Single Soybean Seeds Research Articles

Non-destructive prediction of protein contents of soybean seeds using near-infrared hyperspectral imaging

Protein content is one of the most crucial factors in soybean quality. However, the breeding procedure necessitates the time-consuming and costly selection of elite genotypes from many experimental lines in a destructive manner. The present work aims to predict protein content in single soybean seeds non-destructively using Near-Infrared (NIR) Hyperspectral Imaging (HSI). 1491 seed samples from 3 varieties of the low, medium, and high protein content (consisting of 371, 560, and 560 samples, respectively) were measured using the NIR-HSI system with a range of 900–1800 nm. The spectral data extracted from the HSI 3D hypercube were synchronised to the reference values examined from chemical analysis. The calibration model was constructed using partial least square regression (PLSR) methods based on the 70 % spectral data and then validated using the remaining 30 % of data. The result showed that the NIR-HSI technique is a promising method to predict protein content in soybean seeds, as shown by an R2 of 0.92 and a root mean square error (RMSE) of 1.08 %. In addition, the chemical images visualised the distribution of protein content for the multiple soybean seed showed the possibility of the developed technique for the use of rapid evaluation of massive samples in the processing line.

Nondestructive phenotyping fatty acid trait of single soybean seeds using reflective hyperspectral imagery

AbstractSoybean (Glycine max) is one of the most economically important crops in the world and is used widely for different purposes. Breeding programs have developed new varieties with desired traits, including altered fatty acid profiles and levitated protein content. The breeding process involves the selection of elite genotypes from a large number of experimental lines, which is time‐consuming and costly. This study aimed to evaluate the feasibility of a reflective hyperspectral imaging system in classifying the fatty acid content of single soybean seeds for high‐throughput phenotyping high oleic soybean cultivars. A hyperspectral imager was used to take hyperspectral images of 100 soybean seeds of two varieties. After comparing the performance of different methods for data preprocessing, featured spectral patterns were extracted to predict oleic acid and linoleic acid content using a successive projection algorithm. Results showed that the spectral reflectance of soybean seeds was able to differentiate different cultivars. The spectral information of selected regions of interest from the hyperspectral images of single seeds could reflect the spectral information of whole seeds. It is found that the classification models based on raw spectra performed the best in estimating the content of oleic acid and linoleic acid. The classification accuracy of the calibration for oleic acid and linoleic acid was both 100%, and the classification accuracy of validation was 90 and 93.3%, respectively. The results indicate that the reflective hyperspectral imaging technology might be used as a rapid and nondestructive tool to quantify the oleic acid content and linoleic acid content of single soybean seeds for high‐throughput phenotyping high oleic soybean.Practical ApplicationsIt is the interest of soybean breeders to develop high oleic soybean varieties that are in great demand. Studies indicate that the major factors affecting soybean oil content are oleic acid and linoleic acid. However, the nutritional values of soybean seeds, such as sugar, protein, oil, and fatty acids, are commonly measured using wet chemistry analysis, which is destructive, time‐consuming, and expensive. Although NIR spectroscopy can improve efficiency and simplify the measurement procedure, it still requires a large number of seeds. In breeding programs, a single seed with known oleic content is needed for further seed development. Bulk‐seed measurement using spectroscopy is not selective and would not determine the oleic profile of single seeds. Therefore, there is a pressing need to develop a high‐throughput and nondestructive method to quantify the nutritional values of individual seeds for precision breeding and improve the efficiency of breeding programs.

