Pattern recognition-based analysis of the material basis of five flavors of Chinese herbal medicines in Lamiaceae

doi:10.19852/j.cnki.jtcm.2025.03.014

Abstract

Abstract:

OBJECTIVE: To study the correlation between five flavors (Wuwei) and the chemical substances of Chinese herbal medicines in Lamiaceae and to establish five flavors identification models.
METHODS: A total of 245 herbs belonging to the Lamiaceae family were selected from the Pharmacopoeia of the People's Republic of China 2020 and Chinese Materia Medica. A database of the chemical substances of these herbs was constructed, with the chemical substances obtained from the professional literature and databases. A three-level classification system of the material components was established on the basis of the molecular structure and biosynthetic pathway of these substances. Apriori association rule analysis and feature selection were employed to obtain the material basis of the five flavors. A multiple logistic regression analysis method was employed to establish identification models for the five flavors.
RESULTS: The association rule analysis revealed 34 high-value groups and 30 specific groups for the main flavors, and 39 high-value groups and 36 specific groups for the combined flavors. Sixteen groups of chemical components were the decisive groups for the main flavors, and 13 groups were the decisive groups for the combined flavors. Multiple logistic regression analysis was used to successfully establish identification models with an overall accuracy of 88.8% for the main flavors and 87% for the combined flavors.
CONCLUSIONS: Five flavors are often characterized by the interaction of multiple classes of substances, and a single class of substances cannot be used to characterize flavors. The organic combination of multiple classes of substances is the material basis of the five flavors, both the main and combined flavors. Significant differences exist in the material basis of the main and combined flavors, suggesting that the “natural flavor” and “functional flavor” may have different material bases.

Key words: Lamiaceae, pattern recognition, five flavors, chemical substances, identification model

ZHANG Chuanyao, WANG Xiao, SHI Gaoxiang, ZHOU Qing, BU Feifei, ZHANG Xiaojun, WANG Peng. Pattern recognition-based analysis of the material basis of five flavors of Chinese herbal medicines in Lamiaceae[J]. Journal of Traditional Chinese Medicine, 2025, 45(3): 597-609.

Figures/Tables 10

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Five flavors	CHM (main flavors)		Components (main flavors)		CHM (combined flavors)		Components (combined flavors)
Five flavors	Count	Proportion (%)	Count	Proportion (%)	Count	Proportion (%)	Count	Proportion (%)
Light	2	1.16	29	0.27	2	2.13	217	3.47
Sweet	21	12.21	1020	9.51	13	13.83	749	11.98
Bitter	57	33.14	3298	30.75	47	50.00	3110	49.73
Pungent	92	53.49	6379	59.47	31	32.98	2127	34.01
Astringent	/	/	/	/	1	1.06	51	0.82
Total	172	100.00	10726	100.00	94	100.00	6254	100.00

Five flavors	CHM (main flavors)		Components (main flavors)		CHM (combined flavors)		Components (combined flavors)
Five flavors	Count	Proportion (%)	Count	Proportion (%)	Count	Proportion (%)	Count	Proportion (%)
Light	2	1.16	29	0.27	2	2.13	217	3.47
Sweet	21	12.21	1020	9.51	13	13.83	749	11.98
Bitter	57	33.14	3298	30.75	47	50.00	3110	49.73
Pungent	92	53.49	6379	59.47	31	32.98	2127	34.01
Astringent	/	/	/	/	1	1.06	51	0.82
Total	172	100.00	10726	100.00	94	100.00	6254	100.00

Consequent		The best value of new parameter	Constitute elements of new parameter method			No. of core groups	Logistic regression accuracy (%)	Single interaction accuracy (%)
Consequent		The best value of new parameter	Coefficient of antecedents	Maximum support	Maximum confidence	No. of core groups	Logistic regression accuracy (%)	Single interaction accuracy (%)
Main flavors	Pungent	K₇			√	15	76	90
	Sweet	K₇			√	15	33	85
	Bitter	K₁	√			15	43	93
Combined flavors	Pungent	K₂	√	√		15	58	90
	Sweet	K₁	√			15	46	76
	Bitter	K₇			√	15	68	100

Consequent		The best value of new parameter	Constitute elements of new parameter method			No. of core groups	Logistic regression accuracy (%)	Single interaction accuracy (%)
Consequent		The best value of new parameter	Coefficient of antecedents	Maximum support	Maximum confidence	No. of core groups	Logistic regression accuracy (%)	Single interaction accuracy (%)
Main flavors	Pungent	K₇			√	15	76	90
	Sweet	K₇			√	15	33	85
	Bitter	K₁	√			15	43	93
Combined flavors	Pungent	K₂	√	√		15	58	90
	Sweet	K₁	√			15	46	76
	Bitter	K₇			√	15	68	100

No.	Consequent	Independent variables	No. of variables	Sig. (P-values)	Nagelkerke R²values	Percent correct (%)
No.	Consequent	Independent variables	No. of variables	Sig. (P-values)	Nagelkerke R²values	Pungent	Sweet	Bitter	Total
1	Main flavors	High-value groups	34	0.000	0.617	84.8	61.9	70.2	77.1
2	Main flavors	High-value groups+specific groups	64	0.000	0.785	87	81	77.2	82.9
3	Main flavors	High-value groups (single interaction)	76	0.628	0.991	100	95.2	96.5	98.2
4	Main flavors	High-value groups (all 2-way interactions)	159	0.650	0.990	100	95.2	96.5	98.2
5	Combined flavors	High-value groups	39	0.000	0.929	90.3	100	93.8	93.5
6	Combined flavors	High-value groups+specific groups	75	0.001	1.000	100	100	100	100
7	Combined flavors	High-value groups (single interaction)	60	0.428	1.000	100	100	100	100
8	Combined flavors	High-value groups (all 2-way interactions)	88	0.428	1.000	100	100	100	100

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