WO2020252999A1 - 一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法 - Google Patents

一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法 Download PDF

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WO2020252999A1
WO2020252999A1 PCT/CN2019/111533 CN2019111533W WO2020252999A1 WO 2020252999 A1 WO2020252999 A1 WO 2020252999A1 CN 2019111533 W CN2019111533 W CN 2019111533W WO 2020252999 A1 WO2020252999 A1 WO 2020252999A1
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fruit
peak
distribution
water
cell
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French (fr)
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孙大文
李冬梅
朱志伟
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华南理工大学
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Publication of WO2020252999A1 publication Critical patent/WO2020252999A1/zh

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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65Raman scattering
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84Systems specially adapted for particular applications
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0098Plants or trees
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02Food
    • G01N33/025Fruits or vegetables
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/0004Microscopes specially adapted for specific applications
    • G02B21/002Scanning microscopes
    • G02B21/0024Confocal scanning microscopes (CSOMs) or confocal "macroscopes"; Accessories which are not restricted to use with CSOMs, e.g. sample holders
    • G02B21/0052Optical details of the image generation
    • G02B21/0064Optical details of the image generation multi-spectral or wavelength-selective arrangements, e.g. wavelength fan-out, chromatic profiling
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/0004Microscopes specially adapted for specific applications
    • G02B21/002Scanning microscopes
    • G02B21/0024Confocal scanning microscopes (CSOMs) or confocal "macroscopes"; Accessories which are not restricted to use with CSOMs, e.g. sample holders
    • G02B21/008Details of detection or image processing, including general computer control
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28Investigating the spectrum
    • G01J3/44Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
    • G01J2003/4424Fluorescence correction for Raman spectrometry
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N2021/8466Investigation of vegetal material, e.g. leaves, plants, fruits
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00Features of devices classified in G01N21/00
    • G01N2201/06Illumination; Optics
    • G01N2201/061Sources
    • G01N2201/06113Coherent sources; lasers
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00Features of devices classified in G01N21/00
    • G01N2201/12Circuits of general importance; Signal processing
    • G01N2201/129Using chemometrical methods

Definitions

  • the invention belongs to the technical field of spectral detection, and specifically relates to a method for testing the cell-level moisture content and distribution in fruit and vegetable tissues based on Raman spectroscopy.
  • Malinchi experiment method In order to accurately measure the content and distribution of water in different binding states in fruits and vegetables, the usual methods are Malinchi experiment method, differential scanning calorimetry, differential thermal analysis method, bioimpedance analysis method and low-field nuclear magnetic resonance response. law. Each of these methods has advantages and disadvantages.
  • the Malinchi experiment method, differential scanning calorimetry and differential thermal analysis methods can all determine the content of bound water and free water in plant tissues, but these methods can only measure the water content into two types, namely bound water and free water.
  • the bioimpedance analysis method can determine the content of intracellular water and extracellular water, but It is impossible to evaluate the degree of binding of intracellular water and extracellular water;
  • the low-field nuclear magnetic resonance method is currently the most convenient and quick method to determine free water, non-flowing water, and bound water. It can be based on the T 2 relaxation time of the water in the sample. To judge the degree of binding between water and other substances, and finally divide the water in the sample into free water (the longest relaxation time), non-flowing water, and bound water (the shortest relaxation time) according to the length of relaxation time.
  • the purpose of the present invention is to provide a method for testing the cell-level moisture content and distribution in fruit and vegetable tissues based on Raman spectroscopy.
  • This method can visually image and quantify the distribution and content of water at the cell level in fruit and vegetable tissues, so as to accurately obtain the location information of water in different states of fruit and vegetable tissues (ie, free water, difficult-to-flow water, and bound water). Content information.
  • a method based on Raman spectroscopy to test the cell-level moisture content and distribution in fruit and vegetable tissues including the following steps:
  • the cell area is selected from the sample through the confocal microscope objective lens, and then the selected cell area is meshed to obtain a uniform distribution Intersection point, mark the corresponding coordinate information of each intersection point in the selected cell area, scan each intersection point, and obtain the Raman spectrum of the corresponding moisture at each intersection point in the cell area;
  • the grid step size is 3 ⁇ 5 ⁇ m
  • step (3) The Raman spectrum obtained in step (2) is processed for smoothing noise reduction and fluorescence background removal, and then performing Gaussian peak fitting. For each Raman spectrum, 5 sub-peaks and 5 sub-peaks located at 3000-3800 cm -1 are obtained. 2 to 3 sub-peaks located at 2700 ⁇ 3000cm -1 ;
  • the fruits and vegetables in step (1) are preferably one of apples, potatoes, grapes, pears and cabbage stems.
  • Step (1) The sample is preferably stored in a quartz cavity before and during the test.
  • the cavity temperature of the quartz cavity is 2-10°C
  • the humidity is greater than 80%
  • the cavity thickness of the quartz cavity is 0.3 mm quartz cover glass is sealed.
  • the purpose is to make the moisture evaporation of the sample in the environment during the test almost negligible, so as to maintain the stability of the moisture content of the sample during the test.
  • step (2) the cell area size is determined by the cell size, and the average diameter of conventional fruit and vegetable cells is between 100 and 300 ⁇ m.
