CN111579736B - Method for controlling processing production degree and evaluating quality of gardenia jasminoides ellis - Google Patents
Method for controlling processing production degree and evaluating quality of gardenia jasminoides ellis Download PDFInfo
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Abstract
Aiming at the control method which is adopted in the processing production process of the traditional Chinese medicine gardenia in the prior art and takes personal experience to judge color as a unique index, the invention introduces a novel method for processing and controlling gardenia based on smell. The method has the advantages that the sensor response value changed in the processing process of the gardenia is acquired by using an electronic nose technology capable of accurately quantifying the smell value, the technical problem that the sensor response value is lack of objectivity in the evaluation process is effectively solved, and the method of determining the digital standard, the modeling standard and the 'hot weather' discriminant function based on the smell value as an index is used for online control in the processing process of the gardenia decoction pieces through statistical calculation. And by means of correlation analysis of the electronic nose and gas chromatography-mass spectrometry, index components causing change of the processed smell of the gardenia are determined, and limit is set. The method is used in the processing production of gardenia, can ensure the stability of the quality of gardenia decoction pieces, has the advantages of objectivity, rapidness and reliability, and solves the technical problem that the objectivity is lacked in the manual judgment in the prior art.
Description
Technical Field
The invention relates to the field of processing technology and quality control of gardenia Chinese medicinal decoction pieces, in particular to a discrimination method for controlling the processing degree and quality of gardenia Chinese medicinal decoction pieces by taking smell data as a new important index.
Background
Fructus Gardeniae is dry mature fruit of Gardenia jasminoides Eill of Rubiaceae, and has effects of purging pathogenic fire, relieving restlessness, clearing heat, promoting urination, cooling blood and removing toxic substance. Different decoction pieces such as raw gardenia, fried gardenia, scorched gardenia and gardenia charcoal are common in clinical application of gardenia, and the descriptions of the processing degree and the finished product characters of the processed products with different specifications of gardenia in the current version Chinese pharmacopoeia, 88 version national Chinese traditional medicine decoction piece processing standard, each province Chinese traditional medicine decoction piece processing standard, Chinese traditional medicine processing science and the like are all based on the appearance color as an important index. The method comprises the following specific steps: the surface of the raw product is reddish yellow or brownish red, the color of the inner surface is lighter, and the seeds are deep red or reddish yellow; the surface of the fried gardenia is yellow brown or yellow red; charred fructus Gardeniae has brown, charred yellow or charred black surface, brown pericarp inner surface, and yellow-brown or brown seed surface; the charcoal product has black brown or burnt black surface. Therefore, the color of the gardenia is only artificially evaluated as the only index in the processing process at present, but the smell of the gardenia obviously changes along with the increase of the processing temperature and the processing time in the actual production process, such as obvious burnt fragrance is generated. Because the index of smell is difficult to control in actual evaluation, the index is not always listed in the standard for detecting the processing degree and quality of gardenia, but the addition of the index can ensure that the processing control of the gardenia decoction pieces and the quality standard of the decoction pieces are more reasonable and stable.
The change of the smell of Chinese herbs is one of the means for judging the processing degree of Chinese herbs, i.e. the change of the smell of Chinese herbs is often used to describe the processing degree (duration) of Chinese herbs, but the key factor "duration" of Chinese herbs in processing is still a fuzzy concept so far, and its scientific connotation is not yet elucidated. Meanwhile, the smell of the decoction pieces is also one of the important indexes for evaluating the quality of the traditional Chinese medicine decoction pieces, and the method has long-term practical foundation. However, the description of the quality control of the indexes such as the odor and the like is fuzzy, and the evaluation method such as the aroma escape and the burnt aroma … … only by the sense of smell of human beings can be influenced by factors such as environment, evaluators and the like, and cannot meet objective requirements, namely, the data measurement standard is not favorable for the implementation of the standardization of the traditional Chinese medicine processing process, the quality of the decoction pieces cannot be stably controlled, and meanwhile, the judgment standard is not available in the decoction piece supervision, so that the law enforcement action is difficult to pay attention to. Therefore, objective quantification of the appearance and character indexes of decoction pieces represented by odor is an important problem to be solved in the industry of traditional Chinese medicine decoction pieces.
With the rapid development of modern bionic technology, the electronic nose technology simulating human olfaction can effectively solve the problem of lack of objectivity in smell evaluation by means of updating of sensor technology. The electronic nose is an artificial olfactory system, simulates a human recognition mechanism on smell, adsorbs smell molecules by a sensor array and generates signals, and simulates the process of combining the smell molecules with receptor proteins on the surface of human olfactory cells; the generated signal is processed and transmitted by a signal processing system, and the analog signal is further processed and amplified by an olfactory cell neural network and olfactory bulbs; and finally, judging the processed signals by a pattern recognition system, and simulating the process of judging the odor by the human brain.
