Method for researching tomato light penetration depth mechanism based on light transmission simulation

Abstract

The invention relates to a method for researching a tomato light penetration depth mechanism based on light transmission simulation. The objective of the invention is to explore the depth rule of photons penetrating tomato tissues under different conditions. The method comprises the following specific steps: 1, determining a wavelength starting point and an end point of a spatial resolution spectrum and collecting a tomato spatial resolution spectrum; 2, calculating optical characteristic parameters of the tomatoes by using a diffusion approximation equation inversion algorithm; 3, according to the obtained tomato optical characteristic parameters, utilizing Monte Carlo to simulate a transmission path of photons in tomato tissues, and obtaining a penetration depth and a light source detector distance; 4, determining the maturity, moisture, pectin and lycopene content of the tomatoes through physical and chemical experiments; 5, researching a correlation mechanism between the penetration depth of photons in tomato tissues and the spatial resolution spectrum wavelength; 6, researching a correlation mechanism between the penetration depth of photons in tomato tissues and quality parameters; and 7, researching a correlation mechanism between the penetration depth of photons in tomato tissues and the distance of a light source detector.

Description

Technical Field

[0001] The present invention relates to a method for studying the optical property mechanism of biological tissue, in particular to a method for studying the light penetration depth mechanism of tomatoes based on light transmission simulation. Background Art

[0002] Growing conditions, maturity, and post-harvest grading, storage, and transportation all directly or indirectly affect the quality of fresh tomatoes. Selective grading and post-harvest processing of tomatoes can effectively increase their commercialization rate and market price. In recent years, spectral analysis has been widely used in product analysis. In agriculture, it has primarily focused on non-destructive testing of crop and agricultural product quality, such as for detecting plant lesions and the sugar content and firmness of fruits and vegetables. Visible and near-infrared spectroscopy, combined with chemometric methods, provides a powerful tool for assessing the quality and safety of agricultural and food products. This technique relies on obtaining spectral reflectance from a sample under illumination with a specific light source. The spectral information is then processed using chemometric methods and correlated with quality parameters. Due to its non-destructive, rapid, simple, and easy-to-use characteristics, spectral technology has been widely used to assess the internal and external quality of agricultural products. However, traditional spectral detection methods are limited not only by spectral variability during signal acquisition but also by the fact that the measured signal is affected by instrument configuration, resulting in significant challenges in model interoperability across different measurement instruments. These factors limit the applicability and development of predictive models based on visible and near-infrared spectroscopy.

[0003] Recognizing the limitations of visible and near-infrared spectroscopy, researchers have long sought alternatives, with optical property measurement proving to be a viable option. Optical property measurement involves measuring optical parameters of food and biological materials using spectral or image information and inversion algorithms such as the diffusion approximation equation. Absorption and scattering coefficients are two commonly used optical property parameters. These are intrinsic material properties that characterize the interaction of light with biological tissue. When a photon encounters a particle in a tissue sample or interacts with the interface between two media with different refractive indices, it may be directly absorbed by the particle and subsequently converted to other energy, a process known as absorption. Alternatively, it may be scattered, changing its direction of propagation and encountering another particle in the medium, repeating the process until it is ultimately absorbed or leaves the medium. The propagation of a photon in a tissue sample can be quantified using the absorption and scattering coefficients. Conventional visible / near-infrared spectroscopy only measures the overall absorption and scattering of light within the tissue, failing to distinguish between absorption and scattering. This ignores information about the photon's propagation within the tissue sample, limiting the ability to fully characterize the sample. Therefore, measuring the optical properties of biological tissue is crucial. Furthermore, due to a lack of understanding of light propagation in biological tissue and knowledge of its optical properties, instrument light source design and placement are often optimized through empirical comparison with post-production modeling results, a cumbersome and unreliable process. Therefore, studying the transmission pathways and penetration processes of light within biological tissue can provide a theoretical basis for the future development of more sophisticated detection methods and instruments. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for studying the mechanism of light penetration depth in tomatoes based on light transmission simulation. It simulates the transmission path of photons in biological tissues through Monte Carlo statistical methods, and studies the correlation mechanism between the penetration depth of photons in tomato tissues and the spatially resolved spectral wavelength and tomato quality parameters, as well as the correlation mechanism between the penetration depth of photons in tomato tissues and the distance between the light source and the detector. This method can further understand the relevant knowledge of the propagation process of light in biological tissues and the optical properties of biological tissues, providing a theoretical basis for future non-destructive testing of optical properties.

