OBJECTIVE：To evaluate the correlation between color difference values and active constituent contents of crude and processed Zingiber officinale . METHODS ：HPLC method was adopted to determint the content of 6 active constituents. The color difference values of crude and processed Z. officinale [lightness（L＊），red-green axis component （a＊），yellow-blue axis component（b＊）] were determined by chromatic aberration meter . SPSS 24.0 software was adopted for the correlation analysis between color difference values and active constituent contents. RESULTS ：The linear range of zingiberone ，6-gingerol， 8-gingerol， 6-shogaol， diacetoxy-6-gingerol and 10-gingerol were 2.65-105.90， 10.15-406.00， 4.87-194.80， 5.28-211.20， 6.14-245.70，7.02-280.80 μg/mL（r＞0.999）. The limits of quantification were 7.46，13.68，14.37，16.62，17.03，17.99 ng，and the limits of detection were 2.24，4.11，4.31，4.99，5.11，5.40 ng，respectively. RSDs of precision ，stability，and repeatability tests were all lower than 3％. The average recovery rates were 101.34％，102.14％，101.22％；103.12％，103.74％，103.54％；103.06％，properties critical for cell migration and invasion. induced EMT through downregulation of NF-κB-Snail sig- naling in breast cancer cells enchymal transition and activation of TLR 4/JNK signal - 98.55％，99.43％；99.36％，103.51％，101.21％；100.85％，99.42％，99.60％；100.39％，97.69％，103.84％（RSD were all lower than 3％，n＝3），respectively. The contents of them were 0-0.66，0.06-7.57，0.03-1.45，0.29-3.47，0.15-2.85，0.04-2.83 mg/g， respectively. L＊ and b＊ values were negative correlated with the processing degree of Z. officinale significantly（P＜0.01），a＊ showed a significantly positive correlation with the processing degree （P＜0.05）. L＊and b＊ values showed a significantly negative correlation with the content of zingiberone before and after processing ，but positively correlated with the other five components （P＜0.01）. a＊ showed a significantly positive correlation with the content of zingiberone ，but had no correlation with other five components（P＞0.05）. The crude and processed Z. officinale were positive correlated with the content of zingiberone ，negatively correlated with other five components （P＜0.01）. CONCLUSIONS ：There is a certain correlation between the color difference values of crude and processed Z. officinale and the contents of their active constituents. With the deepening of the processing ，a＊ values is increased ，L＊ values and b＊ values is decreased ；the content of zingiberone increases ，the contents of 6-gingerol， 8-gingerol，6-shogaol，diacetoxy-6-gingerol，10-gingerol reduce.

OBJECTIVE To explore the discrimination of processing degree of Zingiber officinale charcoal and the correlation between color and component content based on machine vision system， and provide reference for quality evaluation and processing degree control of Z. officinale charcoal. METHODS High-performance liquid chromatography method was used to determine the contents of 5 components in Z. officinale charcoal and its different processed products， such as 6-gingerol， 8-gingerol， 10-gingerol， 6-shogaol， gingerone. Machine vision system was used to obtain the image of the decoction pieces and extract the color features of the decoction pieces in RGB， L*a*b* and HSV color spaces. Machine learning methods， such as principal component analysis （PCA）， linear discriminant analysis （LDA）， partial least squares-discriminant analysis （PLS-DA） and support vector machine （SVM）， were used to establish qualitative identification model for Z. officinale charcoal processed products of different processing degree. The correlation between the color eigenvalues and the contents of measured 5 components were analyzed， and the color- component content prediction model was established.RESULTS With the deepening of processing， gingerone was produced after processing and the content firstly increased and then decreased， and the content of gingerone in standard carbon was the highest； the contents of 6-gingerol， 8-gingerol and 10-gingerol decreased gradually； the content of 6-shogaol increased firstly and then decreased. The prediction accuracy of qualitative discriminant model， which was established on the basis of objective quantization of image and color combined with LDA and SVM of supervised discriminant pattern recognition method， reached 100% in cross-validation training and 95.83% in the external validation. Content prediction model of 5 components was established on the basis of objective quantization of image and color combined with SVM， the RPD values were all greater than 2， the R2P and R2C values of gingerone were 0.633 9 and 0.683 3， and the values of other components were all greater than 0.75， indicating SVM had good prediction ability for the contents of 4 components except for gingerone. CONCLUSIONS The machine vision system is excellent for the discrimination of the processing degree of Z. officinale charcoal and the content prediction， which can provide a reference for the quality control of Z. officinale charcoal decoction pieces and the judgment of the processing degree.