Non‐destructive Determination of High Oleic Acid Content in Single Soybean Seeds by Near Infrared Reflectance Spectroscopy

AbstractSoybean [Glycine max (L.) Merr] with increased oleic acid is desirable to improve oxidative stability and functionality of soybean seed oil. Recently, soybean genotypes with high oleic acid (≥70 %) were developed by breeding programs. Efficient and effective identification of high oleic acid soybean genotypes using non‐destructive near infrared reflectance (NIR) on whole seeds would greatly enhance progress in breeding programs. The objective of this study was to develop a calibration equation for NIR determination of high oleic acid from single soybean seeds. A total of 600 intact, single F2 seeds were scanned by NIR. Spectral data were collected between 400 and 2,500 nm at 2 nm intervals. The relationship between NIR spectral patterns of each soybean seed and its oleic acid content was examined. The best predicted equations for oleic acid were selected on the basis of minimizing the standard error of cross‐validation and increasing the coefficient of determination. Validation demonstrated that the equations for determining total oleic acid and over 50 % oleic acid content had high predictive ability (r2 = 0.91 and r2 = 0.99, respectively). To validate the newly developed equation, F2 seeds from a different genetic background were tested. Again, high oleic acid from single soybean seeds was accurately predicted from various genetic backgrounds. Therefore, applying the calibration equations to NIR will be useful to rapidly and efficiently select high oleic acid soybean genotypes in breeding programs.

Determination of Soybean Oil, Protein and Amino Acid Residues in Soybean Seeds by High Resolution Nuclear Magnetic Resonance (NMRS) and Near Infrared (NIRS)

AbstractA detailed account is presented of our high resolution nuclear magnetic resonance (HR-NMR) and near infrared (NIR) calibration models, methodologies and validation procedures, together with a large number of composition analyses for soybean seeds. NIR calibrations were developed based on both HR-NMR and analytical chemistry reference data for oil and twelve amino acid residues in mature soybeans and soybean embryos. This is our first report of HR-NMR determinations of amino acid profiles of proteins from whole soybean seeds, without protein extraction from the seed. It was found that the best results for both oil and protein calibrations were obtained with a Partial Least Squares Regression (PLS-1) analysis of our extensive NIR spectral data, acquired with either a DA7000 Dual Diode Array (Si and InGaAs detectors) instrument or with several Fourier Transform NIR (FT-NIR) spectrometers equipped with an integrating sphere/InGaAs detector accessory. In order to extend the bulk soybean samples calibration models to the analysis of single soybean seeds, we have analized in detail the component NIR spectra of all major soybean constituents through spectral deconvolutions for bulk, single and powdered soybean seeds. Baseline variations and light scattering effects in the NIR spectra were corrected, respectively, by calculating the first-order derivatives of the spectra and the Multiplicative Scattering Correction (MSC). The single soybean seed NIR spectra are broadly similar to those of bulk whole soybeans, with the exception of minor peaks in single soybean NIR spectra in the region from 950 to 1,000 nm. Based on previous experience with bulk soybean NIR calibrations, the PLS-1 calibration model was selected for protein, oil and moisture calibrations that we developed for single soybean seed analysis. In order to improve the reliability and robustness of our calibrations with the PLS-1 model we employed standard samples with a wide range of soybean constituent compositions: from 34% to 55% for protein, from 11% to 22% for oil and from 2% to 16% for moisture. Such calibrations are characterized by low standard errors and high degrees of correlation for all major soybean constituents. Morever, we obtained highly resolved NIR chemical images for selected regions of mature soybean embryos that allow for the quantitation of oil and protein components. Recent developments in high-resolution FT-NIR microspectroscopy extend the NIR sensitivity range to the picogram level, with submicron spatial resolution in the component distribution throughout intact soybean seeds and embryos. Such developments are potentially important for biotechnology applications that require rapid and ultra- sensitive analyses, such as those concerned with high-content microarrays in Genomics and Proteomics research. Other important applications of FT-NIR microspectroscopy are envisaged in biomedical research aimed at cancer prevention, the early detection of tumors by NIR-fluorescence, and identification of single cancer cells, or single virus particles in vivo by super-resolution microscopy/ microspectroscopy.

