WO2007041755A1 - Imagerie hyperspectrale de contaminants dans des produits et procedes d'agriculture - Google Patents

Imagerie hyperspectrale de contaminants dans des produits et procedes d'agriculture Download PDF

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Publication number
WO2007041755A1
WO2007041755A1 PCT/AU2006/000999 AU2006000999W WO2007041755A1 WO 2007041755 A1 WO2007041755 A1 WO 2007041755A1 AU 2006000999 W AU2006000999 W AU 2006000999W WO 2007041755 A1 WO2007041755 A1 WO 2007041755A1
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sample
grape
image
grapes
infective agent
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PCT/AU2006/000999
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English (en)
Inventor
Robert George Dambergs
Belinda Eva Stummer
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The Australian Wine Research Institute
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Priority claimed from AU2005905552A external-priority patent/AU2005905552A0/en
Application filed by The Australian Wine Research Institute filed Critical The Australian Wine Research Institute
Publication of WO2007041755A1 publication Critical patent/WO2007041755A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/02—Details
    • G01J3/10—Arrangements of light sources specially adapted for spectrometry or colorimetry
    • G01J3/108—Arrangements of light sources specially adapted for spectrometry or colorimetry for measurement in the infrared range
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/009—Sorting of fruit
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods
    • G01N2201/1293—Using chemometrical methods resolving multicomponent spectra

Definitions

  • the present invention relates to an electromagnetic light method for characterizing a sample(s).
  • the present invention relates to near infrared (NIR) and/or visible-near infrared (VIS-NIR) and/or UV imaging methods for characterizing sample(s).
  • NIR near infrared
  • VIS-NIR visible-near infrared
  • UV imaging methods for characterizing sample(s).
  • Previous methods which have been used to assess fruit include for example, visual inspection and grading, tasting and laboratory analytical techniques.
  • Visual methods of inspection tend to be time consuming and potentially subjective.
  • Laboratory analytical techniques have major disadvantages resulting from the large amount of sample handling. The samples must be harvested, collected, bagged, labeled, dried, and finally sent to the laboratory, ground and analyzed for constituent analysis. This excessive sample handling adds both cost and time to the analysis.
  • Agriculturalists would prefer to make informed decisions regarding the fruit before harvesting and these kinds of methods do not lend themselves to an assessment out in the field.
  • Viticulturists and winemakers are particularly interested in accessing analytical techniques which can quickly and efficiently assess grape qualities that can often be related to the characteristics of the wine produced.
  • grapes and grape vines are susceptible to a variety of infective agent(s) which can affect the grape vine, the grapes, and the wine produced from infected grapes.
  • Infective agents may be microorganisms and may include viral, bacterial and fungal infection. Some infective agents may be pathogenic.
  • viticulturists Of particular concern to viticulturists is fungal infection which can have major deleterious effects on both the plant and the agricultural products derived from the plants.
  • powdery mildew caused by Erisyphe necator, is one of the most economically devastating diseases affecting grapevines (Vitis vinifera).
  • Costly fungicide programs are widely used to prevent disease development on grapes.
  • low levels of infection can persist on susceptible varieties in seasons with favourable weather conditions for disease, especially if sprays have been missed between flowering and berry set.
  • Powdery mildew not only reduces the yield and marketability of grapes, but also the quality of the wine.
  • Levels of disease on grapes as low 3% can taint wines. Low levels of disease are difficult to assess visually in the vineyard and to quantify in large consignments of grapes.
  • Botrytis bunch rot caused by Botrytis cinerea.
  • Botrytis infection in harvested grapes can have a significant detrimental impact on wine quality through oxidative reactions caused by fungal laccase. The presence of fungal storage polysaccharides in juice and wine can also cause clarification problems.
  • secondary infections that cause other fruit rots and produce off-flavours in wine are also associated with Botrytis rot.
  • Ochratoxin A is a mycotoxin that can cause kidney disease and affect the immune system of humans. It may also be carcinogenic and have teratogenic effects.
  • OA Ochratoxin A
  • Aspergillus carbonarius, and sometimes Aspergillus niger (both black Aspergillus spp.) are present in Australia and may produce OA.
  • the Aspergillus fungi involved in OA production appeared to be secondary invaders that infected grapes only after damage caused by pre-harvest rain, infection by other fungi or mechanical damage.
