Corn is not only an important staple food for human consumption but is used to create many different types of products and by-products. These include animal feed, biofuel, corn starch, corn oil, and many different forms of corn syrup. More corn is produced than any other cereal plant worldwide. Sweet corn has become particularly appealing to consumers for its good taste and high nutritional content. Corn is comprised of four parts: endosperm, germ, pericarp, and tip cap, all showing distinct characteristics and containing different portions of the precursors of corn products. There are many different biotypes that can be grown in different parts of the world under varying climate conditions. Demand for corn is increasing rapidly and market growth is expected to be strong over the next decade, especially for animal feed and corn starch products. Corn is processed by either wet milling or dry milling. Wet milling separates the corn into separate components which can then be processed into various products. Dry milling is similar to wheat flour production and is used mostly for producing flour and as the precursor for processing corn into ethanol. Genetic engineering of corn has been going on for decades and always remains the subject of great debate. Many transgenic varieties have been created with traits like herbicide, pest, and drought resistance. Research continues to be conducted at a fast pace to improve the quality of corn and its products. With demand continuing to grow and research moving forward at a rapid pace, there is a need for new testing methods to meet the challenges of optimizing corn breeding, harvesting, growing, and processing. Traditional methods are often expensive, time-consuming, and impractical for use on a large scale. One method which has shown potential for measuring parameters of interest in corn that is fast, non-invasive, and able to be implemented for large-scale testing is NIR spectroscopy.
- Sweet corn cultivar sorting
- Viable and non-viable supersweet corn seed sorting
- Effects of varieties, producing areas, ears, and ear positions of single maize kernels on NIR spectra
- Non-Structural Carbohydrates (NSC)
- Water Soluble Carbohydrates (WSC)
- In Vitro Organic Dry Matter Digestibility (IVOMD)
- Organic Matter (OM)
- Crude Protein (CP)
- Neutral Detergent Fiber (NDF)
- Acid Detergent Fiber (ADF)
- Nutritional value of silage from Brazil
- Corn seed germination rate
- Detection of fusarium infused diseased maize grains
- Gross Calorific Value (GCV)
- Cell wall residues
- Lignin content
- Lignin structure
- p-Hydroxycinnamic acids
- Structural sugars
- Transgenic vs. non-transgenic discriminant analysis
- Macronutrient content as a tool for breeding selection
Summary of Published Papers, Articles, and Reference Materials
Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis
The sweetness of sweet corn is a major factor in consumer satisfaction and breeders are always working to breed sweeter cultivars. It is usually consumed when immature because of high nutrition and increased sweet flavor. A uniform maturity time is important to choose the optimal harvest time and then to obtain a good shelf-life time as the sweet flavor changes quickly after harvesting. Different cultivars of sweet corn vary in the maturity cycle, even when planted under the same conditions. Mixing of cultivars is undesirable for a number of reasons, such as differences in maturity cycle times, variation in nutritional value, and resistance to diseases and pests. The purity of a seed cultivar is defined as the ratio of seeds belonging to a cultivar to the total tested seeds. Improving purity maximizes quality and yield, leading to increased economic benefits but traditional methods for determining cultivar purity like protein electrophoresis and DNA molecular markers are expensive, time-consuming, and impractical for large-scale use. FT-NIR spectroscopy was examined for distinguishing between different cultivars of single-kernel sweet corn seeds. Three hundred and eighty sweet corn seeds from each of two separate cultivars were procured for the study. Initial examination was done by scanning a few seeds on both the embryo side and endosperm side of the seeds. After visual examination of the NIR spectra, it was determined that the embryo side spectra would be used for the study. All seeds were scanned from 10000 cm-1 to 4000 cm-1 at 4 cm-1 intervals. Thirty-two scans were collected per reading and averaged into one spectrum per sample for a total of seven hundred and sixty spectra. Various pre-processing methods were applied to the NIR spectra before chemometric analysis. Principle Component Analysis (PCA) was performed for outlier detection and to determine differences in the spectral data. After PCA, a genetic algorithm (GA) was applied to determine the feature wavelengths in spectral differences and one hundred and twenty-six wavelengths were selected. Four separate classification algorithms were used with various wavelength ranges for modeling: K-Nearest Neighbor (KNN), Soft Independent Modeling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Support Vector Machine Discriminant Analysis (SVM-DA). The spectra were split into two groups: two-thirds for a calibration group and one-third for a validation group.