[0005] The present invention uses a step-by-step approach. First, the absorption and scattering coefficients of tomato tissue optical properties are calculated based on the spatially resolved spectral reflectance of tomatoes in the wavelength range of 548.15 nm to 1300.21 nm. Monte Carlo statistical simulations are then performed based on the optical properties obtained in the first step to derive the photon transmission path in tomato tissue. Tomato quality parameters are then measured through physical and chemical experiments. Finally, the correlation mechanisms between the photon penetration depth in tomato tissue, the spatially resolved spectral wavelength, and the tomato quality parameters are studied, as well as the correlation mechanisms between the photon penetration depth in tomato tissue and the distance between the light source and the detector are studied.

[0006] (1) The spatially resolved spectrum of tomato fruit is collected, and the effective interval of the collected spatially resolved spectrum of tomato is selected to determine the starting point and end point of the wavelength interval of the spatially resolved spectrum used to calculate the optical characteristic parameters of tomato tissue.

[0007] (2) Based on the reflectance of the spatially resolved spectrum of tomatoes collected in (1), the optical characteristic parameters of tomato tissue, namely the absorption coefficient and scattering coefficient, are calculated using the diffusion approximation equation inversion algorithm;

[0008] (3) Based on the optical characteristic parameters of tomato tissue calculated in (2), the Monte Carlo statistical method is used to simulate the transmission path of photons in tomato tissue to obtain data on the photon penetration depth and the distance between the light source and the detector;

[0009] (4) Tomato quality parameters such as maturity, moisture content, pectin content, and lycopene content were obtained through physical and chemical experiments to study the effects of different quality parameters on the penetration depth of photons in tomato tissues;

[0010] (5) Compare the depth of photon penetration into tomato tissue obtained in (3), study the effect of wavelength change on the depth of photon penetration into tomato tissue, and obtain the relevant mechanism.

[0011] (6) Compare the tomato quality parameters obtained in (4) to study the effects of different quality parameters on the depth of photon penetration into tomato tissue and obtain the relevant mechanism.

[0012] (7) Compare the data on the penetration depth of photons in tomato tissue and the distance between the light source and the detector obtained in (3) to study the correlation mechanism between the penetration depth of photons and the distance between the light source and the detector.

[0013] In the above-mentioned method for studying the light penetration depth of tomato tissue based on light transmission simulation, tomato samples were divided into six maturity levels, namely Green, Breaker, Turning, Pink, Light red and Red, and numbered 1-6.

[0014] The effective interval in (1) is selected from 156 wavelength points from 548.15 nm to 1300.21 nm, and the interval between two wavelength points is 4.852 nm. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the method of the present invention

[0016] Figure 2 This is a line graph showing the variation of penetration depth with wavelength for six types of tomatoes with different maturity levels.

[0017] Figure 3This is a line graph showing the distance between the light source and the detector as a function of wavelength for six different levels of ripeness of tomatoes.

[0018] Figure 4 The linear fitting diagram of penetration depth and light source detector distance of six tomatoes with different maturity levels is shown in Figure 2. DETAILED DESCRIPTION

[0019] 1. A spatially resolved spectrum system was used to collect spatially resolved spectra of tomatoes. Due to the attenuation of light transmission in tissues, the signal received by the receiving optical fiber with a light source detector distance exceeding 12.5 mm is too weak. Therefore, 9 spatially resolved spectra close to the light source optical fiber (light source detector distance 1.5-12.5 mm) were used to analyze and evaluate the absorption and scattering characteristics of tomatoes. Affected by the strong absorption of water, the signal-to-noise ratio of the spectral region after 1300 nm is relatively small, and only the spectral region of 548.15-1300.21 nm is used to analyze and calculate the absorption and scattering coefficients of tomatoes. After spectral normalization, the sample calibration curve was used to correct the spatially resolved spectrum in the wavelength range of 548.15-1300.21 nm to obtain the corrected spatially resolved reflectance of the tomato sample. Finally, the absorption coefficient μ was calculated according to the inversion algorithm of the diffuse approximation equation. a and scattering coefficient μ s The analytical equation for spatially resolved diffuse reflectance is shown below:

[0020]

[0021] Where r is the light source-detector distance, r1 is the distance from the detector to the actual light source, r2 is the distance from the detector to the mirror light source, z0 = (μ′ t ) -1 =(μ a +μ′ s ) -1 , μ s ′=μ s (1-g), g = 0.9 is the anisotropy factor, which is determined by the material. μ' t is the total attenuation coefficient, is the attenuation coefficient, z b =2AD, D=(3(μ a +μ′ s )) -1 is the diffusion coefficient, A=(1+R f ) / (1-R f ) is the reflection coefficient in the tissue, R f Determined by the refractive index. and is a coefficient derived from the refractive index. For common fruit and vegetable tissues, the refractive index n = 1.35, and C1 and C2 are 0.1277 and 0.3269, respectively. These values ​​are used in the inversion algorithm of the diffusion approximation equation to calculate the absorption and scattering coefficients of tomatoes.

[0022] 2 Monte Carlo (MC) simulation is based on probability theory and statistics and can accurately simulate the real motion trajectory of photons in turbid media. Assign a weight to each photon and specify the position and direction cosine of the photon perpendicular to the incident direction. Set the initial weight of the photon to w = 1, the initial position to (x, y, z) = (0, 0, 0), and the direction cosine to (μ x , μ y , μ z )=(0,0,1). When a photon moves from its current position to the next position, the coordinates of the photon at the next position are determined by the current position, the moving step length and the direction cosine.

[0023] The step length of a photon moving from its current position to the next position is random, and the step length of the photon's trajectory is determined by the probability distribution of the mean free path s, that is, s = -lnξ / μ t (μ t =μ a +μ s is the interaction coefficient, and ξ is a random number between [0, 1]. Each scattering of a photon causes a change in direction. The new direction of the photon moving to the next position is given by the deflection angle θ and the azimuth angle ψ, where:

[0024]

[0025] Once the deflection angle and azimuth are determined, the new direction of the photon's motion can be determined.

[0026]

[0027] Therefore, the coordinates of the photon moving to the next position are (x′, y′, z′) = (x+μ′ x s,y+μ′ y s, z+μ′ z s). Photons will be absorbed during the simulated transmission process, which will cause the weight to decay. The weight change value is calculated using Δw=(μ a / μ t )w, where w is the weight of the photon before absorption and Δw is the change in the photon's energy weight after absorption. The weight after absorption is w′ = w - Δw. When a photon moves to the next step, it is necessary to determine whether it crosses the boundary and reaches other tissues, repeats absorption and scattering within the tissue, or exits the tissue and stops tracking.

[0028] There are two ways to determine when a photon has stopped tracking: 1) When a photon exits the upper or lower surface of the tissue, the vertical coordinate of the photon is compared with the depth of the surface to determine whether it has left the upper or lower surface. Since the tissue is considered a semi-infinite medium in this simulation, it is only necessary to determine whether the photon has exited the upper surface. First, calculate the distance between the current photon position and the upper boundary d = (z0-z) / μ z , if d<0, μ z <0, it can be determined that the photon is about to reach the tissue surface, where z0 = 0 is the location of the upper boundary. When it is determined that the photon will reach the tissue surface, it is necessary to determine whether the photon will undergo total internal reflection or emit the tissue surface. Let the incident angle of the photon at this time be α i =arccos(|μ z |), the transmission angle is α t , and from the law of refraction we get n i α i =n t α t , the reflectivity R(α i ) is obtained from the Fresnel formula:

[0029]

[0030] When ξ≤R(α i ), the photon undergoes total internal reflection and continues to track the photon; when ξ>R(α i ), the photon is emitted from the tissue surface and tracking stops.

[0031] 2) When the photon weight is less than the set weight threshold (0.1%), an integer m = 10 is given, and then a uniformly distributed random number ξ is generated. If ξ ≤ 1 / m, the photon weight is updated to mw, and the photon continues to transmit with the new weight. If ξ> 1 / m, the photon weight is zero, and the tracking of the photon is stopped at this time. After the photon is emitted from the tissue surface, its emission position, maximum penetration depth, emission angle and other information can be recorded, among which the maximum penetration depth is the maximum value of z among all (x, y, z) coordinates of the photon, and the light source detector distance is Where (x′, y′, z′) is the coordinate of the photon emission position, thereby obtaining data on the penetration depth and the distance between the light source and the detector, which can help analyze the distribution law of photon emission and information such as penetration depth.