Near Infrared Reflectance Spectroscopy (NIRS) Determination of Isoflavone Contents for Selected Soybean Accessions

AbstractSoybean isoflavones are of considerable interest in relation to their possible health effects in human diets. The rapid and economical determination of soybean isoflavone concentrations is essential for the investigation and development of soybean health foods as well as the selection of soybean seeds with optimal isoflavone levels for such foods. Fourier transforms near infrared reflectance spectroscopy (FT-NIRS) calibrations were developed for the rapid and cost-effective analysis of isoflavones in soybean seeds. FT-NIRS measurements were carried out in quadruplicate for 50 soybean lines selected from the USDA Soybean Germplasm Collection. The selected soybean seeds provided a wide range of isoflavone concentrations (from 0.3 to 6.0 mg/g) that is necessary for development of high-quality calibrations. Laboratory reference values of isoflavone composition were obtained by HPLC analysis of extracted soybean powders. Single soybean seeds were selected for each standard sample and were cut in half in order to avoid screening of the isoflavones NIR absorption bands by the seed coat. For comparison purposes, measurements were also made on soybean powders of the same samples. FT -NIR spectra were collected with a spectral range from 4000 to 12000 cm-1 at a resolution of 8 cm-1 on a Perkin-Elmer Spectrum one NTS spectrometer model. This spectrometer is optimized for high sensitivity analysis of single seed composition, being equipped with an NIRA, integrating sphere accessory and an extended range InGaAs detector.

Soybean Composition Database from NIR, NMR and GC-MS Analyses- (v.3)

AbstractThis novel Soybean Composition Database from the AFC-NMR & NIR Spectroscopy Facility of the College of ACES at the University of Illinois at Urbana includes more than 12,000 NIR measurements on soybeans from the International Soybean Germplasm Collection, such as those received from Peking at the National Soybean Collection.Excel files (.xls) of our novel spectroscopic data are currently available for all 80,000 + NIR and FT-NMR measurements; such data are made available from an ultra-fast and secure supercomputer server utilizing the current version of the Scientific-Linux OS-based software.A detailed account is also presented of our high-resolution nuclear magnetic resonance (HR-NMR) and near infrared (NIR) calibration models, methodologies and validation procedures, together with a large number of composition analyses for soybean seeds. NIR calibrations were developed based on both HR-NMR and analytical chemistry reference data for oil and twelve amino acid residues in mature soybeans and soybean embryos. Moreover, this is our first detailed report of HR-NMR determinations of amino acid profiles of proteins from whole soybean seeds, without protein extraction from the seed. It was found that the best results for both oil and protein calibrations were obtained with a Partial Least Squares Regression (PLS-1) analysis of our extensive NIR spectral data, acquired with either a DA7000 Dual Diode Array (Si and InGaAs detectors) instrument or with several Fourier Transform NIR (FT-NIR) spectrometers equipped with an integrating sphere/InGaAs detector accessory. In order to extend the bulk soybean samples calibration models to the analysis of single soybean seeds, we have analized in detail the component NIR spectra of all major soybean constituents through spectral deconvolutions for bulk, single and powdered soybean seeds. Baseline variations and light scattering effects in the NIR spectra were corrected, respectively, by calculating the first-order derivatives of the spectra and the Multiplicative Scattering Correction (MSC). The single soybean seed NIR spectra are broadly similar to those of bulk whole soybeans, with the exception of minor peaks in single soybean NIR spectra in the region from 950 to 1,000 nm. Based on previous experience with bulk soybean NIR calibrations, the PLS-1 calibration model was selected for protein, oil and moisture calibrations that we developed for single soybean seed analysis. In order to improve the reliability and robustness of our calibrations with the PLS-1 model we employed standard samples with a wide range of soybean constituent compositions: from 34% to 55% for protein, from 11% to 22% for oil and from 2% to 16% for moisture. Such calibrations are characterized by low standard errors and high degrees of correlation for all major soybean constituents. Morever, we obtained highly resolved NIR chemical images for selected regions of mature soybean embryos that allow for the quantitation of oil and protein components. Recent developments in high-resolution FT-NIR microspectroscopy extend the NIR sensitivity range to the picogram level, with submicron spatial resolution in the component distribution throughout intact soybean seeds and embryos. Such developments are potentially important for biotechnology applications that require rapid and ultra- sensitive analyses, such as those concerned with high-content microarrays in Genomics and Proteomics research. Other important applications of FT-NIR microspectroscopy are envisaged in biomedical research aimed at cancer prevention, the early detection of tumors by NIR-fluorescence, and identification of single cancer cells, or single virus particles in vivo by super-resolution microscopy/ microspectroscopy.