  • Matter other than grapes can include leaves, canes, vine-wood, trellising material, stones and is currently graded visually with the aid of reference charts. Hyperspectral imaging could make this more objective.
  • an NIR image and/or a VIS-NIR image can be used to determine the presence of an infective agent in a sample.
  • the present invention provides a method of determining the presence of "matter other than grapes" (MOG) associated with a sample(s), the method comprising: obtaining a near infrared (NIR) reflectance image(s) of at least a portion of the sample(s); and analysing the image(s) to determine the presence of "matter other than grapes” (MOG).
  • the MOG is the presence of one or more infective agent(s).
  • the present invention provides a method of determining the presence of one or more infective agent(s) associated with a sample(s), the method comprising: obtaining a near infrared (NIR) reflectance image(s) of at least a portion of the sample(s); and analysing the image(s) to determine the presence of one or more infective agent(s).
  • NIR near infrared
  • the present invention provides a method of determining the presence of one or more infective agent(s) associated with a sample(s), the method comprising: obtaining a visible-near infrared (VIS-NIR) reflectance image(s) of at least a portion of the sample(s); and analysing the image(s) to determine the presence of one or more infective agent(s).
  • the sample may be an agricultural product.
  • the agricultural product may be selected from one or more of the group comprising fruit, berry, bulb, grain, seed, leaf, flower, stem, vine, root, petal, and/or part thereof.
  • the agricultural product is a fruit.
  • the fruit is a red or a white grape.
  • the infective agent may be a microorganism.
  • the microorganism may be selected from one or more of the group comprising a virus, bacteria, protozoa and/or fungus.
  • the microorganism is a fungus and may be selected from one or more the group comprising Erysiphe necator, Botrytis cinerea, Aspergillus fungi such as
  • the microorganism may be a pathogenic microorganism.
  • the sample is a grape(s) and the fungal infection is by a fungus which causes powdery mildew in grapes.
  • the fungus is Erysiphe necator.
  • the determination may be quantitative or qualitative.
  • the method may include the use of a light source to illuminate the sample(s).
  • the light source may be selected from any one or more of the group comprising tungsten halogen lamp, light emitting diode, laser diode, tuneable diode laser and / or flash lamp.
  • the present invention provides a method of determining one or more characteristics of an agricultural sample(s), the method comprising obtaining an NIR and/or VIS-NIR reflectance image of at least a portion of the agricultural sample(s); and analysing at least a portion of the image using chemometric analysis to determine one or more characteristics of the agricultural sample.
  • the characteristic(s) may be selected from one or more of the group comprising: sugars, total soluble solids, anthocyanin, tannin, pigments selected from the group comprising yellow, orange, brown and red; acidity; colour; pH; total acidity; firmness; internal and / or external disorder; the presence of infective agent(s); insect(s), and eating quality.
  • the method may be applied to the agricultural product prior to harvesting of the agricultural product.
  • This application of the method may be used by the agriculturalist to assist in the management of the agricultural product, for example, fertilizer needs, whether or not to harvest the product, timing of harvesting, water needs and treatment with anti-infestation agents, such as fungicide agents.
  • the imaging method may be used during harvesting of the agricultural product.
  • the imaging camera may be mounted on the harvesting device and a real-time assessment of the characteristics of the product obtained. This kind of information is useful as the results could be used to determine whether the agricultural product meets a predetermined standard. For example, the product may be separated at the time of harvest into different groups depending on the results of the imaging analysis.
  • the method may be applied during transportation of the agricultural product(s).
  • the imaging equipment may be mounted onto the transport vehicle where one or more characteristics of the product(s) could be determined during transport of the product.
  • the method may also be performed at the weigh-bridge where it could be used to assess the quality of the product and potentially used in determining the price paid for the product(s) and whether to accept the product(s).
  • the method may be used to determine whether further processing of the product is required before transportation to the buyer of the product.
  • the method may be applied to the agricultural product during processing of the agricultural product.
  • winemakers are desirous to assess one or more characteristics of the grapes before making the wine.
  • the level of infective agent(s) associated with the grapes for example Erysiphe necator, can influence the quality of wine produced.
  • Knowledge of the infective agent levels of the grape can be used to tailor the wine making process to suit the characteristics of the grape.
  • the method may be applied to detect the presence of foreign material with the product such as leaves, wood, stones, trellising material.
