Application of a data fusion strategy combined with multivariate statistical analysis for quantification of puerarin in Radix puerariae

Wang, Yaqi; Yang, Yuanzhen; Sun, Haojie; Dai, Junping; Zhao, Manxi; Teng, Chuanzhen; Ke, Zun-hong; Yang, Ming; Zhong, Lei; Zhu, Weifeng

doi:10.1016/j.vibspec.2020.103057

Cited by 19 publications

(4 citation statements)

References 24 publications

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“…Data fusion allows for the integration of data collected using various detection techniques. The strategy combines multiple data blocks into one model and improves predictive performance by working collaboratively between the individual blocks ( 19 ). Depending on the fusion structure, data fusion is typically divided into three levels: pixel-, feature-, and decision-level ( 20 ).…”

Section: Methodsmentioning

confidence: 99%

Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying

Wang,

et al. 2024

Front. Nutr.

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The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.

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Section: Methodsmentioning

confidence: 99%

Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying

Wang,

et al. 2024

Front. Nutr.

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“…MSC and SNV often produce similar evaluation values. [ 13 17 20 ] MSC works to correct the spectrum by estimating the correction coefficient of the raw spectrum and calculating it with the average reference spectrum used in the calibration model. Since it uses the average spectrum, correction errors might occur if there is a dominant spectrum used as a reference spectrum.…”

Section: Quantification Of Phytochemical Compound and Adulterantmentioning

confidence: 99%

“…Besides using spectral preprocessing, reducing the complexity of the model can also be done by selecting variables. From the summary of the research, it can be seen that the selection of variables using principal component analysis (PCA),[ 9 ] interval PLS (iPLS),[ 13 ] Competitive adaptive reweighted sampling (CARS),[ 18 ] or genetic algorithm (GA)[ 39 ] reduce the number of variables by eliminating irrelevant variables. PCA will change the original variables correlated into new variables that contain a combination of the original variables.…”

Section: Quantification Of Phytochemical Compound and Adulterantmentioning

confidence: 99%

Infrared spectroscopy technique for quantification of compounds in plant-based medicine and supplement

Junaedi

Lestari

Muchtaridi

2021

Journal of Advanced Pharmaceutical Technology &Amp; Research

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Quality control of plant-based medicine and supplements must be carried out to ensure uniformity in quality and safety in their use, resulting in the need for effective and accurate analytical methods. Infrared spectroscopy is a method of qualitative and quantitative analysis that is fast, time-saving, cost-effective,accurate, and nondestructive. This method has been applied for quantitative analysis of compounds in complex matrices such as plant-based medicine and supplements supported by chemometrics techniques. The success of infrared spectroscopy applications for quantitative analysis of phytochemicals and adulterants content in plant-based medicine and supplement can happen by several factors. This article highlights the effect of spectral preprocessing and variable selection on quantitative analysis of phytochemical and adulterant in plant-based medicine and supplements using infrared spectroscopy. Literature search was conducted with PubMed, Google Scholar, and Science Direct by selecting quantitative analysis research on plant-based medicines and supplements that utilize spectral preprocessing techniques and variable selection in processing data analysis. The preprocessing spectra and variables selection can affect the accuracy and precision of infrared spectroscopy methods. The variable selection can be done using the wavenumber point technique, the wavenumber interval, or a combination thereof. Variable selection is more commonly used for near-infrared data than for IR data. The optimization of the preprocessing spectra and variables selection technique will be useful in increasing the ability of infrared spectroscopy in predicting compound levels.

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“…As a non-destructive technique, NIR employs measurements of photon energy to obtain qualitative and quantitative information based on the interaction of the sample with NIR radiation [ 20 , 21 ]. It covers the wavelength range of 800–2500 nm and mainly reflects vibration information of hydrogen bonds in a molecular structure, such as carbon–hydrogen (C-H), oxygen–hydrogen (O-H), nitrogen–hydrogen (N-H) [ 22 , 23 ]. Particularly, hydrogen bonds own high spectral stability in the NIR region, which aid in qualitative and quantitative analyses.…”

Section: Introductionmentioning

confidence: 99%

A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis

Xia

Che²,

et al. 2023

Molecules

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A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong’e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.

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Application of a data fusion strategy combined with multivariate statistical analysis for quantification of puerarin in Radix puerariae

Cited by 19 publications

References 24 publications

Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying

Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying

Infrared spectroscopy technique for quantification of compounds in plant-based medicine and supplement

A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis

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