Origin identification of dried distillers grains with solubles using attenuated total reflection Fourier transform mid-infrared spectroscopy after in situ oil extraction

Vermeulen, Philippe; Pierna, Juan Antonio Fernández; Abbas, Ouissam; Dardenne, Pierre; Baeten, Vincent

doi:10.1016/j.foodchem.2014.09.103

Cited by 15 publications

(7 citation statements)

References 19 publications

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“…Similar results can be found in the literature. For example, Vermeulen et al [35] applied attenuated total reflection-Fourier transform infrared (ATR-FTIR) to the oil fraction extracted from the dried distillers grains with solubles for classification of origins. The model developed from their study provided a classification accuracy higher than 95% using an external validation set.…”

Section: Classification Of Geographic Originsmentioning

confidence: 99%

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms

Zhu

2020

Molecules

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Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700–4350 cm−1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.

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Section: Classification Of Geographic Originsmentioning

confidence: 99%

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms

Zhu

2020

Molecules

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“…The selected wavenumbers (835.0262, 911.2006, and 1061.6211 cm −1 ) were related to polysaccharides [45] [46]. The band around 2923 cm −1 (2927.4128 cm −1 ) was characteristic of the length of the fatty acid chain [44].…”

Section: Optimal Wavenumbers Selection the Firstmentioning

confidence: 97%

“…In all, 29 optimal wavenumbers were selected, and the selected optimal wavenumbers are shown in Table 1. The wavenumber near 1745 cm −1 (1741.4056 cm −1 ) was characteristic of the C=O ester double bond of the triglyceride [44]. The selected wavenumbers (835.0262, 911.2006, and 1061.6211 cm −1 ) were related to polysaccharides [45] [46].…”

Section: Optimal Wavenumbers Selection the Firstmentioning

confidence: 99%

Mid-Infrared Spectroscopy for Coffee Variety Identification: Comparison of Pattern Recognition Methods

Zhang

Wang

Liu

et al. 2016

Journal of Spectroscopy

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The potential of using mid-infrared transmittance spectroscopy combined with pattern recognition algorithm to identify coffee variety was investigated. Four coffee varieties in China were studied, including Typica Arabica coffee from Yunnan Province, Catimor Arabica coffee from Yunnan Province, Fushan Robusta coffee from Hainan Province, and Xinglong Robusta coffee from Hainan Province. Ten different pattern recognition methods were applied on the optimal wavenumbers selected by principal component analysis loadings. These methods were classified as highly effective methods (soft independent modelling of class analogy, support vector machine, back propagation neural network, radial basis function neural network, extreme learning machine, and relevance vector machine), methods of medium effectiveness (partial least squares-discrimination analysis,Knearest neighbors, and random forest), and methods of low effectiveness (Naive Bayes classifier) according to the classification accuracy for coffee variety identification.

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“…Apart from IRMS and ICP-MS, there were other publications that suggest that IR and chemometrics can be used for the detection of the origin of agri-food products [20,21,[40][41][42][43][44][45][46].…”

Section: Published Research For Geographical Origin Authentication Frmentioning

confidence: 99%

Geographical Origin Authentication of Agri-Food Products: A Review

Katerinopoulou

Kontogeorgos

Salmas

et al. 2020

Foods

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This study is a systematic literature review of geographical origin authentication by elemental analytical techniques. Authentication and certification of geographic origin of agri-food products is a useful tool toward the protection of the quality for products. The aim of this work was to map the current state of research in the area of agricultural products and food, identifying emerging fields to the geographical origin of products. The article is divided in three parts. The first part of the article deals with the analytical techniques applied in the food authentication. Special mention is made to elemental analysis and multiple isotope ratio. The second section focuses on statistically published data concerning published research for geographical origin authentication for the period 2015–2019. Specific results are presented inter alia: number of articles according to the type of product, articles according to the type of the analytical techniques, and others. The third part contains characteristic results from articles that were published in the period 2015–2019, on certification of geographical origin on specific agricultural products.

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Origin identification of dried distillers grains with solubles using attenuated total reflection Fourier transform mid-infrared spectroscopy after in situ oil extraction

Cited by 15 publications

References 19 publications

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms

Mid-Infrared Spectroscopy for Coffee Variety Identification: Comparison of Pattern Recognition Methods

Geographical Origin Authentication of Agri-Food Products: A Review

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