  • the depth of the imaging spectrum collection in step (2) is preferably 50-100 ⁇ m, which can minimize the influence of the slicing process on the moisture content in the tissue cells, and can also penetrate deep into the cells to measure the distribution of moisture inside the cells.
  • Step (2) The laser used for imaging spectrum acquisition is preferably a 532nm laser, wherein the size of the grating is 600gr/mm, the Hole is 500, and the scanning range is 2700-3800cm -1 ; the acquisition conditions are preferably: acquisition time 3-5s , The cumulative number of times is 2 to 3 times, the attenuation of laser energy is 25-50%.
  • the multiple of the objective lens in step (2) is preferably 10 times.
  • Step (3) The smoothing and noise reduction processing preferably adopts the smooth function in the matlab software, and the Savitzky-golay convolution smoothing algorithm is selected; the fluorescent background removal processing preferably adopts the adaptive iterative weighted penalty least squares background subtraction algorithm (airPLS). algorithm).
  • airPLS adaptive iterative weighted penalty least squares background subtraction algorithm
  • the method of Gaussian peak fitting in step (3) is preferably performed by matlab software using the peakfit function.
  • the peakfit function is a nonlinear iterative curve fitting function using Gaussian equation as the peak shape, and the maximum fitting determination coefficient can be obtained by adjusting the number of iterations, peak shape and peak position.
  • the Gaussian sub-peak fitting is preferably a fixed-peak position Gaussian sub-peak fitting, that is, a fixed-peak position Gaussian iterative curve fitting algorithm is used to perform Gaussian curve fitting on the spectrum with a fixed peak position; its peak position
  • the method of determination is as follows: randomly select 50 Raman spectra, use peakfit software to perform iterative peak splitting processing on them, divide each Raman spectrum into 7-8 sub-peaks, and then divide the sub-peaks obtained from 50 Raman spectra The position information is averaged to obtain the average peak position information of 7 to 8 sub-peaks, which is used as the basis for the peak position of the Gaussian sub-peaks.
  • the peakfit software is more preferably peakfit v4.12 software.
  • Step (3) located at 2700 ⁇ 3000cm -1 2 to 3 sub CH- stretching vibration peak of carbohydrate; a ⁇ at 3000 3800cm -1 peak, five sub-hydrogen bonds of water molecules in the OH- The five sub-peaks of the resulting stretching vibration peak correspond to water molecules with different hydrogen bonds.
  • the Gaussian peak fitting can also obtain the coefficient of determination of the peak fitting and the relative error between the fitted spectrum and the original spectrum.
  • the sum of the peak areas of the five sub-peaks located at 3000 ⁇ 3800cm -1 in step (4) represents the content of corresponding water molecules at the intersection of step (2), that is, the content of moisture, so the moisture at the intersection can be characterized Content; the ratio R of the area of the sub-peak with the center at 3410 ⁇ 3440cm -1 and the area of the sub-peak with the center at 3200 ⁇ 3220cm -1 , which can reflect the hydrogen bonding of water molecules and further reflect the combination of water molecules Degree, where the smaller the ratio, the greater the degree of water molecule binding, the greater the ratio, the higher the degree of freedom of water molecules.
  • the pseudo-color imaging in step (5) is preferably performed by matlab software, using pcolor and colormap function methods, wherein the shading method is shading interp, and the pseudo-color map with the water content A as the pixel point realizes the visualization of the moisture content at the cell level , The pseudo-color map with the ratio R as the pixel points realizes the visualization of the cell level in the water-bound state.
  • the high resolution characteristics of Raman spectroscopy in the present invention can capture subtle changes in intermolecular or intramolecular hydrogen bonds.
  • the subtle changes in hydrogen bonds are easily affected by the external environment, such as temperature, pressure, ion concentration, and space size. Used for the study of water molecular structure.
  • Raman spectrum is a water molecule having two distinct broad peak protrusions, two protrusions are located near the center of 3240cm -1 and 3440cm -1, both closely linked to changes in the relative size of the projections hydrogen bond when the water molecules hydrogen bonded reinforcing projections located near the center of 3240cm -1 Raman relative intensity increases, and when hydrogen bonded water molecules is reduced, the raised central near 3440cm -1 Raman relative intensity Increase; in the formation of hydrogen bonds, water molecules can act as both electron donors (D) and electron acceptors (A).
  • the Raman spectrum of water at 293K and 0.1MPa can be divided into 5 sub-peaks by Gaussian peak fitting algorithm, the centers of which are located at 3041cm -1 , 3220cm -1 , 3430cm -1 , 3572cm -1 , 3636cm -1 .
  • these five sub-peaks are defined as DAA (two electron acceptors, one electron donor) and DDAA (two electron donors).
  • DAA two electron acceptors
  • DA one electron donor, one electron acceptor
  • DDA two electron donors, one electron acceptor
  • the ratio of the peak area of the DA sub-peak and the DDAA sub-peak can characterize the degree of hydrogen bonding around the water molecule.
  • the hydrogen bonding of the water molecules makes the water molecules have a higher degree of binding; on the contrary, the water molecules have a higher degree of freedom. Therefore, the ratio R of DA/DDAA can be used to characterize the binding state of water to determine whether it is free water or bound water.
  • the water in fruits and vegetables mainly exists between and within cells, and interacts with other substances in the cells (including metal ions, small biological molecules and biological macromolecules) to form hydrogen bonds.
