FluorCam叶绿素荧光成像文献 2011 Hyperspectral and Chlorophyll Fluorescence Imaging
Sensors2011, 11, 3765-3779; doi:10.3390/s110403765
OPEN ACCESS
sensors
ISSN 1424-8220
https://www.wendangku.net/doc/8f2372314.html,/journal/sensors Article
Hyperspectral and Chlorophyll Fluorescence Imaging
to Analyse the Impact of Fusarium culmorum on the Photosynthetic Integrity of Infected Wheat Ears
Elke Bauriegel 1,*, Antje Giebel 1 and Werner B. Herppich 2
1Department of Engineering for Crop Production, Leibniz-Institute for Agricultural Engineering Potsdam-Bornim, D-14469 Potsdam, Germany; E-Mail: agiebel@atb-potsdam.de
2Department of Horticultural Engineering, Leibniz-Institute for Agricultural Engineering Potsdam-Bornim, D-14469 Potsdam, Germany; E-Mail: wherppich@atb-potsdam.de
* Author to whom correspondence should be addressed; E-Mail: ebauriegel@atb-potsdam.de;
Tel.: +49-331-5699-414; Fax: +49-331-5699-849.
Received: 24 January 2011; in revised form: 23 March 2011 / Accepted: 25 March 2011 / Published: 28 March 2011
Abstract: Head blight on wheat, caused by Fusarium spp., is a serious problem for both
farmers and food production due to the concomitant production of highly toxic mycotoxins
in infected cereals. For selective mycotoxin analyses, information about the on-field status
of infestation would be helpful. Early symptom detection directly on ears, together with the
corresponding geographic position, would be important for selective harvesting. Hence, the
capabilities of various digital imaging methods to detect head blight disease on winter
wheat were tested. Time series of images of healthy and artificially Fusarium-infected ears
were recorded with a laboratory hyperspectral imaging system (wavelength range: 400 nm
to 1,000 nm). Disease-specific spectral signatures were evaluated with an imaging software.
Applying the ?Spectral Angle Mapper‘ me thod, healthy and infected ear tissue could be
clearly classified. Simultaneously, chlorophyll fluorescence imaging of healthy and
infected ears, and visual rating of the severity of disease was performed. Between six and
eleven days after artificial inoculation, photosynthetic efficiency of infected compared to
healthy ears decreased. The severity of disease highly correlated with photosynthetic
efficiency. Above an infection limit of 5% severity of disease, chlorophyll fluorescence
imaging reliably recognised infected ears. With this technique, differentiation of the
severity of disease was successful in steps of 10%. Depending on the quality of chosen
regions of interests, hyperspectral imaging readily detects head blight 7 d after inoculation
up to a severity of disease of 50%. After beginning of ripening, healthy and diseased ears
were hardly distinguishable with the evaluated methods.
Keywords: chlorophyll defect; fungal diseases; non-destructive; non-invasive sensor
application; potential maximum photochemical efficiency of PSII (F v/F m); Triticum aestivum
L. ?Taifun‘
1. Introduction
Many Fusarium spp. may cause serious grain contamination with mycotoxins (trichothecenes), mainly deoxynivalenol (DON) and 3-acetyl DON (3-ADON), on wheat [1]. Up to now, true infection of ears can only be determined with time consuming and expensive pre- and postharvest laboratory tests (serological rapid tests, Fast-DON-ELISA-test, counting methods; [2]). Thus, the ability to identify infections in the field could prevent exploitation of contaminated grain lots, finally, reducing toxin-burden of food- and feedstuffs.
Head blight, like other fungal and viral infections, is characterised by a complete destruction of the cellular integrity of the impacted tissues leading to cell death and degradation of chlorophylls. Damage is mostly accompanied by a transient increase in transpiration, followed by tissue desiccation. The resulting variation of tissue temperature has been successfully detected by thermal imaging, at least under controlled conditions [3]. On the other hand, chlorophyll degradation by means of spectral analysis in the visible range [4] allows detection of infected plant parts [5,6]. Application of spectral indices such as Normalized Difference Vegetation Index (NDVI), which has been shown to rapidly indicate plant stress [7], may provide a good discrimination between healthy and infected plant parts [8]. Including the NIR range in spectral analyses may increase the efficiency of discrimination because this wavelength range additionally includes information about tissue water content [9,10].
Providing further knowledge about local centres of infections, imaging methods are especially suitable for rapid and non-invasive identification of the effective stage of plants disease in the field. In this context, spectral imaging was used to diagnose viral infections [11] and to identify spatially variable physiological processes of leaves [12]. Hyperspectral analyses have been applied to detect fungal-based grain crop diseases [13-15]. Furthermore, by the combination of both hyperspectral reflection measurements and chlorophyll fluorescence analyses (CFA) the discrimination of yellow rust on winter wheat could have been improved to an accuracy of 94–95% [5].
CFA does not only measure the externally visible effects of infection-induced chlorophyll breakdown; it also provides comprehensive insight into potential and actual photosynthetic activity [16,17]. Photosynthesis is often considerably affected by both biotic and abiotic stresses at very early stages [18,19]. In addition, CFA imaging has already been universally applied, for instance for evaluation of the vitality of plant tissues [20,21], diseases like leaf rust and powdery mildew on cereals [22] or the infection by the tobacco mosaic virus [23].
For the detection of head blight, both methods have been shown to have great potential [24,25]. In this study, both imaging methods were applied in parallel to comprehensively analyse the respective ability of early detection of head blight disease in winter wheat ears both under laboratory and field
conditions. Additional aims were: (1) to determine the highest possible accuracy of the detection of Fusarium infection; (2) to characterise the exact timeframe for meaningful head blight detection and (3) to determine the effect of the level of infection on detection accuracy.
2. Experimental Section
2.1. Materials and Experimental Design
Sixteen wheat caryopses (Triticum aestivum L. ?Taifun‘) were sown in eight pots (0.18 m × 0.18 m). From these, four pots were used for controls and four for infected samples, respectively. After germination, plants were cultivated in a greenhouse. After the start of flowering, plants were inoculated with a water suspension of Fusarium culmorum spores at a concentration of 250,000 spores per mL on three successive days. To guarantee the spread of germs, plants were kept at 20 ± 2 °C, high humidity (70%) and an illumination period of 12 h (high-pressure sodium-vapour lamps, SON-T Plus 400 W, Philips GmbH, Hamburg, Germany). Measurements started immediately after inoculation.
Developmental stages of ears were always graded according to the BBCH scale [26], which empirically describes plant development from dry seeds (BBCH 00) until the harvest product (BBCH 99). In the laboratory, plant infection levels were visually rated three times a week. Using sample pictures of infected ears, percentage infection of blighted spikelets per ear was estimated following [27]. Estimation of severity of disease occurred in distinct steps of 1%, 2%, 3%, 5% of damage at low infection levels and in 10%-steps at higher severity. All measurements were performed on intact plants. As a unit of visual rating of head blight disease pattern, the ―severity of disease‖ (sod) was defined. During the course of this study three independent experiments were performed to comprehensively analyse both disease development and disease recognition accuracy.
2.2. Chlorophyll Fluorescence Imaging
Chlorophyll fluorescence imaging was performed with a modular system (FluorCAM 700MF, PSI, Brno, Czech Republic) measuring sequences of fluorescence images with a user-defined timing of set points, measurement intervals and irradiance [21,28,29]. Basic fluorescence F0was induced by two panels of super-bri ght orange light emitting diodes (λmax= 620 nm, 345 LED per panel; approx.
3 μmol m?2 s?1). Maximum fluorescence (F m) was triggered by short-term (1 s) saturation light pulses (max. 2,500 μmol photons m?2 s?1) generated by an electronic shutter-equipped halogen lamp (250 W).
The ratio of variable fluorescence (F v = F m– F0) to maximum fluorescence, F v/F m, is an indicator of the potential maximum photochemical efficiency of photosystem II. It ranges between 0 (chlorophyll-less, dead plants) and 0.84 for healthy, intact plant parts under optimal conditions [17]. F v/F m is well-known as a valuable tool to determine both capacity and stability of photosynthesis [29,30]. A CCD camera with a F1.2/2.8–6 mm objective and a filter system (high pass 695 nm, low pass 780 nm) recorded fluorescence images (12-bit, 512 × 512 pixel; maximal frequency 50 images s?1) synchronously with the weak, non-actinic measuring-light pulses. The system was controlled by the FluorCam software (PSI, Brno, Czech Republic). In the laboratory, measurements lasted 4 s (F0: 3 s duration, 4 frames recorded; F m: 1 s duration, 25 frames recorded) and were performed on plants, dark-adapted for 10 min. Six samples of both infected and control plants were continuously recorded in time series
experiments. In addition, fifteen plants with pronounced head blight symptoms were investigated at BBCH stage 75. All plants were measured from the side at a distance of 0.2 m between ears and CCD-camera.
