Classification of Single‐ and Double‐Mutant Corn Endosperm Genotypes by Near‐Infrared Transmittance Spectroscopy

Campbell, Mark; Sykes, J. F.

doi:10.1094/cchem.2000.77.6.774

Cited by 30 publications

(16 citation statements)

References 5 publications

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“…NIR spectroscopy technique [7][8][9], has been widely used in crop breeding and in the cereal industry for predicting specific chemical parameters of different crop varieties. For example, NIR spectroscopy analysis method has already been applied to starch analysis of cereals [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of The Total Starch and Amylose Content in Barley using Near-Infrared Reflectance Spectroscopy

Hua¹,

Wang²,

Ren³

2013

Intelligent Automation & Soft Computing

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Near-infrared reflectance (NIR) spectroscopy combined with reference data was used to predict the total starch content (TSC) and amylose content (AC) in different barley varieties. One hundred and twelve barley samples were analyzed for TSC and AC by AOAC methods. Out of the total samples, ninety were used to develop the calibration models, and twenty two were used to validate the models. The total starch model showed very good correlation with coefficient of determination (R 2 ) of 0.96 and root mean square error of cross-validation (RMSECV) of 0.79; the correlation of amylose model was calculated to be R 2 ¼ 0.94, RMSECV ¼ 0.94. The results showed that NIR analysis was sufficiently accurate for the determination of total starch and amylose in barley varieties.

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Section: Introductionmentioning

confidence: 99%

Prediction of The Total Starch and Amylose Content in Barley using Near-Infrared Reflectance Spectroscopy

Hua¹,

Wang²,

Ren³

2013

Intelligent Automation & Soft Computing

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“…The discriminative analyses of corn kernels found in the literature are based on endosperm characteristics (vitreousness, hardness). Accurate discrimination among kernels with vitreous and floury endosperm have been reported (Campbell et al, 2000;Williams et al, 2009). A few studies attempted to discriminate corn kernels according to their toxin contamination level (Dowell et al, 2002;Pearson et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels

Agelet

Ellis²,

Duvick

et al. 2012

Journal of Cereal Science

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The current US corn grading system accounts for the portion of damaged kernels, measured by timeconsuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and frost damage. Four classification algorithms were utilized: Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), k-nearest neighbors (K-NN), and least-squares support vector machines (LS-SVM). The feasibility of NIRS for discriminating normal or viable-germinating corn kernels and soybean seeds from abnormal or dead seeds was also tested. This application could be highly valuable for seed breeders and germplasm-preservation managers because current viability tests are based on a destructive method where the seed is germinated. Heat-damaged corn kernels were best discriminated by PLS-DA, with 99% accuracy. The discrimination of frost-damaged corn kernels was not possible. Discrimination of non-viable seeds from viable also was not possible. Since previous results in the literature contradict the current damagediscrimination results, the threshold of seed damage necessary for NIRS detection should be analyzed in the future. NIRS may accurately classify seeds based on changes due to damage, without any correlation with germination. KeywordsAgronomy, Seed Science Center, single seed, near infrared spectroscopy, discrimination The current US corn grading system accounts for the portion of damaged kernels, measured by timeconsuming and inaccurate visual inspection. Near infrared spectroscopy (NIRS), a non-destructive and fast analytical method, was tested as a tool for discriminating corn kernels with heat and frost damage. Four classification algorithms were utilized: Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), k-nearest neighbors (K-NN), and least-squares support vector machines (LS-SVM). The feasibility of NIRS for discriminating normal or viable-germinating corn kernels and soybean seeds from abnormal or dead seeds was also tested. This application could be highly valuable for seed breeders and germplasm-preservation managers because current viability tests are based on a destructive method where the seed is germinated. Heat-damaged corn kernels were best discriminated by PLS-DA, with 99% accuracy. The discrimination of frost-damaged corn kernels was not possible. Discrimination of non-viable seeds from viable also was not possible. Since previous results in the literature contradict the current damage-discrimination results, the threshold of seed damage necessary for NIRS detection should be analyzed in the future. NIRS may accurately classify seeds based on changes due to damage, without any correlation with germination.

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“…In some cases (e.g., breeding), single-seed samples can be also used and analysed using NIR spectroscopy in order to measure different chemical properties. For example, maize kernels can be classified according to characteristics, such as starch composition, hardness, avidin or mycotoxin levels [22,35]. However, samples still need to be sent to the lab, causing unavoidable time delays and cost.…”

Section: Determining Grain Composition At Harvestmentioning

confidence: 99%

An Overview on the Use of Infrared Sensors for in Field, Proximal and at Harvest Monitoring of Cereal Crops

2015

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Farmers are increasingly demanding rapid, cost-effective, easy-to-use and non-destructive methods for monitoring changes in the physical and chemical characteristics of crops and plants from the early stages of crop development until harvest. Remote and proximal sensor tools have been used recently to monitor different aspects of cereal production (e.g., fertilization, crop diseases). Most of these tools are characterized as non-destructive, non-invasive and easy-to-use, and most of them are based in near-infrared (NIR) spectroscopy. This article reviews recent and potential applications for the use of proximal sensors based on NIR spectroscopy to monitor dry matter (DM), yield, nitrogen and diseases in different cereal crops.

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Classification of Single‐ and Double‐Mutant Corn Endosperm Genotypes by Near‐Infrared Transmittance Spectroscopy

Cited by 30 publications

References 5 publications

Prediction of The Total Starch and Amylose Content in Barley using Near-Infrared Reflectance Spectroscopy

Prediction of The Total Starch and Amylose Content in Barley using Near-Infrared Reflectance Spectroscopy

Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels

An Overview on the Use of Infrared Sensors for in Field, Proximal and at Harvest Monitoring of Cereal Crops

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