The raw data curve detected by the electronic nose represents the change process of the response intensity of each sensor along with time. When the volatile gas reaches the measuring chamber, the resistance value of the electronic nose sensor changes due to the oxidation-reduction reaction, the positive response value of the sensor represents that the action of the reducing gas is greater than that of the oxidizing gas, the negative response value represents that the action of the oxidizing gas is greater than that of the reducing gas, the relative resistance change rate of the electronic nose sensor is used as the response degree of the electronic nose sensor to the sample, and is also called the smell response intensity, and the calculation process is' R ═ (R ═ is 0 -R t )/R 0 ", where R is the sensor response value, R 0 Is the resistance value of the sensor at 0 second, R t Is the instantaneous resistance value of the sensor when it is exposed to the sample odor. During the test, the change in the resistance value of the sensor is recorded and calculated every 1 second. In addition, the maximum response intensity value of each sensor, i.e., the data of the peak or trough of the curve, is low in Relative Standard Deviation (RSD) when the same sample is measured, and the discrimination is generally the largest for different samples. Therefore, in data analysis and processing of the electronic nose, a peak point or a valley point in a raw response curve of the sensor is mostly selected as a characteristic point for analysis.
Aiming at the characteristics that no odor index exists in the quality control of the gardenia decoction pieces at present, and the odor has strong subjectivity and is difficult to control, the invention introduces a technology capable of accurately quantifying the odor of the gardenia, namely a method for measuring the odor of each gardenia decoction piece by using an electronic nose technology, quantifies the odor data, formulates the related evaluation standard of the odor data, and realizes the identification and evaluation of each gardenia decoction piece; the method is applied to the control of the processing process of the gardenia decoction pieces, and ensures the objective, rapid and reliable detection data to ensure the stable quality of the gardenia decoction pieces.
Disclosure of Invention
In order to solve the above problems, the present invention aims to provide a method for controlling the processing production degree and evaluating the quality of gardenia based on the odor value. In order to achieve the purpose, the following technical scheme is adopted:
according to the method, the percentile method, the discrimination function and the DFA analysis are carried out on the odor data obtained after the electronic nose detection of 4 different gardenia decoction pieces, and the correlation analysis of the GC-MS method and the electronic nose is combined to determine the substance components causing the odor change, so that the gardenia decoction pieces are rapidly judged. The method comprises the following specific steps:
the invention provides a method for controlling the processing production degree and evaluating the quality of gardenia, which comprises the following steps: measuring the odor value of the gardenia by using an electronic nose, wherein the odor value completely accords with a specified range established by a percentile method, or accords with a scientific expression function of 'duration of fire' established by a Bayes discrimination method, or accords with a discrimination model established by discrimination factor analysis to control the degree of the processing process of the gardenia and the quality of processed gardenia products; or analyzing the component content by gas chromatography-mass spectrometry, wherein the component content completely meets the specified range established by content limitation method, to control the degree of fructus Gardeniae processing process and the quality of fructus Gardeniae processed product.
The method comprises the following steps: measuring odor value of fructus Gardeniae with electronic nose, wherein the odor value is in accordance with the specified range established by percentile method, or in accordance with the scientific expression function of "duration" established by Bayesian discrimination method, or in accordance with the discrimination model established by discrimination factor analysis, to control the degree of fructus Gardeniae processing process and the quality of fructus Gardeniae processed product
The gardenia odor value is a sensor response value of the electronic nose, and the determination comprises the following steps: weighing a sample, setting the type and flow rate of carrier gas, setting headspace incubation parameters, headspace injection parameters, acquisition parameters and odor detection; the processing production degree of the gardenia and the quality control of the processed gardenia product comprise the following steps: and determining the type of the corresponding decoction pieces processed by the sample by using the response values of 18 or 8 sensors which all accord with the specified range established by a percentile method or using the response values of 8 sensors which accord with the scientific expression function of 'fire' established by a Bayesian discrimination method.