In addition, chlorophyll fluorescence imaging was applied on artificially infected winter wheat plants (BBCH stage 77/79) of the cultivars ?Cardos‘, ?Winnetou‘ and ?Drifter‘ (all resistance class 5) directly in the field. To eliminate the effects of direct sunlight on fluorescence and to pre-darken (10 min) the plants, they were partially shielded with a paper box (approx. 0.9 m × 0.9 m × 0.9 m) during measurements. The duration of the fluorescence measurements was reduced to 2 s (i.e., 1 s for F0measurements and 1 s for F m). In total, 50 ears of varying levels of infection were investigated. Only optimally illuminated images with clearly distinguishable ears (n = 30) were further analysed in this experiment.
For a further evaluation and derivation of severity of disease (sod), in the chlorophyll fluorescence images, all F v/F m-pixel values were allocated to ―efficiency classes‖ of photosynthetic activity at steps of 0.05. In addition, they were accumulated to a ―cumulative F v/F m‖ (%), starting from the lowest values (0.00).
2.3. Hyperspectral Imaging
The laboratory hyperspectral imaging device recorded reflection spectra in a wavelength range of 400 to 1,000 nm with a spectral resolution of 2.5 nm. General pixel-resolution of the camera was 1,392 ×1,024 px; however, two pixels per axis were combined to yield an effective resolution of 696 × 512 px. The field of view achieved generally resulted in a spatial resolution of approx. 0.4 mm per pixel. The system comprised a spectrograph (ImSpector V10E, Spectral Imaging Ltd., Oulu, Finland), a 12 bit, digitally temperature-compensated b/w camera (Pixelfly qe, PCO AG, Kelheim, Germany) and an accessory rotating mirror with a micro-step motor. The hyperspectral camera stored the spectra of all pixels of an image line by line. A program, developed under LabView 8.2 (National Instruments Corporation, Austin, TX, USA), was used to control the camera system and for data pre-processing, including the black/white calibration of the spectra. For this calibration, images for the black and the white adjustment were recorded with each measurement. The b/w-balance [(sample–black)/(white–black)] was performed for the entire frame during the following conversion of the hyperspectral images into the byte stream format. Heterogeneities in the pixel response across the sensor area were generally rather low and, therefore, not compensated for. The samples were illuminated with a stabilised halogen lamp (150 W). In addition to the six ears per variant used for chlorophyll fluorescence analysis, further six plants were examined by hyperspectral imaging with a time lag of approximately two days during three weeks (total n infected = n controls = 12). All plants were recorded from the side at a distance of 0.5 m between plant and camera. To avoid vibrations, the ears were fixed on a black background. Exposure time, adjusted for the respective samples, was in the range of 20 to 25 ms; a complete record lasted 20 to 30 min.
2.4. Data Analysis
The classification of diseased and healthy areas was performed with the software ENVI (Research Systems Inc., Boulder, CO, USA) by means of monitored classifications in the ―Spectral Angle
Mapper‖ (SAM)evaluation algorithm. SAM compares the classifying spectrum of an image with a reference spectrum. The classes are allocated according to their similarity. The distinction of two reflection spectra is described with an angle, which span between related vectors [31,32]. In this paper, a vector in a multidimensional space (512 bands) was used. In addition, SAM was chosen because it is insensitive to variations of illumination [33].
The threshold of similarity of compared spectral angles was 0.1. For this purpose, regions of interest (ROIs) were established as the bases for the classification according to the two classes, diseased (8 ROIs) and healthy (10 ROIs). In a three-band false colour image (450 nm, 550 nm, 650 nm), diseased and healthy areas could be distinguished by visual inspection, which facilitated the proper manual setting of ROIs. These 18 ROIs was used to build an endmember, applied in the further calculations. To consider the effect of plant development, single hyperspectral images were repeatedly used as training images for setting ROIs. These images were excluded from later evaluation. After classification, the relative portions of pixels per image belonging to the healthy, diseased and unclassified object classes were determined. In this context, all pixels which could not be allocated to the defined classes ―healthy‖ and ―diseased‖ were assigned to the class ―unclassified―. The proportion of unclassified pixels was calculated as the difference between 100% and the sum of healthy and diseased pixels.
3. Results
The development of Fusarium infection, rated as severity of disease (sod), and the relevant BBCH stage of plants at respective days after inoculation (dai) are shown for the first time-series experiment in Table 1. The first symptoms of the disease became visible at the BBCH stage 71/75 (7 dai), first ripening symptoms developed in the BBCH stage 81.
Table 1.Representative example of plant development (BBCH stage) and rated disease
symptoms of head blight (severity of disease, sod, n = 6).
dai 5 7 9 11 14 16 18 21 23 25
BBCH 65/71 71/75 75 75/77 77/79 79/81 79/81 81/85 85 89
sod (%) 0 3 5 6 9 16 19 60 82 90
3.1. Chlorophyll Fluorescence Imaging
Distribution analyses of chlorophyll fluorescence images of ears with a sod between 2 and 100% revealed that, in weakly diseased ears (2%), as in healthy ears, pixel-values of photosynthetic efficiency (F v/F m) concentrated in classes of high efficiency [0.55–0.75; Figure 1(a)]. The pixelwise F v/F m-distribution was nearly identical in control plants and very weakly diseased ears (data not shown).
In medium infected ears (50%), the distribution of F v/F m broadened [Figure 1(b)] due to the co-existence of both healthy (pixel-value range 0.40–0.75) and diseased tissues (pixel-value range 0.00–0.40). In strongly infected ears [Figure 1(c)], pixel-values of photosynthetic efficiency only concentrated in the low-value range (0.00–0.20).
Figure 1. Pixelwise distribution of the maximum photochemical efficiency (F v/F m) of (a)
weakly (2%), (b) medium (50%) and (c) strongly infected (90%) wheat ears.
Cumulative F v/F m appropriately characterised the head blight development of representative ears (Figure 2). At an early infection state [Figure 2(a), dai 4–6], only a few pixel values were found in low efficiency classes, while 80% of F v/F m-pixel values concentrated in high photosynthesis efficiency classes (>0.6) in a healthy or weakly diseased (2–3% infection) ear. Continuous development of infection during the course of the experiment from dai 6 to dai 22 could be easily identified by an increasing concentration of accumulated F v/F m–values in low efficiency classes. This means that moderately diseased ears (40–60%) comprised both photosynthetically active and inactive areas. In contrast, cumulative F v/F m obtained only low efficiency classes (<0.3) if the ear was strongly diseased (90%) 22 d after infection. This is also verified if the variation of average cumulative F v/F m values of various plants of different sod is analysed [Figure 2(b)]. In this context, a cumulative F v/F m at 0.3 seems to represent a relevant threshold to differentiate diseased and healthy ears.
Figure 2. Cumulative F v/F m-values (%) (a)during several stages of head blight
development of a single representative ear (b)Average cumulative percentage of
F v/F m-values of ears at different levels of infection at dai 11.
During early infection and at low infection state (sod 2% to 3%), cumulative photosynthetic efficiency of investigated ears overlapped indicating that visual rating and CFA imaging did not obtain completely identical results [Figure 2(b)]. On the other hand, controls and clearly infected ears (sod ca. 5% at dai 11) could be successfully differentiated by analysing the cumulative F v/F m classes at 0.3
(Figure 3). Even at the first day of measurement (dai 6) the cumulative proportion of low efficiency classes was 3% higher than the control value, and rose to a median of nearly 8% within one week (dai 11). Up to a sod of 4%, F v /F m of diseased and control ears did not differ. Even with a sod of 10%, plants showed only minor visible symptoms of head blight one week after inoculation.
Figure 3. Cumulative F v /F m values at 0.3 of controls (blue) and strongly (defined as 5%
sod at dai 11) infected ears (red). 02
46810
Days after inoculation Control plants
Strong symptoms 6 dai 11 dai
C u m u l a t i v e F v /F m a t 0.3 (%) 6 dai 11 dai
Field application of CFA imaging yielded in more variable differentiation of ears according to the severity of disease than laboratory studies (Figure 4). Discrepancy between visual inspection and CFA was more pronounced at low infection (sod < 30%). Nevertheless, ears with medium (40–50%) and high (70–80% and 90%, respectively) sod could easily be identified. Overall, correlation between the cumulative F v /F m and sod was high in this experiment yielding a coefficient of determination of 0.658 (Figure 4).
Figure 4. Correlation between the cumulative F v /F m at 0.3 and the severity of disease
obtained from visual rating (circles: 23 June 2009, triangles: 24 June 2009).