  • the strength of the hydrogen bonds also affects the moisture content.
  • the state of existence in tissue cells Using the high-resolution feature of confocal Raman microscopy, it can capture the changes in the hydrogen bond at the cellular level, thereby judging the presence of water in the tissue based on the strength of the hydrogen bond, and detecting the water molecules based on the strength of the water molecule signal The content is determined.
  • the present invention has the following advantages and beneficial effects:
  • the present invention uses confocal Raman microscopy imaging technology combined with mathematical calculations to provide a method for testing the moisture content of different states in fruit and vegetable tissues, and for the first time realizes the visualization of cell-level moisture content and moisture status, which can be more intuitive Understanding the state and location of water in tissue cells provides a research basis for studying the migration of water during the processing of fruits and vegetables and the impact of water migration on tissue structure.
  • the airPLS algorithm used in this application can not only accurately fit the Raman spectrum background, but also has a great advantage in computing speed. Due to the large number of spectra to be processed, it can save spectra to a greater extent Time of preprocessing.
  • Figure 1 shows the hydrogen bond connection of water molecules.
  • Figure 2 shows the Raman spectra of water molecules with different hydrogen bonding modes under the conditions of 293K and 0.1MPa and their sub-peak spectra.
  • Fig. 3 is a view of the complete field of view of an optical microscope under a 10-fold objective lens at a depth of 50 ⁇ m in the test apple tissue of Example 1 of the present invention.
  • Fig. 4 is an optical microscope image of a single cell in the test area at a depth of 50 ⁇ m in the apple microstructure measured in Example 1 of the present invention (it is an enlarged view of the central test area in Fig. 3).
  • Fig. 5 is all the original Raman spectra obtained after scanning each site in the test apple tissue cells in Example 1 of the present invention.
  • Fig. 6 is a spectrum diagram (apple tissue) obtained after smoothing and removing the fluorescent background of a certain original spectrum in Example 1 of the present invention.
  • Fig. 7 is a graph of sub-peaks and total peaks (apple tissue) of a certain spectrum after pre-processing in Example 1 of the present invention after peak position and peak splitting are performed.
  • Fig. 8 is a distribution diagram (apple tissue) of the degree of fit (number of spectra) of the Gaussian sub-peak fitting of the pre-processed spectrum in Example 1 of the present invention.
  • Fig. 9 is a histogram (apple tissue) of the degree of fit (coefficient of determination) of the Gaussian peak fitting of the pre-processed spectrum in Example 1 of the present invention.
  • Fig. 10 is a distribution diagram of water content at the cell level of apple tissue in Example 1 of the present invention.
  • Fig. 11 is a diagram showing the position distribution of water in different states of apple tissue cell level in Example 1 of the present invention.
  • Figure 12 shows the inversion result of Apple tissue using the NMR method.
  • a method for measuring the moisture content and state distribution in plant tissues by using Raman spectroscopy including the following steps:
  • the 10 ⁇ objective lens of the laser confocal Raman microscope to align the sample, adjust the focal length to obtain a clear microscopic image of the tissue structure, and select the cell with a complete structure as the measurement area.
  • the area size is 200 ⁇ 200 ⁇ m
  • the selected area is meshed with a step length of 5 ⁇ m, so a total of 1681 intersection points are generated.
  • the light source of laser confocal Raman microscopy is a point light source. When scanning, the intersection of the light source extends the grid from top to bottom, and from left to right.
  • Each intersection will generate a Raman spectrum, so each spectrum is in The measurement area has corresponding coordinate information; when the spectrum is collected, the scan spectrum range is set to 2700 ⁇ 3800cm -1 , 532nm laser is selected, the grating is 600gr/mm, the Hole is 500, the laser attenuation is 25%, and the acquisition time is 3s, accumulation The number of times is 3 times, and the measuring depth is set to 50 ⁇ m. The time required to complete all scans is about 3h; all the original Raman spectra collected are shown in Figure 5.
  • the smoothing processing uses the smooth function in the matlab software, selects Savitzky-golay as the smoothing algorithm, and calculates each smoothed spectrum.
  • the number of points used by the element is set to 15, and the degree of the polynomial in the Savitzky-golay algorithm is 1.
  • the use of airPLS to remove the fluorescent background is an adaptive iterative weighted penalty least squares algorithm, which can well remove the fluorescent background of the spectrum.
  • the adjustment parameter ⁇ in the airPLS algorithm is 10e12
  • the order of the difference penalty is 3
  • the weight anomaly ratio at the beginning and end of the spectrum is set to 0.08
  • the asymmetry at the beginning and end The parameter is 50 and the maximum number of iterations is set to 10.
  • Figure 6 shows the pre-processed spectrum of a certain original Raman spectrum after smoothing and removing the fluorescent background.
  • the method of fixed peak position and split peak fitting is used to perform peak fitting for all spectra.
  • the method to determine the peak position is: randomly select 50 pre-processed Raman spectra, use peakfit v4.12 (Seasolve software Inc.) software to iteratively split the peaks, and determine the peak fit based on the peak fitting determination coefficient and the number of iterations.
