Comparison of different CCD detectors and chemometrics for predicting total anthocyanin content and antioxidant activity of mulberry fruit using visible and near infrared hyperspectral imaging technique

Huang, Lingxia; Zhou, Yibin; Meng, Liuwei; Wu, Di; He, Yong

doi:10.1016/j.foodchem.2016.12.037

Cited by 79 publications

(29 citation statements)

References 36 publications

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“…Based on SPA and stepwise regression (SWR) for selecting optimal wavelengths related with anthocyanin content in lychee pericarp, the radial basis function support vector regression (RBF‐SVR) and radial basis function neural network (RBF‐NN) models were fused into a single simplified model, and achieved a better performance ( R 2 P = 0.872) of predicting and visualizing anthocyanin content in lychee during storage (Yang and others ). Similar results were found in studies by Liu and others () and Huang and others (). Anthocyanin contents in mulberry and sweet potato were effectively determined by optimal RC‐MLR and CARS‐LS‐SVM models with R 2 P of 0.866 and R 2 CV of 0.959, respectively.…”

Section: Determination Of Quality Parameters Of Plant Foodssupporting

confidence: 92%

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Sun

2018

Comp Rev Food Sci Food Safe

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The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor‐based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant‐based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS‐SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.

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Section: Determination Of Quality Parameters Of Plant Foodssupporting

confidence: 92%

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Sun

2018

Comp Rev Food Sci Food Safe

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“…In addition, to further verify that the method proposed in this study has higher accuracy than the model established by the general variable selection method in modeling. Two commonly used variable selection methods of successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were performed to select the optimal variables from corn data, and the number of the optimal variables selected by SPA and CARS algorithm is 14 and 4. And then the MLR models (MLR‐SPA and MLR‐CARS) were established based on the optimal variables.…”

Section: Resultsmentioning

confidence: 99%

Tuning parameter identification for variable selection algorithm using the sum of ranking differences algorithm

Nie

Meng

Chen

et al. 2019

Journal of Chemometrics

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Variable selection algorithms are often adopted to select the optimal variable from a full set of variables and are efficient for reducing the variable dimension and improving the model accuracy. Nonetheless, the parameters of the variable selection method and regression model, such as the number of latent variables of the partial least squares (PLS) model and the threshold value of the variable importance index, need to be identified. The parameters directly determine the final performance of the model. Currently, these parameters are often determined subjectively. As a result, the model results may be accidental because of the subjective determination of the parameters. To objectively identify these parameters, the sum of ranking differences (SRD) coupled with partial least squares‐variable importance in projection (PLS‐VIP‐SRD) and partial least squares‐uninformative variable elimination (PLS‐UVE‐SRD) algorithms was applied to determine the latent variable of the PLS model and the threshold value of the variable importance index. Furthermore, public near‐infrared data of corn were used as the calculation data. The final results show that the PLS‐VIP‐SRD and PLS‐UVE‐SRD models can more effectively and objectively determine the optimal parameter combination than the PLS‐VIP and PLS‐UVE models. Moreover, the selected variables are easier to interpret, and the prediction accuracy is also improved to some extent.

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“…PLS can perform data processing on X-variables and Y-variables at the same time. The main information in the two sets of matrices is extracted by optimizing the covariance of X-variables and Y-variables [30]. The establishment of the PLS regression model and ten-fold cross-validation were performed in the Unscrambler X 10.1 (Camo AS, Oslo, Norway).…”

Section: Regression Modelsmentioning

confidence: 99%

Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis

Xiao

Bai

2020

Foods

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Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), L*/b*) and water content of fresh-cut potato tuber slices. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic wavelengths, partial least squares (PLS) and least squares support vector machine (LS-SVM) were utilized to establish regression models. For color prediction, R2c, R2p and RPD of all the LSSVM models established for the five color indicators L*, a*, b*, BI, L*/b* were exceeding 0.90, 0.84 and 2.1, respectively. For water content prediction, R2c, R2p, and RPD of the LSSVM models were over 0.80, 0.77 and 1.9, respectively. LS-SVM model based on full spectra was used to reappear the spatial distribution of color and water content in fresh-cut potato tuber slices by pseudo-color imaging since it performed best in most cases. The results illustrated that hyperspectral imaging could be an effective method for color and water content prediction, which could provide solid theoretical basis for subsequent grading and processing of fresh-cut potato tuber slices.

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Comparison of different CCD detectors and chemometrics for predicting total anthocyanin content and antioxidant activity of mulberry fruit using visible and near infrared hyperspectral imaging technique

Cited by 79 publications

References 36 publications

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Tuning parameter identification for variable selection algorithm using the sum of ranking differences algorithm

Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis

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