3.2. Hyperspectral Measurements
Time series experiments showed that differentiation by hyperspectral imaging was most effective at 14 ± 2 dai (Figure 5, dai 16 shown). It was less effective and, hence, results less reliable soon after infection and, again, after the beginning of maturation ca. four weeks after infection.
Figure 5. Samples of classification results using SAM classification (green: healthy
classified tissues, red: diseased classified tissues). Upper row: infected ears, lower row:
controls).
Results of classification of the levels of infection, obtained from hyperspectral imaging by the SAM algorithm, reflected those of the visual rating [Figure 6(a)].
Figure 6. (a) Results SAM-based classification of infected and control ears (n = 12) in
comparison with the severity of disease obtained by visual rating during the course of
infection development. (b)Proportion of pixels classified as infected and those
unclassified by the SAM algorithm in comparison with the severity of disease obtained by
visual rating (n = 6). Results of healthy ears are not shown.
For this comparison, proportions of the whole ears, classified as diseased, were related to the total number of classified pixels (healthy + diseased, see Figure 5). During early infection, starting from BBCH-stage 75, the sod obtained by spectral classification was always lower than that rated visually; it became closer at the beginning of ripening. Head blight was well separated from healthy tissues after the onset of ripening (from BBCH 81, dai 21). However, at this stage, pixels, which previously were classified as healthy, were now increasingly ascribed as unclassified [Figure 6(b)]. Generally, results of the SAM evaluation algorithm applied to hyperspectral image analysis were highly correlated (R2 = 0.964) with visually evaluated sod (data not shown). In all cases, the quality of classification strongly depended on the appropriate setting of ROIs. For this purpose, it is necessary to inspect the respective region of the image at the highest resolution.
3.3. Effects of Ear Development on Quality of Head-Blight Detection
The presented results indicate that during early ear development (starting from BBCH-stage 75), initial symptoms of infection can be eye-detected at 7 dai (Figure 7).
Figure 7. Time bar for the detection of head blight under indoor conditions.
first symptoms no reliable detection
Supervised classification of hyperspectral images in visual range identified first head blight symptoms at the same time. Most efficient classification of Fusarium-affected ears was possible in the BBCH-stage 75 to 77. With progressing maturation, the number of ears that could not be classified correctly largely increased. However, these limitations were also partially valid for CFA imaging. On the other hand, F v/F m-measurements detect symptoms of infection of ears earlier than visual rating and
hyperspectral imaging. In general, highest accuracy of detection of Fusarium infection may be achieved if two successive measurement dates were performed at the growth stage ―medium milk‖(grain content is milky, BBCH 71-77; dai 6 to 11).
4. Discussion
To the best of our knowledge, this is the first investigation on the combined application of chlorophyll fluorescence and hyperspectral imaging for the early in vivo detection of head blight disease in winter wheat. It could be convincingly shown that both methods can indentify Fusarium infection of wheat ears non-invasively and with high reliability at a very early stage of disease.
Based on the physiology of photosynthesis, chlorophyll fluorescence imaging allowed detection of the initial phase of tissue damage. After the penetration of kernels by the mycelia, there are distinct cellular changes such as degeneration of cytoplasm and cell organelles, decomposition of the host‘s cell walls and deposition of material in vessel walls of the diseased ears [34]. Infection may lead to a complete inhibition of the metabolic activity, including a pronounced disturbance of photosynthetic performance. This can be easily identified by a rapid decline in the photochemical efficiency in the infected ears [24], even before visible chlorophyll degradation occurs.
Chlorophyll fluorescence imaging is a well-established effective tool to comprehensively assess the development and the effects of bacterial, fungal and viral infections on leaves of many crop plants (e.g., [8,23,35]). One topic of this study was to optimize both application of this technique and analysis of obtained results for rapid and early detection of head blight on wheat ears in laboratory and in field. To establish the level of infection of intact ears, the potential maximum photochemical efficiency of PSII (F v/F m) was applied. In contrast to the analysis of absolute F v/F m values, the statistical evaluation of its relative distribution in the entire image provided a successful approach for this purpose. The broadening of the overall distribution of F v/F m with developing infection closely reflected the increased number of diseased kernels per ear.
The analysis of the cumulative F v/F m allows an accurate evaluation of the changed distribution pattern. Considering a cumulative percentage at 0.3 as the differentiation threshold, levels of infection can be differentiated in 10%-steps. Hence, even from the sixth dai, infected and control plants could be effectively separated in laboratory experiments.
Using this approach, it could be shown that the fungi seriously affect photosynthetic performance and, thus, chlorophyll fluorescence at an early stage. This has also been reported by [8,36] for leaf pathogens. For instance, [8] found reduced F v/F0-values two to three days before leaf rust and powdery mildew infection became visible on leaves of winter wheat.
In several studies, other image analysis approaches have been applied. Investigating yellow rust infection on wheat, [5] recorded fluorescence images at 550 and 690 nm. From the relative signal intensities at these two wavelengths, the authors built a disease index (f G) and defined pixels exceeding the f G value of 0.65 as ―diseased‖. For each leaf of Tulip Breaking Virus (TBV)-infected plants, [37] calculated mean and standard deviation in photochemical efficiency classes (0–1) of fluorescence images. With this procedure, they got higher error rates (31–46%) than found by [5] or obtained in the present study. Classifying infected or healthy ears by the cumulative F v/F m at 0.3, as used in this study, is the fastest method of analysis.
The current techniques of chlorophyll fluorescence imaging for identification of head blight in the field certainly need improvement. Due to their complex physiological nature [16], the fluorescence signals directly depend on the prevailing photosynthetic photon flux density. Hence, fluctuating light and direct exposure to sunlight must be avoided during measurements. Furthermore, before measurement of F0and F m, plants need to be dark-adapted [5,17]. As shown in this study, these requirements can certainly be achieved.
Although the applied measuring system was developed for use in the laboratory, a suitable (R2= 0.658) correlation between fluorescence analysis and visible head blight inspection has been obtained under field conditions. The reduction of correlation quality may be due to the high subjectivity of visual rating. The scale applied for visual rating had a step size of 10%; therefore, the absolute rating error would be 10% in the worst case. Methodological problems with the FluorCam measurements could not be entirely excluded but can be largely minimised by proper handling of the system.
Furthermore, movement of ears, induced by strong wind during recording of the sequences of F0 and Fm images, may result in non-overlaying frames of these two parameters. Therefore, overall recording time was reduced to 2 s. Nevertheless, peripheral areas of ears, which were influenced by wind, may have incorrectly low F v/F m. However, the resulting poorly observable marginal regions at the border area of the ROIs may be excluded from further data analysis. Also, incomplete, uneven shading in the measuring box occasionally provide another problem, leading to an overestimated basic fluorescence and, hence, erroneously low F v.
Elimination of all identified outliers reduced the amount of analysable ears by one third. As a consequence, the degree of determination of the correlation between fluorescence analysis and visible disease inspection rose to R2= 0.80. This clearly indicates the high potential of chlorophyll fluorescence imaging for non-invasive disease detection after further improvement of measuring technique and protocols.
In case of hyperspectral imaging, the SAM classification algorithm used resulted in good and reliable detection of diseased ears. According to [32] SAM has a great potential for analysis in multi- and hyperspectral imaging. Nevertheless, the best classification method always depends on the complexity of the initial sets of data. To distinguish between Fusarium-infected and non-infected wheat ears, [38] evaluated RGB images with only three available channels and achieved better classification results by applying the Maximum-Likelihood-Method compared to the application of SAM. With increasing spectral information, other classification methods such as SAM [39], k-Nearest Neighbour, Decision Tree or Support Vector Machines [40] are certainly indispensable. However, basically improved imaging techniques, which allow reproducible and reliable data recording, may represent a necessary first step to optimize and, most important, automatize disease detection.
In general, hyperspectral images have a much higher information density than RGB. Hyperspectral image analysis is based on the entire spectral range investigated and it not only refers to three colour channels. Using spectral images (400–750 nm), [41] clearly separated different ripening stages of tomatoes, whereas application of RGB-images was not successful. In addition, the use of distinct ratios of different wavelengths for disease control and quality analysis has been widely reported [42–44]. In this context, to apply spectral imaging under field conditions, data gained by hyperspectral systems may be used to extract relevant wavelength ranges for rapid multispectral devices.
An important problem with the disease classification by spectral imaging is the choice of the correct stage of development; otherwise the results may be inaccurate. If the measurements start too early, floral residues (anthers) and sterile ears caused by growth disorders are classified as diseased. Hence, in initial phases of the present studies, the low level of infection (ca.3%) classified by SAM on healthy ears (see Figure 6) was not based on head blight but reflected developmental disorders such as barren middle ears or tips. This means that damage other than that caused by Fusarium, were inevitably classified as diseased. Both types of damages could not be readily distinguished.