  • Seven sub-peaks can be obtained in the spectral range of 2700 ⁇ 3800cm -1 , among which the five sub-peaks in the spectral range of 3000 ⁇ 3800cm -1 are the stretching vibration peaks of water molecules or OH groups; the peak positions of the seven sub-peaks of 50 preselected spectra After averaging, the information obtained is the peak position information of the fixed peak position and the peak position; the matlab software uses the peakfit function to perform peak separation processing on all pre-processed spectra according to the peak position information obtained to obtain all sub-peaks after the spectral peaks are separated The peak position, peak height, peak width and peak area information can be obtained. At the same time, the coefficient of determination of the peak fitting of each spectrum, the relative error of the fitted spectrum and the original spectrum, and the fitting residual can be obtained; The results of Mann spectroscopy peaks are shown in Figure 7 and Table 1.
  • Table 1 The peak position, peak height, peak width and peak area data of the sub-peaks after a certain Raman spectrum is split
  • the NMR test method is as follows:
  • the peak of the T 2 relaxation peak in the range of 100 ⁇ 1000ms in the inversion result is considered as the peak of free water in the sample, and the percentage corresponding to the peak area is the percentage of free water; the T 2 relaxation peak is in the range of 10 ⁇ 100ms
  • the peak of is considered to be the peak of non-flowing water, and the percentage corresponding to its peak area is the percentage of non-flowing water; the peak with T 2 relaxation peak in the range of 0-10ms is considered as the peak of bound water, and its peak area corresponds to the percentage That is the percentage corresponding to bound water; the result is shown in Figure 12.
  • Table 2 is a comparison of the bound water and free water content measured by the method of this application and the bound water and free water content measured by the Malinchico method and NMR.

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Abstract

一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法。包括样品的预处理、成像光谱的采集及其预处理、对成像光谱进行高斯分峰拟合并根据拟合结果进行伪彩成像、对细胞水平的水分含量及结合状态分布进行可视化并根据可视化图像。方法完成了对果蔬组织中细胞水平的水分含量及结合状态的分布的可视化,并根据可视化成像获得了相对可靠的不同结合状态水分含量量化分析结果,可以作为一种新型的果蔬组织中细胞水平水分状态含量的测试方法,解决了目前果蔬加工过程中细胞水平水分变化无法检测的难题,对于果蔬加工的研究具有较好的前景。

Description

一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法 技术领域
本发明属于光谱检测的技术领域,具体涉及一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法。
