Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour

Wang, Zhuzhu; Wu, Qianyi; Kamruzzaman, Mohammed

doi:10.1016/j.foodcont.2022.108970

Cited by 46 publications

(16 citation statements)

References 66 publications

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“…One of the most commonly used techniques for developing classification models in food authentication is partial PLS-DA, which focuses on the differences among samples from different classes and operates by splitting the hyperspace of the variables, which is based on a comparison of the predicted response values from Y with a fixed scalar threshold, usually 0.5 ( Jiménez-Carvelo et al, 2021 ). The VIP is a commonly used method for variable selection, where the basic idea is that the average VIP score of all variables is 1; therefore, any variable with a VIP score greater than 1 indicates its importance, and values less than 1 can be eliminated ( Wang et al, 2022 ). OriginPro (Version 2021.…”

Section: Methodsmentioning

confidence: 99%

Investigating the mechanism of the flavor formation in Sichuan sun vinegar based on flavor-orientation and metagenomics

Zhang

et al. 2023

Current Research in Food Science

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Section: Methodsmentioning

confidence: 99%

Investigating the mechanism of the flavor formation in Sichuan sun vinegar based on flavor-orientation and metagenomics

Zhang

et al. 2023

Current Research in Food Science

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“…(2020), Wang et al . (2022), and Da Costa Filho et al . (2022) involving the quantification of adulterants present in different samples using spectroscopy.…”

Section: Resultsmentioning

confidence: 95%

Application of portable near‐infrared spectroscopy for rapid detection and quantification of adulterants in baobab fruit pulp

Yegon

Ojijo

Tybussek

et al. 2023

Int J of Food Sci Tech

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Baobab fruit pulp powder (BFPP) is susceptible to economically driven adulteration owing to its incredible nutrient density and rapidly expanding demand worldwide. In this study, a portable NIR spectroscopy (wavelength 900-1700 nm) coupled with chemometrics was used for the detection of BFPP adulteration. BFPP samples separately adulterated with rice flour (RF), wheat flour (WF), and maize flour (MF) at 0%, 1%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50% and 60% concentrations were subjected to NIR spectroscopy. Two-class models proved to be reliable with sensitivity and specificity of above 0.98 and an error of below 0.01. Four-class models attained sensitivity and specificity of above 0.68 and an error of below 0.276. The correlation coefficient (R 2 ) and root mean square error (RMSE) of the prediction set were above 0.88 and below 6.20% respectively for PLSR models. The LODs were also below 13.79%. Therefore, NIR spectroscopy has a promising potential for rapid screening of BFPP adulterations.

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“…On the one hand, the processing of a large number of features requires a robust computer performance to handle the computational load. On the other hand, a large part of these feature bands are often redundant bands with a large amount of collinear information and useless information, which will only hinder the processing performance of the model [44]. As a result, the insufcient processing of the really important information leads to the instability of the model and poor experimental results.…”

Section: Feature Band Screening Methodmentioning

confidence: 99%

Evaluation of Flavor Type of Tobacco Blending Module: A Prediction Model Based on Near-Infrared Spectrum

Wang,

Guan,

Zhang

2023

Discrete Dynamics in Nature and Society

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Near-infrared spectrum technology is extensively employed in assessing the quality of tobacco blending modules, which serve as the fundamental units of cigarette production. This technology provides valuable technical support for the scientific evaluation of these modules. In this study, we selected near-infrared spectral data from 238 tobacco blending module samples collected between 2017 and 2019. Combining the power of XGBoost and deep learning, we constructed a flavor prediction model based on feature variables. The XGBoost model was utilized to extract essential information from the high-dimensional near-infrared spectra, while a convolutional neural network with an attention mechanism was employed to predict the flavor type of the modules. The experimental results demonstrate that our model exhibits excellent learning and prediction capabilities, achieving an impressive 95.54% accuracy in flavor category recognition. Therefore, the proposed method of predicting flavor types based on near-infrared spectral features plays a valuable role in facilitating rapid positioning, scientific evaluation, and cigarette formulation design for tobacco blending modules, thereby assisting decision-making processes in the tobacco industry.

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Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour

Cited by 46 publications

References 66 publications

Investigating the mechanism of the flavor formation in Sichuan sun vinegar based on flavor-orientation and metagenomics

Investigating the mechanism of the flavor formation in Sichuan sun vinegar based on flavor-orientation and metagenomics

Application of portable near‐infrared spectroscopy for rapid detection and quantification of adulterants in baobab fruit pulp

Evaluation of Flavor Type of Tobacco Blending Module: A Prediction Model Based on Near-Infrared Spectrum

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