WO2021089001A1 - 一种检测水果甜度的方法、装置、系统及存储介质 - Google Patents

一种检测水果甜度的方法、装置、系统及存储介质 Download PDF

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WO2021089001A1
WO2021089001A1 PCT/CN2020/127203 CN2020127203W WO2021089001A1 WO 2021089001 A1 WO2021089001 A1 WO 2021089001A1 CN 2020127203 W CN2020127203 W CN 2020127203W WO 2021089001 A1 WO2021089001 A1 WO 2021089001A1
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fruit
sweetness
diffusion coefficient
apparent diffusion
sample
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PCT/CN2020/127203
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English (en)
French (fr)
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王成波
王心培
张继昌
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宁波诺丁汉大学
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Priority to US17/775,235 priority Critical patent/US11965842B2/en
Publication of WO2021089001A1 publication Critical patent/WO2021089001A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food
    • G01N33/025—Fruits or vegetables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
    • G01N24/08—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/56341—Diffusion imaging

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  • the present invention relates to the field of food detection, and more specifically to a method, device, system and storage medium for detecting fruit sweetness.
  • Sweetness is one of the most important indicators for measuring fruit quality, and it has been widely concerned as an important parameter for quality classification and rating.
  • the existing detection methods of fruit sweetness mainly include destructive chemical detection and non-destructive near-infrared spectroscopy.
  • Destructive chemical testing methods will cause a part of the loss of fruit products, and can only be used for sampling fruit testing, and cannot meet the needs of large-scale product testing.
  • Non-destructive near-infrared spectroscopy is widely used as a green non-destructive testing method for the evaluation and grading of fruit sweetness, but there are still the following shortcomings: 1) Infrared detection can only measure the sweetness of fruit flesh within 5 mm, and cannot be used for thick peel Fruits and their internal sweetness are tested; 2) In order to ensure the quality of testing, the conveyor belt must maintain a low speed and only one fruit can be tested at a time, which cannot meet the high-speed testing requirements.
  • the present invention was made in consideration of the above-mentioned problems.
  • the invention provides a method, device, system and storage medium for detecting fruit sweetness.
  • a method for detecting fruit sweetness including:
  • the sweetness of the fruit to be detected is determined according to the effective apparent diffusion coefficient.
  • the method further includes:
  • the determination of the sweetness of the fruit to be detected according to the effective apparent diffusion coefficient is by using the first sweetness detection model.
  • the method further includes:
  • a second sweetness detection model is established according to the apparent diffusion coefficient of the first reference object, the effective apparent diffusion coefficient of the sample fruit, and the sample sweetness, wherein the second sweetness detection model represents the fruit's The relationship between sweetness and the effective apparent diffusion coefficient of the fruit and the apparent diffusion coefficient of the reference object;
  • the apparent diffusion coefficient of the fruit to be tested is obtained without damage
  • the apparent diffusion coefficient of a second reference object is also obtained
  • the second reference object is the same substance as the first reference object
  • the determining the sweetness of the fruit to be detected is also based on the apparent diffusion coefficient of the second reference object and the second sweetness detection model is used.
  • the coefficients, a2 and b2 are parameters in the second sweetness detection model.
  • the reference object is water.
  • the apparent diffusion coefficient is represented by an apparent diffusion coefficient image
  • the determining the effective apparent diffusion coefficient of the fruit to be detected according to the obtained apparent diffusion coefficient includes:
  • the average value of the pixels in the region of interest is calculated as the effective apparent diffusion coefficient.
  • a device for detecting the sweetness of fruits including:
  • the imaging module is used for MRI diffusion weighted imaging to obtain the apparent diffusion coefficient of the fruit to be tested without damage;
  • a calculation module configured to determine the effective apparent diffusion coefficient of the fruit to be detected according to the apparent diffusion coefficient of the fruit to be detected
  • a system for detecting the sweetness of fruits including a processor and a memory, wherein computer program instructions are stored in the memory, and the computer program instructions are used when the processor is running. To perform the above-mentioned method of detecting the sweetness of fruits.
  • a storage medium on which program instructions are stored, and the program instructions are used to execute the aforementioned method for detecting fruit sweetness during operation.
  • the apparent diffusion coefficient of the fruit to be detected is obtained based on magnetic resonance imaging when the fruit to be detected is not damaged, and the apparent diffusion coefficient is used to determine the fruit's Sweetness, realizing non-destructive and reliable fruit sweetness detection.
  • this technical solution can accurately detect multiple fruits at one time.
  • Fig. 1 shows a schematic flowchart of a method for detecting fruit sweetness according to an embodiment of the present invention
  • Figures 2A, 2B, 2C, and 2D respectively show an optical image of a fruit, a T2-weighted image when the DW-MRI diffusion sensitivity parameter of the fruit is 0, and a DW-MRI diffusion sensitivity of the fruit according to an embodiment of the present invention.
  • Fig. 3 shows a schematic diagram of fitting a first sweetness detection model according to the sample sweetness and effective apparent diffusion coefficient of a sample fruit according to an embodiment of the present invention
  • Fig. 4 shows a schematic block diagram of an apparatus for detecting fruit sweetness according to an embodiment of the present invention.
  • the present invention proposes a method for detecting the sweetness of fruits.
  • the method for detecting the sweetness of fruits can be implemented based on the magnetic resonance diffusion weighted imaging (DWI) technology.
  • DWI magnetic resonance diffusion weighted imaging
  • magnetic resonance imaging is mainly used in the field of medical diagnosis.
  • radio frequency pulses are used to excite the magnetization vector in the imaging target.
  • the space position information of the magnetization vector is marked by phase gradient coding and frequency coding, and the image is reconstructed by Fourier transform.
  • the magnetic resonance imaging method is a non-destructive imaging method, which can provide a variety of weighted imaging including diffusion and high-resolution intra-target structure maps.
  • Magnetic resonance diffusion-weighted imaging is a kind of magnetic resonance imaging technique that is widely used to study the diffusion of water molecules in substances. Using the diffusion rate of water molecules in the imaging target, the diffusion coefficient of water molecules inside the imaging target can be calculated. The inventor has conducted a large number of experimental tests for the detection of fruit sweetness, and the test results show that there is a strong correlation between the fruit sweetness and the apparent diffusion coefficient of the fruit.