  • the NIR reflectance image obtained comprises thousands of linearly independent, spatially-resolved NIR reflectance spectra which are collected with each collection.
  • One or more of these individual spectra may be processed using chemometric analysis techniques to either qualitatively or quantitatively detect, classify, identify and/or visualize one or more characteristic(s) of the product(s).
  • the present invention provides a method for sorting grape(s) according to the level of infection of an infective agent(s), the method comprising: obtaining a NIR and / or VIS-NIR reflectance image(s) of at least a portion of the grape(s); analysing the image(s) to assess the level of infective agent(s) present; comparing the level of assessed infection to a predetermined level and sorting the grape(s) accordingly.
  • the infective agent is a fungus, most preferably the fungus is Erysiphe necator.
  • the sorting may be performed before harvesting, during harvesting and/or after harvesting. Analysis of the image according to the first and second aspects of the present invention may be performed using the analysis method of the third aspect.
  • Figure 1 illustrates the plot of DNA content (ng/150 ng) vs visually graded infection level.
  • Figure 2 (a) illustrates the plot of absorbance vs wavelength for raw spectra.
  • Figure 2 (b) illustrates the plot of the first derivative of absorbance vs wavelength.
  • Figure 3 (a) illustrates the plot of absorbance standard deviation for all samples vs wavelengths for raw spectra.
  • Figure 3 (b) illustrates the plot of the first derivative absorbance standard deviation for all samples vs wavelengths.
  • Figure 4 (a) illustrates the plot of total soluble solids (°Brix) vs visually graded infection level.
  • Figure 4 (b) illustrates the plot of pH vs visually graded infection level.
  • Figure 5 (a) illustrates the mean values of the first principal component (PCl) at each powdery mildew infection level, error bars show 1 standard deviation.
  • Principal component analysis (PCA) was done on first derivative spectra with a wavelength range of 450 - 1884 nm.
  • Figure 5 (b) illustrates the mean values of the second principal component (PC2) at each powdery mildew infection level, error bars show 1 standard deviation.
  • Principal component analysis (PCA) was done on first derivative spectra with a wavelength range of 450 - 1884 nm.
  • Figure 5 (c) illustrates the mean values of the third principal component (PC3) at each powdery mildew infection level, error bars show 1 standard deviation.
  • Principal component analysis (PCA) was done on first derivative spectra with a wavelength range of 450 - 1884 nm.
  • Figure 5 (d) illustrates the mean values of the fourth principal component (PC4) at each powdery mildew infection level, error bars show 1 standard deviation.
  • Principal component analysis (PCA) was done on first derivative spectra with a wavelength range of 450 - 1884 nm.
  • Figure 8 illustrates the wavelength loadings for the first 3 factors of a PLS calibration for Botrytis infection of Shiraz grapes.
  • Figure 9 illustrates a plot of absorbance vs. wavelength for three grape wood E. lata culture extracts, with varying Eutypinol concentrations as measured by HPLC.
  • the present invention provides a system which is effective, fast, has high resolution, and which has a greater accuracy and discrimination rate than prior art devices or systems. It is desirable that the present invention provide a method and apparatus for the detection and discrimination of defects in products, such as agricultural products.
  • the present invention provides a method and apparatus for sorting products based on the character, number, type or aggregation of defects.
  • an "agricultural product” refers to any plant material that is being interrogated by a method of the present invention.
  • An agricultural product can be, for example, a fraction of a grape, a whole grape, more than one grape, and other plant tissues, among others.
  • Controls can include grapes known to be susceptible and resistant. The correlation of the disease to a particular structural change can be established by an appropriate statistical analysis. It is understood that controls need not be run against a particular grape or grape batch once a correlation has been established.
  • Other agricultural products or plant tissues can be substituted for grapes.
  • agricultural products include, but are not limited to, plant tissues such as fruit, but also include non-plant based material such as non-organic matter or non-plant based matter that occur in an agricultural context.
  • plant tissues include, but are not limited to, any plant part such as leaf, flower, root, and petal.
  • the samples were examined using an optical microscope and categorized, however, insufficient samples could be found at level 3 and 4 so they were combined to allow adequate replication.
  • the samples were stored at -18°C until they were further processed.
  • Grape samples were thawed and then homogenised while still cool (approximately 4°C) with a Grindomix GM 200 (Retsch GmbH & Co., Haan, Germany) for 20 seconds at 8,000 rprn using a floating lid to maintain contact of sample with the blades. Homogenates were immediately sub-sampled and frozen for comparative DNA analysis. NIR scans were performed immediately on the remaining homogenates.