Soybean Composition Database from NIR, NMR and GC-MS Analyses

AbstractThis novel Soybean Composition Database from the AFC-NMR & NIR Spectroscopy Facility of the College of ACES at the University of Illinois at Urbana includes more than 12,000 NIR measurements on soybeans from the International Soybean Germplasm Collection, such as those received from Peking at the National Soybean Collection.Excel files (.xls) of our novel spectroscopic data are currently available for all 80,000 + NIR and FT-NMR measurements; such data are made available from an ultra-fast and secure supercomputer server utilizing the current version of the Scientific-Linux OS-based software.A detailed account is also presented of our high-resolution nuclear magnetic resonance (HR-NMR) and near infrared (NIR) calibration models, methodologies and validation procedures, together with a large number of composition analyses for soybean seeds. NIR calibrations were developed based on both HR-NMR and analytical chemistry reference data for oil and twelve amino acid residues in mature soybeans and soybean embryos. Moreover, this is our first detailed report of HR-NMR determinations of amino acid profiles of proteins from whole soybean seeds, without protein extraction from the seed. It was found that the best results for both oil and protein calibrations were obtained with a Partial Least Squares Regression (PLS-1) analysis of our extensive NIR spectral data, acquired with either a DA7000 Dual Diode Array (Si and InGaAs detectors) instrument or with several Fourier Transform NIR (FT-NIR) spectrometers equipped with an integrating sphere/InGaAs detector accessory. In order to extend the bulk soybean samples calibration models to the analysis of single soybean seeds, we have analized in detail the component NIR spectra of all major soybean constituents through spectral deconvolutions for bulk, single and powdered soybean seeds. Baseline variations and light scattering effects in the NIR spectra were corrected, respectively, by calculating the first-order derivatives of the spectra and the Multiplicative Scattering Correction (MSC). The single soybean seed NIR spectra are broadly similar to those of bulk whole soybeans, with the exception of minor peaks in single soybean NIR spectra in the region from 950 to 1,000 nm. Based on previous experience with bulk soybean NIR calibrations, the PLS-1 calibration model was selected for protein, oil and moisture calibrations that we developed for single soybean seed analysis. In order to improve the reliability and robustness of our calibrations with the PLS-1 model we employed standard samples with a wide range of soybean constituent compositions: from 34% to 55% for protein, from 11% to 22% for oil and from 2% to 16% for moisture. Such calibrations are characterized by low standard errors and high degrees of correlation for all major soybean constituents. Morever, we obtained highly resolved NIR chemical images for selected regions of mature soybean embryos that allow for the quantitation of oil and protein components. Recent developments in high-resolution FT-NIR microspectroscopy extend the NIR sensitivity range to the picogram level, with submicron spatial resolution in the component distribution throughout intact soybean seeds and embryos. Such developments are potentially important for biotechnology applications that require rapid and ultra- sensitive analyses, such as those concerned with high-content microarrays in Genomics and Proteomics research. Other important applications of FT-NIR microspectroscopy are envisaged in biomedical research aimed at cancer prevention, the early detection of tumors by NIR-fluorescence, and identification of single cancer cells, or single virus particles in vivo by super-resolution microscopy/ microspectroscopy.