|Full Wavelength Range||99.59% Correct Classification|
|Featured Wavelengths||99.19% Correct Classification|
The best results were obtained using the SVM-DA algorithm and as shown, there was over 99% accuracy using both the full wavelength range and featured wavelength range for the models. This study proved the feasibility of classifying different corn cultivars using FT-NIR spectroscopy and the SVM-DA classification algorithm. Using a classification model like this could be a method used in seed sorting machinery to select high-purity seeds of the same cultivar, helping to optimize product quality and yield in sweet corn.
Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis
Sweet corn has become a very popular vegetable in many countries because of pleasant flavor and high nutritional value. However, low germination rate and seedling vigor of sweet corn seed have limited the development of the sweet corn industry. High soluble sugar content and lower starch cause seeds to rapidly deteriorate compared to other corn seeds. This likely occurs because less starch means less endosperm tissue can be reserved as an energy source for seed metabolism. This effect is magnified with supersweet corn seeds and the high soluble sugar content also inhibits the drying of the seed crop in the field, often necessitating artificial drying after harvesting. Proper temperature is essential during drying as it has a strong effect on germination and storability. The sensitivity of supersweet corn seeds creates a need for determining seed quality to prevent non-viable seeds from entering the market and ultimately the planting and sowing process. However, conventional methods like the germination test and tetrazolium test are time-consuming, expensive, destructive to samples, and impractical for implementing for large-scale testing. NIR spectroscopy was examined as a method for determining viability in supersweet corn seeds. Three hundred supersweet corn seeds from Huameitian No. 8, a well-known variety in China were procured for the study from South China Agricultural University. More seeds were provided but three hundred seeds that were not cracked, broken, or discolored were chosen. It is assumed that deterioration of supersweet corn seeds is caused by either excessive heating during the drying process or improper storage conditions. In order to simulate this, one hundred seeds were subjected to deterioration by tempering the moisture to 20%, placement in a plastic bag and treating by incubation for seven days, and then dried back to the 20% moisture content. This process simulates artificial aging. Another one hundred seeds were subjected to microwave treatment. Both groups exhibited no changes to the naked eye and the remaining one hundred seeds had no treatment. All seeds were scanned using an FT-NIR spectrometer from 10000 cm-1 to 4000 cm-1 at 4 cm-1 intervals on both the embryo and endosperm side. Thirty-two scans were collected per reading and averaged into a single spectrum per sample. After scanning, germination rate was checked for all seeds using the standard germination test method. Seeds in the control group had a germination rate as high as 98% while the treated seeds had a 2% rate for the deterioration group and 5% for the microwaved group. In reality, these seeds are considered non-viable because of their weak roots which would be unable to support healthy seedlings. Various pre-processing methods were applied to the spectral data before chemometric analysis. Six separate Partial Least Squares Discriminant Analysis (PLS-DA) models were created: Artificially Aged vs. Control, Heat-Damaged vs. Control, and All Damaged vs Control (for both embryo and endosperm spectra).
|Heat Damaged vs. Control (Embryo)||100% Viable||96.0% Non-Viable Classification|
|Heat Damaged vs. Control (Endosperm)||99.3% Viable||98.0% Non-Viable Classification|
|Artificially Aged vs. Control (Embryo)||100% Viable||98.0% Non-Viable Classification|
|Artificially Aged vs. Control (Endosperm)||99.3% Viable||94.0% Non-Viable Classification|
|All Damaged vs. Control (Embryo)||99.6% Viable||98.7% Non-Viable Classification|
|All Damaged vs. Control (Endosperm)||99.1% Viable||98.7% Non-Viable Classification|
The prediction models determined that FT-NIR spectroscopy and PLS-DA classification models can be used to accurately detect non-viable supersweet corn seeds damaged by overheating and artificial aging. Results were similar for both the embryo and endosperm sides of the seeds. Results can be explained by the physical and chemical changes caused by the deterioration process. There are other reasons that can cause seeds to be non-viable that have not been examined and published in an NIR spectroscopy study yet, such as frost damage during growth and natural aging. Further work would entail examining some different types of seed damage as well as encompassing a larger sample set with different varieties and batches of seeds. There is great potential to use NIR spectroscopy as a sorting tool for viable and non-viable corn seeds.
Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis – PubMed (nih.gov)
Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability
Maize is an important source of food and industrial materials and demand for seeds is high, especially in China. The quality of a maize seed is related to varieties and producing areas and identification of seeds is important to prevent adulteration. Seeds from different provinces exhibit different characteristics even among the same variety, usually related to environmental factors like climate, daylight conditions, and soil. While successful studies using NIR spectroscopy for identifying different varieties of maize seeds and wheat of the same species cultivated in different areas have shown distinct differences in NIR spectra that are sufficient for identification, no comprehensive study examining different factors and their degree of influence on NIR spectra of maize seeds has been conducted until now. In this study, NIR spectroscopy was used to determine the degree of influence of genetic and environmental factors on large amounts of maize seeds of different varieties and from different producing areas. A total of one hundred and thirty maize inbred lines harvested from Hainan and Beijing in China were procured for the study, all from the same harvest year. Five ears were randomly selected from each inbred line harvested from Hainan. Five seeds were collected from each ear from each position: ear tip, middle of the ear, and bottom of the ear. For each line harvested in Beijing, twenty seeds were collected randomly. In total, twelve thousand three hundred and fifty seeds were procured for the study. Seeds were scanned with an FT-NIR spectrometer from 12000 cm-1 to 4000 cm-1 at 8 cm-1 resolution. Twenty scans were collected per reading and averaged into a single spectrum per seed. All seeds were scanned on the embryo side. Various pre-processing methods were applied to the spectral data and the wavenumbers from 12000 cm-1 to 9000 cm-1 were eliminated from the data because of noise. Principle Component Analysis (PCA) was first performed to examine differences in the NIR spectra. The NIR spectral difference was calculated to determine the degree of influence of varieties, producing areas, ears, and different ear positions on the NIR spectra. The one hundred thirty inbred lines from Hainan were used to calculate the influence from degree of variety. After pretreatment, the difference between the spectra of each inbred line and the average spectrum was calculated and the average of these differences was the influence of degree of variety on the spectra. Likewise, differences between the spectra from the two different producing areas was calculated to determine the degree of influence of producing areas. Spectral differences between the spectra of different ears and different ear positions from the Hainan samples were also determined. It was determined that wavelength bands from 1300 nm to 1470 nm, 1768 nm to 1949 nm, 2010 nm to 2064 nm, and 2235 nm to 2311 nm were strongly influenced by the producing area. The degree of influence for the four factors was as follows: 45.40% for variety, 42.66% for producing areas, 8.22% for ears, and 3.72% for ear positions. These results show that genetic differences among maize inbred lines are the main factor in differences in NIR spectra, with producing area accounting for a slightly smaller degree of influence. The results provide a basis for variety authentication and breeding optimization. Further study should be conducted on seeds from different harvest years to determine the degree of influence from year to year.
Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability (plos.org)
NIRS Determination of Non-structural Carbohydrates, Water Soluble Carbohydrates and Other Nutritive Quality Traits in Whole Plant Maize with Wide Range Variability
Forage maize is an important source of fodder for dairy farms in Spain, where the need for silage for cow feeding extends from five to seven months each year. The nutritional content of forage is extremely important in determining the end product of cow milk as even small differences in nutritional content of forage can change the output and nutritional content of milk. These parameters include non-structural carbohydrates (NSC), water soluble carbohydrates (WSC), in vitro organic dry matter digestibility (IVOMD), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF). NSC has a strong influence on the utilization of other nutrients while WSC is the substrate for growth of lactobacilli needed for acid lactic fermentation. The other parameters are all important for energy and digestibility in cow forage. Current methods for testing these parameters are time-consuming, expensive, and impractical for implementation on a large scale. NIR spectroscopy was examined for determining components of forage maize. Maize whole plant samples were collected over an eight year period from different locations in Spain and in diverse environmental conditions. To further expand the diversity of the sample set, samples were also collected from different genetic diversity sources, different countries, different tillage systems, and different maturity stages. Over three thousand samples were scanned using an NIR spectrometer from 1100 nm to 2500 nm. Various preprocessing methods were applied to the NIR spectra before chemometric analysis. All samples were analyzed for the parameters of interest using traditional methods. In total, four hundred and fifty samples were chosen for a calibration set and eighty-seven were chosen for a validation set. Sample selection was based on expanding the variability of the spectra by adding appropriate outlier samples to previous calibrated equations after determination of reference values. Partial Least Squares (PLS) calibration models were created to correlate the NIR spectra to the parameters of interest. Results are shown below.
