ObjectiveTo explore the changes in color， odor and chemical components during wine-processing of Chuanxiong Rhizoma（CR）， identify differential markers， and provide a basis for standardizing the process and establishing quality standards. MethodsFifteen batches of CR samples from 4 producing areas were collected. Colorimeter and electronic nose were used to detect the color changes and odor components of CR before and after wine-processing. Multivariate statistical methods including partial least squares-discriminant analysis（PLS-DA）， principal component analysis（PCA）， discriminant factor analysis（DFA） and Fisher discriminant analysis were applied to identify wine-processed CR at different processing stages and establish discriminant models， and differential components were screened out based on variable importance in the projection（VIP） value1. Then， high performance liquid chromatography（HPLC） was employed to detect the content changes of four components（ferulic acid， senkyunolide I， senkyunolide A and ligustilide） during the processing stages. ResultsThe differences of wine-processed CR at various stages were primarily reflected in color parameters L^*（brightness value）， a^*（red-green value） and b^*（yellow-blue value）. Based on chromaticity differences， the color reference ranges were established for moderately processed CR， including L^* of 46.75-48.24， a^* of 5.37-6.07 and b^* of 20.32-21.70. In odor analysis， DFA revealed significant differences among processing stages， and 11 odor markers were identified， with four differential markers（4-hydroxy-3-butylphthalide， isopropyl butyrate， L-limonene and 1-methoxyhexane） based on VIP values. HPLC results showed that there was no significant difference of the four components except for ligustilide in wine-processed CR at different stages. ConclusionThis study achieved rapid identification of wine-processed CR with different processing degrees by electronic sensory technology and differential component content detection， with discrimination accuracy rates of 92.4% and 93.272% for color and odor， respectively. This paper also established the reference ranges of main colorimetric parameters for wine-processed CR at different stages， and four differential components were screened out， providing a basis for standardizing the processing of wine-processed CR and establishing quality standards for this decoction pieces.

ObjectiveTo explore the changes in color， odor and chemical components during wine-processing of Chuanxiong Rhizoma（CR）， identify differential markers， and provide a basis for standardizing the process and establishing quality standards. MethodsFifteen batches of CR samples from 4 producing areas were collected. Colorimeter and electronic nose were used to detect the color changes and odor components of CR before and after wine-processing. Multivariate statistical methods including partial least squares-discriminant analysis（PLS-DA）， principal component analysis（PCA）， discriminant factor analysis（DFA） and Fisher discriminant analysis were applied to identify wine-processed CR at different processing stages and establish discriminant models， and differential components were screened out based on variable importance in the projection（VIP） value1. Then， high performance liquid chromatography（HPLC） was employed to detect the content changes of four components（ferulic acid， senkyunolide I， senkyunolide A and ligustilide） during the processing stages. ResultsThe differences of wine-processed CR at various stages were primarily reflected in color parameters L^*（brightness value）， a^*（red-green value） and b^*（yellow-blue value）. Based on chromaticity differences， the color reference ranges were established for moderately processed CR， including L^* of 46.75-48.24， a^* of 5.37-6.07 and b^* of 20.32-21.70. In odor analysis， DFA revealed significant differences among processing stages， and 11 odor markers were identified， with four differential markers（4-hydroxy-3-butylphthalide， isopropyl butyrate， L-limonene and 1-methoxyhexane） based on VIP values. HPLC results showed that there was no significant difference of the four components except for ligustilide in wine-processed CR at different stages. ConclusionThis study achieved rapid identification of wine-processed CR with different processing degrees by electronic sensory technology and differential component content detection， with discrimination accuracy rates of 92.4% and 93.272% for color and odor， respectively. This paper also established the reference ranges of main colorimetric parameters for wine-processed CR at different stages， and four differential components were screened out， providing a basis for standardizing the processing of wine-processed CR and establishing quality standards for this decoction pieces.