  • Fig. 1 shows a schematic flowchart of a method 100 for detecting fruit sweetness according to an embodiment of the present invention.
  • the method 100 includes step S110, step S120, and step S130.
  • step S110 the apparent diffusion coefficient of the fruit to be detected without damage is obtained through magnetic resonance diffusion-weighted imaging.
  • the basis of MRI diffusion-weighted imaging is the movement of water molecules, which can directly reflect the strength of the dispersion of water molecules inside the imaging target.
  • the sugar molecules in the fruit whether it is a monosaccharide molecule, a disaccharide molecule or a polysaccharide molecule, will affect the movement of water molecules in the fruit. Generally speaking, sugar molecules will significantly reduce the dispersion speed of water molecules.
  • the apparent dispersion coefficient is an indicator of the strength of the dispersion of soluble substances when they pass through a permeable medium.
  • the dispersion coefficient is related to the structure of the medium, the uniformity of the permeation path, the average permeation flow rate, and the physical and chemical properties of the fluid.
  • the dispersion of water molecules is affected by the sugar molecules in the fruit. Generally, the greater the concentration of sugar molecules, the weaker the dispersion.
  • the sweetness of the fruit can be determined by performing magnetic resonance diffusion-weighted imaging on the fruit without damage.
  • Figures 2A, 2B, 2C, and 2D respectively show an optical image of a fruit (orange) to be detected, a T2-weighted image when the DW-MRI dispersion sensitivity parameter of the fruit is 0, and a fruit image according to an embodiment of the present invention.
  • the DW-MRI diffusion sensitivity parameter is the same direction diffusion weighted DWI image and the ADC image of the fruit when the higher value.
  • the magnetic resonance imaging of the fruit to be detected under the condition of 0 magnetic field intensity (as shown in FIG. 2B) and the magnetic resonance imaging of the fruit to be detected under the condition of high magnetic field intensity (as shown in FIG. 2C) can be used.
  • the apparent diffusion coefficient of each corresponding position of the fruit to be detected can be calculated according to the corresponding pixel values in the two images.
  • the apparent diffusion coefficient ADC of the fruit to be tested can be calculated by formula (1).
  • b represents the dispersion-sensitive parameter in the same direction dispersion weighted image
  • S 1 represents the signal intensity of the magnetic resonance image under high magnetic field conditions .
  • b represents the dispersion-sensitive parameter in the same direction dispersion weighted image
  • S 1 represents the signal intensity of the magnetic resonance image under high magnetic field conditions .
  • b represents the dispersion-sensitive parameter in the same direction dispersion weighted image
  • S 1 represents the signal intensity of the magnetic resonance image under high magnetic field conditions .
  • b represents the dispersion-sensitive parameter in the same direction dispersion weighted image
  • S 0 represents the signal intensity of the magnetic resonance image when b
  • step S110 if the fruit to be detected is not damaged, image it to obtain its apparent diffusion coefficient.
  • the apparent diffusion coefficient of each part of the fruit to be detected is different, which is related to the detected part. For example, for oranges, the apparent diffusion coefficient of the peel part and the central part of the orange network is relatively small. The apparent diffusion coefficient of the pulp is larger.
  • Step S120 Determine the effective apparent diffusion coefficient of the fruit to be detected according to the apparent diffusion coefficient of the fruit to be detected.
  • a fruit usually has inedible parts such as the skin, cavity, pit, etc.
  • the sweetness does not affect the quality of the fruit, such as the peel part of an orange.
  • fruits may also rot during storage or transportation.
  • the spoiled part of the fruit should not be used as a test part.
  • the apparent diffusion coefficients of these parts are not only worthy of consideration, but also have a negative impact on the accurate determination of fruit sweetness. Therefore, in this step S120, the effective apparent diffusion coefficient is determined according to the apparent diffusion coefficient of the fruit to be detected.
  • the effective apparent diffusion coefficient is a single value.
  • the apparent diffusion coefficients of the skin, cavity, core and other parts of the fruit to be detected are discarded, and the remaining apparent diffusion coefficients are averaged to obtain the effective apparent diffusion coefficient.
  • Step S130 Determine the sweetness of the fruit to be detected according to the effective apparent diffusion coefficient.
  • the sweetness of fruit has a clear correlation with its apparent diffusion coefficient.
  • the solid pulp tissue of the fruit does not have a significant effect on the dispersion of water molecules in the fruit. Therefore, the sweetness of the fruit can be determined based on the apparent diffusion coefficient obtained without damage to the fruit, instead of having to squeeze the fruit.
  • the effective apparent diffusion coefficient of the fruit to be detected is determined based on the apparent diffusion coefficient obtained in a non-destructive condition, and the sweetness of the fruit can be determined by the mapping relationship between it and the sweetness of the fruit.
  • the apparent diffusion coefficient of the fruit to be detected is obtained based on magnetic resonance imaging and the apparent diffusion coefficient is used to determine the sweetness of the fruit, thereby achieving a non-destructive and reliable fruit sweetness.
  • Degree detection when the fruit to be tested is non-destructive, the apparent diffusion coefficient of the fruit to be detected is obtained based on magnetic resonance imaging and the apparent diffusion coefficient is used to determine the sweetness of the fruit, thereby achieving a non-destructive and reliable fruit sweetness.
  • the apparent diffusion coefficient obtained in step S110 can be represented by an apparent diffusion coefficient image.
  • the above step S120 determining the effective apparent diffusion coefficient according to the acquired apparent diffusion coefficient may include: firstly, performing image segmentation on the apparent diffusion coefficient image to determine the region of interest containing the pulp of the fruit to be detected .
  • the image segmentation method of region growth can be used to segment the apparent diffusion coefficient image.
  • the pixel values of all pixels of the apparent diffuse image are averaged to determine the seed pixel.
  • the surrounding pixels are continuously added in a certain rule to achieve the goal of finally combining all the pixels representing the effective part of the fruit into one area.
  • the obtained area is the desired area of interest.
  • the average value of the pixels in the region of interest is calculated as the effective apparent diffusion coefficient of the fruit to be detected.
  • the sweetness of the fruit can be determined more ideally.
  • step S110 statistical analysis may also be performed on all apparent diffusion coefficients obtained in step S110. Based on the probability distribution of the apparent diffusion coefficient indicated by the statistical analysis results, the apparent diffusion coefficients with probabilities higher than the preset probability threshold are averaged, and the mean value is taken as the effective apparent diffusion coefficient.