Further, the gardenia odor value is a sensor response value of the electronic nose, and the determination comprises the following steps: weighing a sample, setting the type and flow rate of carrier gas, setting headspace incubation parameters, headspace injection parameters, acquisition parameters and odor detection; the processing production degree of the gardenia and the quality control of the processed gardenia product comprise the following steps: and (3) carrying out discriminant factor analysis on the response values of the sensors by adopting a discriminant model established by discriminant factor analysis, obtaining a three-dimensional score map by adopting 3 characteristic factors with the largest contribution rate, and judging the type of the processed decoction pieces to be corresponding according to the contact ratio of the response values of the 8 sensors obtained by carrying out electronic nose detection on the unknown sample and the corresponding region of the three-dimensional map. The headspace incubation parameters comprise incubation time, incubation temperature and stirring speed, the headspace injection parameters comprise injection volume, injection speed and injection needle temperature, and the acquisition parameters comprise acquisition time, flushing time and delay time.
Further, the number of the sensors is 18, and the specified range of the response value is as follows:
the odor value range of raw gardenia simultaneously meets the following requirements: odor value of sensor LY2/LG is 0.041-0.157, odor value of sensor LY2/G is-0.231-0.101, odor value of sensor LY2/AA is-0.164-0.077, odor value of sensor LY2/GH is-0.316-0.129, odor value of sensor LY2/gCTl is-0.251-0.101, odor value of sensor LY2/gCT is-0.064-0.029, odor value of sensor T30/1 is 0.394-0.610, odor value of sensor P10/1 is 0.566-0.731, odor value of sensor P10/2 is 0.442-0.526, odor value of sensor P40/1 is 0.511-0.624, odor value of sensor T25/2 is 0.425/0.651, odor value of sensor PA/2 is 0.469-0.656, odor value of sensor P40/1 is 0.656-0.592, odor value of sensor P839/592 is 0.685/2 is 0.685-0.9, odor value of sensor P592 is 0.685/2 is 0.685-0.9, the odor value of the sensor T40/2 is 0.308-0.421, the odor value of the sensor T40/1 is 0.337-0.366, and the odor value of the sensor TA/2 is 0.392-0.484.
The odor value range of the fried gardenia simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.028-0.496, the odor value of a sensor LY2/G is-0.484-0.086, the odor value of a sensor LY2/AA is-0.358-0.066, the odor value of a sensor LY2/GH is-0.717-0.112, the odor value of a sensor LY2/gCTl is-0.640-0.084, the odor value of a sensor LY2/gCT is-0.133-0.026, the odor value of a sensor T30/1 is 0.355-0.824, the odor value of a sensor P10/1 is 0.539-0.875, the odor value of a sensor P10/2 is 0.433-0.571, the odor value of a sensor P40/1 is 0.495-0.765-0.443, the odor value of a sensor T70/2 is 0.383-0.383, the odor value of a sensor PA/2 is 0.433-0.571, the odor value of a sensor P40/1 is 0.573-0.443, the odor value of a sensor P/443 is 0.7/7, the odor value of the sensor T40/2 is 0.288-0.567, the odor value of the sensor T40/1 is 0.338-0.495, and the odor value of the sensor TA/2 is 0.387-0.667.
The odor value range of the fructus gardeniae preparata simultaneously meets the following requirements: odor value of sensor LY2/LG is 0.049-0.295, odor value of sensor LY2/G is-0.345-0.118, odor value of sensor LY2/AA is-0.241-0.089, odor value of sensor LY2/GH is-0.473-0.153, odor value of sensor LY2/gCTl is-0.389-0.120, odor value of sensor LY2/gCT is-0.058-0.029, odor value of sensor T30/1 is 0.373-0.596, odor value of sensor P10/1 is 0.559-0.726, odor value of sensor P10/2 is 0.439-0.523, odor value of sensor P40/1 is 0.503-0.617, odor value of sensor T70/2 is 0.417-0.586, odor value of sensor PA/2 is 0.646-0.657.7/7, odor value of sensor P/7, odor value of sensor P2/7/2 is-0.473, the odor value of the sensor T40/2 is 0.297-0.418, the odor value of the sensor T40/1 is 0.331-0.359, and the odor value of the sensor TA/2 is 0.387-0.481.
The odor value range of the gardenia charcoal simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.036-0.149, the odor value of a sensor LY2/G is-0.177-0.079, the odor value of a sensor LY2/AA is-0.132-0.060, the odor value of a sensor LY2/GH is-0.223-0.101, the odor value of a sensor LY2/gCTl is-0.178-0.076, the odor value of a sensor LY2/gCT is-0.045-0.019, the odor value of a sensor T30/1 is 0.298-0.523, the odor value of a sensor P10/1 is 0.492-0.663, the odor value of a sensor P10/2 is 0.414-0.483, the odor value of a sensor P40/1 is 0.465-0.568, the odor value of a sensor T70/2 is 0.323-0.559, the odor value of a sensor PA 10/2 is 0.414-0.483, the odor value of a sensor P40/1 is 0.592, the odor value of a sensor P632/2 is 0.527-0.75/75, the odor value of a sensor P2 is 0.7/2 is 0.75-0.75/2, the odor value of the sensor T40/2 is 0.251-0.376, the odor value of the sensor T40/1 is 0.326-0.342, and the odor value of the sensor TA/2 is 0.369-0.427.
