Using Intact Nuts and Near Infrared Spectroscopy to Classify Macadamia Cultivars

Carvalho, Lívia Cirino de; Morais, Camilo L. M.; Lima, Kássio M. G.; Leite, Gustavo Walace Pacheco; Oliveira, Gabriele S.; Casagrande, Izabela P.; Neto, João Paixão dos Santos; Teixeira, Gustavo Henrique de Almeida

doi:10.1007/s12161-017-1078-9

Cited by 18 publications

(8 citation statements)

References 30 publications

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“…Later, Garcia-Molina et al [19] used NIR spectroscopy in combination with partial least square (PLS) analysis to successfully distinguish low gliadin wheat grain from non-transgenic wheat lines with 96% of classification accuracy. It has been shown that using spectroscopic and machine learning algorithms makes it possible to distinguish not only GM and non-GM plants, but also plant species [11,20] and even varieties [21]. However, there is no study that discriminates the GM and non-GM plants with their interspecific hybrids.…”

Section: Introductionmentioning

confidence: 99%

Vis-NIR Spectroscopy and Machine Learning Methods for the Discrimination of Transgenic Brassica napus L. and Their Hybrids with B. juncea

Sohn

Subramanian

et al. 2022

Processes

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The rapid advancement of genetically modified (GM) technology over the years has raised concerns about the safety of GM crops and foods for human health and the environment. Gene flow from GM crops may be a threat to the environment. Therefore, it is critical to develop reliable, rapid, and low-cost technologies for detecting and monitoring the presence of GM crops and crop products. Here, we used visible near-infrared (Vis-NIR) spectroscopy to distinguish between GM and non-GM Brassica napus, B. juncea, and F1 hybrids (B. juncea X GM B. napus). The Vis-NIR spectra were preprocessed with different preprocessing methods, namely normalization, standard normal variate, and Savitzky–Golay. Both raw and preprocessed spectra were used in combination with eight different chemometric methods for the effective discrimination of GM and non-GM plants. The standard normal variate and support vector machine combination was determined to be the most accurate model in the discrimination of GM, non-GM, and hybrid plants among the many combinations (99.4%). The use of deep learning in combination with Savitzky–Golay resulted in 99.1% classification accuracy. According to the findings, it is concluded that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used to distinguish between GM and non-GM B. napus, B. juncea, and F1 hybrids.

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Section: Introductionmentioning

confidence: 99%

Vis-NIR Spectroscopy and Machine Learning Methods for the Discrimination of Transgenic Brassica napus L. and Their Hybrids with B. juncea

Sohn

Subramanian

et al. 2022

Processes

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“…In this case, it was clear that LDA had better performance. In another case [ 68 ], PCA-LDA and GA-LDA were used to establish the identification model of macadamia cultivars. Experiments showed that, with Savitzky–Golay smoothing preprocessed spectra, the accuracy of GA-LDA was higher than 94.44%.…”

Section: Traditional Pattern Recognition Methodsmentioning

confidence: 99%

A Review of the Discriminant Analysis Methods for Food Quality Based on Near-Infrared Spectroscopy and Pattern Recognition

et al. 2021

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Near-infrared spectroscopy (NIRS) combined with pattern recognition technique has become an important type of non-destructive discriminant method. This review first introduces the basic structure of the qualitative analysis process based on near-infrared spectroscopy. Then, the main pretreatment methods of NIRS data processing are investigated. Principles and recent developments of traditional pattern recognition methods based on NIRS are introduced, including some shallow learning machines and clustering analysis methods. Moreover, the newly developed deep learning methods and their applications of food quality analysis are surveyed, including convolutional neural network (CNN), one-dimensional CNN, and two-dimensional CNN. Finally, several applications of these pattern recognition techniques based on NIRS are compared. The deficiencies of the existing pattern recognition methods and future research directions are also reviewed.

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“…The selected variables are of the same scale as the original spectral data and are selected according to the lowest risk of misclassification (G). The value of G is calculated from the validation set according to the following equation described by Carvalho et al (2018):…”

Section: Chemometricsmentioning

confidence: 99%

Estimation and classification of popping expansion capacity in popcorn breeding programs using NIR spectroscopy

Oliveira

Murray

Júnior

et al. 2020

Journal of Cereal Science

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Using Intact Nuts and Near Infrared Spectroscopy to Classify Macadamia Cultivars

Cited by 18 publications

References 30 publications

Vis-NIR Spectroscopy and Machine Learning Methods for the Discrimination of Transgenic Brassica napus L. and Their Hybrids with B. juncea

Vis-NIR Spectroscopy and Machine Learning Methods for the Discrimination of Transgenic Brassica napus L. and Their Hybrids with B. juncea

A Review of the Discriminant Analysis Methods for Food Quality Based on Near-Infrared Spectroscopy and Pattern Recognition

Estimation and classification of popping expansion capacity in popcorn breeding programs using NIR spectroscopy

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