  • the process of determining the sweetness of the corresponding fruit according to the effective apparent diffusion coefficient can be implemented by relying on a sweetness detection model.
  • the sweetness detection model represents the mathematical relationship between the effective apparent diffusion coefficient and the sweetness of the fruit, which can be expressed by a functional formula.
  • the method for detecting the sweetness of fruits further includes the following steps of establishing a sweetness detection model. Subsequent determination of the sweetness of the fruit to be tested according to the effective apparent diffusion coefficient is to use the sweetness detection model.
  • establishing the sweetness detection model includes the following steps.
  • Step S101 Obtain the apparent diffusion coefficient under the condition that the sample fruit is not damaged by magnetic resonance diffusion weighted imaging.
  • Step S102 Determine the effective apparent diffusion coefficient of the sample fruit according to the apparent diffusion coefficient of the sample fruit.
  • Step S103 detecting the sample sweetness of the sample fruit.
  • Step S104 Establish a first sweetness detection model based on the effective apparent diffusion coefficient of the sample fruit and the sample sweetness, wherein the first sweetness detection model represents the difference between the sweetness and the effective apparent diffusion coefficient relationship.
  • the sample fruit and the fruit to be tested are the same fruit, for example, both are oranges.
  • the obtained first sweetness detection model can more effectively express the universal relationship between the effective apparent diffusion coefficient and the sweetness of the fruit.
  • the implementation process of the foregoing step S101 and step S102 is similar to the foregoing step S110 and step S120, respectively, and will not be repeated here for the sake of brevity.
  • any existing method for detecting fruit sweetness can be used. For example, the use of detrimental fruit sweetness detection methods in order to accurately obtain the sample sweetness of the sample fruit.
  • step S104 the sample points can be determined in the coordinate system according to the effective apparent dispersion coefficient determined in step S102 of step S102 and the sample sweetness of step S103 for all sample fruits, and fitting is performed based on the sample points to obtain the effective apparent dispersion
  • the function curve of the coefficient and the sweetness of the sample is the first sweetness detection model.
  • the sample fruit is used to determine the first sweetness detection model that represents the relationship between the sweetness of the fruit and the effective apparent diffusion coefficient. Based on the sweetness detection model, the sweetness of the fruit can be accurately determined.
  • Fig. 3 shows a schematic diagram of fitting the first sweetness detection model according to the sample sweetness of the sample fruit and the effective apparent diffusion coefficient according to an embodiment of the present invention.
  • the first sweetness detection model shown in Figure 3 involves a variety of fruits, including grapes, oranges, and pears.
  • Figure 3 also shows the first sweetness detection model established based on sugar water.
  • the soluble solids in Figure 3 refer to sugar. It can be seen that the sweetness of the sample fruit is linearly negatively correlated with the effective apparent diffusion coefficient, and the slope of the first sweetness detection model curve for different types of sample fruits is not much different.
  • the higher the sweetness of the fruit the more viscous the macroscopic appearance of the sugar solution.
  • the microscopic interpretation is that the dispersion of water molecules is more restricted, that is, the lower the apparent diffusion coefficient. Therefore, the sweetness of the sample fruit has a linear negative correlation with the effective apparent diffusion coefficient, and the parameter a1 is a negative number. Since the main components that affect the dispersion of water molecules in each fruit sample are sugars, there is little difference in the slope of the first sweetness detection model curve for different types of sample fruits, and the difference in the value of a1 in the first sweetness detection model of each sample is small. .
  • the parameters a1 and b1 can be set according to the above theory and experience to omit the above-mentioned complicated operation of establishing the first sweetness detection model.
  • the detection result according to the first sweetness detection model ignores the difference in apparent diffusion coefficient caused by temperature.
  • the second sweetness detection model can be established according to the following steps.
  • step S101' the apparent diffusion coefficient of the sample fruit and the apparent diffusion coefficient of the first reference object under the condition that the sample fruit is not damaged are obtained at the same time through magnetic resonance diffusion weighted imaging.
  • step S102' the effective apparent diffusion coefficient of the sample fruit is determined according to the apparent diffusion coefficient of the sample fruit.
  • Step S103' detecting the sample sweetness of the sample fruit.
  • Step S104' establishing a second sweetness detection model according to the apparent diffusion coefficient of the first reference object, the effective apparent diffusion coefficient of the sample fruit, and the sample sweetness, wherein the second sweetness detection model It shows the relationship between the sweetness of the fruit and the effective apparent diffusion coefficient of the fruit and the apparent diffusion coefficient of the reference object.
  • step S101' while obtaining the apparent diffusion coefficient of the sample fruit, the apparent diffusion coefficient of the first reference object is also obtained.
  • the first reference object can be placed near the sample fruit. It is understandable that because the first reference object and the sample fruit are in the same environment, it can be considered that the temperatures of the two are the same.
  • Step S102' is similar to step S102, determining the effective apparent diffusion coefficient of the sample fruit. Because the first reference object may be a substance with uniform apparent diffusion coefficient, it may not be necessary to perform the operation of step S102' on it.
  • Step S103' is similar to step S103, and will not be repeated for the sake of brevity.
  • step S104' the second sweetness detection model is established.
  • the influence of temperature is also considered, and the first sweetness detection model is also considered.
  • the apparent diffusion coefficient of the reference object is taken into consideration.
  • the second sweetness detection model represents the relationship between the sweetness of the fruit and the effective apparent diffusion coefficient of the fruit and the apparent diffusion coefficient of the reference object.
  • step S110 when the apparent diffusion coefficient of the fruit to be detected is not damaged, the apparent diffusion coefficient of the second reference object is also obtained.
  • the second reference object is the same substance as the aforementioned first reference object. In this way, errors in detection results caused by different reference objects can be avoided.
  • the second reference object may be placed near the fruit to be detected. When imaging multiple fruits to be detected, the second reference object can be placed in the middle of the fruits to be detected. Similarly, if the second reference object and the fruit to be tested are in the same environment, it can be considered that the temperatures of the two are the same.
  • the second sweetness detection model is used to determine the sweetness of the fruit to be detected. This process is not only based on the effective apparent diffusion coefficient of the fruit to be detected but also the apparent diffusion coefficient of the second reference object.