OBJECTIVE To optimize the simmering technology of Rheum palmatum from Menghe Medical School and compare the difference of chemical components before and after processing. METHODS Using appearance score， the contents of gallic acid， 5-hydroxymethylfurfural （5-HMF）， sennoside A+sennoside B， combined anthraquinone and free anthraquinone as indexes， analytic hierarchy process （AHP）-entropy weight method was used to calculate the comprehensive score of evaluation indicators； the orthogonal experiment was designed to optimize the processing technology of simmering R. palmatum with fire temperature， simmering time， paper layer number and paper wrapping time as factors； validation test was conducted. The changes in the contents of five anthraquinones （aloe-emodin， rhein， emodin， chrysophanol， physcion）， five anthraquinone glycosides （barbaloin， rheinoside， rhubarb glycoside， emodin glycoside， and emodin methyl ether glycoside）， two sennosides （sennoside A， sennoside B）， gallic acid and 5-HMF were compared between simmered R. palmatum prepared by optimized technology and R. palmatum. RESULTS The optimal processing conditions of R. palmatum was as follows: each 80 g R. palmatum was wrapped with a layer of wet paper for 0.5 h， simmered on high heat for 20 min and then simmered at 140 ℃， the total simmering time was 2.5 h. The average comprehensive score of 3 validation tests was 94.10 （RSD＜1.0%）. After simmering， the contents of five anthraquinones and two sennosides were decreased significantly， while those of 5 free anthraquinones and gallic acid were increased to different extents； a new component 5-HMF was formed. CONCLUSIONS This study successfully optimizes the simmering technology of R. palmatum. There is a significant difference in the chemical components before and after processing， which can explain that simmering technology slows down the relase of R. palmatum and beneficiate it.

OBJECTIVE To establish the characteristic chromatogram of the volatile oil in the standard decoction of Qingshang juantong decoction， and preliminarily infer the main active components of volatile oil that affect the clinical therapeutic effect. METHODS The volatile oil in the standard decoction of Qingshang juantong decoction was extracted by steam distillation. The ultra-high performance liquid chromatography （UPLC） characteristic chromatograms of 15 batches of samples were established by the Similarity Evaluation System of TCM Chromatographic Fingerprint （2012 edition）， and the similarity evaluation was carried out. The volatile oil of standard decoction was identified by UPLC coupled with quadrupole time-of-flight mass spectrometry （UPLC-Q-TOF/MS）. Then the volatile oil components were analyzed by GC-MS. RESULTS The similarities of UPLC characteristic chromatograms for volatile oil of 15 batches of Qingshang juantong decoction were between 0.949 and 0.997. A total of 12 common peaks were obtained. According to the UPLC-Q-TOF/MS， the main components were methyl eugenol， E-ligustilide， E-butylidenephthalide and so on. A total of 23 components were identified by GC-MS， which were mainly 3，4，5-trimethoxy- methylbenzene， patchouli alcohol， Z-ligustilide and so on. CONCLUSIONS The characteristic chromatograms of the volatile oil in the standard decoction of Qingshang juantong decoction is established， and it is inferred that methyl eugenol， ligustilide， E- butylidenephthalide， patchouli alcohol， 3，4，5-trimethoxy-methylbenzene might be the main active components affecting the clinical therapeutic effect of the volatile oil of Qingshang juantong decoction.

The current production of Chinese herbal decoction pieces faces several issues including strong subjectivity,unstable quality,low production efficiency,and a lack of intelligent systems.In order to expedite the intelligent transformation and upgrading of Chinese medicine processing,this paper delves deeply into the problems and challenges encountered in establishing a digital and intel-ligent production model for Chinese herbal pieces.Addressing the slow progress in fundamental research on traditional Chinese medi-cine processing mechanisms,the absence of online digital quality characterization techniques,the low level of production equipment in-telligence,and the lack of evaluation standards for high-quality decoction pieces,this paper proposes a"New Quality Productive Forces"formation approach driven by technological innovation.Through interdisciplinary integration methods,the paper explores the mechanisms of Chinese medicine processing in depth,clarifies the correlation between the processing procedures and the"medicinal properties-quality"relationship,and employs bionic sensing and artificial intelligence to achieve a holistic quality characterization of decoction pieces.Additionally,the use of cloud-edge collaborative big data systems is proposed to enhance intelligent upgrades of the production lines.The paper also aims to establish a high-quality decoction piece evaluation system integrating"physical-chemical-bio-logical"multimodal data fusion.This approach aims to steer the Chinese medicine processing towards becoming more efficient,precise,and sustainable,thereby promoting high-quality sustainable development of the Chinese herbal decoction industry and providing both theoretical and practical support for the modernization of traditional Chinese medicine.

