Analysis and Identification of Rice Adulteration Using Terahertz Spectroscopy and Pattern Recognition Algorithms

Li, Chao; Li, Bin; Ye, Dapeng

doi:10.1109/access.2020.2970868

Cited by 36 publications

(13 citation statements)

References 25 publications

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“…Under normal circumstances, when the cumulative variance contribution rate of the current n PCs is large enough (generally 85%), the original data can be replaced with the first n PCs. The principal component analysis process is as follows [ 19 ]: Standardize the original spectral data , and then calculate the covariance matrix . where i is the i-th sample and n is the number of samples.…”

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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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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“…As shown in Table 1, labels (1-6) represented the six coumarin-based food additives, respectively. Then the values of accuracy rate and recall rate could also be calculated from the confusion matrix (Caelen, 2017;Li et al, 2020), and the following formula was used:…”

Section: Performance Of the Identification Modelsmentioning

confidence: 99%

Identification of coumarin‐based food additives using terahertz spectroscopy combined with manifold learning and improved support vector machine

Chen

Tang

et al. 2022

Journal of Food Science

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The purpose of this paper is to use terahertz (THz) spectroscopy combined with manifold learning and improved support vector machine (SVM) model to identify the coumarin-based food additives. The 216 THz absorbance spectra (144 for calibration set and 72 for prediction set) of six coumarin-based food additives are measured by using THz time-domain spectroscopy (THz-TDS) in the range of 0.5-2.0 THz. The method (P-t-SNE) combined principal component analysis (PCA) with manifold learning t-distributed stochastic neighbor embedding (t-SNE) is used for feature extraction of the THz spectra. Then, an improved SVM using differential evolution (DE) to improve gray wolf optimization (GWO) to optimize parameters is proposed. Finally, the result shows that the prediction set accuracy of PCA-DEGWO-SVM, P-t-SNE-DEGWO-SVM, and P-t-SNE-GWO-SVM models are 97.22%, 98.61%, and 95.83%, respectively, indicating that the accuracy by P-t-SNE is increased by about 1.39% compared with that processed by PCA, and the accuracy by DEGWO is also increased by about 2.78% compared with that processed by GWO. In conclusion, the improved model (Pt-SNE-DEGWO-SVM) has the best identification effect, and it is proved to be an effective method to identify coumarin-based food additives.

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“…Multispectral imaging systems have been used in applications from various fields such as agriculture [47], microbiology [16], entomology [48], etc. In specific, adulteration and defects of agricultural produce have been analyzed using MIS for turmeric [49], fruits [50]- [52], beef [53], and rice [54], as well as in packed foods [55], and tomato paste [56].…”

Section: Related Workmentioning

confidence: 99%

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

et al. 2022

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Oil reheating has a significant impact on global health due to its extensive consumption, especially in South Asia, and severe health risks. Nevertheless, food image analysis using multispectral imaging systems(MISs) has not been applied to oil reheating analysis despite their vast application in rapid food quality screening. To that end, the paper discusses the application of a low-cost MSI to estimate the 'reheat cycle count classes' (number of times an oil sample is recursively heated) and identify 'critical classes' at which substantial changes in the oil sample have materialized. Firstly, the reheat cycle count class is estimated with Bhattacharyya distance between the reheated and a pure oil sample as the input. The classification was performed using a support vector machine classifier that resulted in an accuracy of 83.34 % for reheat cycle count identification. Subsequently, an unsupervised clustering procedure was introduced using a modified spectral clustering (SC) algorithm to distinguish critical classes under reheating. In addition, laboratory experiments were performed to ascertain the ramifications of the reheating process with a chemical analysis. The chemical analysis of the coconut oil samples used in the experiment coincided with the chemical analysis results and was statistically significant (p < 0.05). Accordingly, the proposed work closes the gap for using multispectral imaging for oil reheating and proposes a novel algorithm for unsupervised detection of critical property changes in the oil. Hence, the proposed research work is significant in its practical implications, contribution to food image analysis, and unsupervised classification mechanisms.

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Analysis and Identification of Rice Adulteration Using Terahertz Spectroscopy and Pattern Recognition Algorithms

Cited by 36 publications

References 25 publications

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

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

Identification of coumarin‐based food additives using terahertz spectroscopy combined with manifold learning and improved support vector machine

Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation

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