背景技术
果蔬作为人们日常生活不可缺少的食物之一,其水分含量高,但是由于其组织结构的不均匀性、多孔性、吸湿性等特点,水分在组织中的分布非常复杂。根据生物组织学的研究,我们通常认为果蔬中的大部分的水分分布在液泡、细胞质、细胞壁和胞外间隙中;而根据水分与组织中其他物质的结合程度,我们又将果蔬中的水分分为自由水、不易流动水和结合水,并且认为液泡和胞外间隙中的水为自由水,细胞质中的水为不易流动水,而细胞壁中的水为结合水。目前为了准确测量果蔬中不同结合状态水分的含量及分布情况,通常采用的方法有马林契可实验法、差式扫描量热法、差热分析法、生物阻抗分析法和低场核磁共振响应法。这些方法都各有优劣。马林契可实验法、差示扫描量热法和差热分析法都能够测定植物组织中结合水和自由水的含量,但是这些方法只能将测得的水分为两种即结合水和自由水,而无法更细致的区分出位于两者之间的不易流动水,并且无法区分这些水分布在细胞内还是细胞外;生物阻抗分析法可以测定出胞内水和胞外水的含量,但是无法对胞内水和胞外水的结合度进行评价;低场核磁共振法是目前测定自由水、不易流动水、结合水最方便快捷的方法,其可以根据样品中水的T 2弛豫时间的长短来判断水与其他物质的结合程度,并最终将样品中的水分根据弛豫时间的长短分为自由水(弛豫时间最长)、不易流动水、结合水(弛豫时间最短),并根据三者间的面积比确定样品中不 同状态水分含量的比值,但是该方法同样无法提供不同状态水分的分布情况和位置信息,同时对于低场核磁共振响应法来说对样品中水分的含量做定量分析也是比较难的。因此,目前没有一种方法可以直接测定果蔬组织中细胞水平水分含量和水分分布情况。
发明内容
为解决现有技术的缺点和不足之处,本发明的目的在于提供一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法。
该方法能够直观地对果蔬组织中细胞水平的水分分布和含量状况进行成像和量化分析,从而准确地获得果蔬组织中不同状态水分(即自由水、不易流动水、结合水)的位置信息及其含量信息。
本发明目的通过以下技术方案实现:
一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,包括以下步骤:
(1)取待测果蔬切成样品;
(2)将样品置于激光共聚焦显微镜的载物台上进行成像光谱采集,具体是通过激光共聚焦显微镜物镜从样品中选取细胞区域,然后将所选细胞区域进行网格划分得到均匀分布的交点,标记每个交点在所选细胞区域中的相应坐标信息,对每个交点处进行扫描,得到细胞区域中各个交点处对应水分的拉曼光谱;
其中网格步长为3~5μm;
(3)将步骤(2)得到的拉曼光谱进行平滑降噪和去除荧光背景处理,然后进行高斯分峰拟合,每个拉曼光谱均得到位于3000~3800cm -1处的5个子峰和位于2700~3000cm -1处的2~3个子峰;
(4)将每个拉曼光谱分峰后位于3000~3800cm -1处的5个子峰的峰面积进行加和,得到细胞区域中该交点处对应水分含量A;根据中心位于3410~3440cm -1处的子峰峰面积与中心位于3200~3220cm -1处的子峰峰面积的比值R判断 该交点处对应水分子的结合状态;
(5)结合各个交点的坐标信息,分别以所有交点处对应水分含量A和比值R为像素点进行伪彩化成像,得到果蔬组织细胞水平的水分含量和水分结合状态分布状况。
步骤(1)所述果蔬优选为苹果、土豆、葡萄、梨和白菜茎中的一种。
步骤(1)所述样品为去皮的果蔬,样品的形状优选为圆片状,其尺寸根据测试前及测试过程中用于存放样品的仪器决定,优选为直径×厚度=12mm×2mm。
步骤(1)所述样品在测试前及测试过程中优选保存在石英腔中,所述石英腔的腔体温度为2~10℃,湿度大于80%,其中石英腔的腔体用厚度为0.3mm的石英盖玻片密封。目的在于使测试过程中样品在环境中的水分蒸发几乎可以忽略不计,从而可以保持测试过程中样品水分含量的稳定性。
步骤(2)所述细胞区域大小由细胞大小决定,常规果蔬细胞的平均直径在100~300μm之间。
步骤(2)所述成像光谱采集的深度优选为50~100μm,该深度可以最大限度降低切片过程对组织细胞中水分含量的影响,同时也能够深入细胞内部,测得细胞内部水分的分布。
步骤(2)所述成像光谱采集所用的激光器优选为532nm激光器,其中光栅的大小为600gr/mm,Hole为500,扫描范围为2700~3800cm -1;采集的条件优选为:采集时间3~5s,累积次数2~3次,激光能量的衰减为25~50%。
步骤(2)所述物镜的倍数优选为10倍。
步骤(3)所述平滑降噪处理优选采用matlab软件中的smooth函数,选择Savitzky-golay卷积平滑算法;所述去除荧光背景处理优选采用自适应迭代重加权惩罚最小二乘背景扣除算法(airPLS算法)。
步骤(3)所述高斯分峰拟合的方法优选为采用matlab软件运用peakfit函 数进行。
所述peakfit函数是一种以高斯方程为峰形的非线性迭代曲线拟合函数,可以通过调节迭代次数、峰形和峰位获得最大的拟合决定系数。
步骤(3)所述高斯分峰拟合优选为定峰位高斯分峰拟合,即采用定峰位高斯迭代曲线拟合算法,在固定峰位的情况下对光谱进行高斯曲线拟合;其峰位的确定方法如下:随机选取50个拉曼光谱,利用peakfit软件对其进行迭代分峰处理,将每个拉曼光谱分为7~8个子峰,然后将50个拉曼光谱得到的子峰峰位信息进行平均化,得到7~8个子峰的平均峰位信息,以此作为定峰位高斯分峰的峰位依据。
所述peakfit软件更优选为peakfit v4.12软件。
步骤(3)所述位于2700~3000cm -1处的2~3个子峰为碳水化合物的CH-伸缩振动峰;位于3000~3800cm -1处的5个子峰为水分子OH-在氢键作用下产生的伸缩振动峰,5个子峰分别对应具有不同氢键的水分子。