Further, 8 sensors are used, and the judgment is carried out by using a scientific expression function of a specified range or 'duration' of the response value of the sensors:
the first judgment method comprises the following steps: the determination is made with a predetermined range of sensor response values:
the odor value range of raw gardenia simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.041-0.157, the odor value of a sensor LY2/G is-0.231-0.101, the odor value of a sensor LY2/gCTl is-0.251-0.101, the odor value of a sensor T30/1 is 0.394-0.610, the odor value of a sensor PA/2 is 0.469-0.656, the odor value of a sensor P30/1 is 0.599-0.760, the odor value of a sensor T40/1 is 0.337-0.366, and the odor value of a sensor TA/2 is 0.392-0.484.
The odor value range of the fried gardenia simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.028-0.496, the odor value of a sensor LY2/G is-0.484-0.086, the odor value of a sensor LY2/gCTl is-0.640-0.084, the odor value of a sensor T30/1 is 0.355-0.824, the odor value of a sensor PA/2 is 0.443-0.850, the odor value of a sensor P30/1 is 0.573-0.906, the odor value of a sensor T40/1 is 0.338-0.495, and the odor value of a sensor TA/2 is 0.387-0.667.
The odor value range of the fructus gardeniae preparata simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.049-0.295, the odor value of a sensor LY2/G is-0.345-0.118, the odor value of a sensor LY2/gCTl is-0.389-0.120, the odor value of a sensor T30/1 is 0.373-0.596, the odor value of a sensor PA/2 is 0.473-0.657, the odor value of a sensor P30/1 is 0.586-0.777, the odor value of a sensor T40/1 is 0.331-0.359, and the odor value of a sensor TA/2 is 0.387-0.481.
The odor value range of the gardenia charcoal simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.036-0.149, the odor value of a sensor LY2/G is-0.177-0.079, the odor value of a sensor LY2/gCTl is-0.178-0.076, the odor value of a sensor T30/1 is 0.298-0.523, the odor value of a sensor PA/2 is 0.396-0.593, the odor value of a sensor P30/1 is 0.491-0.709, the odor value of a sensor T40/1 is 0.326-0.342, and the odor value of a sensor TA/2 is 0.369-0.427.
Or the judgment method II: the scientific expression function of the 'duration' is used for judging:
raw gardenia "fire" function:
F 1 ＝175.9SR LY2/LG -12701.9SR LY2/G +13775.6SR LY2/gCTl -9777.4SR T30/1 -2684.5SR PA/2 +9908.0SR P30/1 +3873.3SR T40/1 +15272.5SR TA/2 -4007.3
stir-fried gardenia "duration" function:
F 2 ＝301.7SR LY2/LG -12571.5SR LY2/G +13779.1SR LY2/gCTl -10659.4SR T30/1 -1898.8SR PA/2 +10019.2SR P30/ 1+3539.7SR T40/1 +15667.0SR TA/2 -4131.4
fructus Gardeniae "fire" function:
F 3 ＝367.7SR LY2/LG -12050.5SR LY2/G +13224.5SR LY2/gCTl -11336.2SR T30/1 -613.4SR PA/2 +9634.9SR P30/1 +3192.1SR T40/1 +15123.8SR TA/2 -3930.5
cape jasmine charcoal "duration" function:
F 4 ＝538.1SR LY2/LG -12128.8SR LY2/G +13518.9SR LY2/gCTl -10888.0SR T30/1 -908.6SR PA/2 +9472.6SR P30/1 +3385.9SR T40/1 +15052.4SR TA/2 -3885.9
where SR is the response of the sample on each sensor and the subscripts are the corresponding sensor name.
Or the judgment method III: and (3) establishing a discrimination model by using discrimination factor analysis, carrying out discrimination factor analysis on the sensor response value, obtaining a three-dimensional score map by using 3 characteristic factors with the largest contribution rate, and judging the type of the processed decoction pieces to be corresponding according to the contact ratio of the unknown sample and the corresponding region of the three-dimensional map after carrying out electronic nose detection on the unknown sample to obtain an odor value. The following 8 sensors were used: LY2/LG, LY2/G, LY2/gCTl, T30/1, PA/2, P30/1, T40/1, TA/2.
The following is a further improvement of the invention, the response value of the electronic nose sensor takes the maximum response value as a calculation value, and the average value calculated after 2 times of measurement of each sample is taken as a sample odor value.
