Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM)
The accurate quantification of amino acids in maize breeding programs is challenging due to the high cost of analysis using High-Performance Liquid Chromatography (HPLC) and other conventional methods. Using the Near-Infrared Spectroscopic (NIRS) method in breeding to screen many genotypes has prove...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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MDPI
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/121931 |
| _version_ | 1855513344361889792 |
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| author | Alamu, Emmanuel Oladeji Menkir, A. Adesokan, Michael Fawole, S. Maziya-Dixon, Busie |
| author_browse | Adesokan, Michael Alamu, Emmanuel Oladeji Fawole, S. Maziya-Dixon, Busie Menkir, A. |
| author_facet | Alamu, Emmanuel Oladeji Menkir, A. Adesokan, Michael Fawole, S. Maziya-Dixon, Busie |
| author_sort | Alamu, Emmanuel Oladeji |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The accurate quantification of amino acids in maize breeding programs is challenging due to the high cost of analysis using High-Performance Liquid Chromatography (HPLC) and other conventional methods. Using the Near-Infrared Spectroscopic (NIRS) method in breeding to screen many genotypes has proven to be a fast, cost-effective, and non-destructive method. Thus, this study aimed to develop and apply the NIRS prediction models for quantifying amino acids in biofortified quality protein maize (QPM). Sixty-three (63) QPM maize genotypes were used as the calibration set, and another twenty (20) genotypes were used as the validation set. The microwave hydrolysis system coupled with post-column derivatization with 6-amino-quinoline-succinimidyl-carbamate as the derivatization reagent and the HPLC method were used to generate the reference data set used for the calibration development. The calibration models were developed for essential and non-essential amino acids using WINSI Foss software. Good coefficients of determination in calibration (R2cal) of 0.91, 0.93, 0.93, and 0.91 and low standard errors in calibrations (SEC) of 0.62, 0.71, 0.26, and 1.75 were obtained for glutamic acids, alanine, proline, and leucine, respectively, while aspartic acids, serine, glycine, arginine, tyrosine, valines, and phenylalanine had fairly good R2Cal values of 0.86, 0.71, 0.81, 0.78, 0.68, 0.79, and 0.75. In contrast, poor (R2cal) was obtained for histidine (0.07), cystine (0.09), methionine (0.09), lysine (0.20), threonine (0.51), and isoleucine (0.09), respectively. The models’ prediction performances (R2pred) and standard error of prediction (SEP) were reasonably good for certain amino acids such as aspartic acid (0.90), glycine (0.80), arginine (0.94), alanine (0.90), proline (0.80), tyrosine (0.83), valine (0.82), leucine (0.90), and phenylalanine (0.88) with SEP values of 0.24, 0.39,0.24, 0.93, 0.47,0.34, 0.78, 2.20, and 0.77, respectively. However, certain amino acids had their R2pred below 0.50, which could be improved to become useful for screening purposes for those amino acids. Further work is recommended by including a training set representing the sample population’s variance to improve the model’s performance. |
| format | Journal Article |
| id | CGSpace121931 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1219312025-12-08T10:29:22Z Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM) Alamu, Emmanuel Oladeji Menkir, A. Adesokan, Michael Fawole, S. Maziya-Dixon, Busie infrared spectrophotometry reflectance amino acids quality proteins maize hplc screening models calibration microbiology food science The accurate quantification of amino acids in maize breeding programs is challenging due to the high cost of analysis using High-Performance Liquid Chromatography (HPLC) and other conventional methods. Using the Near-Infrared Spectroscopic (NIRS) method in breeding to screen many genotypes has proven to be a fast, cost-effective, and non-destructive method. Thus, this study aimed to develop and apply the NIRS prediction models for quantifying amino acids in biofortified quality protein maize (QPM). Sixty-three (63) QPM maize genotypes were used as the calibration set, and another twenty (20) genotypes were used as the validation set. The microwave hydrolysis system coupled with post-column derivatization with 6-amino-quinoline-succinimidyl-carbamate as the derivatization reagent and the HPLC method were used to generate the reference data set used for the calibration development. The calibration models were developed for essential and non-essential amino acids using WINSI Foss software. Good coefficients of determination in calibration (R2cal) of 0.91, 0.93, 0.93, and 0.91 and low standard errors in calibrations (SEC) of 0.62, 0.71, 0.26, and 1.75 were obtained for glutamic acids, alanine, proline, and leucine, respectively, while aspartic acids, serine, glycine, arginine, tyrosine, valines, and phenylalanine had fairly good R2Cal values of 0.86, 0.71, 0.81, 0.78, 0.68, 0.79, and 0.75. In contrast, poor (R2cal) was obtained for histidine (0.07), cystine (0.09), methionine (0.09), lysine (0.20), threonine (0.51), and isoleucine (0.09), respectively. The models’ prediction performances (R2pred) and standard error of prediction (SEP) were reasonably good for certain amino acids such as aspartic acid (0.90), glycine (0.80), arginine (0.94), alanine (0.90), proline (0.80), tyrosine (0.83), valine (0.82), leucine (0.90), and phenylalanine (0.88) with SEP values of 0.24, 0.39,0.24, 0.93, 0.47,0.34, 0.78, 2.20, and 0.77, respectively. However, certain amino acids had their R2pred below 0.50, which could be improved to become useful for screening purposes for those amino acids. Further work is recommended by including a training set representing the sample population’s variance to improve the model’s performance. 2022-09-09 2022-09-23T09:18:13Z 2022-09-23T09:18:13Z Journal Article https://hdl.handle.net/10568/121931 en Open Access application/pdf MDPI Alamu, E.O., Menkir, A., Adesokan, M., Fawole, S. & Maziya-Dixon, B. (2022). Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM). Foods, 11(18): 2779, 1-10. |
| spellingShingle | infrared spectrophotometry reflectance amino acids quality proteins maize hplc screening models calibration microbiology food science Alamu, Emmanuel Oladeji Menkir, A. Adesokan, Michael Fawole, S. Maziya-Dixon, Busie Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM) |
| title | Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM) |
| title_full | Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM) |
| title_fullStr | Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM) |
| title_full_unstemmed | Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM) |
| title_short | Near-Infrared Reflectance Spectrophotometry (NIRS) application in the amino acid profiling of Quality Protein Maize (QPM) |
| title_sort | near infrared reflectance spectrophotometry nirs application in the amino acid profiling of quality protein maize qpm |
| topic | infrared spectrophotometry reflectance amino acids quality proteins maize hplc screening models calibration microbiology food science |
| url | https://hdl.handle.net/10568/121931 |
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