Soybean Composition Database from NIR, NMR and GC-MS Analyses

AbstractThis novel Soybean Composition Database from the AFC-NMR & NIR Spectroscopy Facility of the College of ACES at the University of Illinois at Urbana includes more than 12,000 NIR measurements on soybeans from the International Soybean Germplasm Collection, such as those received from Peking at the National Soybean Collection.Excel files (.xls) of our novel spectroscopic data are currently available for all 80,000 + NIR and FT-NMR measurements; such data are made available from an ultra-fast and secure supercomputer server utilizing the current version of the Scientific-Linux OS-based software.A detailed account is also presented of our high-resolution nuclear magnetic resonance (HR-NMR) and near infrared (NIR) calibration models, methodologies and validation procedures, together with a large number of composition analyses for soybean seeds. NIR calibrations were developed based on both HR-NMR and analytical chemistry reference data for oil and twelve amino acid residues in mature soybeans and soybean embryos. Moreover, this is our first detailed report of HR-NMR determinations of amino acid profiles of proteins from whole soybean seeds, without protein extraction from the seed. It was found that the best results for both oil and protein calibrations were obtained with a Partial Least Squares Regression (PLS-1) analysis of our extensive NIR spectral data, acquired with either a DA7000 Dual Diode Array (Si and InGaAs detectors) instrument or with several Fourier Transform NIR (FT-NIR) spectrometers equipped with an integrating sphere/InGaAs detector accessory. In order to extend the bulk soybean samples calibration models to the analysis of single soybean seeds, we have analized in detail the component NIR spectra of all major soybean constituents through spectral deconvolutions for bulk, single and powdered soybean seeds. Baseline variations and light scattering effects in the NIR spectra were corrected, respectively, by calculating the first-order derivatives of the spectra and the Multiplicative Scattering Correction (MSC). The single soybean seed NIR spectra are broadly similar to those of bulk whole soybeans, with the exception of minor peaks in single soybean NIR spectra in the region from 950 to 1,000 nm. Based on previous experience with bulk soybean NIR calibrations, the PLS-1 calibration model was selected for protein, oil and moisture calibrations that we developed for single soybean seed analysis. In order to improve the reliability and robustness of our calibrations with the PLS-1 model we employed standard samples with a wide range of soybean constituent compositions: from 34% to 55% for protein, from 11% to 22% for oil and from 2% to 16% for moisture. Such calibrations are characterized by low standard errors and high degrees of correlation for all major soybean constituents. Morever, we obtained highly resolved NIR chemical images for selected regions of mature soybean embryos that allow for the quantitation of oil and protein components. Recent developments in high-resolution FT-NIR microspectroscopy extend the NIR sensitivity range to the picogram level, with submicron spatial resolution in the component distribution throughout intact soybean seeds and embryos. Such developments are potentially important for biotechnology applications that require rapid and ultra- sensitive analyses, such as those concerned with high-content microarrays in Genomics and Proteomics research. Other important applications of FT-NIR microspectroscopy are envisaged in biomedical research aimed at cancer prevention, the early detection of tumors by NIR-fluorescence, and identification of single cancer cells, or single virus particles in vivo by super-resolution microscopy/ microspectroscopy.

Near Infrared Microspectroscopy, Fluorescence Microspectroscopy, Infrared Chemical Imaging and High-Resolution Nuclear Magnetic Resonance Analysis of Soybean Seeds, Embryos and Single Cells