|OM||R2= 0.91||SEC= 0.23 g/100 g Dry Matter|
|CP||R2= 0.93||SEC= 0.29 g/100 g Dry Matter|
|NDF||R2= 0.91||SEC= 1.61 g/100 g Dry Matter|
|ADF||R2= 0.92||SEC= 0.98 g/100 g Dry Matter|
|IVOMD||R2= 0.77||SEC= 2.30 g/100 g Organic Matter|
|WSC||R2= 0.90||SEC= 1.34 g/100 g Dry Matter|
|NSC||R2= 0.87||SEC= 2.57 g/100 g Dry Matter|
|Starch||R2= 0.90||SEC= 2.67 g/100 g Dry Matter|
The independent validation set was used for predictions to validate the models and out of the parameters, WSC and NSC showed the best results with good accuracy obtained in the predicted results. Starch, NDF, ADF, OM, and CP showed accurate results as well. The prediction results for IVOMD were not as good as the rest of the parameters and it was speculated that error in the reference method may have contributed to the higher prediction error. Still, the results were considered sufficient for screening purposes and to distinguish between high and low values for IVOMD. This study proved the feasibility of using NIR spectroscopy to determine nutritive components in forage maize and the potential to replace traditional time-consuming and expensive reference methods for determining these parameters.
Nutrition Value of Silage from Corn Hybrids in the State of Mato Grosso, Brazil
Rainfall in the Brazilian savanna between October and March causes considerable seasonality in forage production and thus difficulties in maintaining product regularity and income of producers. Evaluating the chemical composition of silage of corn cultivars is very important because it determines the food quality available for animal intake, especially in the case of neutral detergent fiber (NDF) because reduced NDF increases dry matter (DM) digestibility. In this practical application, NIR spectroscopy was used to determine the nutritional value of corn silage from different hybrids cultivated on an experimental farm in Brazil. Twenty-three different hybrid corn varieties from different seed companies of three repetitions each were planted for the study. Harvesting and slicing of corn plants for ensiling occurred one hundred days after plant emergence when the grains were at the hard flour stage. Forage was cut at an average particle size between two and three cm and placed in sealed silos for ninety days. After opening the silos, samples were collected from the middle of each silo. A portion of each sample was set aside for pH and ammoniacal nitrogen tests. Samples were scanned using an NIR spectrometer and calibration models were used to determine dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and the minerals Ca, P, K, and Mg rates from the NIR spectra. From these parameters, rates of total digestible nutrients (TDN), net lactation energy (NLE), net energy gained (NEg), net energy for maintenance (NEm), digestibility in vitro of DM (DIVDM) and dry matter intake (DMI) were calculated. All pH and ammoniacal nitrogen tests showed expected values. Twelve of the hybrids showed lower NDF and thus higher estimated DMI values. This practical application showed the benefits of using NIR spectroscopy as a tool to determine variation in corn hybrid silage as performing traditional reference tests to determine the parameters of interest in this study would have been expensive, time-consuming, and required the use of expensive chemicals and solvents.
Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination
Seed viability and germination rate are crucial parameters in planning agricultural production. It can be significantly influenced by both ecological factors, biochemical metabolism of the seed, and improper storage after harvesting. Traditional methods for determining these parameters include tetrazolium, conductivity, immunoassay, and germination tests which are not only expensive, time-consuming, and require the use of toxic chemicals and solvents but can also be very dependent on the experience and sensitivity of the technicians performing the tests. There is a need for a fast, non-invasive, and cost-effective testing method to determine corn seed germination rate and NIR spectroscopy was examined for this purpose. Eighteen different commercial samples of corn seed were procured for the study. Fourteen of the eighteen samples belonged to the same genotype of various ages that were stored for periods ranging from three months to two years under uncontrolled room conditions. Samples were scanned using an FT-NIR spectrometer from 10000 cm-1 to 4000 cm-1 using 4 cm-1 resolution. Thirty-two scans were collected per reading and averaged into one spectrum. This process was done three times per sample for a total of fifty-four spectra. Germination rate of the seeds was determined using a standard International Seed Testing Association (ISTA) method. Seeds were placed in moist germination paper, rolled, placed in a plastic container, and incubated at 25°C for seven days. When the emerging radicle was at least 2 mm, the seed was considered germinated. A significant variation was detected between corn genotypes in terms of germination rates and made the effect of seed storage period clear on germination rate. Rates varied from 0% for a non-viable seed to 18% for the slowest germinating seed to 100% germination. A Partial Least Squares (PLS) calibration model was created correlating the germination rate to the NIR spectra. Results are shown below.
Germination Rate R2= 0.98% SEP= 4.61%
The results of this study show promise for determining the germination rate of corn genotypes using NIR spectroscopy and a calibration model. However, it must be noted that the sample set is very limited and in order to use this model in a practical setting, more samples and multiple genotypes must be added to the calibration set. With such a small sample set, it is unclear exactly what parameters the calibration model is using to determine the basis for the correlation. It is recommended that a sample set with a minimum size of one hundred samples using multiple genotypes be used before using this application in a practical setting.
Near Infrared Reflectance Spectroscopy and Multivariate Analyses for Fast and Non-Destructive Prediction of Corn Seed Germination | TR Dizin
An Approach for Identification of Fusarium Infected Maize Grains by Spectral Analysis in the Visible and Near Infrared Region, SIMCA Models, Parametric and Neural Classifiers
Fusarium is a genus of fungi that is widely distributed in soil and associated with plants. While most species are harmless, some produce mycotoxins in cereal crops that can be toxic to humans and animals if consumed. Fusarium spp. Fusarium verticillioides is one such disease and it is important to determine that maize grains are free of this disease before entering the food chain. Traditional testing methods typically involve chromatography and while accurate, they are expensive, time-consuming, and impractical for large-scale testing use. Fluorimetric testing can be used as well but this method still takes about fifteen minutes to examine one single seed. A need exists for a fast, non-invasive testing method that can test large amounts of maize grains for disease and NIR spectroscopy was examined for this purpose. A total of nine hundred grains from the most popular variety of corn in Bulgaria were procured for the study. Samples were sorted into healthy and diseased grains and were scanned on both the endosperm and embryo sides. NIR spectra were collected using an NIR spectrometer from 400 nm to 2498 nm at 2 nm intervals. Each sample was scanned three times for a total of twenty-seven hundred spectra. Sample spectra were divided into a calibration set and validation set with six hundred samples chosen for calibration and three hundred for validation. Various preprocessing methods were applied to the spectral data before chemometric analysis. Three different classification algorithms were used to classify the grains based on NIR spectra: Soft Independent Modeling of Class Analogy (SIMCA), K-Means, and Probabilistic Neural Network (PNN).
|SIMCA||Healthy – 98.7%||Diseased – 96.0%|
|K-Means||Healthy – 95.3%||Diseased – 90.6%|
|PNN||Healthy – 99.3%||Diseased – 98.7%|
|SIMCA||Healthy – 99.3%||Diseased – 93.3%|
|K-Means||Healthy – 94.6%||Diseased – 92.0%|
|PNN||Healthy – 99.3%||Diseased – 99.3%|
Best results were obtained using the PNN algorithm with the NIR spectra of the embryo side of the seeds. Spectra of the validation set were used with the classification algorithm to validate the model. However, it must be noted that the threshold of detection of mycotoxin concentration is far too low to directly detect it using NIR spectroscopy. It is quite possible that the mycotoxin concentration is affecting chemical and physical parameters in the seeds that can be detected using NIR spectroscopy and the results of this study reflect the changes in these parameters that occur in diseased maize grains. In order to properly validate a study of this nature, a thorough examination of the wavelengths showing differences in the NIR spectra of the two groups of seeds and tests on the nutritional content of the two groups of seeds is recommended.