  • the apparent diffusion coefficient of the reference object is used to offset the influence of temperature on the sweetness detection, and a more accurate sweetness detection result is obtained.
  • the diffusion coefficient, a2 and b2 are parameters in the second sweetness detection model.
  • the second sweetness detection model introduces the apparent diffusion coefficient of the reference object, not only the influence of temperature difference on the detection result is avoided, but the model is simple and the calculation is easy.
  • the above-mentioned reference material are all aqueous solutions with uniform apparent diffusion coefficient.
  • the reference object can be contained in a container made of non-metallic substances, such as a plexiglass container. Since the reference object has the uniformity of apparent diffusion coefficient, its apparent diffusion coefficient can be simply used for subsequent operations without further operations.
  • the reference substance can be a homogeneous solution of any substance. Since magnetic resonance diffusion-weighted imaging is to image the dispersion phenomenon of water molecules, the reference object may be water. The uniformity of the apparent dispersion coefficient of water is the highest, and the cost is low and pollution-free.
  • a device for detecting the sweetness of fruits is also provided.
  • Fig. 4 shows a schematic block diagram of an apparatus 400 for detecting fruit sweetness according to an embodiment of the present invention.
  • the device 400 for detecting the sweetness of fruit includes an imaging module 410, a calculation module 420, and a sweetness determination module 430.
  • the imaging module 410 is used to obtain the apparent diffusion coefficient of the fruit to be detected without damage through magnetic resonance diffusion weighted imaging;
  • the calculation module 420 is configured to determine the effective apparent diffusion coefficient of the fruit to be detected according to the apparent diffusion coefficient of the fruit to be detected;
  • the sweetness determination module 430 is configured to determine the sweetness of the fruit to be detected according to the effective apparent diffusion coefficient.
  • each module in the device 400 for detecting the sweetness of fruits is used to specifically execute the corresponding steps in the method for detecting the sweetness of fruits.
  • a detection system for detecting fruit sweetness including a processor and a memory, wherein the memory stores the detection system for detecting fruit sweetness according to an embodiment of the present invention.
  • the processor is used to run computer program instructions stored in the memory to execute the corresponding steps of the method for detecting fruit sweetness according to the embodiment of the present invention, and to implement the device for detecting fruit sweetness according to the embodiment of the present invention.
  • a storage medium on which program instructions are stored, and when the program instructions are run by a computer or processor, the computer or processor is caused to execute the present invention.
  • the corresponding steps of the detection method for detecting fruit sweetness of the embodiment are used to implement the corresponding modules in the device for detecting fruit sweetness according to the embodiment of the present invention.