  • Orion ROSS epoxy body combination electrode (model 815600, Thermo Orion, USA).
  • the infection level of powdery mildew caused by the fungus Erysiphe necator was assessed using a Erysiphe necator-spGci ⁇ c DNA probe, pUnAl as described by
  • Erysiphe necator-specific probe pUnAl, obtained from a plasmid library. Southern hybridisation, probing and autoradiography were according to Sambrook, J., Fritsch,
  • Slot blots contained approximately 50- 100 ng of total DNA (grapevine plus Erysiphe necator). Controls included DNA extracted from healthy grapevine tissue, and from various micro-organisms commonly associated with grapevines.
  • the entire DNA probe analysis process takes approximately 1 week to perform.
  • Homogenates were scanned, without temperature equilibration, in a FOSS NIRSystems 6500 (FOSS NIRSystems, Silver Spring, Maryland, USA), in reflectance mode at 2 nm intervals over the wavelength range of 400-2500 nm.
  • a reference scan was performed before each sample, using a rare earth metal oxide impregnated ceramic tile as a reference. Spectra were stored as the average of 32 scans. Scanning control was performed with the Vision software package (FOSS NIRSystems, Silver Spring, Maryland, USA).
  • chemometric models were developed for each parameter.
  • the model is a mathematical construct developed using samples of the same product or class of products.
  • a Chemometric model is developed by collecting spectral readings from a group of samples that display (a) the maximum variability of the characteristic of interest, and (b) non-correlating or random variability in all other characteristics. The same samples are submitted for independent testing to measure the characteristic of interest by a standard analytical method. The spectral data and independent test data were then analyzed using commercially available chemometrics software.
  • the statistical processes used in quantitative spectral analysis include multiple linear regression, classical least squares, inverse least squares, and principal component regression.
  • the statistical processes used in qualitative spectral analysis include K- nearest neighbours, SIMCA and others.
  • Spectral features correlated with infection level can be observed both in the visible (400 - 700 nm) and NIR regions (700 - 2500 nm).
  • the large variations due to baseline shifts with raw spectra are illustrated by the broad spectral standard deviation, Figure 3 (a).
  • the spectral standard deviation profile is sharpened with first derivative transformation and some distinct areas of spectral variation occurred in both the visible and NIR wavelength ranges, Figure 3(b).
  • An explanation for spectral variations with infection could be that TSS and pH can be correlated with infection level. This would apply particularly in the NIR spectral regions. There appeared to be very variable but high TSS/low pH in the highest infection level Figure 4.
  • Table 1 Tukey pair wise comparison probabilities for one-way analysis of variance (ANOVA), with infection level as the categorical variable compared with (a) total soluble solids (TSS) or (b) pH as the dependant variable.
  • ANOVA analysis of variance
  • Principal component analysis was performed on first derivative spectra. Means and standard deviations for the first four principal components of spectra from each infection level are shown in Figure 5. The first principal component correlated strongly with the infection level and combinations of the other components provided further discrimination of infection levels.
  • a classification matrix for discriminant analysis of infection level, using the first four PCA scores is shown in Table 2(a); 100% classification was achieved. Note that a quadratic function was used (ie. the changes with infection level were not linear). If cross-validation was used (ie. samples sequentially removed from the training set and predicted independently), 92% classification was still achieved, suggesting that the classification algorithm is robust and not over-fitted to this relatively small dataset Table 2 (b). Level 1 Level 2 Level 3 Level 5 % correct
  • Table 2 Classification matrices for discrimination of powdery mildew infection level using the first 4 principal component analysis scores and a quadratic function for the best combination of scores to discriminate levels.
  • 2 (a) shows data without cross- validation.
  • 2 (b) shows data with cross-validation i.e. sequential removal of samples that were predicted with remaining samples. Predicted levels are in columns and actual levels in rows. Correct predictions are italicized. Only one level 2 and one level 3 sample (shaded in bold) was incorrectly classified during cross-validation.
  • Botrytis has a dramatic effect on grape pigments, so to avoid domination of calibrations by visible wavelengths and thereby reducing calibration robustness in the face of grapes with varying intrinsic pigment concentrations, only NIR wavelengths were used (600-1800 nm).