Gross Calorific Value Estimation for Milled Maize Cob Biomass Using Near-Infrared Spectroscopy
Maize cob is the waste product after the maize seed is removed. While it has been used as a fertilizer, it takes a long time to decompose and is not popular among farmers for this reason. However, maize cob can be used as a biofuel. When burned, it has a calorific value of approximately 17,000 kJ/kg and has a long burning time as well. An important measurement in waste residues for biofuel use is gross calorific value (GCV), the total energy released in the burning process. This particular measurement takes the latent heat of vaporization of water into account during the combustion process. The traditional method for determining GCV is a bomb calorimeter which is expensive and takes about thirty minutes per sample, making it impractical for large-scale testing. A need exists for a fast, non-invasive, and cost-effective method to determine GCV in maize cob and NIR spectroscopy was examined for this purpose. Sixty samples of maize cobs were collected from different growing areas for the study. After harvesting, samples were crushed and dried to a constant weight in an oven. Samples were scanned using an FT-NIR spectrometer from 12500 cm-1 to 3600 cm-1 at 8 cm-1 resolution. Each sample was scanned thirty-two times per reading and the scans were averaged into a single spectrum per sample. Out of the sixty sample spectra, fifty were chosen for the calibration set and the remaining ten for a validation set. After scanning, reference tests were performed on 0.5 g of each sample using a bomb calorimeter to determine GCV values. Various pre-processing algorithms were applied to the NIR spectra before chemometric modeling. A Partial Least Squares (PLS) calibration model was created correlating the NIR spectra to GCV. Results are shown below.
|GCV||R2= 0.83||RMSECV= 91 J/g|
Modeling results showed good correlation and the independent validation set was used to confirm the validity of the model. The RMSECV is quite low considering that each sample has a GCV in between 17000 J/g and 18000 J/g. It is possible that using only 0.5 g for the reference tests contributed to a lower correlation coefficient as the variation between that sample size used for the reference test and the sample scanned with the spectrometer may have been significant. The sample set was limited and before using this model in a practical setting, more samples from different growing areas and different varieties of maize cob should be added to the calibration set to make the model more robust and confirm the validity of the calibration. Overall, the results show promise for using NIR spectroscopy as a fast, non-invasive, and cost-effective method for determining GCV in maize cob with the potential to replace traditional time-consuming and expensive reference methods.
(PDF) Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy (researchgate.net)
Water Deficit Responsive QTLs for Cell Wall Degradability and Composition in Maize at the Silage Stage
In order to use lignocellulosic biomass for animal feed or biorefining, optimization of the degradability of the material is required. Much work is put into adapting biomass crops to changing climate and in particular to drought resistance. Lignocellulosic biomass consists primarily of cell wall polymers. Few studies have been conducted that use quantitative trait loci (QTL) to determine agronomical and cell-wall related traits related to water deficit. In this study, the mapping power of a maize recombinant inbred line population was combined with NIR spectroscopy calibration models to track the response to water deficit of traits associated with biomass quality. Over three separate years, the inbred line population was cultivated under two contrasted water regimes and harvested at silage stage. NIR predictive equations were established for various biochemical cell wall related traits, such as cell wall residues, degradability, lignin content, lignin structure, p-Hydroxycinnamic acids, and structural sugars. Results showed that cell wall degradability and β-O-4-linked H lignin subunits were increased in response to water deficit, while lignin and p-coumaric acid contents were reduced. A mixed linear model was fitted to map QTLs for agronomical and cell wall-related traits. These QTLs were categorized as “constitutive” (QTL with an effect whatever the irrigation condition) or “responsive” (QTL involved in the response to water deficit) QTLs. Fifteen clusters of QTLs encompassed more than two-thirds of the two hundred and thirteen constitutive QTLs and thirteen clusters encompassed more than 60% of the one hundred and forty-nine responsive QTLs. The results showed that water deficit favors cell wall degradability and that breeding of varieties that show improved resistance to drought and biomass degradability is possible. NIR spectroscopy proved to be a powerful tool in this study by enabling the quick analysis of the various traits needed to determine the effect of water deficit on the maize samples.