  • the storage medium may include, for example, the storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), USB memory, or any combination of the above storage media.
  • the computer-readable storage medium may be any combination of one or more computer-readable storage media.
  • the disclosed device and method may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or It can be integrated into another device, or some features can be ignored or not implemented.
  • the various component embodiments of the present invention may be implemented by hardware, or by software modules running on one or more processors, or by a combination of them.
  • a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules in the device for detecting fruit sweetness according to embodiments of the present invention.
  • DSP digital signal processor
  • the present invention can also be implemented as a device program (for example, a computer program and a computer program product) for executing part or all of the methods described herein.
  • Such a program for realizing the present invention may be stored on a computer-readable medium, or may have the form of one or more signals.
  • Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

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Abstract

一种检测水果甜度的方法（100）、装置（400）、系统及存储介质。该方法（100）包括：通过磁共振弥散加权成像，获取待检测水果无损的情况下的表观弥散系数（S110）；根据待检测水果的表观弥散系数确定待检测水果的有效表观弥散系数（S120）；以及根据有效表观弥散系数确定待检测水果的甜度（S130）。能够在待检测水果无损的情况下基于磁共振成像获取待检测水果表观弥散系数并利用表观弥散系数来确定水果的甜度，实现了无损且可靠的水果甜度检测。

Description

一种检测水果甜度的方法、装置、系统及存储介质 技术领域
本发明涉及食品检测领域，更具体地涉及一种检测水果甜度的方法、装置、系统及存储介质。
背景技术
甜度是衡量水果质量最重要的指标之一，其作为质量分类评级的重要参数一直被广泛关注。
目前已有的水果甜度的检测方法主要有有损化学检测和无损近红外光谱检测。有损化学检测方法会带来一部分水果产品的损耗，只能用于抽样性水果检测，无法满足大规模的产品检测需求。无损近红外光谱检测作为一种绿色无损检测手段而被广泛运用于水果甜度的评估分级，但仍存在以下不足：1)红外检测只能测定5毫米以内的果肉的甜度，无法对厚果皮水果以及水果内部进行甜度检测；2)为保证检测质量，传送带要保持低速且每次只能检测一个水果，无法满足高速检测需求。
因此，亟需一种新的检测水果甜度的技术，以解决上述问题。
发明内容
考虑到上述问题而提出了本发明。本发明提供了一种检测水果甜度的方法、装置、系统及存储介质。
根据本发明一方面，提供一种检测水果甜度的方法，包括：
通过磁共振弥散加权成像，获取待检测水果无损的情况下的表观弥散系数(ADC)；
根据所述待检测水果的表观弥散系数确定所述待检测水果的有效表观弥散系数；以及
根据所述有效表观弥散系数确定所述待检测水果的甜度。
示例性地，所述方法还包括：
通过磁共振弥散加权成像，获取样本水果无损的情况下的表观弥散系数；
根据所述样本水果的表观弥散系数确定所述样本水果的有效表观弥散系数；
检测所述样本水果的样本甜度；
根据所述样本水果的有效表观弥散系数和所述样本甜度建立第一甜度检测模型，其中所述第一甜度检测模型表示了甜度与有效表观弥散系数之间的关系；
其中，所述根据所述有效表观弥散系数确定所述待检测水果的甜度是利用所述第一甜度检测模型。
示例性地，所述第一甜度检测模型是Y＝a1*x+b1，其中，Y表示甜度，x表示有效表观弥散系数，a1和b1是所述第一甜度检测模型中的参数。
示例性地，所述方法还包括：
通过磁共振弥散加权成像，同时获取样本水果无损的情况下的表观弥散系数和第一参照物的表观弥散系数；
根据所述样本水果的表观弥散系数确定所述样本水果的有效表观弥散系数；
检测所述样本水果的样本甜度；
根据所述第一参照物的表观弥散系数、所述样本水果的有效表观弥散系数和所述样本甜度建立第二甜度检测模型，其中所述第二甜度检测模型表示了水果的甜度与水果的有效表观弥散系数和参照物的表观弥散系数这二者之间的关系；
其中，在所述获取待检测水果无损的情况下的表观弥散系数的同时，还获取第二参照物的表观弥散系数，所述第二参照物与所述第一参照物是同种物质，所述确定所述待检测水果的甜度还根据所述第二参照物的表观弥散系数并利用所述第二甜度检测模型。
示例性地，所述第二甜度检测模型是Y＝a2*x/x0+b2，其中，Y表示水果的甜度，x表示水果的有效表观弥散系数，x0表示参照物的表观弥散系数，a2和b2是所述第二甜度检测模型中的参数。
示例性地，所述参照物是水。
示例性地，所述表观弥散系数用表观弥散系数图像表示，所述根据所获取的表观弥散系数确定所述待检测水果的有效表观弥散系数包括：
对所述表观弥散系数图像进行图像分割，以确定包含所述待检测水果的果肉的感兴趣区域；以及
计算所述感兴趣区域内的像素的平均值，以作为所述有效表观弥散系数。
根据本发明另一方面，还提供了一种检测水果甜度的装置，包括：
成像模块，用于通过磁共振弥散加权成像，获取待检测水果无损的情况下的表观弥散系数；
计算模块，用于根据所述待检测水果的表观弥散系数确定所述待检测水果的有效表观弥散系数；
甜度确定模块，用于根据所述有效表观弥散系数确定所述待检测水果的甜度
根据本发明又一方面，还提供了一种检测水果甜度的系统，包括处理器和存储器，其中，所述存储器中存储有计算机程序指令，所述计算机程序指令被所述处理器运行时用于执行上述的检测水果甜度的方法。
根据本发明再一方面，还提供了一种存储介质，在所述存储介质上存储了程序指令，所述程序指令在运行时用于执行上述的检测水果甜度的方法。