步骤(3)所述高斯分峰拟合还可得到分峰拟合的决定系数和拟合光谱与原光谱的相对误差。
步骤(4)所述位于3000~3800cm -1处的5个子峰的峰面积之和代表了步骤(2)所述交点处对应水分子的含量,即水分的含量,因此可以表征该交点处水分含量;中心位于3410~3440cm -1处的子峰峰面积与中心位于3200~3220cm -1处的子峰峰面积的比值R,可以反映水分子氢键的结合情况,从而进一步反映水分子的结合程度,其中比值越小,水分子结合度越大,比值越大,水分子的自由度越高。
步骤(5)所述伪彩化成像优选采用matlab软件进行,运用pcolor和colormap函数方法,其中shading方式为shading interp,以水分含量A为像素点做的伪彩图实现了细胞水平水分含量的可视化,以比值R为像素点做的伪彩图实现了水分结合状态细胞水平的可视化。
本发明中拉曼光谱高分辨率的特性可以捕捉到分子间或分子内氢键的细微变化,氢键的细微变化易受到外界环境的影响,如温度、压力、离子浓度和空间大小等,因此可以用于水分子结构的研究。水分子的拉曼光谱是一个具有两个明显凸起的宽峰,两个凸起的中心分别位于3240cm -1和3440cm -1附近,这两个凸起的相对大小与氢键的变化紧密相连,当连接水分子的氢键增强时,凸起中心位于3240cm -1附近的拉曼相对强度增加,而当连接水分子的氢键减弱时,凸起中心位于3440cm -1附近的拉曼相对强度增加;在氢键的形成过程中,水分子既可以作为电子供体(D),也可以作为电子受体(A)。为了更好的研究氢键对水分子拉曼光谱的影响,通过高斯分峰拟合算法可将水在293K和0.1MPa条件下的拉曼光谱分为了5个子峰,其中心分别位于3041cm -1、3220cm -1、3430cm -1、3572cm -1、3636cm -1附近,根据研究,这5个子峰依次被定义为DAA(两个电子受体,一个电子供体)、DDAA(两个电子供体,两个电子受体)、DA(一个电子供体,一个电子受体)、DDA(两个电子供体,一个电子受体)和没有连接任何氢键的free-OH。研究过程发现DA子峰和DDAA子峰的峰面积之比可以表征水分子周围氢键的结合程度,DA/DDAA的比值R越小,说明有更多的水分子与周围的分子形成了更多的氢键,从而使水分子具有较高的结合度;相反,水分子的自由度更高。因此,可以用DA/DDAA的比值R来表征水分的结合状态,从而判断其为自由水还是结合水。
果蔬中的水分主要存在于细胞间和细胞内,并且与细胞中的其他物质(包括金属离子、生物小分子和生物大分子)相互作用形成氢键,而氢键的强弱也影响了水分在组织细胞中的存在状态。利用共聚焦显微拉曼高分辨率的特性,可以捕捉到细胞水平的氢键变化,从而根据氢键的强弱判断组织中水分的存在状态,并能根据水分子信号的强弱对水分子的含量进行测定。
与现有技术相比,本发明具有以下优点及有益效果:
(1)本发明采用共聚焦显微拉曼成像技术结合数学计算提供了一种测试果 蔬组织中不同状态水分含量的方法,并首次实现了细胞水平水分含量及水分状态的可视化,可以较为直观地了解水分在组织细胞中的存在状态和存在位置,从而为研究果蔬加工过程中水分的迁移以及水分迁移对组织结构产生的影响提供了研究基础。
(2)本申请采用airPLS算法不仅可以较准确地拟合出拉曼光谱的背景,而且在运算速度上也具有很大的优势,由于要处理的光谱数量庞大,因此可以较大程度的节省光谱预处理的时间。
附图说明
图1为水分子氢键连接方式。
图2为不同氢键连接方式的水分子在293K和0.1MPa条件下的拉曼光谱及其分峰光谱图。
图3为本发明实施例1测试苹果组织50μm深度处10倍物镜下的光学显微镜完整视野图。
图4为本发明实施例1所测苹果微观组织50μm深度处测试区域单个细胞的光学显微镜图(为图3的中心测试区域放大图)。
图5为本发明实施例1测试苹果组织细胞中各个位点进行扫描后获得的全部原始拉曼光谱图。
图6为本发明实施例1某一个原始光谱经过平滑、去除荧光背景处理后得到的光谱图(苹果组织)。
图7为本发明实施例1预处理后某一个光谱经过定峰位分峰拟合后的各个子峰和总峰图(苹果组织)。
图8为本发明实施例1预处理光谱进行高斯分峰拟合的拟合度(光谱数)分布图(苹果组织)。
图9为本发明实施例1预处理光谱进行高斯分峰拟合的拟合度(决定系数)直方分布图(苹果组织)。
图10为本发明实施例1苹果组织细胞水平的水分含量分布图。
图11为本发明实施例1苹果组织细胞水平不同状态水分的位置分布图。
图12为苹果组织采用NMR法测试的反演结果图。
具体实施方式
下面结合实施例和附图对本发明作进一步详细的描述,但本发明的实施方式不限于此。
实施例1
一种利用拉曼光谱测试植物组织中水分含量和状态分布的方法,包括如下步骤:
(1)用取样器沿径向在苹果上取一个12mm×15mm(直径×高度)的样品柱,随后用自制的切片设备将样品柱切成2mm厚的薄片,立即放置在恒温恒湿的石英腔中,用厚度为0.3mm的石英盖玻片密封。设置腔体湿度为80%,温度为4℃,放置在激光共聚焦拉曼显微镜载物台上,等待测试。
(2)选择激光共聚焦显微拉曼设备的10×倍物镜对准样品,调节焦距,获得清晰的组织结构显微图像,选择结构完整的细胞作为测量区域如图4所示,区域大小为200×200μm,对所选区域进行网格划分,步长为5μm,因此共产生1681个交点。激光共聚焦显微拉曼的光源为点光源,扫描时,光源延网格的交点从上至下,从左至右依次扫描,每个交点处都会产生一个拉曼光谱,因此每个光谱在测量区域都有对应的坐标信息;光谱采集时设置扫描的光谱范围为2700~3800cm -1,选用532nm激光器,光栅为600gr/mm,Hole为500,激光衰减为25%,采集时间为3s,积累次数为3次,测量深度设为50μm。完成全部扫描所需的时间大约为3h;采集到的所有原始拉曼光谱如图5所示。