Classification results could, in some cases, be improved by either choosing different angle‘s radian specific to the respective object classes or by manually adjusting the angle‘s radian to lower values, e.g., to 0.05. However, such variations showed to be advantageous only for ears in the BBCH stages 89, because it also decreased the total number of classified pixel.
With the incipient ripeness, the spectra of healthy and diseased ears become more similar, which, again, leads to an increasing misclassification. Unclassified pixels clearly reflect the progressing degradation of chlorophyll during maturation, which occurred in healthy ears without the distinct spectral signature of infected kernels. In the classification procedure applied, such a class has not been specified but will be a next step of optimization. This has to be verified on the control plants which were free of a Fusarium infection.
Both methods investigated here are suitable for the detection of head blight. However, the next step to improve the accuracy of classification should be the dynamic combination of both methods and the addition of form and spot parameters, as proposed by [37]. A highly accurate classification is very important, because minimal levels of infection can lead to a contamination of major harvest lots with the poisonous mycotoxins of the Fusarium-fungi.
5. Conclusions
Laboratory as well as in-field measurements were performed to investigate the applicability of chlorophyll fluorescence and hyperspectral imaging for the detection of head blight. Under laboratory conditions, chlorophyll fluorescence imaging detects even very low levels of infection (ca.5%) as early as 6 dai; visual classification is only possible beginning from 7 dai.
One single measurement enables a distinction between infected and healthy ears, provided the disease is sufficiently strong. However, two measurement dates are recommended to reliably detect even a minimal infestation and to eliminate possible errors of measurement. By the use of the cumulative Fv/Fm threshold of 0.3, the severities of infection can be detected with an accuracy of 10% under laboratory conditions. Under field conditions a differentiation between low (0–10%), medium (40–50%) and high (70–80% and 90%, respectively) level of infection can also be described with a linear model (R2= 0.658, RMSE = 17%). Yet, the accuracy may rise up to 80% after data pre-processing including the elimination of outliers.
The application of the SAM evaluation algorithm yielded relatively good classification results. Nevertheless, the number of unclassified pixels increased during ear development.
The correct growth stage for spectral measurements and classification is therefore very important. From the BBCH-stage 81 (beginning of ripening) on, a distinction between healthy and diseased ears by the methods discussed above is limited.
Acknowledgements
This study is a part of the https://www.wendangku.net/doc/8f2372314.html,2research project ―Sensor based technologies and integrated assessment models in the food production ch ains‖, which is financially supported by the German Federal Ministry of Education and Research (BMBF 0339992). We would like to thank H. Beuche and J. Intre?for their support and instruction on the laboratory hyperspectral device and B. Rodemann from the Julius-Kühn-Institute, Braunschweig, for providing the plant material.
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叶绿素荧光参数及意义
第一节 叶绿素荧光参数及其意义 韩志国，吕中贤（泽泉开放实验室，上海泽泉科技有限公司，上海，200333） 叶绿素荧光技术作为光合作用的经典测量方法，已经成为藻类生理生态研究领域功能最强大、使用最 广泛的技术之一。由于常温常压下叶绿素荧光主要来源于光系统II 的叶绿素a ，而光系统II 处于整个光合 作用过程的最上游，因此包括光反应和暗反应在内的多数光合过程的变化都会反馈给光系统II ，进而引起 叶绿素a 荧光的变化，也就是说几乎所有光合作用过程的变化都可通过叶绿素荧光反映出来。与其它测量 方法相比，叶绿素荧光技术还具有不需破碎细胞、简便、快捷、可靠等特性，因此在国际上得到了广泛的 应用。 1 叶绿素荧光的来源 藻细胞内的叶绿素分子既可以直接捕获光能，也可以间接获取其它捕光色素（如类胡萝卜素）传递来 的能量。叶绿素分子得到能量后，会从基态（低能态）跃迁到激发态（高能态）。根据吸收的能量多少， 叶绿素分子可以跃迁到不同能级的激发态。若叶绿素分子吸收蓝光，则跃迁到较高激发态；若叶绿素分析 吸收红光，则跃迁到最低激发态。处于较高激发态的叶绿素分子很不稳定，会在几百飞秒（fs ，1 fs=10－15 s ）内通过振动弛豫向周围环境辐射热量，回到最低激发态（图1）。而最低激发态的叶绿素分子可以稳定 存在几纳秒（ns ，1 ns=10－9 s ）。 波长吸收荧光红 B 蓝 荧光 热耗散 最低激发态较高激发态基态吸收蓝光吸收红光能量A 图1 叶绿素吸收光能后能级变化（A ）和对应的吸收光谱（B ）（引自韩博平 et al., 2003） 处于最低激发态的叶绿素分子可以通过几种途径（图2）释放能量回到基态(韩博平 et al., 2003; Schreiber, 2004)：1）将能量在一系列叶绿素分子之间传递，最后传递给反应中心叶绿素a ，用于进行光化 学反应；2）以热的形式将能量耗散掉，即非辐射能量耗散（热耗散）；3）放出荧光。这三个途径相互竞 争、此消彼长，往往是具有最大速率的途径处于支配地位。一般而言，叶绿素荧光发生在纳秒级，而光化 学反应发射在皮秒级（ps ，1 ps=10－12 s ），因此在正常生理状态下（室温下），捕光色素吸收的能量主要用 于进行光化学反应，荧光只占约3%～5%(Krause and Weis, 1991; 林世青 et al., 1992)。 在活体细胞内，由于激发能从叶绿素b 到叶绿素a 的传递几乎达到100％的效率，因此基本检测不到 叶绿素b 荧光。在常温常压下，光系统I 的叶绿素a 发出的荧光很弱，基本可以忽略不计，对光系统I 叶 绿素a 荧光的研究要在77 K 的低温下进行。因此，当我们谈到活体叶绿素荧光时，其实指的是来自光系 统II 的叶绿素a 发出的荧光。