OBJECTIVE To screen and quantitatively analyze differential quality markers （Q-Marker） in Zingiber officinale mixed and triturated with Schisandra chinensis （ZMTS） before and after processing. METHODS HPLC fingerprints of before processing [Z. officinale complicated with S. chinensis （ZWS）] and after processing （ZMTS） （10 batches each） were established. The differences of Q-Markers before and after processing were screened by the chemical pattern recognition method and Q-Marker “five principles”， and the contents were determined. RESULTS A total of 14 common peaks were identified in the fingerprints of ZWS， 22 common peaks were identified in the fingerprints of ZMTS， and 8 components were identified. Differential Q-Marker were screened by chemical pattern recognition and Q-Marker “five principles”， i. e. 6-gingerol， schisandrol A schisandrol B， 8-gingerol， 10-gingerol， schisandrin A， schisandrin B， schizandrin C. The average contents of the 8 differential Q-Markers in ZMTS were 229.46， 244.48， 39.96， 44.12， 61.17， 47.82， 100.11 and 9.70 μg/g， respectively. The average contents of the 4 differential Q-Markers （6-gingerol， schisandrol A， schisandrol B， 8-gingerol） in ZWS were 112.58， 19.01， 26.74 and 5.98 μg/g， respectively. CONCLUSIONS In this study， the differential Q-Markers before and after ZMTS processing are screened. The contents of the Q-Markers in ZMTS after processing are higher than those before processing.

OBJECTIVE To optimize the preparation process of Soft-shelled turtle blood lyophilized powder （STBLP）， and to provide a reference for improving the availability and quality stability of soft-shelled turtle blood （STB）. METHODS STBLP was prepared with vacuum freeze-drying. Taking the solubility as the index， the preparation process parameters of STBLP were optimized by single factor experiment and Box-Behnken response surface method. RESULTS The optimal freeze-drying process for STBLP was obtained： pre-freezing time of 4 h， total drying time of 13 h （before at 0 ℃）， and resolution drying temperature of 25 ℃. The average solubility of 3 batches of STBLP prepared according to the optimal process was 95.72% （RSD＝0.68%， n＝3）， the relative error of which was －0.97% to the theoretical solubility （96.66%）. CONCLUSIONS Optimized lyophilization process in this study are stable and feasible， the solubility of the prepared sample is high.

OBJECTIVE To establish the fingerprints of dried Houttuynia cordata and its decoction pieces ，conduct chemometrics analysis and determine the contents of 5 flavonoids such as neochlorogenic acid. METHODS High performance liquid chromatography （HPLC）method was adopted. Using quercitrin as reference ，HPLC fingerprints of 10 batches of dried H. cordata and its decoction pieces were drawn. The similarity evaluation was conducted by Similarity Evaluation System of TCM Chromatographic Fingerprint （2012 edition），the common peaks were also confirmed. SIMCA-P 14.1 software was applied for principal component analysis （PCA）and partial least square-discriminant analysis （PLS-DA），and the variable importance in projection（VIP）value more than 1 was considered as a standard to screen the differential components affecting the quality of these two products ；meanwhile，the contents of 5 components such as neochlorogenic acid in both products were determined by the same HPLC method. RESULTS There were 20 common peaks in 10 batches of dried H. cordata and 10 batches of its decoction pieces with the similarity values more than 0.960. A total of 5 common peaks were identified ，which were neochlorogenic acid （peak 1）， chlorogenic acid （peak 3），cryptochlorogenic acid （peak 4），rutin（peak 7）and quercitrin （peak 11）. The results of PCA and PLS-DA showed that dried H. cordata could be distinguished from its decoction pieces obviously ；the common peaks with VIP value greater than 1 were as follows ：peak 7（rutin），peak 20，peak 5，peak 13，peak 2，peak 18，peak 3（chlorogenic acid ）， peak 14，peak 17 and peak 19. The linear range of neochlorogenic acid ，chlorogenic acid ，cryptochlorogenic acid ，rutin and quercitrin were 3.77-60.29 μg/mL（r＝0.999 7），1.40-22.42 μg/mL（r＝0.999 5），3.76-60.22 μg/mL（r＝0.999 9），2.19-35.06 μg/mL （r＝0.999 9）and 25.49-407.88 μg/mL（r＝0.999 7），respectively. RSDs of precision ，stability（24 h）and reproducibility E-mail：20190394@njucm.edu.cn tests were all lower than 3％. The average recoveries of the above components in these two products were 98.72％-101.12％ and 98.86％ -100.63％ with RSDs less than 3％（n＝9）. In dried H. cordata ，the average contents of 5 components were 0.87，0.33，0.59，0.61 and 6.17 mg/g，while the average contents were 0.42，0.11，0.26，0.23 and 3.16 mg/g in its decoction pieces ，respectively. CONCLUSIONS HPLC fingerprint and the method of content determination are stable and feasible ，which could be used for the quality control of dried H. cordata and its decoction pieces. Besides ，rutin and other components may be the differential components which could affect the quality of these two products ；the average contents of the 5 flavonoids such as neochlorogenic acid in dried H. cordata all decrease after processing.