  • An example of a calibration for detection of Botrytis in Shiraz grapes had an R 2 of 0.95 and a standard error of cross-validation of 1.25 % w/w. Calibration loadings were strongest at approximately 700 nm, a wavelength easily achieved with inexpensive silicon detectors.
  • a Spectral Dimensions' NIR Chemical Imaging (NIR-CI) camera can be used.
  • the camera can be tuned over the wavelengths of approximately 700 to 2500 nm and the imaging system utilizes an indium gallium arsenide (InGaAs) focal-plane array (FPA) detector comprising of 240 * 320 pixels for a total of 76,800 spectra per image cube recorded.
  • InGaAs indium gallium arsenide
  • FPA focal-plane array
  • a camera which can be tuned over wide wavelengths can also be used.
  • a camera which can be tuned to include at least parts of the visible region can also be used.
  • the camera can be tuned to record VIS-NIR spectra from 350 to 2500 nm.
  • Non-limiting examples of suitable cameras include a Chemlmage CONDOR Macroscopic Chemical Imaging System camera and an Electrophysics Jade SWIR imaging camera.
  • the FPA can also be comprised of Si, SiGe, PtSi, InSb, HgCdTe, PdSi, Ge, analog vidicon types.
  • the FPA output is digitized using an analog or digital frame grabber approach.
  • the electromagnetic light reflectance image of a sample(s) can be taken using ambient light from the sun as a light source.
  • the sample area can be illuminated using an appropriate artificial light source. Illumination with a light source can enable the rapid acquisition of reproducible data with good signal/noise (S/N), even in the highly light scattering and absorbing 250-699 nm and the strongly absorbing >950 nm region.
  • the lamp can be a tungsten halogen lamp, for example a 12- Volt, 75-Watt tungsten halogen lamp.
  • Other light sources which can be used include but are not limited to light emitting diode, laser diode, tunable diode laser, flash lamp and other such sources which will provide equivalent light source and will be familiar to a person skilled in the art.
  • the lamp is held at a resting voltage of 2- Volts.
  • the lamp is ramped up to the desired voltage, a brief delay allows the lamp output to stabilize, and then spectra can be acquired.
  • the lamp is ramped down to the resting voltage. This procedure extends lamp life and prevents burning the sample.
  • the lamp can always be lighted, e.g., on a high-speed packing/sorting line or used on harvest equipment, and a light "chopper" or shutter or other equivalent article or method can be utilized to deliver light to the passing sample for a determined period of time.
  • the operation of the light source is important in extending lamp life, reducing operating expense and reducing disruption of operations.
  • the lamp voltage is ramped up and down to preserve lamp life and to lessen the likelihood of burning fruit.
  • a standby voltage keeps the lamp filaments warm.
  • An ambient/room light background measurement is made to correct for the dark spectrum, which can include ambient light. It is stored and subtracted from the sample and reference (if applicable) so that there is no contribution of ambient light to the sample spectrum, which would affect accuracy.
  • Dual intensity illumination is employed to: 1) improve data accuracy above 925 nm and below 700 nm and 2) to normalize path length changes due to scattering. Dual exposure time increases the likelihood of increased data quality with large and small fruit. Utilization of more than one light detector, with each positioned at different distances from the sample, will likewise increase the ability to obtain increased data quality throughout each portion of the spectrum from approximately 350 nm to 1150 nm.
  • the electromagnetic light reflectance image comprises thousands of linearly independent, spatially-resolved spectra which can be collected with each collection. These spectra can then be processed to generate unique contrast intrinsic to analyte species without the use of stains, dyes, or contrast agents. For example, contrast can be generated in a VIS-NIR reflectance image and reveals the spatial distribution of properties revealed in the underlying VIS-NIR spectra. Thus the acquired VIS-NIR reflectance image comprises many thousands of pixels with each pixel represents a full spectrum. This kind of data can be suitable for multivariate (chemometric) analysis techniques such as principal component analysis (PCA), principal component regression (PCR) and partial least squares (PLS) modeling, as discussed above.
  • PCA principal component analysis
  • PCR principal component regression
  • PLS partial least squares
  • Chemometric analysis of one or more of the spectra can then be used to determine one or more of the physical or chemical characteristics of the sample.
