Research on Determination Method of Starch, Protein and Total Flavonoids Content in Buckwheat by Near-Infrared Spectroscopy

Wang, Fenghua; Yang, Ju Yun; Zhu, Hailong

doi:10.1166/sl.2014.3155

Cited by 6 publications

(4 citation statements)

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“…The NIR prediction model for sucrose content in peanut kernels built by Telly et al [25], using 72 peanut resources with edible oil, showed that the coefficient of determination of the optimal model was only 0.822, while Bian et al [26] expanded the number of modeling samples to 119 and the coefficient of determination of the optimal peanut sucrose prediction model increased to 0.898. Wang et al [27] used 40 samples to build the near-infrared model for buckwheat grain protein and total flavonoids, but the prediction value of the built model for protein and flavonoids was not ideal, which might be related to the small sample size. Guo et al [28] used 217 buckwheat samples to build a prediction model for grain protein and found that the coefficient of determination of the prediction model was 0.9481 and the root mean square of the cross-validation was 0.68.…”

Section: Discussionmentioning

confidence: 99%

Construction and Application of Detection Model for Leucine and Tyrosine Content in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy

Zhu

Damaris

et al. 2022

Applied Sciences

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To meet the demand of the breeding and processing industry of Golden Tartary buckwheat, quantitative identification models were established to test the content of leucine (Leu) and tyrosine (Tyr) in Golden Tartary buckwheat leaves by near-infrared reflectance spectroscopy (NIRS) with quantitative partial least squares (PLS). Leu’s modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–9000 cm−1 was appropriate for modeling (calibration sets: validation set = 6:1), the mean coefficient of determination (R2), standard error of calibration (SEC), and relative standard deviation (RSD) for the calibration set were 0.9229, 0.45, and 3.45%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9502, 0.47, and 3.65%, respectively. Tyr modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–10,000 cm−1 was suitable for modeling (calibration sets: validation set = 4:1), the R2, SEC, and RSD for the calibration set was 0.9016, 0.15, and 5.72%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9012, 0.15, and 5.53%, respectively. It was proved that the Leu and Tyr content of Golden Tartary buckwheat could be quantified using the model structured by near infrared spectroscopy combined with the partial least squares method.

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Section: Discussionmentioning

confidence: 99%

Construction and Application of Detection Model for Leucine and Tyrosine Content in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy

Zhu

Damaris

et al. 2022

Applied Sciences

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“…After the tartary buckwheat was mature, the grains were harvested and dried indoors for seven days, so that the water content of all samples was basically at the same level. Then, the grains were shelled, crushed, and screened through a 0.2 mm sieve (Zhangxing Sieve, Shaoxing, China), so that all the crushed samples remained the same thickness [ 24 ]. The sieved samples were used to measure hyperspectral reflectance, total flavonoids, and total phenols content.…”

Section: Methodsmentioning

confidence: 99%

“…Some researchers also used spectral technology to study the flavonoids content in buckwheat grains. For example, Wang et al [ 24 ] used near-infrared spectroscopy to monitor the content of total flavonoids in buckwheat grains and obtained a model with high accuracy, whose validation R 2 reached 0.967. Rutin is a kind of flavonoid.…”

Section: Introductionmentioning

confidence: 99%

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Yang

Song

Wei

et al. 2023

Foods

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Tartary buckwheat is a common functional food. Its grains are rich in flavonoids and phenols. The rapid measurement of flavonoids and phenols in buckwheat grains is of great significance in promoting the development of the buckwheat industry. This study, based on multiple scattering correction (MSC), standardized normal variate (SNV), reciprocal logarithm (Lg), first-order derivative (FD), second-order derivative (SD), and fractional-order derivative (FOD) preprocessing spectra, constructed hyperspectral monitoring models of total flavonoids content and total phenols content in tartary buckwheat grains. The results showed that SNV, Lg, FD, SD, and FOD preprocessing had different effects on the original spectral reflectance and that FOD can also reflect the change process from the original spectrum to the integer-order derivative spectrum. Compared with the original spectrum, MSC, SNV, Lg, FD, and SD transformation spectra can improve the correlation between spectral data and total flavonoids and total phenols in varying degrees, while the correlation between FOD spectra of different orders and total flavonoids and total phenols in grains was different. The monitoring models of total flavonoids and total phenols in grains based on MSC, SNV, Lg, FD, and SD transformation spectra achieved the best accuracy under SD and FD transformation, respectively. Therefore, this study further constructed monitoring models of total flavonoids and total phenols content in grains based on the FOD spectrum and achieved the best accuracy under 1.6 and 0.6 order derivative preprocessing, respectively. The R2c, RMSEc, R2v, RMSEv, and RPD were 0.8731, 0.1332, 0.8384, 0.1448, and 2.4475 for the total flavonoids model, and 0.8296, 0.2025, 0.6535, 0.1740, and 1.6713 for the total phenols model. The model can realize the rapid measurement of total flavonoids content and total phenols content in tartary buckwheat grains, respectively.

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“…The content of total flavonoids in tea trees is determined by factors such as tea tree variety, ecological environment, soil condition, and management methods. Wang et al [15] employed a combination of principal component analysis (PCA) and artificial neural networks (ANN) to establish prediction models for buckwheat starch, protein, and total flavonoids content, respectively. The results revealed low accuracy in predicting protein and total flavonoids content, which may be due to the limited sample size, and no appropriate methods were applied to address this problem.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Flavonoids Content in Ancient Tree Sun−Dried Green Tea under Abiotic Stress Based on LASSO−Cox

Li,

Wu,

Wang

et al. 2024

Agriculture

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To investigate the variation in flavonoids content in ancient tree sun–dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun−dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, and soil composition of the tree. This study uses two machine−learning models, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression, to build a predictive model based on the selection of effective variables. During the process, bootstrap was used to expand the dataset for single−factor and multi−factor comparative analyses, as well as for model validation, and the goodness−of−fit was assessed using the Akaike information criterion (AIC). The results showed that pH, total potassium, nitrate nitrogen, available phosphorus, hydrolytic nitrogen, and ammonium nitrogen have a high accuracy in predicting the flavonoids content of this model and have a synergistic effect on the production of flavonoids in the ancient tree tea. In this prediction model, when the flavonoids content was >6‰, the area under the curve of the training set and validation set were 0.8121 and 0.792 and, when the flavonoids content was >9‰, the area under the curve of the training set and validation set were 0.877 and 0.889, demonstrating good consistency. Compared to modeling with all significantly correlated factors (p < 0.05), the AIC decreased by 32.534%. Simultaneously, a visualization system for predicting flavonoids content in ancient tree sun−dried green tea was developed based on a nomogram model. The model was externally validated using actual measurement data and achieved an accuracy rate of 83.33%. Therefore, this study offers a scientific theoretical foundation for explaining the forecast and interference of the quality of ancient tree sun−dried green tea under abiotic stress.

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Research on Determination Method of Starch, Protein and Total Flavonoids Content in Buckwheat by Near-Infrared Spectroscopy

Cited by 6 publications

References 0 publications

Construction and Application of Detection Model for Leucine and Tyrosine Content in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy

Construction and Application of Detection Model for Leucine and Tyrosine Content in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Prediction Model of Flavonoids Content in Ancient Tree Sun−Dried Green Tea under Abiotic Stress Based on LASSO−Cox

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