After the sample needs to be crushed, screening the crushed sample by a third sieve, and weighing 0.4g of the sample for sample loading analysis; the carrier gas type in the carrier gas parameters is synthetic dry air, and the flow rate is 150 mL/min; the incubation time in the headspace incubation parameters is 300s, the incubation temperature is 50 ℃, and the stirring speed is 500 rpm; the injection volume of the headspace injection parameters is 1500 mu L, the injection speed is 500 mu L/s, and the temperature of the injection needle is 60 ℃; the acquisition time is 120s, the flushing time is 120s, and the delay time is 600 s.
The method 2 comprises the following steps: analyzing the component content by gas chromatography-mass spectrometry, wherein the component content completely meets the specified range established by content limitation method, and controlling the degree of processing fructus Gardeniae and the quality of fructus Gardeniae processed product
Analyzing the component content by adopting a gas chromatography-mass spectrometry, and controlling the processing production degree of the gardenia and the quality of the processed gardenia product by using the component content to completely accord with the specified range, wherein the specified range is that the relative percentage content of methyl acetate, 2, 5-dimethyl pyrazine, acetic acid, furfural and 4-methylene isophorone is an evaluation index for processing the gardenia: calculating the relative percentage ratio of parched fructus Gardeniae, fructus Gardeniae Preparata, and fructus Gardeniae charcoal to raw fructus Gardeniae by using methyl acetate as raw fructus Gardeniae relative percentage of 1, wherein the contents of parched fructus Gardeniae, fructus Gardeniae Preparata, and fructus Gardeniae charcoal should be respectively controlled at 1.75-2.47, 1.74-2.71, and 2.10-3.70; calculating the ratio of the relative percentage of fried gardenia, charred gardenia and gardenia charcoal to the relative percentage of raw gardenia by taking the relative percentage of raw gardenia as 1, wherein the relative percentage of fried gardenia, charred gardenia and gardenia charcoal is respectively controlled to be 9.76-15.64, 4.19-8.15 and 1.86-3.30; calculating the relative percentage ratio of fructus Gardeniae preparata, and fructus Gardeniae charcoal to fructus Gardeniae with acetic acid as raw fructus Gardeniae relative percentage of 1, wherein fructus Gardeniae preparata, and fructus Gardeniae charcoal should be controlled at 5.13-7.40, 3.89-5.34, and 2.48-3.48, respectively; calculating the ratio of the relative percentage of fried gardenia, charred gardenia and gardenia charcoal to the relative percentage of raw gardenia by taking the relative percentage of raw gardenia as 1, wherein the relative percentage of fried gardenia, charred gardenia and gardenia charcoal is respectively controlled to be 246.60-397.65, 111.60-184.80 and 61.20-101.75; the relative percentage of the 4-methylene isophorone in raw gardenia is 1, the ratio of the relative percentage of fried gardenia, charred gardenia and raw gardenia is calculated, and the relative percentage of the fried gardenia, the charred gardenia and the charred gardenia is respectively controlled to be 12.11-18.21, 14.11-18.81 and 6.86-12.46.
Advantageous effects
The invention discloses a method for monitoring change of smell in a processing process to realize control of processing production degree and quality evaluation of gardenia, and aims to measure the smell of a gardenia sample by using an electronic nose, quantify the smell data of the gardenia sample, combine a GC-MS method and realize the smell of the gardenia represented by digitalization, modeling, functionalization and specific substance limitation through statistical calculation. The method is used for the processing online production of gardenia, can ensure the stability of the quality of gardenia decoction pieces, and has the advantages of objectivity, rapidness and reliability.
Drawings
FIG. 1 is a graph showing the effect of different detection conditions of Gardenia on the RSD value of the maximum response value of the sensor. A: crushing particle size, B: sample weighing, C: sample amount, D: incubation temperature, E: incubation time
Fig. 2 is a linear discriminant analysis diagram of a gardenia decoction piece sensor array. A: original array, B: optimizing the array; 1: raw gardenia, 2: stir-frying gardenia, 3: fructus gardeniae preparata, 4: gardenia charcoal
FIG. 3 is a DFA discrimination model diagram of Gardenia jasminoides ellis decoction pieces. SZZ: raw gardenia CZZ: fried gardenia JZZ: charred gardenia ZZT: gardenia charcoal
FIG. 4 is a GC-MS chart of gardenia decoction pieces. A: GC-MS overlay, B: GC-MS fingerprint spectrum common mode (peaks marked in the figure 1,3, 4, 5, 7, 10, 12, 20, 22, 24, 26, 33, 41, 68, 78, 83, 106, 108, 110, 112, 121, 126, 132, 137, 140, 150, 178, 222, 236 and 278 are 30 common chromatographic peaks of gardenia samples)
FIG. 5 is a graph showing the trend of the relative percentage change of various compounds during stir-frying Gardenia jasminoides Ellis.