  • chemometric analysis (or variants thereof such as piecewise direct standardization) are used to relate the spectra(s) to characteristic of the sample such as sugar composition and concentration, total soluble solids, anthocyanin, tannin pigments, including yellow and/or red coloured, acidity, pH, total acidity, firmness, color, presence of micro-organisms or foreign matter, internal or external disorder severity and type, and eating quality.
  • the acquired spectra(s) can be used to assess the level of infective agent(s) of the sample(s). For example, a VIS-NIR reflectance image can be analysed to assess the level of fungal infection of a fruiting body, such as a grape or berry.
  • an electromagnetic light reflectance image of the grape(s) can be taken and following analysis of the results, the level of powdery mildew of the grape(s) can either be qualitatively or quantitatively assessed.
  • an NIR and / or a VIS-NIR reflectance image of the grape(s) can be used to assess the level of powdery mildew infection.
  • the assessment can be qualitative or quantitative.
  • UV ultraviolet
  • the visible wavelength range is useful for monitoring pigmented compounds hi natural products, for example grape anthocyanins.
  • Near infrared can be used to monitor all compounds with hydrogen attached to carbon, nitrogen and oxygen, but in biological samples the spectrum tends to be dominated by a water signal.
  • a wavelength range that provides further information, specificity and high sensitivity is the UV range. Wavelengths below 200 nm (the "vacuum UV” range) are difficult to use as absorbance is too high and all chemical bonds absorb, but the 200-400 nm range can be used to monitor compounds with double bonds such as tannins, proteins, anthocyanins, carotenoids, aldehydes, ketones, C-aromatic compounds, O and S- heteroaromatic compounds and N-heteroaromatic compounds.
  • the absorbance peaks are shifted to longer UV wavelengths and often as far as to visible wavelengths.
  • the main advantage of using the UV range in grapes, for example, is that common constituents such as water, sugars and organic acids contribute very little to the spectrum, whereas minor constituents of interest such as tannins, anthocyanins and phenolic flavour compounds have a strong signal.
  • Some fungal metabolites also have a strong, unique UV fingerprint. Examples are the metabolites of Eutypa lata, a fungus that infects vine tissue resulting in the syndrome known as "dead arm” that causes serious economic loss in older vineyards.
  • the acetylenic phenol metabolites produced by E. lata have an unusual structure with series of conjugated double bonds, triple bonds and aldehyde groups. This gives them a strong and unique UV spectral fingerprint and offers an opportunity for rapid screening with minimal sample preparation using spectroscopy combined with chemometric methods.
  • Figure 9 illustrates examples of UV spectra of unfractionated culture extracts of E. Lata. Eutypinol concentrations were measured by HPLC and spectra from culture extracts showed clear features related to Eutypino ' l, in particular the peaks at 262, 276 and 308 nm. Principal component analysis of first derivative transformed spectra shown in Figure 10, illustrates the discriminatory power of UV spectra with regard to cultures with low medium and high Eutypinol concentration as measured by HPLC.
  • UV imaging of samples requires a UV light source such as a xenon discharge, mercury or halogen lamp.
  • the imaging camera must have sensors responsive to UV wavelengths.
  • the UV spectral image can be combined with a visible image so that spatial data can be combined with chemical data generated from the UV spectra collected within each pixel of the UV image.
  • UV, VIS and NIR images can be combined to cover a wavelength range of at least 200 to 2500 nm. Imaging algorithms can utilize all or part of that wavelength range.
  • UV imaging is the detection of E. lata in specimens of grapevine wood tissue and leaves. Samples are scanned then the spatial image overlaid with a false colour image generated by an algorithm to detect fungal mass or metabolites, or to detect degenerated vine tissue. This method can be both qualitative and quantitative. UV imaging can also be used to detect insects, animals and other foreign biological material in grape loads, using for example a protein UV signal. Grape secondary metabolites such as phenolic compounds can also be detected with UV imaging in grape loads, or on the vine with a portable device. Undesirable vine tissue in grape loads, such as leaves, petioles, canes and woody tissue can also be discriminated by their unique UV fingerprint.
  • insects of concern which can affect the quality of the grape, and/or affect the vine.
  • Non-limiting examples of insects which can be detected using aspects of the present invention include: Grape Berry Moth The grape berry" moth is a key pest of grapes that is distributed in the United
  • Grape Phylloxera is native to eastern United States, but has been distributed to other grape regions of the U.S. and is also established in Europe where it is of great economic importance. The leaf galls caused by grape phylloxera are unsightly and do little damage, however, infestation of the roots can be difficult to control and can lead to decline of vines. Severe infestations can cause defoliation and reduce shoot growth. Hosts include cultivated and wild grapes.
