Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics

Liu, J.; Xie, Hongliang; Zha, B.; Ding, W.; Luo, Jiliang; Hu, Cong

doi:10.1007/s10812-018-0621-9

Cited by 12 publications

(6 citation statements)

References 26 publications

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“…[6][7][8][9][10][11][12] This illustrates the importance of this spectral range in providing a so-called "ngerprint" of the conformational structure of molecules, and a complementary tool to recognize or distinguish chemical compounds such as amino acids and carbohydrates. [13][14][15][16][17][18][19] Zheng et al 13 investigated anhydrous and monohydrated glucose, whose spectra agreed with those calculated by solid-state density functional theory. They concluded that different features of the spectra arise from the intermolecular interactions between water and glucose molecules that occurs in the monohydrate, while glucoseglucose interactions take place in both compounds.…”

Section: Introductionmentioning

confidence: 70%

“…Although a classification with 100% of accuracy was reached, the results can be taken with caution because a limited number of samples as well as varieties were employed in the study. The same strategy was employed by Liu et al 16 for the identification of GM sugar cane, which was performed with an accuracy of 98%. Similarly, the quantitative analysis of ternary mixtures of saccharide isomers, containing d -(−)fructose, d -(+)galactose and d -(+)mannose were performed by Du et al 17 by employing Terahertz Time-Domain Spectroscopy (THz-TDS) and chemometrics.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of sugars and sweeteners via terahertz time-domain spectroscopy

2022

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Section: Introductionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Analysis of sugars and sweeteners via terahertz time-domain spectroscopy

2022

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“…The basic idea of LDA classification is to extract the best identifiable low-dimensional features from high-dimensional features and then use these selected features to classify samples. Make the samples of the same kind cluster together as much as possible, while the samples of the different kinds are separated as much as possible; that is, the between-class variance is the largest, and the intra-class variance is the smallest [ 20 , 21 ] since LDA uses the Fisher criterion function, LDA is also called Fisher linear Discriminant Analysis (FDA) [ 22 ]. The Fisher criterion function is …”

Section: Methodsmentioning

confidence: 99%

Research on anthracnose grade of Camellia oleifera based on the combined LIBS and THz technology

Li¹,

Qiu²,

Zhan³

et al. 2022

Plant Methods

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Background Anthracnose of Camellia oleifera is a very destructive disease that commonly occurs in the Camellia oleifera industry, which severely restricts the development of the Camellia oleifera industry. In the early stage of the Camellia oleifera suffering from anthracnose, only the diseased parts of the tree need to be repaired in time. With the aggravation of the disease, the diseased branches need to be eradicated, and severely diseased plants should be cut down in time. At present, aiming at the problems of complex experiments and low accuracy in detecting the degree of anthracnose of Camellia oleifera, a method is proposed to detect the degree of anthracnose of Camellia oleifera leaves by using terahertz spectroscopy (THz) combined with laser-induced breakdown spectroscopy (LIBS), so as to realize the rapid, efficient, non-destructive and high-precision determination of the degree of anthracnose of Camellia oleifera. Results Mn, Ca, Ca II, Fe and other elements in the LIBS spectrum of healthy and infected Camellia oleifera leaves with different degrees of anthracnose are significantly different, and the Terahertz absorption spectra of healthy Camellia oleifera leaves, and Camellia oleifera leaves with different degrees of anthracnose there are also significant differences. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and linear discriminant analysis (LDA) are used to establish the fusion spectrum anthracnose classification model of Camellia oleifera. Among them, the Root mean square error of prediction (RMSEP) and the prediction determination coefficient R2p of THz-LIBS-CARS-PLS-DA of prediction set are 0.110 and 0.995 respectively, and the misjudgment rate is 1.03%; The accuracy of the modeling set of THz (CARS)-LIBS (CARS)-SVM is 100%, and the accuracy of prediction set is 100%, after preprocessing of the multivariate scattering correction (MSC), the accuracy of the THz-LIBS-MSC-CARS modeling set is 100%, and the accuracy of prediction set is 100%; The accuracy rate of THz-LIBS-MSC-CARS-LDA of modeling set is 98.98%, and the accuracy rate of the prediction set is 96.87%. Conclusion The experimental results show that: the SVM model has higher qualitative analysis accuracy and is more stable than the PLS-DA and LDA models. The results showed that: the THz spectrum combined with the LIBS spectrum could be used to separate healthy Camellia oleifera leaves from various grades of anthracnose Camellia oleifera leaves non-destructively, quickly and accurately.

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“…Machine learning, which is widely used in spectroscopy analysis, could also extend to THz spectroscopy data processing. Linear discriminant analysis (LDA) and support vector machine (SVM) have been proved as effective supervised classification methods in THz spectroscopy applications (37,42). Owing to the multicollinearity and the interference of uncorrelated variables, most machine learning methods are based on spectral features rather than whole spectral data (43,44).…”

Section: Introductionmentioning

confidence: 99%

Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods

Yang

et al. 2021

Front. Nutr.

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Different geographical origins can lead to great variance in coffee quality, taste, and commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great importance for producers and consumers worldwide. In this study, terahertz (THz) spectroscopy, combined with machine learning methods, was investigated as a fast and non-destructive method to classify the geographic origin of coffee beans, comparing it with the popular machine learning methods, including convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) to obtain the best model. The curse of dimensionality will cause some classification methods which are struggling to train effective models. Thus, principal component analysis (PCA) and genetic algorithm (GA) were applied for LDA and SVM to create a smaller set of features. The first nine principal components (PCs) with an accumulative contribution rate of 99.9% extracted by PCA and 21 variables selected by GA were the inputs of LDA and SVM models. The results demonstrate that the excellent classification (accuracy was 90% in a prediction set) could be achieved using a CNN method. The results also indicate variable selecting as an important step to create an accurate and robust discrimination model. The performances of LDA and SVM algorithms could be improved with spectral features extracted by PCA and GA. The GA-SVM has achieved 75% accuracy in a prediction set, while the SVM and PCA-SVM have achieved 50 and 65% accuracy, respectively. These results demonstrate that THz spectroscopy, together with machine learning methods, is an effective and satisfactory approach for classifying geographical origins of coffee beans, suggesting the techniques to tap the potential application of deep learning in the authenticity of agricultural products while expanding the application of THz spectroscopy.

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Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics

Cited by 12 publications

References 26 publications

Analysis of sugars and sweeteners via terahertz time-domain spectroscopy

Analysis of sugars and sweeteners via terahertz time-domain spectroscopy

Research on anthracnose grade of Camellia oleifera based on the combined LIBS and THz technology

Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods

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