Detailed Description
The invention will be further described with reference to specific embodiments as follows:
example 1 determination of odor detection method of Gardenia jasminoides Ellis decoction pieces
A batch of gardenia decoction pieces are selected as experimental objects in the experimental process for single factor investigation. FOX-4000 electronic nose matched with Alpha Soft 11.0 software (French Alpha MOS company) is used as an odor detection instrument. The absolute value of the response value of the sample on the electronic nose sensor is controlled within the range of 0.3-0.9, and the response value of the sample has good stability (the RSD value of the response value is small) as the investigation indexes, so that the experimental parameters are optimized respectively from the aspects of sample weighing amount, crushing granularity, sample feeding amount of an instrument, incubation temperature, incubation time and the like.
1.1 optimization of detection parameters
1.1.1 optimization of crushing particle size
Taking five samples of the gardenia original decoction pieces, sieving the five samples by a first sieve, a second sieve, a third sieve and a fourth sieve, respectively weighing 0.4g of the samples, filling the samples into a headspace sample injection bottle, and detecting under the conditions of controlling the sample injection amount to be 1500 mu L, the incubation temperature to be 50 ℃, the incubation time to be 300s and the like, wherein 6 parts are parallel to each other. The results show that the maximum response value of each sensor gradually increases along with the increase of the crushing degree of the decoction pieces, and the odor response value of the decoction piece powder reaches the maximum when the decoction piece powder passes through a No. four sieve. In combination with the change in RSD for the maximum response value of each sensor, the RSD for each sensor response value was found to be the lowest and most stable when the decoction piece powder was passed through the No. three sieve. Comprehensively considering and determining the crushing degree of the decoction pieces, namely sieving the powder through a third sieve. The RSD value results for the maximum response value are shown in fig. 1 (a).
1.1.2 optimization of the sample weighing
Weighing fructus Gardeniae sample powder (0.1 g, 0.2g, 0.3g, 0.4g, 0.5 g) respectively, loading into headspace sampling bottle, controlling sampling amount to be 1500 μ L, and detecting under the condition of incubation temperature of 50 deg.C and incubation time of 300s, each level is 3 parts in parallel. The results show that the maximum response value of each sensor gradually increases along with the increase of the sample weighing amount. In combination with the change in RSD for the maximum response value of each sensor, it was found that the RSD for each sensor response value was the lowest and most stable at a sample weight of 0.4 g. The sample weight was set to 0.4g taking the noise into account. The RSD value results for the maximum response value are shown in fig. 1 (B).
1.1.3 optimization of sample size
Weighing 0.4g of gardenia sample powder (passing through a third sieve), filling the gardenia sample powder into a headspace sample injection bottle, and setting the sample injection amount to be 500 mu L, L000 mu L, 1500 mu L and 2000 mu L respectively under the condition that the incubation temperature is controlled to be 50 ℃ and the incubation time is controlled to be 300s, and the like, wherein each level is 3 parts in parallel. The results show that the maximum response value of each sensor gradually increases with the increase of the sample amount, and the RSD of the maximum response value of each sensor is the lowest and more stable at the sample amount of 1500 μ L. The total amount of sample was determined to be 1500 μ L. The RSD value results for the maximum response value are shown in fig. 1 (C).
1.1.4 optimization of incubation temperature
Weighing fructus Gardeniae sample powder (0.4 g passing through No. three sieve), placing into a headspace sampling bottle, controlling the sampling amount to be 1500 μ L, and setting the incubation temperatures to be 40 deg.C, 45 deg.C, 50 deg.C, and 55 deg.C, and 6 parts per horizontal line under the condition of uniform incubation time of 300 s. The results showed that the maximum response value of each sensor gradually increased with the rise of the incubation temperature, and the maximum value appeared at the incubation temperature of 50 ℃, and the response value at 55 ℃ was close to that, while the RSD of the maximum response value of each sensor was the lowest and most stable at 50 ℃. The incubation temperature was determined to be 50 ℃ in general consideration. The RSD value results for the maximum response value are shown in fig. 1 (D).
