tail used to evaluate the photosynthetic sample;
The determining step comprises:
The measurement data received in the reception step includes first data in a first time zone including a time point when the delayed light emission is first measured, and a second time zone after the first time zone. Dividing into second data at
Determining the model function to be fitted to the first data by performing a function fitting weighted towards the first data rather than the second data;
Evaluation method for photosynthetic samples. Receiving a measurement data indicating a time-dependent change in delayed luminescence generated from the photosynthetic sample by irradiating the photosynthetic sample with excitation light; and
Determining to determine a model function indicative of a single, heavy tail used to evaluate the photosynthetic sample;
The determining step comprises:
The measurement data received in the reception step includes first data in a first time zone including a time point when the delayed light emission is first measured, and a second time zone after the first time zone. Dividing into second data at
Determining the model function to be fitted to the first data by performing a function fitting weighted towards the first data rather than the second data;
Photosynthesis sample evaluation program.

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