[0032] 3. Determine tomato quality parameters through physical and chemical experiments. Maturity is a key parameter for determining tomato picking time and assessing post-harvest fruit quality. As tomatoes mature, chlorophyll content gradually decreases while lycopene content gradually increases, causing the fruit color to change from green to red. Based on the color standard for different tomato maturity levels, tomato samples were visually classified into six maturity levels: Green, Breaker, Turning, Pink, Light Red, and Red, numbered 1-6 according to maturity. Tomato samples were sliced ​​and dried in a drying oven for moisture content measurement. Pectin and lycopene content in tomato fruits were measured in accordance with national standards NY / T2016-2011 and GB / T14215-2008 and the method "Improvement of Lycopene Detection Methods in Tomatoes" by Yang Dingqing et al.

[0033] The measurement results are shown in Table 1. From the data in the table, it can be seen that the water and lycopene contents of tomato fruits increase with increasing maturity, while the pectin content decreases with increasing maturity.

[0034] Table 1

[0035] Maturity Moisture Pectin content (g / kg) Lycopene (mg / 100g) 1 0.9455 1.2010 1.0385 2 0.9471 1.0445 1.2928 3 0.9479 0.9938 1.7788 4 0.9532 0.8957 1.9487 5 0.9574 0.8002 2.0471 6 0.9590 0.7830 2.1327

[0036] 4. Study the relationship between the penetration depth of photons in tomato tissue, the wavelength of spatially resolved spectra, and tomato quality parameters. With wavelength as the horizontal axis and maximum penetration depth as the vertical axis, draw a wavelength-maximum penetration depth curve for six types of tomatoes of different maturity levels, such as Figure 2 .

[0037] like Figure 2 As shown in the figure, within the wavelength range (548.15 nm to 625.782 nm), the penetration depth of tomatoes at maturity levels 5 and 6 shows a sudden drop, followed by an increase. The spectrum shows a trough at 575 nm before rising sharply again. This change in maturity levels 5 and 6 is primarily due to the increase in anthocyanin, curcumin, and lycopene as the tomato transitions from green to red. This wavelength corresponds to the absorption peaks of anthocyanin and curcumin. Tomatoes at maturity levels 5 and 6 contain more pigment particles, resulting in greater absorption of photons. This reduces scattered photons and consequently reduces the depth of photon penetration into biological tissues.

[0038] At 675nm, the penetration depth of tomatoes of all six maturity levels shows a trough. Chlorophyll's absorption peak is located near 675nm, and this trough is believed to be caused by chlorophyll absorbing photons. Tomatoes at maturity levels 1 and 2 have the highest chlorophyll content, so their penetration depth decreases the most. Tomatoes at maturity levels 5 and 6 have the lowest chlorophyll content, so their penetration depth decreases the least. As tomatoes mature, their chlorophyll content decreases, their absorption of photons decreases, and their penetration depth increases.

[0039] In the wavelength range (616.078 and 936.31 nm), the penetration depth of tomatoes at maturity levels 5 and 6 was significantly greater than that of the other four groups. As tomatoes ripen, the texture gradually softens, accompanied by the depolymerization of pectin and the dissolution of hemicellulose cell wall polysaccharides. This softening process reduces intercellular adhesion and the density of scattering particles within the flesh, facilitating photon penetration. Consequently, more mature tomatoes tend to have greater penetration depths.

[0040] At 970nm, the penetration depths of the six tomatoes of varying maturity levels show a simultaneous trough. This is caused by the absorption bands of water and CH, NH, and OH in tomato tissue, which facilitate photon absorption and thus affect and reduce the penetration depth.

[0041] In the wavelength range (936.31 to 1125.54 nm), the penetration depth curves for the six levels of tomato ripeness are essentially identical. This indicates that within this wavelength range, the ripeness of the tomato no longer affects the depth of photon penetration into biological tissue. This analysis indicates that if spectroscopy is used to distinguish between ripe and unripe tomatoes, the 640-900 nm wavelength range should be used.

