A local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation

Yi-ming, BI; Yuan, Kailong; Xiao, Weiqiang; Wu, Jizhong; Shi, Chunyun; Xia, Junfeng; Chu, Guixin; Zhang, Guangxin; Guang-hong, Zhou

doi:10.1016/j.aca.2016.01.010

Cited by 147 publications

(46 citation statements)

References 34 publications

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“…The MSC and SNV are designed to reduce the (physical) variability between samples due to scatter and adjust for baseline shifts between samples [10]. The MSC and SNV have the capability to remove both additive and multiplicative effects in the spectra [11]. …”

Section: Methodsmentioning

confidence: 99%

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Suhandy

Yulia

2017

International Journal of Food Science

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Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world's priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPDcal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPDp and RER values.

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Section: Methodsmentioning

confidence: 99%

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Suhandy

Yulia

2017

International Journal of Food Science

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“…Reducing the total volume of data results in effective multispectral imaging systems and image acquisition with relatively low spatial resolutions in a few important wavelengths [13]. Standard Normal Variate transformation (SNV) Equation 2was applied in the case of chicken thigh and burger in order to avoid collinear and "noisy" data areas [55]. In contrast, spectral data from chicken breast and marinated souvlaki were pre-processed with baseline offset treatment [56,57] Equation 3.…”

Section: Data Pre-processing and Model Developmentmentioning

confidence: 99%

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

Spyrelli

Doulgeraki²,

Argyri³

et al. 2020

Microorganisms

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The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the “time from slaughter” parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.

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“…SG (Liu, Wang, et al, ) can eliminate noise produced by the environment and instrument to improve signal‐to‐noise ratio. MSC has the ability to correct for changes in light scattering, and SNV is a good way to eliminate interference caused by light scattering and path length changes (Bi et al, ; Qiao, Tang, & Dong, ). By comparing their performance based on partial least squares regression (PLSR) models, the SNV had the best result and was determined as the optimal pretreatment method.…”

Section: Methodsmentioning

confidence: 99%

Total phenolic content prediction in Flos Lonicerae using hyperspectral imaging combined with wavelengths selection methods

Liu

Wang

Gao

et al. 2019

J Food Process Engineering

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Hyperspectral imaging (HSI) method was applied to rapidly and nondestructively predict total phenolic content in Flos Lonicerae. The least squares support vector machine (LS‐SVM) and partial least squares regression (PLSR) models were developed on the basis of full wavelengths data and characteristic wavelengths data chosen by six wavelengths selection ways. The results clarified that standard normal variable (SNV) was the optimal pretreatment method, and the nonlinear LS‐SVM model based on the characteristic wavelengths chosen by the combination (CARS‐SPA) of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) yielded the best prediction performance for the total phenolic content. The overall results demonstrated that the proposed CARS‐SPA was a good method for selecting characteristic wavelengths to enhance prediction performance of HSI, and nonlinear LS‐SVM was more appropriate than linear PLSR for the prediction of total phenolic content in Flos Lonicerae. Practical applications Flos Lonicerae is a well‐known traditional Chinese medicinal herb, and also the raw material of various medicines and herbal tea. The demand for Flos Lonicerae is increasing. Therefore, monitoring the quality of Flos Lonicerae is very important to consumers and industry of Flos Lonicerae. The phenolic compounds content in Flos Lonicerae is one of the key internal quality factors. The traditional methods (such as spectrophotometric techniques or high‐performance liquid chromatography) for detecting total phenolic content are time‐consuming, labor‐consuming, and destructive. Hyperspectral imaging (HSI) is a nondestructive and reliable method for quality inspection. The results showed that SNV was determined as the best method for pretreatment, and the nonlinear CARS‐SPA‐LS‐SVM model was developed for total phenolic content detection with high precision. It could be verified that HSI technology is effective and promising for rapid determination of total phenolic content and quality control of Flos Lonicerae as well as other agricultural products.

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A local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation

Cited by 147 publications

References 34 publications

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

Total phenolic content prediction in Flos Lonicerae using hyperspectral imaging combined with wavelengths selection methods

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