Radiant energy – Invisible radiant energy responsive electric signalling – Infrared responsive
Reexamination Certificate
2000-10-30
2003-11-11
Evans, F. L. (Department: 2877)
Radiant energy
Invisible radiant energy responsive electric signalling
Infrared responsive
C250S339120, C356S305000, C356S326000, C356S328000
Reexamination Certificate
active
06646264
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to a device and method for analyzing agricultural products. More particularly, the present invention relates to a device and method for real time, non-destructive analysis of the physical and chemical characteristics of one or more seeds.
BACKGROUND OF THE INVENTION
Breeding for compositionally enhanced agricultural products can require the analysis of a large number of seed samples from plants to identify those plants with the desired compositional and agronomic properties for use or advancement to the next generation. Analysis of bulk seed batches for certain traits, such as high oil or high protein, on a single plant or ear, in conjunction with an appropriate breeding methodology such as recurrent selection, often allow for the selection of and introduction of such traits into a commercial population. Although the analysis of these seed batches can be performed by various techniques, typically methods that are rapid, low cost, and non-destructive are used.
During the past decade, near infrared (NIR) spectroscopy has become a standard method for screening seed samples whenever the sample of interest has been amenable to this technique. Samples studied include wheat, maize, soybean, canola, rice, alfalfa, oat, and others (see, for example, Massie and Norris, “Spectral Reflectance and Transmittance Properties of Grain in the Visible and Near Infrared”, Transactions of the ASAE, Winter Meeting of the American society of Agricultural Engineers, 1965, pp. 598-600, which is herein incorporated by reference in its entirety). NIR spectroscopy uses near infrared light, which is typically in the range of 770 to 2,500 nanometers, to access overtones and combinations of the fundamental vibrational frequencies of the organic functional groups of O—H, C—H, and N—H. Devices for measuring such light are known in the art. (See, for example Hyvarinen et al., “Direct Sight Imaging Spectrograph: A Unique Add-on Component Brings Spectral Imaging to Industrial Applications”, SPIE Vol. 3,302, 1998, “Handbook of Near-Infrared Analysis”, Eds. Burns and Ciurczak, Marcel Dekker, Inc., 1992, both of which are herein incorporated by reference in their entirety).
Typically, the NIR spectra associated with a batch of seeds is determined (often, for example, a cuvette capable of holding 100 grams of seed is used). This determination can be combined with conventional chemical analysis of samples in order to provide additional data and to build a chemometric calibration model. Chemometric calibration models are often developed for traits that include, but are not limited to: oil, starch, water, fiber, protein, extractable starch, chlorophyll, glucosinolates, and fatty acid (see, for example, Archibald et al. “Development of Short-Wavelength Near-Infrared spectral Imaging for Grain Color Classification,” SPIE Vol. 3,543, 1998, pp. 189-198, Delwiche, “Single Wheat Kernel Analysis by Near-Infrared Transmittance: Protein Content,” Analytical Techniques and Instrumentation, Vol. 72, 1995, pp. 11-16, Dowell, “Automated Color Classification of single Wheat Kernels Using Visible and Near-Infrared Reflectance,” Vol. 75(1), 1998, pp. 142-144, Orman and Schumann, “Comparison of Near-Infrared Spectroscopy Calibration Methods for the Prediction of Protein, Oil, and Starch in Maize Grain,” Vol. 39, 1991, pp.883-886, Robutti, “Maize Kernel Hardness Estimation in Breeding by Near-Infrared Transmission Analysis,” Vol. 72(6), 1995, pp.632-636, U.S. Pat. No. 5,991,025, U.S. Pat. No. 5,751,421, Daun et al., “Comparison of Three whole Seed Near-Infrared Analyzers for Measuring Quality Components of Canola Seed”, Vol. 71, no. 10, 1994, pp. 1,063-1,068, “Corn: Chemistry and Technology”, Eds. Watson and Ramstad, American Association of Cereal Chemists, Inc., (1987), all of which are herein incorporated by reference in their entirety). The development of a chemometric model can then be used to predict the chemical characteristics of untested samples with NIR spectroscopy, without requiring additional conventional chemical analysis.
NIR analysis of bulk samples, either crushed or whole, has been reported (see, for example, Orman and Schumann, “Comparison of Near-Infrared Spectroscopy Calibration Methods for the Prediction of Protein, Oil, and Starch in Maize Grain,” Vol. 39, 1991, pp.883-886, Robutti, “Maize Kernel Hardness Estimation in Breeding by Near-Infrared Transmission Analysis,” Vol. 72(6), 1995, pp.632-636, U.S. Pat. No. 5,991,025, U.S. Pat. No. 5,751,421, Daun et al., “Comparison of Three whole Seed Near-Infrared Analyzers for Measuring Quality Components of Canola Seed”, Vol. 71, no. 10, 1994, pp.1,063-1,068, all of which are herein incorporated by reference in their entirety). Conventional commercial NIR spectrometers for bulk grain analysis have several disadvantages. Conventional spectrometers were designed for use in a laboratory environment, which is typically distant from the breeding fields, under controlled conditions of temperature, humidity and vibration. In addition, the spectrometers necessitate excessive sample handling. The samples must be harvested, sent to the breeding facility, threshed, bagged, labeled, and sent to the NIR lab for analysis. At the NIR lab the samples must be logged in, removed from the sample bags, poured into the sample cuvette, scanned with the NIR spectrometer, returned to the original sample bag, and sent back to the breeding facility. The resulting NIR data must be assembled into a final report, reviewed for any anomalies, and sent back to the breeder, who then locates and sorts the samples based upon the NIR analytical results. The excessive sample handling adds both time and cost to the analysis.
Current NIR based approaches are not only cumbersome and expensive, they are slow. Data processing time can be crucial because selection of appropriate seeds should be carried out prior to the planting time of the next generation. Delays in providing the breeder with the analytical results or the return of the samples can result in the loss of an entire breeding cycle.
Further, the speed of acquisition and analysis of the current technology cannot keep up with the speed at which the processing devices can operate. For example, single ear shellers can process up to 15 ears of corn per minute. Current NIR commercial spectrometers operate at a rate of about one sample every one to two minutes. The spectrometer rate of processing is typically the limiting step in the analytical process.
Conventional spectrometers gather information from a sub-set of the total sample. Commercial spectrometers collect light at a single point or several tens of points with small active areas, which results in only a small portion of the sample actually being interrogated by the technique. In bulk samples, for example, conventional techniques can lead to spot sampling of portions of only a few seeds out of the hundreds of seeds in the bulk sample. Further, since spot sampling of bulk samples analyzes arbitrary portions of the seed, different tissues of the seeds in the samples can be misrepresented by the analytical data. Since qualities like oil content are often present in different amounts in different tissues, these conventional techniques can fail to accurately assess the desired quality. These limitations apply to spectrometers with conventional optical configurations where a lens system collects light from the sample, as well as those that use fiber optic bundles to collect the light from the sample. In addition, since discrete, unrelated sampling points are used, spatial information associated with the sample is lost. Spatial information (which can be used, for instance, to determine morphology) consists of, for example, size, shape, mechanical damage, insect infestation, and fungal damage. Since conventional spectrometers do not collect spatial information at all, a correlation of spatial and spectral data is not possible.
Conventional spectrometers also fail to provide an efficient method for single seed analysis, which can greatly accelerate the rate
Deppermann Kevin L.
Modiano Steven H.
Evans F. L.
Jenkens & Gilchrist P.C.
Monsanto Technology LLC
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