  • the wingless forms of the insect are very small, yellow-brown, oval or pear- shaped, and aphid-like.
  • the winged forms, which are less apt to be seen, are also aphid- like, except that wings are held flat over the back.
  • the presence of grape phylloxera is best recognized by characteristic galls it produces on the leaves or roots.
  • Leaf galls are wart-like, whereas, root galls are knot-like swellings on the rootlets, and may lead to decay of infested parts. The presence of leaf galls can be detected using methods according to the invention.
  • Larvae consume small roots and pit the surface of larger roots, causing an unthrifty condition of the plant, and reduction in yield. Vines may be killed in 3 or more years when damage is severe. Adults make chain-like feeding marks on leaves and may also feed on the surface of green grape berries. Hosts include wild and cultivated grapes.
  • the adult beetle is elongate oval, sub-cylindrical, dark reddish brown, clothed with short pubescence and is about 0.5 cm to 0.8 cm long.
  • the larva is white, hairy, curved, with a brown head.
  • Grape Flea Beetle Grape flea beetle is found in the eastern two-thirds of the United States. Adults eat buds and unfolding leaves, causing leaves to be ragged and tattered. Larvae feed on flower clusters and skeletonize leaves in a manner similar to adult rootworm feeding. Hosts include grape, plum, apple, quince, beech, elm & Virginia creeper.
  • the adult is a black snout beetle about 0.31 cm long.
  • the grub is slightly larger when full grown, and is white with a brown head and legless. It is very similar in appearance to the closely related grape cane gall maker. Grape Cane Gallmaker
  • Grape root borer is potentially the most destructive insect attacking grapes in some parts of the world. Larvae of this insect tunnel into the larger roots and crown of vines below the soil surface. Borer damage results in reduced vine growth, smaller leaves, reduced berry size, and fewer bunches of grapes.
  • Redbanded leafroller is an occasional pest of clusters and fruits, and its symptoms are very similar to grape berry moth. Larvae of this insect will feed on botli foliage and clusters. Unlike grape berry moth larvae, redbanded leafroller larvae do not crawl into the berry but remain concealed in webbing on the cluster stem and feed on the stem as well as berries.
  • the adult redbanded leafroller is a 1.25 cm long reddish-brown moth with small areas of silver, gold and orange. The moth is recognized by the red band extending across the front wings when at rest. The larva is a small, yellowish-green, unmarked caterpillar. The head capsule is the same color as the rest of the body.
  • the method of the invention can be used to identify the presence of a number of insects in agricultural products.
  • insects that can be identified can be selected from the group comprising caterpillar(s), grasshoppers, beetles, moth(s), moth pupa, Grape Berry Moth, Grape Phylloxera, Grape Rootworm, Grape Flea Beetle, Grape Cane Girdler, Grape Cane Gallmaker, Grape Root Borer, Redbanded Leafroller, scale insects, flies, fruit flies, aphids, midges or mealy bugs.
  • the method of the invention can be applied to the analysis of other sample types.
  • the method of the invention can be applied to the determination of the presence of microorganisms on other types of samples, such as hospital surfaces, kitchen surfaces, cooking surfaces, factory surfaces, and other surface where it is desirous to determine the presence of microorganism contamination.
  • the method of the invention can be used in any field including veterinary, industrial or human medicine. In the case of human medicine, the present methods can be applied to determining the presence of pathogenic microorganism on for example surgical gloves.

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Abstract

L'invention concerne des procédés permettant d'évaluer les caractéristiques d'un ou de plusieurs échantillon(s) par spectroscopie de réflectance dans le proche infrarouge (NIRS). L'échantillon peut être de type agricole, en particulier un échantillon de raisin. Le procédé de l'invention peut être utilisé pour évaluer la présence de matières autres que du raisin dans un échantillon, notamment la présence d'agents infectieux de type champignons.
PCT/AU2006/000999 2005-10-07 2006-07-14 Imagerie hyperspectrale de contaminants dans des produits et procedes d'agriculture WO2007041755A1 (fr)

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JPWO2016158820A1 (ja) * 2015-03-31 2017-05-25 三井金属計測機工株式会社 青果物検査装置
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