荧光分析法检测原理及应用举例
1 荧光定义 某些化学物质从外界吸收并储存能量而进入激发态，当其从激发态回到基态时，过剩的能量以电磁辐射的形式放射出去即发光，称之为荧光。可产生荧光的分子或原子在接受能量后引起发光，供能一旦停止，荧光现象随之消失。 2 荧光分类 由化学反应引起的荧光称为化学荧光，由光激发引起的荧光称为光致荧光，课题主要研究光致荧光。按产生荧光的基本微粒不同，荧光可分为原子荧光、X 射线荧光和分子荧光，课题主要研究分子荧光。 3 光致荧光机理 某一波长的光照射在分子上，分子对此光有吸收作用，光能量被分子所吸收，分子具有的能量使分子的能级由最低的基态能级上升至较高的各个激发态的不同振动能级，称为跃迁。分子在各个激发态处于不稳定的状态，并随时在激发态的不同振动能级下降至基态，在下降过程中，分子产生发光现象，此过程为释放能量的过程，即为光致荧光的机理。光致荧光的过程按照时间顺序可分为以下几部分。 分子受激发过程 在波长为10~400nm的紫外区或390~780nm的可见光区，光具有较高的能量，当某一特征波长的光照射分子时，是的分子会吸收此特征波长的光能量，能量由光传递到分子上，此过程为分子受激发过程。分子中的电子会出现跃迁过程，在稳定的基态向不稳定的激发态跃迁。跃迁所需要的能量为跃迁前后两个能级的能量差，即为吸收光的能量。分子跃迁至不稳定的激发态中即为电子激发态分子。 在电子激发态中，存在多重态。多重态表示为2S+1。S为0或1，它表示电子在自转过程中，具有的角动量的代数和。S=0表示所有电子自旋的角动量代数和为0，即所有电子都是自旋配对的，那么2S+1=1，电子所处的激发态为单重态， 用S i 表示，由此可推出，S 即为基态的单重态，S 1 为第一跃迁能级激发态的单重 态，S 2 为第二跃迁能级激发态的单重态。S=1表示电子的自旋方向不能配对，说明电子在跃迁过程中自旋方向有变化，存在不配对的电子为2个，2S+1=3，电子 在激发态中位于第三振动能级，称为三重态，用T i 来表示，T 1 即为第一激发态中 的三重态，T 2 即为第二激发态中的三重态，以此类推。
Fluorcam多光谱荧光成像技术及其应用
FluorCam多光谱荧光成像技术（Multi-color FluorCam） 自上世纪90s年代PSI公司首席科学家Nedbal教授与公司总裁Trtilek博士等首次将PAM脉冲调制叶绿素荧光技术与CCD技术结合在一起，成功研制生产FluorCam叶绿素荧光成像系统（Nedbal等，2000）以来，FluorCam叶绿素荧光成像技术得到长足发展和广泛应用，先后有封闭式、开放式（包括标准版和大型版）、便携式叶绿素荧光成像系统，及显微叶绿素荧光成像系统、大型叶绿素荧光成像平台（包括移动式、样带式、XYZ三维扫描式等）等，近些年还进一步发展了PlantScreen植物表型成像分析平台（Phenotyping）（有传送带版、XYZ三维扫描版及野外版等）及多光谱荧光成像技术。 Multi-color FluorCam多光谱荧光成像技术包括多激发光－多光谱荧光成像技术和UV 紫外光激发多光谱荧光成像技术： 1.多激发光－多光谱荧光成像技术：通过光学滤波器技术，仅使特定波长的光（激发光） 到达样品以激发荧光，同时仅使特定波长的激发荧光到达检测器。不同的荧光发色团（如叶绿素或GFP绿色荧光蛋白等）对不同波长的激发光“敏感”并吸收后激发出不同波长的荧光，根据此原理可以选配2个或2个以上的激发光源、绿波轮及相应滤波器，对不同波长荧光（多光谱荧光）进行成像分析。如FluorCam便携式GFP/Chl.荧光成像仪及FluorCam封闭式GFP／Chl.荧光成像系统具备红光和兰光及相应滤波器，可以对GFP和叶绿素荧光成像分析；FluorCam开放式多光谱荧光成像系统可以进一步选配不同颜色的激发光，如除红光、蓝光外，还可选配绿色光源及相应滤波器，以对YFP进行荧光成像分析等； 2.UV紫外光激发多光谱荧光成像技术：长波段UV紫外光（320nm－400nm）对植物叶片 激发，可以产生具有4个特征 性波峰的荧光光谱，4个波峰 的波长为兰光440nm（F440）、 绿光520nm（F520）、红光690nm （F690）和远红外740nm （F740），其中F440和F520 统称为BGF，由表皮及叶肉细 胞壁和叶脉发出，F690和F740 为叶绿素荧光Chl-F。紫外光 激发多光谱荧光（UV-MCF）可 以用来灵敏、特异性地评估植 物生理状态包括受胁迫状态， 包括干旱、病虫害、环境污染、 氮胁迫等 本文就FluorCam多光谱荧光成像技术产品及最新应用案例做一简单介绍，其中FluorCam便携式GFP/Chl荧光成像仪（Handy GFPCam）和FluorCam封闭式GFP/Chl荧光成像系统（Closed GFPCam）已有较为详细的资料介绍，在此不再专门介绍。
叶绿素荧光研究背景知识介绍
叶绿素荧光研究背景知识介绍 前言 近些年来，叶绿素荧光技术已经逐渐成为植物生理生态研究的热门方向。荧光数据是植物光合性能方面的必要研究内容。目前这种趋势由于叶绿素荧光检测仪的改进而得到发展。然而荧光理论和数据解释仍然比较复杂。就我们所了解的情况来看，目前许多研究者对荧光理论不是很清楚，仪器应用仅仅限于简单的数据说明的基础上，本文在此基础上，目的在于简单明晰地介绍相关理论和研究要点，以求简单明确地使用叶绿素荧光检测设备，充分分析实验数据，重点在于植物生理生态学技术的应用和限制。 荧光测量基础 植物叶片所吸收的光的能量有三个走向：光合驱动、热能、叶绿素荧光。三个过程之间存在竞争，其中任何一个效率的增加都将造成另外两个产量的下降。因此，测量叶绿素荧光产量，我们可以获得光化学过程与热耗散的效率的变化信息。尽管叶绿素荧光的总量很小(一般仅占叶片吸收光能总量的1-2%)，测量却非常简单。荧光光谱不同于吸收光谱，其波长更长，因此荧光测量可以通过把叶片经过给定波长的光线的照射，同时测量发射光中波长较长的部分光线的量来实现。有一点需要注意的是，这种测量永远是相对的，因为光线不可避免会有损失。因此，所有分析必须把数据进行标准化处理，包括其进一步计算的许多参数也是如此。 调制荧光仪的出现是荧光研究技术的革命性的创新。在这类仪器中，测量光源是调制(高频率开关)的，其检测器也被调谐来仅仅检测被测量光激发的荧光。因此，相对的荧光产量可以在背景光线(主要是指野外全光照的条件下)存在的条件下进行测量。目前绝大多数的荧光仪采用了调制系统，同时也强烈建议选择调制荧光仪(Kate Maxwell,2000)。 为什么荧光产量会发生改变？Kautsky效应和Beyond 叶绿素荧光产量的变化最早在1960年被Kautsky和其合作者发现。他们发现，当把植物叶片从黑暗中转入光下，荧光产量瞬间上升(大约在1秒左右)这种上升可以解释为光合途径中电子受体的还原(可接受电子的受体的减少)。一旦PSII吸收光能，初级电子受体Q A(质体醌)接受了电子，它将不能再接受电子，直到它把电子传递给下一级电子载体Q B。此期间，反应中心是关闭的，反应中心关闭的比
对于叶绿素荧光全方面的研究
对于叶绿素荧光全方面的研究 叶绿素荧光现象的发现 将暗适应的绿色植物突然暴露在可见光下后，植物绿色组织发出一种暗红色，强度不断变化的荧光。荧光随时间变化的曲线称为叶绿素荧光诱导动力学曲线。最直观的表现是，叶绿素溶液在透射光下呈绿色，在反射光下呈红色的现象。其本质是，叶绿素吸收光后，激发了捕光色素蛋白复合体，LHC将其能量传递到光系统2或光系统1，期间所吸收的光能有所损失，大约3%-9%的所吸收的光能被重新发射出来，其波长较长，即叶绿素荧光。 叶绿素荧光动力学研究的特点 1、叶绿素荧光动力学特性包含着光合作用过程的丰富信息 光能的吸收和转换 能量的传递与分配 反应中心的状态 过剩光能及其耗散 光合作用光抑制与光破坏 2、可以对光合器官进行“无损伤探查” 3、操作步骤简单快捷 光合作用的光抑制 光抑制是过剩光能造成光合功能下降的过程。过剩光能指植物所吸收的光能超出光化学反应所能利用的部分。过去人们把光抑制与光破坏等同起来，认为发生了光抑制就意味着光和机构遭到破坏。甚至把光抑制、光破坏、光氧化等，沦为一体。 光抑制的基本特征表现为： 光合效率下降说明叶片吸收的光能不能有效地转化为化学能。光破坏：PSII 是光破坏的主要场所，破坏也可能发生在反应中心也可能发生在与次级电子受体结合的蛋白上。发生光破坏后的结果：电子传递受阻、光合效率下降。当过剩的光能，不能及时有效地排散时，会对光合机构造成不可逆的伤害，如光氧化、光漂白等等。一切影响二氧化碳同化的外界因素，如低温、高温、水分亏缺、矿质元素亏缺等都会减少对光能的利用，导致过剩光能增加，进而加重光破坏。 植物防御破坏的措施 1、减少对光能的吸收 增加叶片的绒毛、蜡质 减少叶片与主茎夹角 2、增强代谢能力 碳同化 光呼吸 氮代谢 3、增加热耗散 依赖叶黄素循环的热耗散 状态转换 作用中心可逆失活 光合作用
植物表型组学研究技术(一)FluorCam 叶绿素荧光成像技术
植物表型组学研究技术（一） ——FluorCam叶绿素荧光成像技术
