A novel multivariate calibration method based on variable adaptive boosting partial least squares algorithm

Li, Pao; Du, Guotong; Ma, Yongjun; Zhou, Jun; Jiang, Liwen

doi:10.1016/j.chemolab.2018.03.013

Cited by 18 publications

(7 citation statements)

References 35 publications

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“…The variable number of raw spectra decreases to 440 for the average spectra of four sampling points, whereas the variable number of raw spectra decreases to 480 for the average spectra of six sampling points. the MC‐UVE method obtains more characteristic wavelength variables (>180) than the CARS method, which is similar to the results in the literature (Li et al, 2018). However, very small values of variable numbers do not make the prediction more robust and accurate in quantitative analysis.…”

Section: Resultssupporting

confidence: 89%

“…Both methods are based on Monte Carlo sampling (MCS), which is a common sampling strategy in sample space and variable space. In our previous, a fusion algorithm for characteristic variable selection and modeling, namely variable adaptive boosting partial least squares (VABPLS) (Li et al, 2018), was proposed to obtain robustness models and improve the prediction ability by simultaneous weighing of samples and variables in the boosting step.…”

Section: Introductionmentioning

confidence: 99%

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Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection

Zhang

Shan

et al. 2022

Food Processing Preservation

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This study employs a near‐infrared diffuse reflectance spectroscopy (NIRDRS) system to accurate nondestructive determine citrus soluble solids content (SSC). The penetration experiment results showed that the interference of thick peel is large and NIRDRS light has the ability to penetrate the peel to a certain extent. Partial least squares with different characteristic variable selection methods were used to establish the quantitative model of SSC. The results demonstrated that characteristic variable selection methods can select targeted characteristic variables and improve the accuracy with few variables. Monte Carlo‐uninformative variable elimination method was selected as the optimal prediction performance. In the best prediction model, the correlation coefficient and root mean square error of prediction of the prediction set are 0.854 and 0.7%Brix, respectively, while the variable number decreases to 440 from 1557. Furthermore, the models using the average spectra of four points on the equator are the most appropriate. Novelty impact statement The penetrating ability of near‐infrared diffuse reflectance spectroscopy (NIRDRS) light to thick peel and the prediction of internal quality of citrus is still unsatisfactory with traditional partial least squares (PLS) algorithm due to the interference of thick peel. In this study, a nondestructive method for the analysis of soluble solids content (SSC) in citrus was established by NIRDRS with characteristic variable selection algorithms. The results demonstrated that NIRDRS light has the ability to penetrate the peel to a certain extent, while characteristic variable selection methods can select targeted characteristic variables and improve the accuracy of quantitative analysis models with fewer variables.

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection

Zhang

Shan

et al. 2022

Food Processing Preservation

Self Cite

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“…The latter method mimics the "survival of the fittest" principle which is the basis of Darwin's Evolution Theory and has been successfully adopted to select the key wavelengths (Li et al, 2009). Besides, VABPLS was applied to get higher robustness models and enhance the prediction ability by simultaneous weighting of samples and variables in the boosting step (Li et al, 2018). Furthermore, consensus partial least squares regression (cPLS) and boosting PLS with the same training set and prediction set were used as comparison.…”

Section: Discussionmentioning

confidence: 99%

“…To solve this problem, variable selection methods such as Monte Carlo uninformative variable elimination (MC-UVE) (Cai, Li, & Shao, 2008), randomization test (RT) (Xu, Liu, Cai, & Shao, 2009), competitive adaptive reweighted sampling (CARS) (Li, Liang, Xu, & Cao, 2009), and related techniques (Han, Tan, et al, 2017) were proposed for building robust and accurate models. In our previous work, variable adaptive boosting partial least squares (VABPLS) (Li, Du, Ma, Zhou, & Jiang, 2018) was proposed to obtain robustness models and improve the prediction ability by simultaneous weighting samples and variables in the boosting step.…”

Section: Introductionmentioning

confidence: 99%

A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near‐infrared spectroscopy

et al. 2020

Food Science & Nutrition

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A simple and nondestructive method for the analysis of soluble solid content in citrus was established using portable visible to near‐infrared spectroscopy (Vis/NIRS) in reflectance mode in combination with appropriate chemometric methods. The spectra were obtained directly by the portable Vis/NIRS without destroying samples. Outlier detection was performed by using leave‐one‐out cross‐validation (LOOCV) with the 3σ criterion, and the calibration models were established by partial least squares (PLS) algorithm. Besides, different data pretreatment methods were used to eliminate noise and background interference before calibration, to determine the one that will lead to better model accuracy. However, the correlation coefficients are all <0.62 and the results of all pretreatments are still unsatisfactory. Variable selection methods were discussed for improving the accuracy, and variable adaptive boosting partial least squares (VABPLS) method was used to get higher robustness models. The results show that standard normal variate (SNV) transformation is the best pretreatment method, while VABPLS can significantly simplify the calculation and improve the result even without pretreatment. The correlation coefficient of the best prediction models is 0.82, while the value is 0.48 for the raw data. The high performance shows the feasibility of portable Vis/NIRS technology combination with appropriate chemometric methods for the determination of citrus soluble solid content.

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“…Besides, combination preprocessing methods are typically used to remove multiple interferences in the spectra, since a single method can only suppress one certain interference [27]. Variable selection methods can improve the prediction performance, make the calibration reliable and provide simpler interpretation [28,29]. Principal component analysis (PCA) [14,30,31] and Fisher linear discriminant analysis (FLD) [32,33] are used for the establishment of identification model, while partial least-regression (PLS) and related robust techniques [17,30] are used for the quantitative analysis.…”

Section: Introductionmentioning

confidence: 99%

A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy

Zhang

et al. 2020

Sensors

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Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment.

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A novel multivariate calibration method based on variable adaptive boosting partial least squares algorithm

Cited by 18 publications

References 35 publications

Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection

Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection

A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near‐infrared spectroscopy

A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy

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