根据本发明实施例的检测水果甜度方法、装置、系统及存储介质，在待检测水果无损的情况下，基于磁共振成像获取待检测水果表观弥散系数并利用表观弥散系数来确定水果的甜度，实现了无损且可靠的水果甜度检测。此外，该技术方案能够一次性准确地检测多个水果。
上述说明仅是本发明技术方案的概述，为了能够更清楚了解本发明的技术手段，而可依照说明书的内容予以实施，并且为了让本发明的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明的具体实施方式。
附图说明
通过结合附图对本发明实施例进行更详细的描述，本发明的上述以及其它目的、特征和优势将变得更加明显。附图用来提供对本发明实施例的进一步理解，并且构成说明书的一部分，与本发明实施例一起用于解释本发明，并不构成对本发明的限制。在附图中，相同的参考标号通常代表相同部件或步骤。
图1示出了根据本发明一个实施例的检测水果甜度的方法的示意流程图；
图2A、图2B、图2C和图2D分别示出了根据本发明一个实施例的水果的光学图像、水果的DW-MRI弥散敏感参数为0时的T2加权图像、水果的DW-MRI弥散敏感参数为较高值时的同向弥散加权DWI图像和水果的ADC图像；
图3示出了根据本发明一个实施例的根据样本水果的样本甜度与有效表观弥散系数拟合第一甜度检测模型的示意图；以及
图4示出了根据本发明一个实施例的检测水果甜度的装置的示意性框图。
具体实施方式
为了使得本发明的目的、技术方案和优点更为明显，下面将参照附图详细描述根据本发明的示例实施例。显然，所描述的实施例仅仅是本发明的一部分实施例，而不是本发明的全部实施例，应理解，本发明不受这里描述的示例实施例的限制。基于本发明中描述的本发明实施例，本领域技术人员在没有付出创造性劳动的情况下所得到的所有其它实施例都应落入本发明的保护范围之内。
为了解决上述问题，本发明提出了一种检测水果甜度的方法。该检测水果甜度的方法可以基于磁共振弥散加权成像(Diffusion weighted imaging，DWI)技术实现。目前，磁共振成像主要用于医疗诊断领域。常规磁共振成像中，射频脉冲用于激发成像目标中的磁化矢量。通过相位梯度编码和频率编码对磁化矢量进行空间位置信息标记并通过傅里叶变换进行图像重建。磁共振成像方法是一种无损的成像手段，其能够提供包括弥散在内的多种加权成像和高分辨率的目标内结构图。磁共振弥散加权成像是一种被广泛运用于研究物质内水分子扩散现象的磁共振成像技术。利用成像目标中水分子的扩散速度，可以计算出成像目标内部水分子弥散系数。发明人针对水果甜度的检测已经进行了大量实验测试，测试结果表明水果甜度与水果的表观弥散系数存在较强的相关性。
图1示出了根据本发明一个实施例的检测水果甜度的方法100的示意性流程图。
如图1所示，方法100包括步骤S110、步骤S120以及步骤S130。
步骤S110，通过磁共振弥散加权成像，获取待检测水果无损的情况下的表观弥散系数。
磁共振弥散加权成像的基础是水分子运动，其可以直接体现成像目标内部的水分子弥散现象的强弱。水果中的糖分子，无论是单糖分子、二糖分子还是多糖分子，会影响水果中水分子的运动。通常而言，糖分子会使得水分子的弥散速度显著下降。
表观弥散系数是可溶性物质通过渗透介质时弥散现象强弱的指标。弥散系 数与介质的结构、渗透途径的均匀程度、平均渗透流速、流体的物理化学性质有关。如前所述，水分子的弥散现象受水果中糖分子的影响，通常糖分子的浓度越大，弥散现象越弱。
可以理解，水果甜度主要由其中的糖分子来决定。
基于上述分析，可以在待检测水果无损的情况下，通过对水果进行磁共振弥散加权成像来确定其甜度。
图2A、图2B、图2C和图2D分别示出了根据本发明一个实施例的待检测水果(橘子)的光学图像、水果的DW-MRI弥散敏感参数为0时的T2加权图像、水果的DW-MRI弥散敏感参数为较高值时的同向弥散加权DWI图像和水果的ADC图像。
示例性地，可以通过待检测水果在磁场强度为0的条件下的磁共振成像(如图2B所示)与待检测水果在高磁场强度条件下的磁共振成像(如图2C所示)来获取表观弥散系数。具体地，可以根据两个图像中的对应像素值计算出待检测水果每个对应位置的表观弥散系数。
例如，通过公式(1)计算出待检测水果的表观弥散系数ADC。
ADC＝ln(S 0/S 1)/b                    (1)
其中，b表示同向弥散加权图像中的弥散敏感参数，S 0表示b＝0(无外加弥散磁场对)时磁共振图像的信号强度，S 1表示高磁场条件下的磁共振图像的信号强度。增加b值有利于检测更为细微的弥散现象。通过公式(1)可以利用磁共振弥散加权成像，点对点计算出待检测水果各个部分的表观弥散系数。该表观弥散系数可以用矩阵来表示。
替代地，也可以利用先进的磁共振成像设备直接对待检测水果进行成像，从而直接获得表观弥散系数图像，如图2D所示。表观弥散系数图像中不同的表观弥散系数用不同的像素值来表示。每一个像素的像素值都表示待检测水果对应位置的表观弥散系数。
在本步骤S110中，在待检测水果无损的情况下，对其进行成像，以获得其表观弥散系数。如上所述，待检测水果的各个部分的表观弥散系数是不同的，其与所检测的部分相关。例如，对于橘子来说，其果皮部分和中心的橘络聚集部分的表观弥散系数较小。而果肉部分表观弥散系数较大。
步骤S120，根据所述待检测水果的表观弥散系数确定所述待检测水果的有效表观弥散系数。
一个水果通常会有表皮、空腔、果核等不可食用的部分，其甜度不影响水果的质量，例如橘子的果皮部分等。此外，水果也可能在存储或运输过程中，发生腐烂等问题。水果的腐败部分也不应作为检测部分。这些部分的表观弥散系数不仅不具有考虑价值，而且还会给准确确定水果甜度带来负面影响。因此，在此步骤S120中，根据待检测水果的表观弥散系数确定其有效表观弥散系数。该有效表观弥散系数是单一数值。
示例性地，将待检测水果的表皮、空腔、果核等部位的表观弥散系数舍弃，余下的表观弥散系数取平均值，得到有效表观弥散系数。
步骤S130，根据所述有效表观弥散系数确定所述待检测水果的甜度。
如前所述，水果的甜度与其表观弥散系数存在着较为明显的相关性。经过大量试验，发现水果的固态的果肉组织对水果中水分子的弥散现象影响不十分显著。因此可以基于水果无损情况下所获得的表观弥散系数确定水果的甜度，而非必须将水果榨汁。待检测水果的有效表观弥散系数是基于无损情况下获取的表观弥散系数确定的，能够利用其与水果的甜度之间的映射关系确定水果的甜度。
上述检测水果甜度的方法，在待检测水果无损的情况下，基于磁共振成像获取待检测水果表观弥散系数并利用表观弥散系数来确定水果的甜度，实现了无损且可靠的水果甜度检测。
如前所述，步骤S110所获取的表观弥散系数可以用表观弥散系数图像表示。示例性地，上述步骤S120根据所获取的表观弥散系数确定有效表观弥散系数可以包括：首先，对所述表观弥散系数图像进行图像分割，以确定包含待检测水果的果肉的感兴趣区域。例如，可以用区域生长的图像分割方法，对表观弥散系数图像进行分割。对表观弥散图像的所有像素的像素值进行平均，以确定种子像素。基于种子像素，不断将其周围的像素以一定规则加入其中，达到最终将代表水果的有效部分的所有像素结合成一个区域的目的。则所获得区域即为期望的感兴趣区域。然后，计算感兴趣区域内的像素的平均值，以作为待检测水果的有效表观弥散系数。基于该有效表观弥散系数，能够更理想地确定水果的甜度。该技术方案有效利用了数据并且避免了干扰因素。
替代地，也可以对步骤S110所获取的所有表观弥散系数进行统计分析。基于统计分析结果所表明的表观弥散系数的概率分布，将概率高于预设的概率阈值的表观弥散系数进行平均，以将其均值作为有效表观弥散系数。
示例性地，根据有效表观弥散系数确定对应水果甜度的过程可以依靠甜度检测模型来实现。所述甜度检测模型表示了有效表观弥散系数和水果的甜度之间存在的数学关系，其可以用函数式来表达。可选地，上述检测水果甜度的方法还包括以下建立甜度检测模型的步骤。后续根据有效表观弥散系数确定待检测水果的甜度是利用甜度检测模型。