FluorCam叶绿素荧光成像技术 Rousseau等（High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis.Plant Methods, 2013, 9:17），利用FluorCam开放式叶绿素荧光成像系统作为高通量表型分析平台，采用图像阈值分割等分析方法，对植物病原体感染进行了定量分析检测，根据Fv/Fm将感染分为不同阶段／等级，特别是可以将用其它方法难以分辨出来的感染前期加以分辨，并对5个品种的菜豆对普通细菌性疫病的抗性进行了定量分析评价。 PSI公司首席科学家Nedbal教授与公司总裁Trtilek博士等首次将PAM叶绿素荧光技术（Pulse Amplitude Modulated technique—— 脉冲调制技术）与CCD技术结合在一起，于1996 年在世界上成功研制生产出FluorCam叶绿素荧 光成像系统（Heck等，1999；Nedbal等，2000； Govindjee and Nedbal, 2000）。FluorCam叶 绿素荧光成像技术成为上世纪90年代叶绿素荧 光技术的重要突破，使科学家对光合作用与叶 绿素荧光的研究一下子进入二维世界和显微世 界，广泛应用于植物生理生态、植物胁迫与抗 性监测、作物育种、植物表型分析等。不同于 其它成像分析技术，FluorCam叶绿素荧光成像 只对叶绿素荧光波段敏感，可以有效避免环境 光的干扰，特异性、高灵敏度反映植物生理生 态状况。 主要功能特点如下： 1)高灵敏度CCD，时间分辨率可达50帧／秒，有效抓取叶绿素荧光瞬变；可选配高分 辨率CCD，分辨率1392x1040像素，用于气孔功能成像分析、稳态荧光如GFP荧光测量等
藻类叶绿素荧光仪快讯
藻类研究监测快讯 藻类是水体生态系统中的生产者，在生态系统中起着不可或缺的作用。随着能源与环境方面研究的深入，藻类已经越来越多的被利用到实验当中。叶绿素荧光是藻细胞中的叶绿素吸收光能后受激发而释放出的能量，通过检测荧光的强弱， 可初步判断藻类的光合作用强度及生理状况。该项技术使藻 类的生理生化研究变得更加简单、方便、精确。 重要参数如下： Ft瞬时荧光，与藻细胞浓度、叶绿素浓度有 关。在暗适应状态下测得的Ft值即为Fo最小荧 光值，在给予饱和光照时，即为Fm最大荧光值； QY反映藻类的光合效率，对胁迫非常敏感；暗适应条件下测得的QY值为最大光合效率值即(Fm-Fo)/Fm，反映藻类的潜在光合效率，光照下测得的QY值为有效光量子产量即(Fm’-Ft)/Fm’，反映藻类的实际光合效率。 OJIP曲线快速荧光诱导曲线，可测定藻类在由暗适应转到光照下的瞬间荧光变化，其中 FixArea与藻类叶绿素浓度 呈正相关，可作为藻类浓度 指标；PI为功能指数，对 胁迫非常敏感。有些胁迫不 会影响PSⅡ，也不会导致 QY降低，但可通过PI体 现出来，PI可以反映三个方面：反应中心密度、用于电荷分离过程的光子吸收率、电子传递效率。 NPQ 非光化学荧光淬灭，多余辐射能的散失，反映的藻类的光保护能力。 1、AquaPen探头式藻类荧光仪 AquaPen探头式藻类荧光仪用于水体微藻类的荧光测量，其高灵敏度和便携性可以对水 体较低浓度的浮游植物进行快速测量。检测极限可达0.5 μg Chl/L,测量计算参数：Fo, Ft, Fm, Fm‘, QY, OJIP, NPQ等。 光化学光和饱和光的强度在0 - 3,000 μmol·m-2·s-1可调，光 化光的持续时间可调，界面简单，易于操作，内存可达4Mb， 4节AAA电池供电，数据可通过USB数据线传至计算机或 掌上电脑。检测器前带有暗适应罩子，适合野外测量。
叶绿素荧光成像技术及其在光合作用研究中的应用
Fluorcam荧光成像技术及其在光合作用研究 中的应用 Eco‐lab生态实验室 北京易科泰生态技术有限公司 info@eco‐https://www.wendangku.net/doc/8f2372314.html,
目录 1、叶绿素荧光成像技术发展过程 2、荧光参数及其生理意义 3、PSI介绍（荧光成像的发明者） 4、PSI产品介绍 5、应用案例
叶绿素荧光技术发展历程 ?Kautsky effect: Kautsky and Hirsch(1931)首次用肉眼发现叶绿素荧光现象并发表论文“CO2同化新实验”，后被称作“Kautsky effect” ?PAM(Pulse Amplitude Modulated Fluorometer): Schreiber（1986）等发明了PAM脉冲调制技术测量叶绿素荧光。?FluorCam：KineKc imaging of chlorophyll fluorescence: Ladislav Nedbal（2000）等于上世纪90年代末期发明了与 PAM技术相结合的叶绿素荧光成像技术
成像测量局部放大
荧光参数及其意义 ?Fo、Fm与QY，此外还有PAR_Abs及ETR ?Kautsky诱导效应：Fo，Fp，Fv，Ft_Lss，QY，Rfd ?荧光淬灭分析：Fo，Fm，Fp，Fs，Fv，QY，NPQ，Qp，Rfd 等50多个参数 ?OJIP曲线：快速荧光诱导曲线。Fo，Fj，Fi，P或Fm，Mo（OJIP曲线初始斜率）、FixArea固定面积、Sm（对关闭所有光反应中心所需能量的量度）、QY、PI等 ?LC光响应曲线：Fo，Fm，QY，QY_Ln
叶绿素荧光仪著名厂商 ?PSI：捷克布尔诺Brno（孟德尔在此发现著名的孟德尔遗传定律），Ladislav Nedbal为首席科学家和主要股东（另一股东为David Kramer，美国密执根州立大学教授），1997年为美国华盛顿大学H.Pakrasi教授研制成了第一台FluorCam荧光成像系统。主要产品有： –FluorCam叶绿素荧光成像系列产品 –FL3500/FL5000双调制荧光仪系列产品 –FluorPen及AquaPen等手持式荧光仪产品 –光养生物反应器等藻类培养与在线监测产品 –光源与植物培养室 ?Optics：美国，主要产品为OS5p‐PAM叶绿素荧光仪等?Walz：德国，主要产品为PAM2500叶绿素荧光仪等
浅谈荧光分析法的特点及在环境分析中的应用
荧光分析法的特点及在环境分析中的应用 摘要：论文综述了荧光分析法的特点及在环境分析中的应用。重点分析了荧光分析法的原理、特点，以及常用的荧光分析法的讨论。分析了荧光分析法在环境监测中的应用，测定范围和发展情况。 关键词：荧光分析；环境分析；应用 1.引言 环境中分析、监测的对象往往是微量、超微量的物质，有很多还具有时间性和空间性，因此对分析技术要求越来越高。荧光分析法和分光光度法以其灵敏度高、检测限低、准确性好等优点在近年来得到了迅速发展。荧光分子探针的设计合成以及荧光分析法在环境分析化学中的应用是方兴未艾的研究方向[1]。 分子荧光分析具有检测限低，灵敏度高，选择性好，取样量少，方法简捷快速等特点，是一种重要的光谱化学分析手段，其中荧光分子探针检测技术在环境分析化学中占有重要的地位[2]。因此，在对环境的分析中，荧光分析法应用非常广泛，从天然水、饮用水到废水、污水；从土壤、大气到动植物；从人的头发、骨骼、血液到内脏等各个器官，涉及到的样品和应用范围几乎无所不有[3]。 2.荧光分析法的原理和特点 2.1.荧光分析法 2.1.1荧光及荧光分析 荧光是荧光化合物在受到紫外光、电和化学等能量激发后，电子从基态跃迁到激发态，然后通过辐射衰变释放出光子而回复到基态，即产生荧光。这些物质会在极短的时间内(8-10秒)发射出各种颜色和不同强度的可见光，而当紫外光停止照射时，所发射的光线也随之很快地消失。 荧光分析是指利用某些物质在紫外光照射下产生荧光的特性及其强度进行物质的定性和定量的分析的方法。1852年G.G.斯托克斯(G.G.Strokes)发现荧光，真正的荧光光谱测量则始于本世纪60年代。 2.1.2荧光激发光谱和发射光谱 荧光是一种光致发光现象，由于分子对光的选择性吸收，不同波长的入射光便具有不同的激发效率。如果固定荧光的发射波长不断改变激发光的波长，并记
荧光分析法检测原理及应用举例
1荧光定义 某些化学物质从外界吸收并储存能量而进入激发态，当其从激发态回到基态时，过剩的能量以电磁辐射的形式放射出去即发光，称之为荧光。可产生荧光的分子或原子在接受能量后引起发光，供能一旦停止，荧光现象随之消失。 2荧光分类 由化学反应引起的荧光称为化学荧光，由光激发引起的荧光称为光致荧光，课题主要研究光致荧光。按产生荧光的基本微粒不同，荧光可分为原子荧光、X 射线荧光和分子荧光，课题主要研究分子荧光。 3光致荧光机理 某一波长的光照射在分子上，分子对此光有吸收作用，光能量被分子所吸收，分子具有的能量使分子的能级由最低的基态能级上升至较高的各个激发态的不同振动能级，称为跃迁。分子在各个激发态处于不稳定的状态，并随时在激发态的不同振动能级下降至基态，在下降过程中，分子产生发光现象，此过程为释放能量的过程，即为光致荧光的机理。光致荧光的过程按照时间顺序可分为以下几部分。 3.1 分子受激发过程 在波长为10~400nm的紫外区或390~780nm的可见光区，光具有较高的能量，当某一特征波长的光照射分子时，是的分子会吸收此特征波长的光能量，能量由光传递到分子上，此过程为分子受激发过程。分子中的电子会出现跃迁过程，在稳定的基态向不稳定的激发态跃迁。跃迁所需要的能量为跃迁前后两个能级的能量差，即为吸收光的能量。分子跃迁至不稳定的激发态中即为电子激发态分子。 在电子激发态中，存在多重态。多重态表示为2S+1 o S为0或1,它表示电子在自转过程中，具有的角动量的代数和。S=0 表示所有电子自旋的角动量代数和为0,即所有电子都是自旋配对的，那么2S+仁1,电子所处的激发态为单重态，用S i 表示，由此可推出，S0 即为基态的单重态，S1 为第一跃迁能级激发态的单重态，S2为第二跃迁能级激发态的单重态。S=1表示电子的自旋方向不能配对，说明电子在跃迁过程中自旋方向有变化，存在不配对的电子为2个，2S+仁3,电子在激发态中位于第三振动能级，称为三重态，用T i 来表示，T1 即为第一激发 态中的三重态，T2即为第二激发态中的三重态，以此类推。 分子跃迁至各个激发态中，状态不稳定，随时会释放出能量，释放能量的类型有两种：一种是辐射跃迁，另一种是非辐射跃迁，释放能量会回到稳定的基态。
大白菜叶色突变体的HRM鉴定及其叶绿素荧光参数分析