1.1.5 optimization of incubation time
Weighing 0.4g of gardenia sample powder (passing through a No. three sieve), filling the gardenia sample powder into a headspace sample injection bottle, setting the incubation time to be 150s, 300s and 600s under the conditions of controlling the sample injection amount to be 1500 mu L, controlling the incubation temperature to be 50 ℃ and the like to be consistent, setting 6 parts of incubation time for each level, and observing the change of the maximum response value of the sensor and the RSD value of the maximum response value to optimize the incubation time. The results show that the maximum response value of each sensor gradually increases with the increase of the incubation time, the maximum value appears at the incubation time of 300s, and then the response value of each sensor slightly decreases. In combination with the change in RSD of the maximum response value of each sensor, the RSD of the maximum response value of each sensor was the lowest and most stable at the incubation time of 300 s. The incubation time was set to 300 s. The RSD value result of the maximum response value is shown in fig. 1 (E).
Based on the above results, the optimal conditions for determining the detection of gardenia electronic nose are shown in table 1.
TABLE 1 Gardenia electronic nose detection parameters
1.2 methodological investigation
And (4) carrying out methodology investigation according to the optimal condition determined by the optimization result.
1.2.1 repeatability test
6 parts of the same batch of sample powder are taken and respectively filled into a headspace sample injection bottle for sealing, the best detection condition is adopted for determination, the result shows that the maximum RSD value of 18 sensors is 3.799%, the repeatability is good, and the specific result is shown in table 2.
TABLE 2 repeatability test results of cape jasmine electronic nose detection method
1.2.2 stability Studies
The same batch of sample powder is taken and put into a headspace sampling bottle for sealing, the optimal detection conditions are adopted for respectively carrying out measurement for 0 hour, 2 hours, 4 hours, 6 hours, 8 hours and 10 hours, 3 parts of the sample powder are parallelly measured at each time point, the result shows that the RSD value of the maximum response value of 18 sensors is 4.908 percent at most, the sample is stable within 10 hours, and the result is shown in Table 3.
TABLE 3 stability investigation result of gardenia electronic nose detection method
1.3 sample odor detection
121 batches of samples (57 batches of raw gardenia, 32 batches of fried gardenia, 17 batches of scorched gardenia and 15 batches of gardenia charcoal) of the gardenia decoction pieces are detected under the optimal detection condition of the electronic nose, and each batch of samples are repeated for 2 times to obtain 242 groups of data.
EXAMPLE 2 optimization of electronic nose Sensors
2.1 sensor optimization
Because each sensor of the electronic nose has a broad-spectrum response to all gases, the obtained maximum response intensity of each sensor has high correlation, the correlation causes a great deal of redundancy of data to a certain extent, and meanwhile, some sensors are not sensitive to sample odor information, and the response of the sensors is close to the response to air. In order to simplify the sensor array, realize data dimension reduction, and ensure the integrity, effectiveness and reliability of the acquired information while rejecting redundant information, thereby reducing the complexity of pattern recognition, better realizing classification and discrimination, and optimizing the sensor.
Experiments were performed using Wilks 'Lambda' method for stepwise discriminant analysis to optimize electronic nose sensors. The F value is used as the discrimination statistic. Whether a variable can enter the model depends primarily on the significance level of the F-test in the covariance analysis and the set F-values entering and leaving the model. Specific parameters are set as (default): when F is more than or equal to 3.84, the variable enters the model; when F is less than or equal to 2.71, the variable is moved out of the model. The maximum response values of 18 sensors of the gardenia samples are subjected to stepwise discriminant analysis, and specific results are shown in table 4.
TABLE 4 progressive discrimination analysis result of maximum response values of 18 sensors in gardenia sample
As shown in Table 4, the statistics (F) of LY2/LG, LY2/G, LY2/gCTl, T30/1, PA/2, P30/1, T40/1, TA/2 are all above 3.84, and the P values of the 8 variables are all less than 0.01, except that the sensor LY2/gCT is kicked out of the model because the F value is less than 3.84 in the 8 th step of stepwise discrimination. Finally, after 10 steps, the external variable and the internal variable of the model do not enter or exit, the independent variable selection of the stepwise discriminant analysis is finished, and the determined optimized array of the gardenia sensor comprises the following components: LY2/LG, LY2/G, LY2/gCTl, T30/1, PA/2, P30/1, T40/1, TA/2.
2.2 sensor optimization validation
In order to compare the classification effect of different processed products of gardenia before and after the optimization of the sensor array, a Linear Discriminant Analysis (LDA) is adopted to output a visual classification result. And (3) adopting linear discriminant analysis on the original sensor arrays of all the gardenia samples and the optimized maximum response values of the sensor arrays, and comparing LDA analysis before and after sensor optimization with that shown in figure 2. As shown in the figure, the total contribution rate of the discrimination graphs LD1 and LD2 of the sensor array after the gardenia optimization is 93.6%, which indicates that the established linear discrimination function can explain most information, and ensures the integrity and reliability of the acquired information; meanwhile, the classification effect of the sample is obviously improved on the optimized sensor array, so that redundant information is removed to a certain extent by the optimized array, the data processing efficiency is improved, and the optimized sensor array can replace an original array to complete the task of identifying different gardenia decoction pieces.
