| Chinese traditional medication decoction pieces is that the processed product of Traditional Chinese Medicine (TCM) by using specific method of purifying, cutting, frying, baking, and etc., which always possess the special characters. And according to these special characters, the quality evaluation of decoction pieces can be carried out. Bulbus of Fritillaria (BF), a group Traditional Chinese medicine originated from Fritillaria, always been used in the form of powder. However, these powders are white, lead to the difficult distinguish of different species of BF and adulteration of Fritillariae cirrhosae bulbus (FCB) on the basis of appearance. Additionally, several methods for the quality control of decoction pieces of Coptidis Rhizoma (CR) always recorded as "same to the raw material'" in China Pharmacopoeia. According to the same method, such as microscopical identification or thin layer chroma to graphy identification, CR can be identified qualitatively and its quality can be qualified; however, the special processing characteristics of CR can't be embodied, the difference among four decoction pieces of CR including raw products, processed with wine, ginger and moxibustion can't be performed respectively. Intelligent sensory analysis system, consisted by the machine vision, electronic nose and electronic tongue, which designed in principle of mimic the sensory characteristics, and has the characteristics of high sensitivity. Therefore, the color, odor and taste of BF powder and CR were used as the breakthrough point in this research, and these three technologies of intelligent sensory analysis system were applied to discriminate samples.Based on color analysis, an unobvious difference of the color among different species of BF was obtained, and it is difficult to distinguish different varieties on the basis of the color. Principal component analysis (PCA) showed that different species of BF with the different sensor response "fingerprint" of electronic nose and tongue; According to electronic nose, by using discriminant factor analysis (DFA) for the recognize of unknown samples, the accurate rate of 77.78% was achieved; and the recognition rate of 94.4% of linear discriminant analysis (LDA) for cross validation case group identification were achieved. After the optimization of electronic nose sensors, DFA and LDA model for the identification of unknown sample were improved to 100%. According to the discrimination analysis, accurate rate of 100% was acquired for the recognition of different varieties of BF by electronic tongue.The color analysis indicated that the color difference between FCB and its adulterations that mixed with the powder of other species of Fritillaria was not obvious. In contrast, the color difference was largely between FCB and the adulterations that mixed with coicis semen powder and wheat flour, and the larger color difference combine with the increasingly adulteration proportion. PCA showed that there was an obvious differences between FCB and adulterated samples; Based on electronic nose and electronic tongue, the accurate rate of 100% was achieved by DFA for recognizing FCB and their adulterations; For the discrimination of the specific adulterants, all the unknown samples were correctly recognized by DFA according to E-nose; however, an accurate rate of 80.95% was acquired according to E-tongue, and a successful recognition with an accurate rate of 100% was obtained after data fusion. The discrimination performances based on fusion approaches are better than those based on the sole usage of E-tongue.On the one hand, the color detection showed that there was a significant difference of the color between the processed products of CR and crude material. The wine processed CR, ginger processed CR and moxibustion processed CR possess the similar color, and reflecting the color of CR becomes dimer after processing. Color difference between wine processed CR and ginger processed CR was unobvious, and minor difference of the color can be acquired between moxibustion processed CR and other processed products. On the other hand, sensor LY2/gCT, T30/1, P10/2, P40/1, PA/2, P30/1, P40/2 and ZZ, AB was chosen for the discrimination of CR after the sensor optimization. PCA and discriminant analysis results showed that different processed CR products possess the different sensor response characteristics of electronic nose and electronic tongue. By LDA model, a recognition rate of 96.4% was achieved based on electronic nose, and 73.8% was acquired according to electronic tongue. The recognition rate of LDA can be improved to 100% after data of electronic nose and electronic tongue fusion.Intelligent sensory technologies can quantitatively evaluate the color and qualitatively reflect the "odor and taste" difference of BF powder and decoction pieces of CR Based on the intelligent sensory technologies, BF powders, different processed CR discriminated well. And also, it can reflect the "odor and taste" difference of different decoction pieces of CR which processed combined with different accessories. |
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