ObjectiveIn order to find a fast odor-based method for the identification of sulfur fumigated Gastrodiae Rhizoma， an ultra-fast gas phase electronic nose technology was used to identify the odors of different degrees of sulfur fumigated Gastrodiae Rhizoma decoction pieces. MethodHeracles NEO ultra-fast gas phase electronic nose was employed to collect gas chromatograms of unsulfured and sulfured with different degrees of Gastrodiae Rhizoma decoction pieces， gas chromatograms were performed under programmed temperature （initial temperature of 40 ℃， 0.2 ℃·s^-1 to 60 ℃， and then 4 ℃·s^-1 to 250 ℃）， the sample volume was 5 mL， the incubation temperature was 65 ℃ and incubation time was 35 min. Kovats retention index and the AroChemBase database were used for qualitative analysis， and stoichiometric analysis was performed on this basis. Principal component analysis （PCA）， discriminant factor analysis （DFA） and partial least squares-discriminant analysis （PLS-DA） models were established to identify the Gastrodiae Rhizoma decoction pieces with different degrees of sulfur fumigation. ResultAccording to the comparative analysis of AroChemBase database， there were significant differences in the odor characteristics of sulfur fumigated and non-sulfur fumigated Gastrodiae Rhizoma， cyclopentane， acetone and heptane might be the odor components to distinguish the degree of sulfur fumigation in Gastrodiae Rhizoma decoction pieces. The identification index of PCA model was 81， the accumulative discriminant index of the discriminating factors was 92.09% in DFA model， the supervisory model interpretation rate of PLS-DA model was 0.963 and the predictive ability parameter was 0.956， indicating that PCA， DFA and PLS-DA models could well distinguish Gastrodiae Rhizoma decoction pieces with different sulfur fumigation degrees. ConclusionHeracles NEO ultra-fast gas phase electronic nose can be used as a rapid method to identify and distinguish Gastrodiae Rhizoma decoction pieces with different levels of sulfur fumigation. Meanwhile， it can provide a rapid， simple and green method and technology for identification of traditional Chinese medicine decoction pieces by sulfur fumigation.

OBJECTIVE To establish the method for monitoring the dynamic changes of odor components in Cornus officinalis during processing . METHODS The decoction pieces of C. officinalis with different processing time were prepared by the wine steaming method . The dynamic changes of odor components were obtained by using ultra -fast gas electronic nose ；odor components were identified by comparing with AroChemBase database ；the dynamic changes of odor compounds were analyzed in combination with peak area ，and the chemical pattern recognition analysis were carried out . RESULTS A total of 12 common peaks of odor components were identified in the fingerprints of raw C. officinalis，and 21 in the fingerprints of decoction pieces of C. officinalis. Eight odor components with the high proportion of peak area during processing were ethanol ， isopropyl alcohol ， 2- methylpropylaldehyde，ethyl acetate ，2-methylbutanal，isoamyl alcohol ，2-hexanol and furfural ，among which ，the peak areas of ethanol，isoamyl alcohol and 2-hexanol showed a trend of first increasing and then decreasing ；at 24 h of processing ，their peak areas were still higher than those of raw products . The peak areas of ethyl acetate ，2-methylbutanal and furfural nearly increased with the increase of processing time . Variable importance in projection of above eight odor components were all greater than 1. CONCLUSIONS The method is established for monitoring the dynamic changes of odor components of C. officinalis during processing. Eight odor components such as ethanol can be used as monitoring indicators of C. officinalis dring processing .