在一个示例中，建立甜度检测模型的包括以下步骤。
步骤S101，通过磁共振弥散加权成像，获取样本水果无损的情况下的表观弥散系数。
步骤S102，根据所述样本水果的表观弥散系数确定所述样本水果的有效表观弥散系数。
步骤S103，检测所述样本水果的样本甜度。
步骤S104，根据所述样本水果的有效表观弥散系数和所述样本甜度建立第一甜度检测模型，其中所述第一甜度检测模型表示了甜度与有效表观弥散系数之间的关系。
其中，样本水果与待检测水果是同一种水果，例如都是橘子。为了获得更准确的第一甜度检测模型，这里可以对多于预设个数阈值的样本水果进行检测。由此，所获得的第一甜度检测模型能够更有效地表示有效表观弥散系数与该种水果的甜度的普遍性关系。上述步骤S101和步骤S102的实现过程分别与上述步骤S110和步骤S120类似，为了简洁，在此不再赘述。步骤S103可以采用任何现有的水果甜度的检测方法。例如，采用有损的水果甜度检测方法，以便于准确获取样本水果的样本甜度。在步骤S104中，可以根据所有样本水果的步骤S102所确定的有效表观弥散系数与步骤S103样本甜度，在坐标系中确定样本点，并基于样本点进行拟合，以得到有效表观弥散系数与样本甜度的函数曲线，即所述第一甜度检测模型。
上述技术方案中，利用了样本水果确定了表示水果的甜度与有效表观弥散系数之间的关系的第一甜度检测模型。基于该甜度检测模型，能够准确地确定水果的甜度。
经过大量实验表明，有效表观弥散系数与样本甜度呈线性相关。第一甜度检测模型可以表示为Y＝a1*x+b1，其中，Y表示水果的甜度，x表示水果的有效表观弥散系数，a1和b1是第一甜度检测模型中的参数。
图3示出了根据本发明一个实施例的根据样本水果的样本甜度与有效表观 弥散系数拟合第一甜度检测模型的示意图。图3所示出的第一甜度检测模型涉及多种水果，包括葡萄、橙子、梨。图3还示出了根据糖水建立的第一甜度检测模型。图3中可溶性固形物指糖分，可以看出样本水果的甜度与有效表观弥散系数呈线性负相关，并且不同种类的样本水果的第一甜度检测模型曲线斜率差异并不大。
可以理解，水果的甜度越高，糖溶液的宏观表现越粘稠，其微观解释为水分子弥散受到了更大的限制，即表观弥散系数越低。所以样本水果的甜度与有效表观弥散系数呈线性负相关，参数a1为负数。由于各个水果样本中影响水分子弥散的主要成分都是糖，所以不同种类的样本水果的第一甜度检测模型曲线斜率差异不大，各个样本第一甜度检测模型中a1的值差异较小。
示例性地，可以依据上述理论，根据经验设置参数a1和b1，以省略上述建立第一甜度检测模型的繁复操作。
众所周知，温度会使分子的运动速度加快，导致水分子弥散的速度也相应的提高。比如：在冷水中滴一滴墨水和在开水中滴一滴墨水，开水中的墨水扩散速度大于冷水中的墨水扩散速度。这就是因为开水中的水分子运动速度大于冷水中的水分子运动速度。显然，根据第一甜度检测模型的检测结果忽略了温度给表观弥散系数带来的差异。为了避免温度差异对检测结果的准确性带来的影响，可以根据以下步骤建立第二甜度检测模型。
步骤S101’，通过磁共振弥散加权成像，同时获取样本水果无损的情况下的表观弥散系数和第一参照物的表观弥散系数。
步骤S102’，根据所述样本水果的表观弥散系数确定所述样本水果的有效表观弥散系数。
步骤S103’，检测所述样本水果的样本甜度。
步骤S104’，根据所述第一参照物的表观弥散系数、所述样本水果的有效表观弥散系数和所述样本甜度建立第二甜度检测模型，其中所述第二甜度检测模型表示了水果的甜度与水果的有效表观弥散系数和参照物的表观弥散系数这二者之间的关系。
上述步骤S101’中，在获取样本水果的表观弥散系数的同时，还获取了第一参照物的表观弥散系数。在磁共振弥散加权成像时，可以将第一参照物放置于样本水果的附近。可以理解，因为第一参照物与样本水果处于同样的环境，可以认为二者的温度是一致的。步骤S102’与步骤S102类似，确定样本水果 的有效表观弥散系数。因为第一参照物可以是具有表观弥散系数均一性的物质，所以可以无需对其执行步骤S102’的操作。步骤S103’与步骤S103类似，为了简洁，不再赘述。步骤S104’中，建立第二甜度检测模型，除了与建立第一甜度检测模型类似地依据样本水果的有效表观弥散系数和样本甜度之外，还考虑了温度的影响，将第一参照物的表观弥散系数纳入考虑。由此，该第二甜度检测模型表示了水果的甜度与水果的有效表观弥散系数和参照物的表观弥散系数这二者之间的关系。
在该技术方案中，在步骤S110获取待检测水果无损的情况下的表观弥散系数的同时，还获取第二参照物的表观弥散系数。所述第二参照物与前述第一参照物是同种物质。由此可以避免参照物不同带来的检测结果错误。在步骤S110磁共振成像时，可以将第二参照物放在待检测水果附近。当对多个待检测水果成像时，可以将第二参照物放在待检测水果中间。类似地，该第二参照物与待检测水果处于同样的环境，可以认为二者的温度是一致的。步骤S130确定待检测水果的甜度是利用第二甜度检测模型，该过程不仅根据待检测水果的有效表观弥散系数还根据第二参照物的表观弥散系数。
上述技术方案中，通过参照物的表观弥散系数，抵消了温度对甜度检测的影响，获得了更准确的甜度检测结果。
示例性地，所述第二甜度检测模型为：Y＝a2*x/x0+b2，其中，Y表示水果的甜度，x表示水果的有效表观弥散系数，x0表示参照物的表观弥散系数，a2和b2是所述第二甜度检测模型中的参数。
所述第二甜度检测模型由于引入了参照物的表观弥散系数，不仅避免了温度差异对检测结果带来的影响，而且模型简单，计算容易。
可选地，上述参照物(包括第一参照物和第二参照物)都是具有表观弥散系数均一性的水溶液。参照物可以利用非金属物质制成的容器盛放，例如有机玻璃容器。由于参照物具有表观弥散系数均一性，可以简单地利用其表观弥散系数进行后续操作，而无需进一步的操作。所述参照物可以是任何物质的均匀溶液。由于磁共振弥散加权成像是对水分子的弥散现象进行成像，所述参照物可以是水。水的表观弥散系数的均一性最高，而且成本低、无污染。
根据本发明另一个实施例，还提供了一种用于检测水果甜度的装置。图4示出了根据本发明一个实施例的用于检测水果甜度的装置400的示意性框图。如图4所示，用于检测水果甜度的装置400包括成像模块410、计算模块420 以及甜度确定模块430。
成像模块410用于通过磁共振弥散加权成像，获取待检测水果无损的情况下的表观弥散系数；
计算模块420用于根据所述待检测水果的表观弥散系数确定所述待检测水果的有效表观弥散系数；
甜度确定模块430用于根据所述有效表观弥散系数确定所述待检测水果的甜度。
总之，用于检测水果甜度的装置400中的各个模块用于具体执行上述检测水果甜度的方法中的相应步骤。通过阅读上述关于该方法的描述，本领域普通技术人员可以理解上述用于检测水果甜度的装置400的具体实现和技术效果。
根据本发明又一方面，还提供了一种用于检测水果甜度的检测系统，包括处理器和存储器，其中，所述存储器中存储用于实现根据本发明实施例的检测水果甜度的检测方法中的各个步骤的计算机程序指令。所述处理器用于运行所述存储器中存储的计算机程序指令，以执行根据本发明实施例的检测水果甜度的方法的相应步骤，并且用于实现根据本发明实施例的检测水果甜度的装置中的成像模块410、计算模块420以及甜度确定模块430。
此外，根据本发明再一方面，还提供了一种存储介质，在所述存储介质上存储了程序指令，在所述程序指令被计算机或处理器运行时使得所述计算机或处理器执行本发明实施例的检测水果甜度的检测方法的相应步骤，并且用于实现根据本发明实施例的用于检测水果甜度的装置中的相应模块。所述存储介质例如可以包括平板电脑的存储部件、个人计算机的硬盘、只读存储器(ROM)、可擦除可编程只读存储器(EPROM)、便携式紧致盘只读存储器(CD-ROM)、USB存储器、或者上述存储介质的任意组合。所述计算机可读存储介质可以是一个或多个计算机可读存储介质的任意组合。