(3)对采集到的所有原始拉曼光谱进行Savitzky-golay平滑处理和airPLS法去除荧光背景处理,平滑处理运用matlab软件中的smooth函数,选择Savitzky-golay作为平滑算法,计算平滑后光谱每个元素所使用的点数设置为15, Savitzky-golay算法中多项式的次数为1;荧光背景的去除运用airPLS就是自适应迭代重加权惩罚最小二乘算法,该算法可以很好地去除光谱的荧光背景,并最大限度地保留光谱的有效信息;airPLS算法中调节参数λ为10e12,差分惩罚项的阶数为3,光谱起始和结束处的权重异常比例设为0.08,起始和结束时的不对称参数为50,最大迭代次数设为10。其中某一个原始拉曼光谱经平滑和去除荧光背景预处理后的光谱如图6所示。
(4)对预处理后的拉曼光谱进行高斯分峰拟合,由于样品为成分复杂的果蔬组织,因此在测定过程中会对光谱产生一定的影响,从而使分峰拟合过程中的峰位具有一定的差异,不利于后续的数据处理,因此采用定峰位分峰拟合的方式对所有的光谱进行分峰拟合。确定峰位的方法为:随机选取50个预处理后的拉曼光谱,采用peakfit v4.12(Seasolve software Inc.)软件对其进行迭代分峰,根据分峰拟合决定系数和迭代数确定从2700~3800cm -1光谱范围内可以获得7个子峰,其中从3000~3800cm -1光谱范围内的5子峰为水分子或OH基的伸缩振动峰;将50个预选光谱7个子峰的峰位进行平均化,得到的即为定峰位分峰的峰位信息;用matlab软件采用peakfit函数根据得到的峰位信息,对所有预处理后的光谱进行分峰处理,得到所有光谱分峰后子峰的峰位、峰高、峰宽及峰面积信息,同时可以得到每个光谱定分峰拟合的决定系数、拟合光谱与原光谱的相对误差和拟合残差等信息;其中某一个拉曼光谱分峰结果如图7和表1所示。
表1某一个拉曼光谱分峰后子峰的峰位、峰高、峰宽和峰面积数据
Figure PCTCN2019111533-appb-000001
Figure PCTCN2019111533-appb-000002
(5)将分峰后位于3000~3800cm -1范围内的5个子峰的峰面积进行加和,得到各个拉曼光谱在3000~3800cm -1范围内的峰面积之和A,即为水分子的含量;3000~3800cm -1范围内5个子峰的第三个子峰(峰中心位于3428cm -1)与第二个子峰(峰中心位于3219cm -1)的峰面积之比,得到的比值R可用于判断水分子的结合状态;所有光谱的分峰拟合光谱数为1681,获得的拟合决定系数如图8所示,决定系数的直方分布图如图9所示。
(6)将每个光谱的水分子含量与其对应的坐标信息,以A作为像素点用matlab进行伪彩化成像如图10所示,得到所选范围内细胞水平的水分分布图,A值越大,表明该点处的水分含量越高;而A值越小,表明该点处的水分含量越低;同时以比值R作为像素点,可以判断各点水分的结合状态如图11所示,R值越大,表明结合度越弱;而R值越小,表明结合度越强;从而实现对果蔬组织中细胞水平的水分含量及水分结合状态分布的可视化。Matlab伪彩化成像选用其自带的pcolor和colormap函数,shading方式选择为shading interp。
(7)根据图11中不同结合度水分的比值R的差异,将R值≤1.2的水定义为结合水,R值≥1.4的水定义为自由水,1.2<R值<1.4的水定义为不易流动水,对应坐标信息根据图10中的水分含量计算不同状态水分的含量,每个苹果取三个圆柱,每个圆柱取三片薄片,均通过本申请的方法测试从而得到9组数据,对其进行分析计算,分别取自由水、结合水和不易流动水的平均值,并分别与NMR法(相同的苹果测三个相同的圆柱取平均值,即测试样品与本申请方法所用样品相同)和马林契可法实验测得的结果(相同的苹果测试两组且每组均是三个相同的圆柱,即测试样品与本申请方法所用样品相同,其中一组用于做蔗糖浸渍实验,另一组用于做烘干测水分实验,对得到的结果取平均值)进行对比,结果如表1所示。
NMR测试方法如下:
①沿径向从相同的苹果的不同部位取3个12mm×15mm(直径×高度)的样品柱,置于20mm直径的核磁测试管中,用封口膜密封后置于4℃的恒温冰箱中2h,使样品温度保持为4℃。
②本实验采用低场核磁共振仪,测试前,采用标准样品对设备进行校正,并利用FID序列寻找中心频率,然后选择CPMG测试序列,设置参数:SW=200kHz,RFD=0.02μs,RG1=5,DRG1=3,DR=1,PRG=0,NS=3,TW=8000ms,TE=0.5ms,NECH=2500准备测试。
③将样品放入磁体箱内,选择累积采样采集样品的T 2弛豫信号,采样结束后,数据会自动保存到数据库,选中所测的数据,对其进行数据反演,从而得到最终的结果。反演结果中T 2弛豫峰在100~1000ms范围内的峰认为是样品中自由水的峰,其峰面积对应的百分比即为自由水的百分比;T 2弛豫峰在10~100ms范围内的峰认为是不易流动水的峰,其峰面积对应的百分比即为不易流动水的百分比;T 2弛豫峰在0~10ms范围内的峰认为是结合水的峰,其峰面积对应的百分比即为结合水对应的百分比;结果如图12所示。
马林契可实验方法如下:
①沿径向从相同的苹果的不同部位取6个12mm×15mm(直径×高度)的样品柱。将其中三个样品柱切成2mm厚的样品圆片分别置于3个已知质量为m 0的称量皿中,分别称量其总质量为m 1,置于105℃的烘箱中烘干10h至恒重后,称量烘干后的总质量为m 2;按照公式计算组织含水量,具体公式为:
Figure PCTCN2019111533-appb-000003
Figure PCTCN2019111533-appb-000004
②将另外三个样品柱也切成2mm的样品圆片,分别置于另外3个已知质量为M 0的称量皿中,称量总质量为M 1,用移液枪分别在三个称量皿中加入质量百分比浓度为60%的蔗糖溶液,轻轻摇动称量皿,使溶液与样品混合均匀,再称量其质量为M 2
