A selective ensemble preprocessing strategy for near-infrared spectral quantitative analysis of complex samples

Bian, Xihui; Wang, Kaiyi; Tan, Erxuan; Diwu, Pengyao; Zhang, Fei; Guo, Yibing

doi:10.1016/j.chemolab.2019.103916

Cited by 80 publications

(25 citation statements)

References 46 publications

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“…The strategies are time‐consuming especially for the analysis of large dataset. In this paper, the best preprocessing method was selected by employing the principle “pick the best of the best.” First, according to the literature (Bian et al., 2020 ; Gerretzen et al., , 2015 , 2016 ), eight preprocessing methods were divided into three classes: baseline correction, scattering and trend correction, and scaling, shown in Table 1 . CWT and derivative methods (1st Der and 2nd Der) are baseline correction methods which can subtract the influence of background or drift in the signal.…”

Section: Methodsmentioning

confidence: 99%

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A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy

et al. 2021

Food Science & Nutrition

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Poria cocos is the dried sclerotia of Poria cocos (Schw.) Wolf, and has been used for several thousand years as a kind of food and medicine function (Tian et al., 2019). The most commonly consumed product of Poria cocos is Poria cocos powder. The main active ingredients of Poria cocos include pachyman, poriatin, triterpenoids, etc.It is also rich in amino acids, choline, and potassium salt. Modern

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Section: Methodsmentioning

confidence: 99%

“…However, the signal noise ratio may decrease in the same time. In order to eliminate multiple interferences in the spectra, a combination of various preprocessing methods is often needed (Bian et al., 2020 ). How to choose the best preprocessing method and its combination is a problem that must be considered.…”

Section: Introductionmentioning

confidence: 99%

A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy

et al. 2021

Food Science & Nutrition

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“…However, the noise level increases apparently in higher order derivative calculation. 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].…”

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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“…Maximum and minimum normalization (MinMax) method is a scaling technique that normalizes all the variables into a certain range (Bian et al., 2020). First‐order derivative (1st Der), second‐order derivative (2nd Der), and continuous wavelet transform (CWT) can subtract the influence of instrument background or drift on signal (Bian et al., 2020). However, in the results of higher order derivative, the noise level increases significantly (Li et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, single pretreatment method can only suppress one certain interference in the spectra, and the optimal pretreatment method is usually different for different dataset. To solve the problem, the combination pretreatment methods are often used to eliminate various interferences in the spectra (Bian et al., 2020). PCA (Li et al., 2012), soft independent modeling of class analogy (SIMCA) (Szabó et al., 2018), and Fisher's linear discriminant analysis (FLD) (Witjes et al., 2003; Yan et al., 2018) have been applied for the classification, while partial least squares (PLS), boosting partial least squares (Shao et al., 2010), and related robust techniques (Li et al., 2018; Li et al., 2020; Ma, Liu, et al., 2020; Ma, Pang, et al., 2020; Melssen et al., 2007) were used for the quantitative analysis.…”

Section: Introductionmentioning

confidence: 99%

A novel method for the nondestructive classification of different‐age Citri Reticulatae Pericarpium based on data combination technique

Zhang

et al. 2020

Food Science & Nutrition

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The quality of Citri Reticulatae Pericarpium (CRP) is closely correlated with the aging time. However, CRPs in different storage ages are similar in appearance, and the young CRP may be labeled as the aged one to obtain the excess profit by some unscrupulous traders. Most traditional analysis methods are laborious and time‐consuming, and they can hardly realize the nondestructive classification. In this paper, a novel method based on near‐infrared diffuse reflectance spectroscopy (NIRDRS) and data combination technique for the nondestructive classification of different‐age CRPs was proposed. The CRPs in different storage ages (5, 10, 15, 20, and 25 years) were measured. The near‐infrared spectra of outer skin and inner capsule were obtained. Principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and Fisher's linear discriminant analysis (FLD), with different data pretreatment methods, were used for the classification analysis. Data combination of the outer skin and inner capsule spectra was discussed for further improving the classification results. The results show that multiple sensors provide more useful and complementary information than a single sensor does for improving the prediction accuracy. With the help of data combination strategy, 100% prediction accuracy can be obtained with both second‐order derivative–FLD and continuous wavelet transform–multiplicative scatter correction–FLD methods.

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A selective ensemble preprocessing strategy for near-infrared spectral quantitative analysis of complex samples

Cited by 80 publications

References 46 publications

A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy

A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy

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

A novel method for the nondestructive classification of different‐age Citri Reticulatae Pericarpium based on data combination technique

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