园艺学报，（）：– 2014411122152224 http: // www. ahs. ac. cn Acta Horticulturae Sinica E-mail: yuanyixuebao@https://www.wendangku.net/doc/8f2372314.html, 收稿日期：2014–08–22；修回日期：2014–10–24 基金项目：河北省海外高层次人才百人计划项目（E2013100011）；河北省杰出青年科学基金项目（C2013204118）；‘十二五’农村领域国家科技计划课题（2012AA100202-5）；农业部农业科研杰出人才培养计划项目（2130106）；高等学校博士学科点专项基金项目（20121302110006） 大白菜叶色突变体的HRM 鉴定及其叶绿素荧光参数分析 刘梦洋，卢 银，赵建军，王彦华，申书兴* （河北农业大学园艺学院，河北省蔬菜种质创新与利用重点实验室，河北保定 071000） 摘 要：将大白菜经甲基磺酸乙酯（EMS ）诱变种子获得的42株叶色突变体按照生殖时期叶片颜色和叶绿素含量分为9种类型：深绿色、灰绿色、绿色、浅绿色、白绿色、白浅绿色、黄绿色、黄浅绿色、黄色；利用高分辨率熔解曲线（high resolution melting ，HRM ）技术对叶绿素荧光基因HCF164突变进行了筛选并结合叶绿素荧光参数测定，获得了1株黄绿色高光合效率突变体A29，1株黄绿色光合结构损伤突变体A35和1株浅绿色光合电子传递受阻突变体A21；对另外7个叶色相关基因的突变进行了HRM 鉴定，表明叶绿素相关基因ATRCCR 、CLH2、PORA 突变可能是造成18个突变体叶色变化的主要原因，黄叶特异基因家族YLS 突变与叶色变化也有关系。 关键词：大白菜；诱变；突变体叶色；HRM ；叶绿素荧光 中图分类号：S 634.1 文献标志码：A 文章编号：0513-353X （2014）11-2215-10 HRM Identification and Chlorophyll Fluorescence Characteristics on Leaf Color Mutants in Chinese Cabbage LIU Meng-yang ，LU Yin ，ZHAO Jian-jun ，WANG Yan-hua ，and SHEN Shu-xing * （College of Horticulture ，Agricultural University of Hebei ，Key Laboratory for Vegetable Germplasm Enhancement and Utilization of Hebei ，Baoding ，Heibei 071001，China ） Abstract ：Forty-two leaf color mutants of Chinese cabbage obtained through EMS seeds mutagenesis were used as materials in this study. According to leaf color and leaf chlorophyll content at generative growth mutations were suggested to be divided into 9 types ：Dark green ，gray-green ，green ，light green ，white-green ，light white-green ，yellow-green ，light yellow-green and yellow. By detecting the nucleotide variation of the gene HCF164 related to chlorophyll fluorescence using HRM technology and by measuring chlorophyll fluorescence characteristics ，we identified one yellow-green leaf color mutant A29 with high photosynthesis efficiency ，one yellow-green leaf color mutant A35 with photosynthetic structure damages ，one light green mutant A21 with photosynthetic electron transport obstruction. Through identifying other 7 leaf-color-related genes by HRM ，mutation of chlorophyll-related genes ATRCCR ，CLH2 and PORA could be the main reason resulted in 18 leaf color mutants ，mutation of yellow-leaf- specific genes was also affected the variation of leaf color. * 通信作者 Author for correspondence （E-mail ：shensx@https://www.wendangku.net/doc/8f2372314.html, ）
时间分辨荧光分析技术
1.1 时间分辨荧光分析技术 时间分辨荧光生化分析技术是基于稀土荧光配合物特殊的荧光性质而建立起来的，自1978年提出以来[1]，已广泛的应用于免疫分析、核酸测定、荧光显微镜成像、细胞识别、单细胞原位测定、生物芯片等生化领域，并发展出了相应的时间分辨荧光免疫测定法、时间分辨荧光DNA 杂交测定法、时间分辨荧光显微镜成像测定法、时间分辨荧光细胞活性测定法及时间分辨荧光生物芯片测定法等分支。 本节主要对稀土荧光配合物的发光机理、荧光性质，时间分辨荧光测定的原理，时间分辨荧光免疫分析技术，时间分辨荧光显微镜成像技术的研究进展等加以介绍。 1.1.1 稀土荧光配合物的发光机理及荧光性质 稀土元素指的是元素周期表中IIIB 族的镧系元素以及钪和钇，共17种元素。其中镧系元素的外层电子结构为4f 0-145d 0-106s 1-2，由于5s 和5p 电子对4f 电子的屏蔽作用，导致这些金属及其离子的性质十分相似。图1.1给出了四种三价稀土离子的基态及激发态电子能级图[2]。 1020 152530355 E N E R G Y ,103c m -1 6 H 5/2 G 5/2 6 H 15/2 7 F 0 F 2D 0 5D 1 7F 6 F 5 4 5D 3 13/2 4 9/2 Sm 3+ Eu 3+ Tb 3+ Dy 3+ H 9/2 图1.1 部分三价稀土离子的电子能级图 Fig. 1.1 Electronic energy levels of certain lanthanide(III) ions 大部分稀土离子本身是不具有荧光性质的，只有Sm 3+、Eu 3+、Tb 3+和Dy 3+的水溶液在紫外光或可见光的激发下能够发出微弱的荧光。当Sm 3+、Eu 3+、Tb 3+和Dy 3+与某些有机配位体形成配合物时其荧光强度会显著增强，这种发光是基于配合物由配位体到中心稀土离子的能量转移所产生的[3-8]。以铕(III)配合物为例，其荧
FluorCam叶绿素荧光成像文献 2011 Hyperspectral and Chlorophyll Fluorescence Imaging
Sensors2011, 11, 3765-3779; doi:10.3390/s110403765 OPEN ACCESS sensors ISSN 1424-8220 https://www.wendangku.net/doc/8f2372314.html,/journal/sensors Article Hyperspectral and Chlorophyll Fluorescence Imaging to Analyse the Impact of Fusarium culmorum on the Photosynthetic Integrity of Infected Wheat Ears Elke Bauriegel 1,*, Antje Giebel 1 and Werner B. Herppich 2 1Department of Engineering for Crop Production, Leibniz-Institute for Agricultural Engineering Potsdam-Bornim, D-14469 Potsdam, Germany; E-Mail: agiebel@atb-potsdam.de 2Department of Horticultural Engineering, Leibniz-Institute for Agricultural Engineering Potsdam-Bornim, D-14469 Potsdam, Germany; E-Mail: wherppich@atb-potsdam.de * Author to whom correspondence should be addressed; E-Mail: ebauriegel@atb-potsdam.de; Tel.: +49-331-5699-414; Fax: +49-331-5699-849. Received: 24 January 2011; in revised form: 23 March 2011 / Accepted: 25 March 2011 / Published: 28 March 2011 Abstract: Head blight on wheat, caused by Fusarium spp., is a serious problem for both farmers and food production due to the concomitant production of highly toxic mycotoxins in infected cereals. For selective mycotoxin analyses, information about the on-field status of infestation would be helpful. Early symptom detection directly on ears, together with the corresponding geographic position, would be important for selective harvesting. Hence, the capabilities of various digital imaging