③将称量皿置于回旋式振荡器上振荡6h,设置振荡器的旋转速率,使称 量皿中的溶液可以延一个方向轻轻摇动且液体无法溅出称量皿。
④振荡结束后,充分摇动溶液,用移液枪取200μL样品,滴在阿贝折光仪的毛玻璃面上,旋紧棱镜,在20℃条件下测溶液的糖浓度为D 2,同时测定原来糖液的浓度为D 1,按下面的公式求组织中自由水的含量(%):
Figure PCTCN2019111533-appb-000005
Figure PCTCN2019111533-appb-000006
则自由水的含量占总含水量的百分比(%)=(组织含水量-组织自由水含量)×100/组织含水量,此百分比即为自由水的含量;而结合水的含量为1-自由水的含量。
(7)表2为采用本申请方法测得的结合水和自由水含量与采用马林契可法实验和NMR测得的结合水与自由水含量的比较。
表2不同测试方法测得的苹果中结合水与自由水含量
Figure PCTCN2019111533-appb-000007
上述实施例为本发明较佳的实施方式,但本发明的实施方式并不受上述实施例的限制,其他的任何未背离本发明的精神实质与原理下所作的改变、修饰、替代、组合、简化,均应为等效的置换方式,都包含在本发明的保护范围之内。

Claims (10)

  1. 一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,包括以下步骤:
    (1)取待测果蔬切成样品;
    (2)将样品置于激光共聚焦显微镜的载物台上进行成像光谱采集,具体是通过激光共聚焦显微镜物镜从样品中选取细胞区域,然后将所选细胞区域进行网格划分得到均匀分布的交点,标记每个交点在所选细胞区域中的相应坐标信息,对每个交点处进行扫描,得到细胞区域中各个交点处对应水分的拉曼光谱;
    其中网格步长为3~5μm;
    (3)将步骤(2)得到的拉曼光谱进行平滑降噪和去除荧光背景处理,然后进行高斯分峰拟合,每个拉曼光谱均得到位于3000~3800cm -1处的5个子峰和位于2700~3000cm -1处的2~3个子峰;
    (4)将每个拉曼光谱分峰后位于3000~3800cm -1处的5个子峰的峰面积进行加和,得到细胞区域中该交点处对应水分含量A;根据中心位于3410~3440cm -1处的子峰峰面积与中心位于3200~3220cm -1处的子峰峰面积的比值R判断该交点处对应水分子的结合状态;
    (5)结合各个交点的坐标信息,分别以所有交点处对应水分含量A和比值R为像素点进行伪彩化成像,得到果蔬组织细胞水平的水分含量和水分结合状态分布状况。
  2. 根据权利要求1所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,步骤(2)所述成像光谱采集所用的激光器为532nm激光器,其中光栅的大小为1200gr/mm,Hole为500,扫描范围为2700~3800cm -1;采集的条件为:采集时间3~5s,累积次数2~3次,激光能量的衰减为25~50%。
  3. 根据权利要求1所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含 量和分布的方法,其特征在于,步骤(2)所述成像光谱采集的深度为50~100μm。
  4. 根据权利要求1或2或3所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,步骤(3)所述高斯分峰拟合的方法为采用matlab软件运用peakfit函数进行。
  5. 根据权利要求4所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,步骤(3)所述平滑降噪处理采用matlab软件中的smooth函数,选择Savitzky-golay卷积平滑算法;所述去除荧光背景处理采用自适应迭代重加权惩罚最小二乘背景扣除算法。
  6. 根据权利要求4所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,步骤(3)所述高斯分峰拟合为定峰位高斯分峰拟合,即定峰位高斯迭代曲线拟合算法,在固定峰位的情况下对光谱进行高斯曲线拟合;其峰位的确定方法如下:随机选取50个拉曼光谱,利用peakfit软件对其进行迭代分峰处理,将每个拉曼光谱分为7~8个子峰,然后将50个拉曼光谱得到的子峰峰位信息进行平均化,得到7~8个子峰的平均峰位信息,以此作为定峰位高斯分峰的峰位依据。
  7. 根据权利要求4所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,步骤(2)所述细胞区域大小由细胞大小决定,常规果蔬细胞的平均直径在100~300μm之间;所述物镜的倍数为10倍。
  8. 根据权利要求4所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,步骤(1)所述果蔬为苹果、土豆、葡萄、梨和白菜茎中的一种。
  9. 根据权利要求8所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,步骤(1)所述样品为去皮的果蔬;所述样品的形状为圆片状,其尺寸为直径×厚度=12mm×2mm;所述样品在测试前及测试过程中保存在石英腔中,所述石英腔的腔体的温度为2~10℃,湿度大于80%,其中石英腔体用厚度为0.3mm的石英盖玻片密封。
  10. 根据权利要求4所述一种基于拉曼光谱测试果蔬组织中细胞水平水分含量和分布的方法,其特征在于,步骤(5)所述伪彩化成像采用matlab软件进行,运用pcolor和colormap函数方法,其中shading方式为shading interp。
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