Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms

Sampaio, Pedro Sousa; Soares, Andreia; Castanho, Ana; Almeida, Ana Sofia; Oliveira, Jorge C.; Brites, Carla

doi:10.1016/j.foodchem.2017.09.058

Cited by 160 publications

(91 citation statements)

References 39 publications

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“…The PLS-DA model is developed based on the PLS algorithm, which is a sophisticated multivariate regression model and can establish mathematical relationships between descriptors and dependent variables [31,32]. PLS algorithm obtains latent variables (LVs) by linear combination of original variables and ranks the LVs.…”

Section: Modeling Methodsmentioning

confidence: 99%

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

Hao

Geng

et al. 2019

Molecules

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Background: In recent years, genetically modified technology has developed rapidly, and the potential impact of genetically modified foods on human health and the ecological environment has received increasing attention. The currently used methods for testing genetically modified foods are cumbersome, time-consuming, and expensive. This paper proposed a more efficient and convenient detection method. Methods: Near-infrared diffuse reflectance spectroscopy (NIRDRS) combined with multivariate calibration methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), were used for identification of different rice varieties and transgenic (Bt63)/non-transgenic rice. Spectral pretreatment methods, including Norris-Williams smooth (NWS), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky-Golay 1st derivative (SG 1st-Der), were used for spectral noise reduction and effective information enhancement. Accuracy was used to evaluate the qualitative discriminant models. Results: The results showed that the SG 1st-Der pretreatment method, combined with the SVM, provided the optimal model to distinguish different rice varieties. The accuracy of the optimal model was 98.33%. For the discrimination model of transgenic/non-transgenic rice, the SNV-SVM model, MSC-SVM model, and SG 1st-Der-PLS-DA model all achieved good analysis results with the accuracy of 100%. Conclusion: The results showed that portable NIR spectroscopy combined with chemometrics methods could be used to identify rice varieties and transgenic characteristics (Bt63) due to its fast, non-destructive, and accurate advantages.

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Section: Modeling Methodsmentioning

confidence: 99%

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

Hao

Geng

et al. 2019

Molecules

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“…The PCA-DA model was developed using pre-processed spectra (combination of SNV+SG smoothing). In the SG smoothing, there are many different parameters include polynomial degree (PD), the derivative order of polynomial (DOP) and the number of smoothing points (NSP) or the size of smoothing window (SW) [15]. A too-small NSP is prone to cause calculation error, resulting in a decreased model precision, while a toolarge NSP would over smooth the spectral data, most of the information were missing and leading to decreased accuracy of model [15].…”

Section: Development Of Pca-da Classification Modelmentioning

confidence: 99%

The classification of arabica gayo wine coffee using UV-visible spectroscopy and PCA-DA method

Suhandy¹,

Yulia

2018

MATEC Web Conf.

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The unique processing of Arabica Gayo Wine coffee produces special attributes to the beverage and could increase its value. However, it is important to prove the authenticity of Arabica Gayo Wine coffee using reliable methods. The objective of this study was to evaluate the potential of UV-visible spectroscopy and principal component analysis-discriminant analysis (PCA-DA) method for classification of ground roasted Arabica Gayo Wine coffee. A number of 200 samples of Arabica Gayo Wine coffee and 200 samples of Arabica Gayo normal (not Wine) coffee was used. The spectral data obtained in the UV-visible region were analyzed using PCA-DA with standard normal variate (SNV) and followed by Savitzky-Golay smoothing with different number of smoothing point (NSP). The results showed that the best PCA-DA model was obtained with NSP = 23 with coefficient of determination for calibration (R 2 ) = 0.99, root mean square error of calibration (RMSEC) = 0.005692 and root mean square error of validation (RMSEV) = 0.006112. Using this model, a good classification between Gayo Wine and Gayo normal in prediction step was achieved with 100% accuracy, sensitivity and specificity. Thus, the proposed method can be used for the evaluation of authenticity of ground roasted Arabica Gayo Wine coffee.

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“…In order to solve the problems, a large number of chemometric methods have been developed. Partial least squares (PLS) regression and related robust techniques are the most commonly used methods for establishing quantitative models (De Luca et al, 2019;Li, Shao, & Cai, 2007;Sampaio et al, 2018). Furthermore, a large number of spectral pretreatment methods for baseline correction and background removal were developed, while each possesses advantage and drawbacks (Bian, Li, Shao, & Liu, 2016;Shao, Bian, & Cai, 2010).…”

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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Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms

Cited by 160 publications

References 39 publications

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

The classification of arabica gayo wine coffee using UV-visible spectroscopy and PCA-DA method

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

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