Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration

Haruna, Suleiman A.; Li, Huanhuan; Wei, Wu; Geng, Wenhui; Adade, Selorm Yao-Say Solomon; Zareef, Muhammad; Ivane, Ngouana Moffo A.; Chen, Quansheng

doi:10.1039/d2ay00875k

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…Overall, it appears that the efficiency of the NIRS in predicting forage quality differs from one parameter to another [39]. Even though the R 2 c value is a great statistical indicator by which to assess the accuracy of the calibration model, it is not the best indicator for this purpose, as it depends on the range [40]. Thus, the wide range observed in the mineral composition values of the studied elements and within certain parameters in terms of concentration can be explained by the diversity of the leguminous species, the stages of maturity at the time of sampling, and the differences in the plant fractions (leaves versus stems) used [31].…”

Section: Nirs Models Accuracy: Calibration and Cross Validationmentioning

confidence: 99%

Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management

Ndiaye,

Obour,

Harmoney

et al. 2023

Sustainability

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Cowpea fodder has been one of the favored livestock forages for centuries in sub-Saharan Africa, particularly in Senegal. However, little research has been conducted on quantifying the nutritional quality of cowpea fodder because of the costly wet chemistry analysis. The main objective of this study was to develop predictive equations for a sustainable quantification of the nutritional quality of dual-purpose cowpea fodder using near infrared spectroscopy (NIRS) and to investigate the influence of cropping system, fertilizer, genotype, and their interaction on biomass yield and cowpea forage nutritional value. In this study, 120 samples from a dual-purpose cowpea variety trial were used to develop NIRS equations to estimate forage quality parameters including concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), calcium (Ca), phosphorus (P), potassium (K), and iron (Fe). Partial least squares (PLS) regression generated prediction equations using NIRS wavelength measurements, and reference wet chemistry analysis from calibration samples were developed. The PLS prediction equations for the different forage quality parameters had an R2 of calibration 0.94, 0.93, 0.88, 0.63, 0.69, 0.87, and 0.94 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. Using these prediction equations, correlation of the predicted values of the calibration subset and the prediction test subset resulted in significant positive relationships, with R2 of 0.83, 0.74, 0.70, 0.63, 0.59, 0.75, and 0.83 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The corresponding RMSE of these relationships was 0.91, 2.68, 3.45, 0.23, 0.06, 0.11, and 100 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The range and mean concentrations of the calibration subset overlapped with that of the prediction subset for all parameters evaluated. Cross-validation procedures indicated good correlations between wet chemistry analysis and NIRS forage quality estimates. Results of the second experiment showed that the cropping system had no significant effect on cowpea forage yield and nutritive value. However, cowpea variety and fertilizer, both individually and their interaction, had a significant effect on fodder yield and cowpea forage quality. We conclude that the NIRS calibration equations developed can be used to accurately predict the cowpea forage quality parameters evaluated in this study.

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Section: Nirs Models Accuracy: Calibration and Cross Validationmentioning

confidence: 99%

Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management

Ndiaye,

Obour,

Harmoney

et al. 2023

Sustainability

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“…We placed the sample in a laboratory environment for 24 hours before the test to eliminate the influence of temperature and humidity fluctuations. 17 The specific experimental procedure was to first measure the maximum equatorial diameter, minimum equatorial diameter, and longitudinal diameter with a vernier caliper (DWGR-3042, DELIXI, China) and weigh the potato sample with an electronic scale (HZY-B2000, Huazhi Electronic Technology, China) (Table 1). In the second step, two detection modes (diffuse reflection and transmission) (Fig.…”

Section: Methodsmentioning

confidence: 99%

Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization

et al. 2023

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“…Te resulting solution obtained through the iterative process represents the algorithm's outcome. However, in complex high-dimensional optimization problems, the convergence rate of the RF algorithm may be slow, requiring more iterations to achieve an optimal solution [35,36].…”

Section: Algorithmic Advantagesmentioning

confidence: 99%

Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy

Jia,

Mai,

Xiang

et al. 2024

Journal of Spectroscopy

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The precise and prompt determination of quality control indicators such as moisture, stilbene glycosides, and anthraquinone glycosides is crucial in assessing the quality of Polygoni Multiflori Radix. Near-infrared spectroscopy is a nondestructive analytical technique that offers a more desirable approach than traditional methods for assessing content levels. In this study, various spectral preprocessing techniques were used to preprocess the raw spectral data. The spectral data were correlated with the determination of three-component contents using the partial least squares regression (PLSR) method. Then different algorithms, such as competitive adaptive weighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), and random frog hopping (RF), were used for model simplification and feature selection. The data suggest that the first-order deconvolution derivative (1st Dev.) processing of the spectral data is superior to other methods in all three model evaluation metrics. The PLSR model for moisture, stilbene glycosides, and anthraquinone glycosides produced the calibration coefficient of determination (R2C) of 0.82, 0.52, and 0.58, the root mean square error of cross validation (RMSECV) of 0.91%, 0.77%, and 0.69%, the prediction coefficient of determination (R2P) of 0.72, 0.28, and 0.54, the root mean square error of prediction (RMSEP) of 0.65%, 0.81%, and 0.75%, and relative percentage differences (RPDs) of 1.7, 1.0, and 0.8. After optimizing the model using CARS, R2C increased by 0.15%, 0.41%, and 0.34%, RMSECV decreased by 0.53%, 0.32%, and 0.24%, R2P increased by 0.21%, 0.63%, and 0.35%, RMSEP decreased by 0.36%, 0.41%, and 0.31%, and RPD increased by 1.1, 0.9, and 0.6, significantly improving the predictive capacity of the model. This research provides a feasible method for rapid compliance testing of Polygoni Multiflori Radix. To further improve the model’s performance and applicability, it is necessary to continuously expand the sample set with different varieties and locations for wide variation.

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Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration

Abstract: Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds.

Cited by 13 publications

References 34 publications

Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management

Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management

Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization

Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy

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