AbstractChemical analysis of soybean seeds, somatic embryos and single cells were carried out by Fourier Transform Infrared (FT-IR), Fourier Transform Near Infrared (FT-NIR) Microspectroscopy, Fluorescence and High-Resolution NMR (HR-NMR). The first FT-NIR chemical images of biological systems approaching 1 micron (1μ) resolution are presented here. Chemical images obtained by FT-NIR and FT-IR Microspectroscopy are presented for oil in soybean seeds and somatic embryos under physiological conditions. FT-NIR spectra of oil and proteins were obtained for volumes as small as 2μ3. Related, HR-NMR analyses of oil contents in somatic embryos are also presented here with nanoliter precision. Such 400 MHz 1H NMR analyses allowed the selection of mutagenized embryos with higher oil content (e.g. ~20%) compared to non-mutagenized control embryos. Moreover, developmental changes in single soybean seeds and/or somatic embryos may be monitored by FT-NIR with a precision approaching the picogram level. Indeed, detailed chemical analyses of oils and phytochemicals are now becoming possible by FT-NIR Chemical Imaging/ Microspectroscopy of single cells. The cost, speed and analytical requirements of plant breeding and genetic selection programs are fully satisfied by FT-NIR spectroscopy and Microspectroscopy for soybeans and soybean embryos. FT-NIR Microspectroscopy and Chemical Imaging are also shown to be potentially important in functional Genomics and Proteomics research through the rapid and accurate detection of high-content microarrays (HCMA). Multi-photon (MP), pulsed femtosecond laser NIR Fluorescence Excitation techniques were shown to be capable of Single Molecule Detection (SMD). Therefore, such powerful techniques allow for the most sensitive and reliable quantitative analyses to be carried out both in vitro and in vivo. Thus, MP NIR excitation for Fluorescence Correlation Spectroscopy (FCS) allows not only single molecule detection, but also molecular dynamics and high resolution, submicron imaging of femtoliter volumes inside living cells and tissues. These novel, ultra-sensitive and rapid NIR/FCS analyses have numerous applications in important research areas, such as: agricultural biotechnology, food safety, pharmacology, medical research and clinical diagnosis of viral diseases and cancers.

Classification of Fungal-Damaged Soybean Seeds Using Near-Infrared Spectroscopy#

Fungal damage has a devastating impact on soybean quality and end-use. The current visual method for identifying damaged soybean seeds is based on discoloration and is subjective. The objective of this research was to classify healthy and fungal-damaged soybean seeds and discriminate among various types of fungal damage using near-infrared (NIR) spectroscopy. A diode-array NIR spectrometer, which measured reflectance [log(1/R)] from 400 to 1700 nm, was used to obtain spectra from single soybean seeds. Partial least square (PLS) and neural network models were developed to differentiate healthy and fungal-damaged seeds. The highest classification accuracy was more than 99% when the wavelength region of 490–1690 nm was used under a two-class PLS model. Neural network models yielded higher classification accuracy than the PLS models for five-class classification. The average of correct classifications was 93.5% for the calibration sample set and 94.6% for the validation sample set. Classification accuracies of the validation sample set were 100, 99, 84, 94, and 96% corresponding to healthy seeds, Phomopsis, Cercospora kikuchii, soybean mosaic virus (SMV), and downy mildew damaged seeds, respectively. #Contribution No. 03-163-J from the Kansas Agricultural Experiment Station. Mention of a trademark or proprietary product does not constitute a guarantee or warranty of the product by the U.S. Department of Agriculture and does not imply its approval to the exclusion of other products that also may be suitable.

Microwave resonator technique for moisture content and mass determination in single soybean seeds

Principles are presented relating to the use of a microwave resonant cavity for determining simultaneously the mass and moisture content of individual soybean seeds. Measurement of the resonant frequency shift and change in the transmission characteristics of the cavity when the soybean seed is inserted provides the necessary information for calculating the mass of water in the seed and the dry matter mass, from which the moist mass of the seed and its moisture content can be determined. Uncertainties of the measurements of mass and moisture content were 3 percent and less than 1 percent moisture content, wet basis, respectively, at the 95-percent confidence level.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Nondestructive Single-seed Moisture Determination in Soybeans by RF Impedance Measurements

Impedance measurements were taken at frequencies of 1 and 5 MHz on a small parallel-plate capacitor holding single soybean seeds between and in contact with the plates on samples from two cultivars each from soybean producing areas in the Midwest and the Southeast. Moisture contents ranged from 7 to 20%, wet basis. Calibration equations were developed from these measurements and oven moisture determinations on individual soybeans. Two calibration equations are presented, one with a single variable involving only capacitance measurements at the two frequencies, and another with three variables including capacitance differences and complex admittance differences. The two equations performed comparably in these tests with standard errors of 0.5 to 0.6% moisture content. Thus, the technique can provide rapid nondestructive tests for moisture content of single soybeans and offers promise for practical use.

