Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods

Sampaio, Pedro Sousa; Castanho, Ana; Almeida, Ana Sofia; Oliveira, Jorge; Brites, Carla

doi:10.1007/s00217-019-03419-5

Cited by 66 publications

(38 citation statements)

References 39 publications

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“…The SVM model performed better than PLSDA and RF, similar to the finding of Tankeu et al (2018), who reported that by using of hyperspectral imaging data, the SVM model yielded better predictions for the identification of true black cohosh, and Sun et al (2017), who showed that the SVM model combined with a hyperspectral reflectance imaging technique had high accuracy (92.96-97.28%) in the detection of chilling peaches. SVM combined with NIR spectroscopy also yielded a promising result for the prediction of the chilling storage stage of eggplants (Tsouvaltzis et al 2020) and different rice flour types (Sampaio et al 2020). In addition, SVM methods can be applied to image classification; for example, the SVM model has been used to classify different soil types using soil images.…”

Section: Discussionmentioning

confidence: 97%

Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods

et al. 2020

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The nutrition and quality of bamboo shoots from different species have a large variation, and traditional methods used for the classification of different bamboo shoot species are expensive and time-consuming. Here, the capability of near-infrared reflectance (NIR) spectroscopy to identify bamboo shoot species in a time-and cost-effective manner was examined. The NIR spectra of four bamboo shoot species were collected. Three classification models, a support vector machine (SVM), partial least squaresdiscriminant analysis (PLSDA), and random forest (RF), were calibrated. Several spectra pre-processing methods and their combination were trained before model calibration for the best classification model collection; each model was run 200 times for the calculation of the prediction error and model stability checking. The SVM model combined with the Det+2nd derivative had the best performance with an overall accuracy of 0.95. The use of less than 16% of the full-length NIR spectra produced a similar high accuracy of 0.91. Eight important regions,

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Section: Discussionmentioning

confidence: 97%

Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods

et al. 2020

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“…Another challenge in NIR spectroscopy is determining authenticity and the geographical location of certain agricultural products like grains. Studies carried out by Sampaio et al developed a strong and accurate classification model based on machine learning methods and NIR spectroscopy, allowing to sorting two genotypes of rice with high accuracy based on these characteristics [100]. Barnaby et al correlated the grain chalk of rice to the genomic regions of NIR spectra.…”

Section: Practical Applications Of Nir Spectroscopy and Chemometrics 21 Nir Spectroscopy In Rice Analysis: Identification And Classificatmentioning

confidence: 99%

Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

Sampaio¹,

Brites²

2022

Integrative Advances in Rice Research

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Nowadays, the conventional biochemical methods used to differentiate and characterize rice types, biochemical properties, authentication, and contamination issues are difficult to implement due to the high cost of reagents, time requirement and environmental issues. Actually, the success of agri-food technology is directly related to the quality of analysis of experimental data acquired by sensors or techniques such as the infrared-spectroscopy. To overcome these technical limitations, a rapid and non-destructive methodology for discrimination and classification of rice has been investigated. Near-infrared spectroscopy is considered as fast, clean, and non-destructive analytical tools and its spectra present significant biomolecular information that must be analysed by sophisticated methodologies. Machine learning plays an important role in the analysis of the spectral data being used several methods such as Partial Least Squares, Principal Component Analysis, Partial Least Squares-Discriminant Analysis, Support Vector Machine, Artificial Neuronal Network, among others which can successfully be applied for food classification and discrimination as well as in terms of authentication and contamination issues. The quality control of rice is extremely important at every stage of production, beginning with estimation of raw agricultural materials and monitoring their quality during storage, estimating food quality during the production process and of the final products as well as the determination of their authenticity and the detection of adulterants.

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“…In contrast, FTIR and NIR techniques are rapid, cost-effective, do not require specialized laboratory facilities and could therefore be used for pre-screening samples for SITE confirmatory or orthogonal analysis in enforcement work. Although FTIR and NIR spectroscopy were applied in several recent studies for the authentication of rice [27][28][29][30][31]33], to our knowledge, there have been no studies published that applied and compared benchtop FTIR-ATR and handheld NIR spectroscopy for differentiating the geographical origins of Thai Hom Mali rice.…”

Section: The Potential Of Ftir and Nir Spectroscopy Techniques For Geographical Differentiation Of Thai Hom Mali Ricementioning

confidence: 99%

“…NIR spectroscopy was also reported as successfully discriminating authentic (n = 20) and adulterated (n = 140) rice from China [29]. Sampaio, Castanho, Almeida, Oliveira and Brites [30] applied NIR spectroscopy with principal component analysis (PCA), PLS-DA and support vector machines (SVM) for the discrimination and classification of rice varieties (Indica and Japonica) grown in Portugal. The accuracy, cross-validation and prediction achieved by the SVM model were 97%, 93% and 91%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy

Srinuttrakul¹,

Mihailova

Islam

et al. 2021

Foods

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Although Hom Mali rice is considered the highest quality rice in Thailand, it is susceptible to adulteration and substitution. There is a need for rapid, low-cost and efficient analytical techniques for monitoring the authenticity and geographical origin of Thai Hom Mali rice. In this study, two infrared spectroscopy techniques, Fourier-transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) and near-infrared (NIR) spectroscopy, were applied and compared for the differentiation of Thai Hom Mali rice from two geographical regions over two production years. The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model, built using spectral data from the benchtop FTIR-ATR, achieved 96.97% and 100% correct classification of the test dataset for each of the production years, respectively. The OPLS-DA model, built using spectral data from the portable handheld NIR, achieved 84.85% and 86.96% correct classification of the test dataset for each of the production years, respectively. Direct NIR analysis of the polished rice grains (i.e., no sample preparation) was determined as reliable for analysis of ground rice samples. FTIR-ATR and NIR spectroscopic analysis both have significant potential as screening tools for the rapid detection of fraud issues related to the geographical origin of Thai Hom Mali rice.

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Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods

Cited by 66 publications

References 39 publications

Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods

Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods

Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy

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