methods to detect head blight disease on winter wheat were tested. Time series of images of healthy and artificially Fusarium-infected ears were recorded with a laboratory hyperspectral imaging system (wavelength range: 400 nm to 1,000 nm). Disease-specific spectral signatures were evaluated with an imaging software. Applying the ?Spectral Angle Mapper‘ me thod, healthy and infected ear tissue could be clearly classified. Simultaneously, chlorophyll fluorescence imaging of healthy and infected ears, and visual rating of the severity of disease was performed. Between six and eleven days after artificial inoculation, photosynthetic efficiency of infected compared to healthy ears decreased. The severity of disease highly correlated with photosynthetic efficiency. Above an infection limit of 5% severity of disease, chlorophyll fluorescence imaging reliably recognised infected ears. With this technique, differentiation of the severity of disease was successful in steps of 10%. Depending on the quality of chosen regions of interests, hyperspectral imaging readily detects head blight 7 d after inoculation
叶片荧光测量实验报告
叶片荧光测量实验报告 1.实验目的 2.实验方法 利用PAM100,荧光成像系统测量叶绿素荧光 3.实验原理及一些参数的意义 荧光的变化反映光合与热耗散的变化。 光化学淬灭（Photochemical Quenching）:由于光合作用引起的荧光下降，反映了光合活性的高低。 qP=(Fm’-Fs)/Fv’=1-(Fs-Fo’)/(Fm’-Fo’) （基于“沼泽模型”） qL=(Fm’-F)/(Fm’-Fo’)·Fo’/F=qP·Fo’/F （基于“湖泊模型”） 非光化学淬灭（Non-Photochemical Quenching）:由于热耗散引起的荧光下降。 qN=(Fv-Fv’)/Fv=1-(Fm’-Fo’)/(Fm-Fo) NPQ=(Fm-Fm’)/Fm’=Fm/Fm’-1 ，不需测定Fo’，适合野外调查qN或NPQ反映了植物耗散过剩光能转化为热的能力，反映了植物的光保护能力。 Fv/Fm =(Fm-Fo)/Fm : PS II的最大量子效率，反映植物潜在最大光合能力，高等植物一般在0.8－0.84之间，当植物受到胁迫（Stress）时，Fv/Fm显著下降。 ΦPS II = Yield = (Fm’-Fs)/Fm’ = ΔF/Fm’= qP·Fv’/Fm’: 任一光照状态下PS II的实际量子产量（实际光合能力、实际光合效率）
不需暗适应，不需测定Fo’，适合野外调查。 Y(NPQ)=1-Y(II)-1/(NPQ+1+qL(Fm/Fo-1))：调节性能量耗散，PS II 处调节性能量耗散的量子产量。若Y(NPQ)较高，一方面表明植物接受的光强过剩，另一方面则说明植物仍可以通过调节（如将过剩光能耗散为热）来保护自身。Y（NPQ）是光保护的重要指标。 Y(NO)=1/(NPQ+1+qL(Fm/Fo-1))：非调节性能量耗散 PS II处非调节性能量耗散的量子产量。若Y(NO)较高，则表明光化学能量转换和保护性的调节机制（如热耗散）不足以将植物吸收的光能完全消耗掉。也就是说，入射光强超过了植物能接受的程度。这时，植物可能已经受到损伤，或者（尽管还未受到损伤）继续照光的话植物将要受到损伤。Y（NO）是光损伤的重要指标。 P：光合速率，即相对电子传递速率rETR Pm: 最大光合速率，即最大相对电子传递速率rETRmax α：初始斜率，反映了光能的利用效率 β：光抑制参数 Ik=Pm/α：半饱和光强，反映了样品对强光的耐受能力。
第4章第1节_叶绿素荧光参数及意义-v2
第四章 叶绿素荧光技术应用 第一节 叶绿素荧光参数及其意义 韩志国，吕中贤（泽泉开放实验室，上海泽泉科技有限公司，上海，200333） 叶绿素荧光技术作为光合作用的经典测量方法，已经成为藻类生理生态研究领域功能最强大、使用最广泛的技术之一。由于常温常压下叶绿素荧光主要来源于光系统 II 的叶绿素 a ，而光系统 II 处于整个光合作用过程的最上游，因此包括光反应和暗反应在内的多数光合过程的变化都会反馈给光系统 II ，进而引起叶绿素 a 荧光的变化，也就是说几乎所有光合作用过程的变化都可通过叶绿素荧光反映出来。与其它测量方法相比，叶绿素荧光技术还具有不需破碎细胞、简便、快捷、可靠等特性，因此在国际上得到了广泛的应用。 1 叶绿素荧光的来源 藻细胞内的叶绿素分子既可以直接捕获光能，也可以间接获取其它捕光色素（如类胡萝卜素）传递来的能量。叶绿素分子得到能量后，会从基态（低能态）跃迁到激发态（高能态）。根据吸收的能量多少，叶绿素分子可以跃迁到不同能级的激发态。若叶绿素分子吸收蓝光，则跃迁到较高激发态；若叶绿素分析吸收红光，则跃迁到最低激发态。处于较高激发态的叶绿素分子很不稳定，会在几百飞秒（fs ，1 fs=10－15 s ）内通过振动弛豫向周围环境辐射热量，回到最低激发态（图 1）。而最低激发态的叶绿素分 子可以稳定存在几纳秒（ns ，1 ns=10－9 s ）。 A 较高激发态 B 热耗散 吸收蓝光 吸收红光 最低激发态 能量 荧光 基态 蓝 波长 红 荧光 图 1 叶绿素吸收光能后能级变化（A ）和对应的吸收光谱（B ）（引自韩博平 et al., 2003） 处于最低激发态的叶绿素分子可以通过几种途径（图 2）释放能量回到基态(韩博平 et al., 2003; Schreiber, 2004)：1）将能量在一系列叶绿素分子之间传递，最后传递给反应中心叶绿素 a ，用于进行光化学反应；2）以热的形式将能量耗散掉，即非辐射能量耗散（热耗散）；3）放出荧光。这三个途径相互竞争、此消彼长，往往是具有最大速率的途径处于支配地位。一般而言，叶绿素荧光发生在纳秒级，而 光化学反应发射在皮秒级（ps ，1 ps=10－12 s ），因此在正常生理状态下（室温下），捕光色素吸收的能 量主要用于进行光化学反应，荧光只占约 3%～5%(Krause and Weis, 1991; 林世青 et al., 1992)。 在活体细胞内，由于激发能从叶绿素 b 到叶绿素 a 的传递几乎达到 100％的效率，因此基本检测不到叶绿素 b 荧光。在常温常压下，光系统 I 的叶绿素 a 发出的荧光很弱，基本可以忽略不计，对光系统 I 叶绿素 a 荧光的研究要在 77 K 的低温下进行。因此，当我们谈到活体叶绿素荧光时，其实指的是来自光系统 II 的叶绿素 a 发出的荧光。
PlantScreen 植物表型成像分析平台应用案例
PlantScreen 一、植物表型组学与PlantScreen 植物表型成像分析技术 自 20 世纪 90 年代初以来，生命科学领域出现了最为引人注目的“组学”新概念和新学科，如基因组学（genomics ）、转录组学（transcriptomics ）、蛋白质组学（proteomics ）和代谢组学（metabolomics ）等。伴随各种组学的不断兴起和发展，90年代末，人们提出了表型组（phenome ）和表型组学（phenomics ）的概念。2013年Monya Baker 在《Nature 》发表文章“THE ‘OMES PUZZLE ”将表型组学称为“前景光明（Aspiring ）”的组学研究项目[1]。 表型组定义为：在细胞、组织、器官、生物体或 种属水平上表现出的所有表型的组合。表型组学可定 义为一门在基因组水平上系统研究某一生物或细胞 在各种不同环境条件下所有表型的学科[2] DNA 芯片技术的进一步完善，为植物功能基因 组学研究提供契机[3]。而之前植物表型组学一直缺乏 合适的研究仪器，研究者不得不使用传统方法来获取 表型组学的海量数据。随着近几年FluorCam 叶绿素 荧光成像技术、RGB 彩色成像分析技术乃至集合了 多种最先进表型成像分析技术和植物自动培养技术 的PlantScreen 植物表型成像分析技术逐渐成熟，直 接促进了植物表型组学的发展，同时为基因组、蛋白 组、代谢组及表型组数据进一步整合起来研究提供了 新的挑战和可行性。关于FluorCam 叶绿素荧光成像 技术与RGB 彩色成像分析技术在表型组学中的应用， 请见： 植物表型组学研究技术（一）——FluorCam 叶 绿素荧光成像技术 植物表型组学研究技术（二）——叶绿素荧光成 像与 RGB 彩色成像分析系统 PSI 公司在功能强大的FluorCam 叶绿素荧光成像技术基础上，结合LED 植物智能培养、自动化控制系统、植物热成像分析、植物近红外成像分析、植物高光谱分析、自动条码识别管理、RGB 真彩 3D 成像、自动称重与浇灌系统等多项先进植物表型技术，开发出了PlantScreen 植物表型成像分析系统。这一大型系统切合国际最新的植物表型组学研究，以最优化的方式实现了拟南芥、小麦、水稻、玉米乃至 目前各种热门与不热门的组学项目，表型组(phenome)被认为是“前景极为光明的”[1]