On-line supercritical fluid extraction-supercritical fluid reaction-capillary gas chromatography analysis of the fatty acid composition of oilseeds

The versatility of supercritical fluid extraction (SFE) directly coupled with gas chromatography (GC) is further expanded by the addition of an on-line derivatization technique that is performed under supercritical fluid conditions. The integrated extraction and derivatization technique has been applied to the determination of the fatty acid composition of oilseeds. Triglycerides are extracted from seed samples using SC-CO2 and transesterified to methyl esters, in situ, over a solid catalyst. Experimental conditions were selected such that the methyl esters are preferentially eluted from the catalyst. A linear capillary restrictor was used to deposit the effluent from the supercritical fluid extractor/reactor onto a retention gap precolumn in the GC. Applications of the on-line SFE-SFR-GC technique include the analysis of single soybean, evening primrose, and peanut seeds.

MOISTURE DETERMINATION IN SINGLE SOYBEAN SEEDS BY NEAR-INFRARED TRANSMITTANCE

ABSTRACT A calibration set of 140 soybeans from seven different varieties, ranging in moisture from 5 to 22%, wet basis, was used to calibrate a spectrophotometer for predicting moisture of single soybean seeds. Near-infrared absorbance (A) of individual soybean seeds was measured over the spectral region from 800 to 1100 nm by 0.5 nm increments. The axis of the soybean seed parallel to the incident light beam was measured as an estimate of optical pathlength. Three mathematical techniques were used to develop calibration equations: linear correlation with a difference in absorbance (AA), stepwise multiple linear regression (MLR), and partial least squares (PLS). A validation set contained 100 soybeans from 5 different varieties, ranging in moisture from 5 to 20%, wet basis. The standard error of prediction (SEP) for equations using absorbance data only was 0.88% for AA, 0.82% for MLR, and 0.81% for PLS. The SEP for equations using pathlength and absorbance data was 0.73% for AA, 0.69% for MLR, and 0.65% for PLS.

Analysis of single soybean seeds for oil and protein

Microprocedures were developed to analyze single soybean seeds for protein, oil, and moisture. Ten mature Forrest soybean plants were taken from a yield plot and sampled, so that seeds were taken from the top, middle, and bottom areas. Also, seeds were taken from distal and proximal racemes, from distal and proximal pods on a raceme, and from distal and proximal seeds within a pod. The range in protein for the 241 seeds analyzed was 32 to 51% (dry weight basis) with a standard deviation of 2.96%. The range in oil was 16.5 to 25.5% (dry weight basis) with a standard deviation of 1.84%. The middle area had significantly more oil and significantly less protein than the top and bottom areas. Also, significant differences in protein and oil were found between plants. There was no significant difference in protein or oil due to position of a raceme, position of a pod on a raceme, or position of a seed within a pod. The standard deviation found for protein and oil can be used to estimate the sample size needed to achieve a certain degree of accuracy in protein and oil analyses.

Microwave resonator technique for moisture content determination in single soybean seeds

A rectangular waveguide cavity in the H/sub 105/ and H/sub 107/ modes at 4.8 and 6.0 GHz, respectively, was used to measure the resonant frequency shift and the change in the Q-factor caused by seeds of various dimensions and different hydration levels, located in the center of the cavity. Significant statistical correlations were found between measured parameters and the amount of water in the seeds and their dry mass. An empirical approach consists of relating the ratio of these two cavity parameters with the seed moisture content by a curve fitting method. With either of these techniques, the moisture content of soybeans, Glycine max L., in the range from 7 to 16% can be determined with an uncertainty lower than 1% at the 95% confidence level.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>