Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics

Wang, Yangyang; He, Haixiang; Jiang, Shengqi; Ma, Haile

doi:10.25165/j.ijabe.20221501.6612

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…In this paper, these two methods were adopted to build regression models on both VIS-NIR and SW-NIR wavebands. R to 1 and the closer the RMSE to 0, indicating the stability and accuracy of the model (Wang et al, 2022c).…”

Section: Prediction Modelmentioning

confidence: 89%

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

et al. 2023

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Ginkgo biloba leaf is a kind of food raw material with high concentration of total flavonoid content (TFC). Since conventional measurements of TFC are time-consuming and destructive, the approach of hyperspectral imaging-based prediction was investigated in this study. Hyperspectral images were collected in visual near-infrared (VIS-NIR) and short-wave near-infrared (SW-NIR). Initial comparative analysis on the full spans of the two wavelengths using partial least squares regression (PLSR) and support vector regression (SVR) showed that the TFC of the leaves could be barely captured only when PLSR working on short-wave infrared, with 2 p R and p RMSE on testing set being respectively 0.5496 and 0.6384 mg/g. To further improve prediction performance meeting industrial requirements, another comparative study on feature selection was conducted to fine-tune the PLSR on SW-NIR using a genetic algorithm (GA) and successive projections algorithm (SPA). Results showed that GA-PLSR with 50 characteristic wavelengths mostly from the ranges of 1100-1200 nm and 1400-1500 nm had a significant improvement in performance, giving 0.8482 and 0.2967 mg/g for 2 p R and p RMSE , respectively. Therefore, an approach, GA-PLSR modeling on SW-NIR hyperspectral images, was established for the ginkgo leaves industry to rapidly and non-invasively predict the leaf concentration from the total flavonoid of ginkgo biloba seedling leaf.

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Section: Prediction Modelmentioning

confidence: 89%

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

et al. 2023

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“…The predictive performance of the PLS model is evaluated mainly using r and RMSE in the calibration set ( r C & RMSE C ), cross-validation set ( r CV & RMSE CV ), and prediction set ( r P & RMSE P ) [ 45 ]. The cross-validation is implemented by leaving one sample out from the calibration set in turn, and then rebuilding a model with the remaining samples to predict the excluded sample, i.e., leave-one-out cross-validation [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

Toward Achieving Rapid Estimation of Vitamin C in Citrus Peels by NIR Spectra Coupled with a Linear Algorithm

Zhang

Lin

et al. 2023

Molecules

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Citrus peels are rich in bioactive compounds such as vitamin C and extraction of vitamin C is a good strategy for citrus peel recycling. It is essential to evaluate the levels of vitamin C in citrus peels before reuse. In this study, a near-infrared (NIR)-based method was proposed to quantify the vitamin C content of citrus peels in a rapid way. The spectra of 249 citrus peels in the 912‒1667 nm range were acquired, preprocessed, and then related to measured vitamin C values using the linear partial least squares (PLS) algorithm, indicating that normalization correction (NC) was more suitable for spectral preprocessing and NC-PLS model built with full NC spectra (375 wavelengths) showed a better performance in predicting vitamin C. To accelerate the predictive process, wavelength selection was conducted, and 15 optimal wavelengths were finally selected from NC spectra using the stepwise regression (SR) method, to predict vitamin C using the multiple linear regression (MLR) algorithm. The results showed that SR-NC-MLR model had the best predictive ability with correlation coefficients (rP) of 0.949 and root mean square error (RMSEP) of 14.814 mg/100 mg in prediction set, comparable to the NC-PLS model in predicting vitamin C. External validation was implemented using 40 independent citrus peels samples to validate the suitability of the SR-NC-MLR model, obtaining a good correlation (R2 = 0.9558) between predicted and measured vitamin C contents. In conclusion, it was reasonable and feasible to achieve the rapid estimation of vitamin C in citrus peels using NIR spectra coupled with MLR algorithm.

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“…In SPA method, three operations such as selection of candidate subsets by projection, evaluation of candidate subsets by predicted residual error sum of squares (PRESS), and elimination of variables through F-test criterion are needed, and the wavelengths with the minimum number and the lowest values of PRESS are found and used as informative wavelengths (Zhu et al, 2021). SR is performed by repeating the process of forward addition and reverse deletion of variables, and ending with smallest values of residual sum of squares through increasing variables (Wang, He, Jiang, & Ma, 2022). CARS is running to evaluate the importance of every wavelength by using the absolute value of regression coefficient, based on the principle of survival of the fittest (He et al, 2023a).…”

Section: Informative Wavelength Selection and Model Optimizationmentioning

confidence: 99%

Simultaneous Quantifying And Visualizing Moisture, Ash and Protein Distribution in Sweet Potato [ Ipomoea Batatas (L.) Lam] By Nir Hyperspectral Imaging

Wang

et al. 2023

Preprint

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Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics

Cited by 9 publications

References 45 publications

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

Toward Achieving Rapid Estimation of Vitamin C in Citrus Peels by NIR Spectra Coupled with a Linear Algorithm

Simultaneous Quantifying And Visualizing Moisture, Ash and Protein Distribution in Sweet Potato [ Ipomoea Batatas (L.) Lam] By Nir Hyperspectral Imaging

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