Rapid spectroscopic method for quantifying gluten concentration as a potential biomarker to test adulteration of green banana flour

Ndlovu, Phindile Faith; Magwaza, Lembe Samukelo; Tesfay, S.Z.; Mphahlele, Rebogile R.

doi:10.1016/j.saa.2021.120081

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…To assess the success of data preprocessing and model performance, the following parameters were calculated: coefficient of determination for calibration ( R 2 C ), coefficient of determination for prediction ( R 2 P ), coefficient of determination for cross-validation ( R 2 CV ), root mean square error of estimation (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and the values of prediction to deviation (RPD, the ratio of stander deviation to RMSEP). A good calibration model should have high values of R 2 C and R 2 P , and with low values of RMSEC and RMSEP ( Ndlovu et al, 2021 , Ye et al, 2018 ). RMSECV was the result by 7-fold cross-validation procedure and mainly used to assess the modeling performance of the PLSR models.…”

Section: Methodsmentioning

confidence: 99%

“…RMSECV was the result by 7-fold cross-validation procedure and mainly used to assess the modeling performance of the PLSR models. The RPD values reflect the overall predictive capability of the PLSR models and the performance indicates excellent when the RPD values are greater than 3 ( Ndlovu et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…With the development of chemometrics, NIRs coupled to multivariate statistical analysis has been successfully applied for qualitative and quantitative analyses in food, agricultural, and herbal medicine areas ( Xue et al, 2021 ). Various studies have revealed the great potential of NIRs to distinguish spice adulteration, such as turmeric powder adulterated with corn flour ( Kar et al, 2019 ), green banana flour adulterated with wheat flour ( Ndlovu et al, 2021 ), and paprika powder mixed with potato starch and acacia gum ( Oliveira et al, 2020 ). Comparing to Fourier transform-infrared spectroscopy (FT-IR), the quantitative performance of NIRs is even more prominent.…”

Section: Introductionmentioning

confidence: 99%

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Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics

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Zhang

et al. 2022

Food Chemistry: X

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics

Guo

Zhang

et al. 2022

Food Chemistry: X

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“…The pre-processing method can amplify the original hidden signal differences in the spectral data. Meanwhile, spectral pre-processing techniques can achieve the purpose of improving the resolution of THz spectral data, making the identification more accurate and reliable ( Ndlovu et al, 2021 ; Tafintseva et al, 2021 ). In this paper, seven pre-processing methods were used, including: mean-centering, auto scaling, standard normal variate (SNV), minimum and maximum values to [0 1], multiplicative scatter correction (MSC), first derivative, and second derivative ( Lu, 2006 ; Chu, 2011 ).…”

Section: Methodsmentioning

confidence: 99%

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

et al. 2022

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Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

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“…At the same time, t-SNE is also a nonlinear dimensionality reduction algorithm to explore high-dimensional data. The principles of the t-SNE algorithm are as follows [41].…”

Section: Feature Dimensionality Reduction and Data Visualizationmentioning

confidence: 99%

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Huang

et al. 2021

Agriculture

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A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

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Rapid spectroscopic method for quantifying gluten concentration as a potential biomarker to test adulteration of green banana flour

Cited by 18 publications

References 30 publications

Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics

Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

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