[0042] 5. Study the relationship between the penetration depth of photons in tomato tissue and the distance between the light source and the detector

[0043] The penetration depth and light source detector distance of tomatoes at maturity level 6 simulated by Monte Carlo method were plotted on the same graph for research, and it was found that the two had similar trend changes, such as Figure 3 Therefore, linear regression was used to fit the penetration depth and light source detector distance data of 6 kinds of tomatoes with different maturity levels. It was found that there was a very high linear correlation between the two. The linear expression is shown in Table 2, and the determination coefficient R was calculated. 2 value.

[0044] Table 2

[0045] Maturity Linear relationship <![CDATA[R 2 ]]> 1 y=0.8407x-0.09205 0.9832 2 y=0.8601x-0.2078 0.9858 3 y=0.8611x-0.2424 0.9856 4 y=0.8648x-0.2508 0.9909 5 y=0.818x+0.0118 0.9911 6 y=0.7739x+0.2838 0.9917

[0046] The data in the table demonstrates a linear relationship between tomato penetration depth and light source-detector distance, with tomatoes at maturity level 6 exhibiting the highest correlation coefficient. This pattern suggests that future optical nondestructive testing could utilize detector positioning tailored to the desired depth in biological tissue, yielding even more accurate results.

Claims

1. A method for studying the light penetration depth mechanism of tomatoes based on light transmission simulation, comprising the following steps: (1) The spatially resolved spectrum of tomato fruit was collected, and the effective interval of the collected spatially resolved spectrum of tomatoes was selected to determine the starting and ending points of the wavelength range of the spatially resolved spectrum used to calculate the optical characteristic parameters of tomato tissue, that is, 548.15 nm to 1300.21 nm. (2) Based on the reflectance of the spatially resolved spectrum of tomatoes collected in (1), the optical characteristic parameters of tomato tissue, namely the absorption coefficient and scattering coefficient, are calculated using the diffusion approximation equation inversion algorithm; (3) Based on the optical characteristic parameters of tomato tissue calculated in (2), the Monte Carlo statistical method is used to simulate the transmission path of photons in tomato tissue to obtain data on the photon penetration depth and the distance between the light source and the detector; (4) Tomato quality parameters such as maturity, moisture content, pectin content, and lycopene content were obtained through physical and chemical experiments to study the effects of different quality parameters on the penetration depth of photons in tomato tissues; (5) Compare the depth of photon penetration into tomato tissue obtained in (3), study the effect of wavelength change on the depth of photon penetration into tomato tissue, and explore the mechanism of photon penetration into tomato tissue. (6) Compare the tomato quality parameters obtained in (4), study the effects of different quality parameters on the depth of photon penetration into tomato tissue, and explore the mechanism of the depth of photon penetration into tomato tissue. (7) Compare the data on the penetration depth of photons in tomato tissue and the distance between the light source and the detector obtained in (3) to study the correlation mechanism between the penetration depth of photons and the distance between the light source and the detector.

2. The method for studying the light penetration depth mechanism of tomatoes based on light transmission simulation according to claim 1, characterized in that: The distance between the probe and the light source for collecting spatially resolved spectra of tomatoes is between 1.5 and 12.5 mm.

3. The method for studying the light penetration depth mechanism of tomatoes based on light transmission simulation according to claim 2, characterized in that: The wavelength range of the spatially resolved diffuse reflectance spectrum used for calculating the optical characteristic parameters of tomato tissue is from 548.15 nm to 1300.21 nm, the interval between two wavelength points is 4.852 nm, and there are 156 wavelength points in total.

4. The method for studying the light penetration depth mechanism of tomatoes based on light transmission simulation according to claim 1, characterized in that: The Monte Carlo simulation considers the tomato tissue as a semi-infinite medium, the refractive index inside the medium is constant, and the boundary is uniform and smooth.

5. The method for studying the light penetration depth mechanism of tomatoes based on light transmission simulation according to claim 1, characterized in that: The light source detector distance refers to the distance between the incident light source and the detector at the outgoing light, and the depth of photon penetration into biological tissue is defined as the depth at which the light signal intensity decays to 1%.

6. The method for studying the light penetration depth mechanism of tomatoes based on light transmission simulation according to claim 1, characterized in that: The maturity quality of tomatoes is classified into six maturity levels: Green, Breaker, Turning, Pink, Light red and Red by visual observation.

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