尽管这里已经参考附图描述了示例实施例，应理解上述示例实施例仅仅是示例性的，并且不意图将本发明的范围限制于此。本领域普通技术人员可以在其中进行各种改变和修改，而不偏离本发明的范围和精神。所有这些改变和修改意在被包括在所附权利要求所要求的本发明的范围之内。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用 和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
在本申请所提供的几个实施例中，应该理解到，所揭露的设备和方法，可以通过其它的方式实现。例如，以上所描述的设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个设备，或一些特征可以忽略，或不执行。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本发明的实施例可以在没有这些具体细节的情况下实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本发明并帮助理解各个发明方面中的一个或多个，在对本发明的示例性实施例的描述中，本发明的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该本发明的方法解释成反映如下意图：即所要求保护的本发明要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如相应的权利要求书所反映的那样，其发明点在于可以用少于某个公开的单个实施例的所有特征的特征来解决相应的技术问题。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本发明的单独实施例。
本领域的技术人员可以理解，除了特征之间相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如，在权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本发明的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本发明实 施例的检测水果甜度的装置中的一些模块的一些或者全部功能。本发明还可以实现为用于执行这里所描述的方法的一部分或者全部的装置程序(例如，计算机程序和计算机程序产品)。这样的实现本发明的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
应该注意的是上述实施例对本发明进行说明而不是对本发明进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包含”不排除存在未列在权利要求中的元件或步骤。位于元件之前的单词“一”或“一个”不排除存在多个这样的元件。本发明可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
以上所述，仅为本发明的具体实施方式或对具体实施方式的说明，本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。本发明的保护范围应以权利要求的保护范围为准。

Claims (10)

  1. 一种检测水果甜度的方法，其特征在于，包括：
    通过磁共振弥散加权成像，获取待检测水果无损的情况下的表观弥散系数；
    根据所述待检测水果的表观弥散系数确定所述待检测水果的有效表观弥散系数；以及
    根据所述有效表观弥散系数确定所述待检测水果的甜度。
  2. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    通过磁共振弥散加权成像，获取样本水果无损的情况下的表观弥散系数；
    根据所述样本水果的表观弥散系数确定所述样本水果的有效表观弥散系数；
    检测所述样本水果的样本甜度；
    根据所述样本水果的有效表观弥散系数和所述样本甜度建立第一甜度检测模型，其中所述第一甜度检测模型表示了甜度与有效表观弥散系数之间的关系；
    其中，所述根据所述有效表观弥散系数确定所述待检测水果的甜度是利用所述第一甜度检测模型。
  3. 根据权利要求2所述的方法，其特征在于，所述第一甜度检测模型是Y＝a1*x+b1，其中，Y表示甜度，x表示有效表观弥散系数，a1和b1是所述第一甜度检测模型中的参数。
  4. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    通过磁共振弥散加权成像，同时获取样本水果无损的情况下的表观弥散系数和第一参照物的表观弥散系数；
    根据所述样本水果的表观弥散系数确定所述样本水果的有效表观弥散系数；
    检测所述样本水果的样本甜度；
    根据所述第一参照物的表观弥散系数、所述样本水果的有效表观弥散系数和所述样本甜度建立第二甜度检测模型，其中所述第二甜度检测模型表示了水果的甜度与水果的有效表观弥散系数和参照物的表观弥散系数这二者之间的关系；
    其中，在所述获取待检测水果无损的情况下的表观弥散系数的同时，还获取第二参照物的表观弥散系数，所述第二参照物与所述第一参照物是同种物质， 所述确定所述待检测水果的甜度还根据所述第二参照物的表观弥散系数并利用所述第二甜度检测模型。
  5. 根据权利要求4所述的方法，其特征在于，所述第二甜度检测模型是Y＝a2*x/x0+b2，其中，Y表示水果的甜度，x表示水果的有效表观弥散系数，x0表示参照物的表观弥散系数，a2和b2是所述第二甜度检测模型中的参数。
  6. 根据权利要求4或5所述的方法，其特征在于，所述参照物是水。
  7. 根据权利要求1所述的方法，其特征在于，所述表观弥散系数用表观弥散系数图像表示，所述根据所获取的表观弥散系数确定所述待检测水果的有效表观弥散系数包括：
    对所述表观弥散系数图像进行图像分割，以确定包含所述待检测水果的果肉的感兴趣区域；以及
    计算所述感兴趣区域内的像素的平均值，以作为所述有效表观弥散系数。
  8. 一种检测水果甜度的装置，包括：
    成像模块，用于通过磁共振弥散加权成像，获取待检测水果无损的情况下的表观弥散系数；
    计算模块，用于根据所述待检测水果的表观弥散系数确定所述待检测水果的有效表观弥散系数；
    甜度确定模块，用于根据所述有效表观弥散系数确定所述待检测水果的甜度
  9. 一种检测水果甜度的系统，包括处理器和存储器，其中，所述存储器中存储有计算机程序指令，所述计算机程序指令被所述处理器运行时用于执行如权利要求1至7任一项所述的检测水果甜度的方法。
  10. 一种存储介质，在所述存储介质上存储了程序指令，所述程序指令在运行时用于执行如权利要求1至7任一项所述的检测水果甜度的方法。
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CN107949325A (zh) * 2014-12-26 2018-04-20 东芝医疗系统株式会社 磁共振成像装置、扩散加权图像的生成方法以及图像处理装置
CN108369196A (zh) * 2015-10-12 2018-08-03 Mm(英国)有限公司 食品特性mri检测系统
CN107860722A (zh) * 2017-10-30 2018-03-30 内蒙古农业大学 一种蜜瓜内部品质在线检测方法及系统
CN111366600A (zh) * 2019-11-08 2020-07-03 宁波诺丁汉大学 一种检测水果甜度的方法、装置、系统及存储介质

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