Identifying the geographical origin and processing technology of Moyao (Myrrh) on the basis of near-infrared spectroscopy combined with chemometrics

doi:10.19852/j.cnki.jtcm.20240308.002

Abstract

Abstract:

OBJECTIVE: To evaluate the quality of Moyao (Myrrh) in the identification of the geographical origin and processing of the products.

METHODS: Raw Moyao (Myrrh) and two kinds of Moyao (Myrrh) processed with vinegar from three countries were identified using near-infrared (NIR) spectroscopy combined with chemometric techniques. Principal component analysis (PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm (PLS-DA) and two machine learning algorithms [K-nearest neighbor (KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao (Myrrh) samples, and their discriminative performance was evaluated.

RESULTS: Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. In all of the chemometric analyses, the NIR spectrum of Moyao (Myrrh) preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins, and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best. The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively.

CONCLUSIONS: NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao (Myrrh) and can also provide a reference for evaluations of its quality and the clinical use.

Key words: Moyao (Myrrh), near-infrared spectroscopy, geographical origin, processing technology

XU Ningning, YAN Ganming, XU Fengjie, DENG Linfeng, QIAO Xinjiang, LU Changzheng, CHENG Shaomin. Identifying the geographical origin and processing technology of Moyao (Myrrh) on the basis of near-infrared spectroscopy combined with chemometrics[J]. Journal of Traditional Chinese Medicine, 2024, 44(3): 505-514.

Figures/Tables 7

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Method	Group	Numbers		Total
Method	Group	Training set	Prediction set	Total
Origin	Kenya	71	19	90
	Ethiopia	62	28	90
	Somalia	69	21	90
	Total	202	68	270
Processing technology	Raw Moyao (Myrrh)	62	28	90
	sprayed vinegar Moyao (Myrrh)	75	15	90
	soaked vinegar Moyao (Myrrh)	65	25	90
	Total	202	68	270

Method	Group	Numbers		Total
Method	Group	Training set	Prediction set	Total
Origin	Kenya	71	19	90
	Ethiopia	62	28	90
	Somalia	69	21	90
	Total	202	68	270
Processing technology	Raw Moyao (Myrrh)	62	28	90
	sprayed vinegar Moyao (Myrrh)	75	15	90
	soaked vinegar Moyao (Myrrh)	65	25	90
	Total	202	68	270

Method	Preprocessing	Precision	Accuracy of training test	Accuracy of test	F₁score	Recall
KNN	Raw	0.8889	1	0.8971	0.8987	0.8935
	SNV	0.9833	1	0.9853	0.9853	0.9841
	MSC	0.9833	1	0.9853	0.9853	0.9841
	FD	0.9683	1	0.9706	0.9706	0.9683
	SD	0.9683	1	0.9706	0.9706	0.9683
	SNV+FD	0.9666	1	0.9706	0.9706	0.9666
SVM	Raw	0.8929	0.9109	0.8676	0.8663	0.8611
	SNV	0.9545	0.9703	0.9559	0.9558	0.9524
	MSC	0.9545	0.9703	0.9559	0.9558	0.9524
	FD	0.9683	0.995	0.9706	0.9706	0.9683
	SD	0.9683	0.995	0.9706	0.9706	0.9683
	SNV+FD	0.9683	0.9901	0.9706	0.9706	0.9683
PLS-DA	Raw	0.9666	0.9208	0.9706	0.9706	0.9666
	MSC	0.9833	0.9752	0.9853	0.9853	0.9841
	SNV	0.971	0.9356	0.9706	0.9705	0.9666
	FD	0.9833	0.9653	0.9853	0.9853	0.9841
	SD	0.9833	0.9752	0.9853	0.9853	0.9841
	SNV+FD	0.9683	0.9604	0.9706	0.9706	0.9683

Method	Preprocessing	Precision	Accuracy of training test	Accuracy of test	F₁score	Recall
KNN	Raw	0.8889	1	0.8971	0.8987	0.8935
	SNV	0.9833	1	0.9853	0.9853	0.9841
	MSC	0.9833	1	0.9853	0.9853	0.9841
	FD	0.9683	1	0.9706	0.9706	0.9683
	SD	0.9683	1	0.9706	0.9706	0.9683
	SNV+FD	0.9666	1	0.9706	0.9706	0.9666
SVM	Raw	0.8929	0.9109	0.8676	0.8663	0.8611
	SNV	0.9545	0.9703	0.9559	0.9558	0.9524
	MSC	0.9545	0.9703	0.9559	0.9558	0.9524
	FD	0.9683	0.995	0.9706	0.9706	0.9683
	SD	0.9683	0.995	0.9706	0.9706	0.9683
	SNV+FD	0.9683	0.9901	0.9706	0.9706	0.9683
PLS-DA	Raw	0.9666	0.9208	0.9706	0.9706	0.9666
	MSC	0.9833	0.9752	0.9853	0.9853	0.9841
	SNV	0.971	0.9356	0.9706	0.9705	0.9666
	FD	0.9833	0.9653	0.9853	0.9853	0.9841
	SD	0.9833	0.9752	0.9853	0.9853	0.9841
	SNV+FD	0.9683	0.9604	0.9706	0.9706	0.9683

Model	Preprocessing	Precision	Acc_Train	Acc_Test	F₁ score	Recall
KNN	Raw	0.9122	1.0000	0.9118	0.9089	0.9200
	SNV	0.8833	1.0000	0.8971	0.8987	0.8978
	MSC	0.9267	1.0000	0.9412	0.9418	0.9378
	FD	0.9753	1.0000	0.9706	0.9701	0.9556
	SD	0.9246	1.0000	0.9265	0.9245	0.8978
	SNV+FD	0.9444	1.0000	0.9559	0.9561	0.9511
SVM	Raw	0.6205	0.7723	0.6324	0.6250	0.6383
	SNV	0.9540	0.9406	0.9412	0.9388	0.9111
	MSC	0.9540	0.9307	0.9412	0.9388	0.9111
	FD	0.9505	0.9653	0.9559	0.9556	0.9422
	SD	0.9139	0.9802	0.9265	0.9259	0.9067
	SNV+FD	0.9139	0.9455	0.9265	0.9259	0.9067
PLS-DA	Raw	0.8500	0.9109	0.8676	0.8697	0.8622
	SNV	0.7491	0.8564	0.7647	0.7681	0.7643
	MSC	0.7750	0.8416	0.7941	0.7963	0.7895
	FD	0.7776	0.9010	0.7794	0.7722	0.8057
	SD	0.8136	0.8515	0.8088	0.8012	0.8192
	SNV+FD	0.8521	0.9257	0.8382	0.8382	0.8310

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