Water Deficit-Responsive QTLs for Cell Wall Degradability and Composition in Maize at Silage Stage – PubMed (nih.gov)
Screening of Transgenic Maize Using Near Infrared Spectroscopy and Chemometric Techniques
Plant breeding uses molecular biology to produce new crop varieties and lines by using genetic engineering to introduce desirable traits into plants. One important technique in breeding is selection, the process of effectively propagating plants with desirable traits and eliminating those with less desirable traits. Breeders must screen large populations of crops to find plants with desired traits. Traditional screening methods used for this purpose are DNA and protein based, such as polymerase chain reaction (PCR) and microarrays. Such methods are time-consuming, expensive, and impractical for use when studying large numbers of samples, especially in the procedure of leaf DNA extraction. NIR spectroscopy was examined for the purpose of classifying transgenic and non-transgenic maize plants. Seeds of transgenic maize created with both herbicide and insect tolerant traits along with seeds from its parental line were procured for the study. Seeds were sown and grown in a greenhouse for two months. The second or third leaf that formed from each plant was selected for NIR sampling. Before NIR scanning, PCR was used to check the integrity of the copies of the genes introduced during the breeding phase. In total, one hundred and sixty-three of each of the transgenic and non-transgenic leaves were chosen for NIR scanning. Samples were scanned using an NIR spectrometer from 900 nm to 1700 nm. Each leaf was scanned three times and the three scans were averaged into one spectrum. Various pre-processing methods were applied to the spectral data before chemometric analysis. Principle Component Analysis (PCA) was performed on the NIR spectra to analyze spectral differences and wavelength ranges that were relevant to differences in the spectra. A total of five separate classification algorithms were applied to build discrimination models that can separate the transgenic and non-transgenic samples using the NIR spectra. Models were created using both the full wavelength range and sensitive wavelengths identified from PCA. The best results are shown below:
|Extreme Learning Machine (ELM)||95.20%|
In order to validate the models, cross-validation was performed by removing sample spectra from the models and then classifying the samples based on the spectra that were removed. The best results were obtained with the ELM algorithm using the full wavelength range. The sensitive wavelength range models showed worse results. It is common for natural products to show variations in NIR spectra that are not related to the parameters of interest and models using the full wavelength range for agricultural products in particular need a large wavelength range to be robust and predict accurately. The potential was demonstrated for using NIR spectroscopy as a screening tool for transgenic plants that could replace expensive, time-consuming, and slow traditional reference methods.
Grain Quality of Drought Tolerant Accessions Within the MRI Zemun Polje Maize Germplasm Collection
Maize is among the three most widely grown crops in the world. Breeders are conducting research to identify superior genotypes, particularly in relation to drought tolerance as drought is one of the most important factors that limits production of maize. Even in areas where the average rainfall is sufficient for maize growing, the distribution of rainfall can be insufficient and causes yield loss in the crops. The incorporation of genetic research in breeding programs that can create lines that are more drought resistance and thus having most stable yields is growing rapidly, but current testing methods for determining genetic lines with these traits are impractical for large-scale use. In this study, NIR spectroscopy was used as a tool for determining nutritional content of forty different accessions that were created from an elite drought tolerant core gene bank from multiple inbred lines, introduced populations, and landraces. The purpose was to determine if macronutrient content gain among the different generic groups could be correlated to genetic gain and thus identify these groups as potentially favorable sources for a specific trait, in this case drought tolerance. The forty different accessions from the core were grown, multiplied, and at least eighty ears of maize were collected per multiplied population. Samples were scanned using an NIR spectrometer and calibration models were used to determine values for oil, protein, and starch. It was noted that the oil, protein, and starch contents were significantly higher in the introduced populations than for the landraces. Oil in particular showed the greatest progress from the selection based on the expected genetic gain at 14.74%, indicating that the greatest progress in breeding could be determined from increased oil content with accessions from an unknown group. The potential was shown in this study to use NIR spectroscopy as a tool for determining macronutrient content in maize which can then be used to identify accessions with favorable traits to assist breeders in selecting plants with desirable qualities for improved breeding.
Grain quality of drought tolerant accessions within the MRI Zemun Polje maize germplasm collection – Dialnet (unirioja.es)