Example 3 establishment of Gardenia odor Standard
In the experiment, the digital standard and the mode standard of the sample odor are respectively established by using the electronic nose odor response value data of gardenia decoction pieces (raw products, fried products, scorched products and charcoal products) based on the optimized sensor array, so that a basis is provided for the quality control of the odor. The digital standard can express the odor standard in a digital form, and is favorable for being directly listed in the existing paper standard for evaluation execution, while the modeling standard expresses the odor standard in a database form, and can be adjusted and perfected at any time along with the increase of samples, and the standard can be directly applied to the self-checking process of enterprise samples.
3.1 establishment of numerical Standard of smell of decoction pieces
In the experiment, the odor digital standard range of the collected electronic nose odor data of each decoction piece is established based on the optimized maximum response value of the sensor array. The acquired odor data of different gardenia decoction pieces are analyzed by SPSS 23.0 software, firstly, the data of different sensors are subjected to multivariate normality test by exploratory analysis, and the result shows that the odor original data of most different gardenia decoction pieces do not accord with normal distribution, so that the odor numerical ranges of 90% intervals at both sides of each sensor of different gardenia decoction pieces are established by P5 and P95 percentile indexes of percentile methods (Percentiles), and the results are shown in tables 5 and 6.
TABLE 5 odor digital standard of fructus Gardeniae for different decoction pieces based on raw sensor array
TABLE 6 smell number standards of gardenia different decoction pieces based on optimized sensor array
In order to judge the reasonability of the reference range of the odor value of the decoction pieces, the response values of the sensors of the collected decoction pieces of gardenia are subjected to statistical analysis by adopting independent sample test under a non-parameter test item, and the specific order and test results are shown in table 7.
TABLE 7 nonparametric test results on Gardenia jasminoides Ellis
As shown in table 7, the P values in the test results of the response values of the sensors of the electronic noses of the 4 groups of raw gardenia, fried gardenia, coke and charcoal decoction pieces are all less than 0.01, which indicates that the difference between the odor numerical ranges of the 4 groups of different gardenia decoction pieces has statistical significance, and the established odor numerical standard range can better distinguish the gardenia decoction pieces with different processing specifications.
3.2 creation of decoction piece odor model Standard
Based on the optimized array, Discriminant Factor Analysis (DFA) is adopted to respectively establish a Discriminant model for the electronic nose odor data of the gardenia decoction pieces, and the model is evaluated through cross Validation score (Validation score). The DFA discrimination model and the verification score of the different decoction pieces of gardenia jasminoides are shown in figure 3.
As shown in fig. 3, the total contribution rate of the discrimination factors DF1, DF2 and DF3 reaches 100%, original data information is well reflected, the processing result is reliable, DFA can distinguish different processed product samples of gardenia, raw gardenia and fried gardenia are distributed in the negative direction of DF1, and coke and charcoal are distributed in the positive direction of DF 1; meanwhile, the raw product and the stir-fried product are distributed on two sides of DF2, and the scorched product and the charcoal product are distributed on two sides of DF 3. The cross validation score of the model is larger than 85, which indicates that the established discrimination model of the gardenia decoction pieces is better.
The established decoction piece DFA discrimination model can visually discriminate unknown samples in a visual mode. When the unknown sample enters a library established by the DFA discrimination model, the sample can be determined to belong to a corresponding region when being projected to the corresponding region, otherwise, the unknown sample is judged to be in a 'unrecognized' state. The established discrimination library can be expanded along with the entry of the standard decoction pieces, and has adjustability.
Example 4 establishment of mathematical discriminant formula for "duration of fire" in cape jasmine processing
The theory describing the degree of processing Chinese herbs is the fire condition, but so far, the theory still stays at the level of empirical expression, and no scientific explanation exists, and the experiment preliminarily clarifies the scientific connotation about the fire condition through the established mathematical discriminant function.
4.1 creation of mathematical discriminant formula for degree of concocting decoction pieces
SPSS 23.0 (American IBM company) software is adopted, Bayesian discriminant analysis is carried out on the maximum response value of the sensor array optimized by the gardenia decoction pieces, a mathematical discriminant function formula of the processing fire of the gardenia decoction pieces, which is characterized by sensor data, is established, and the variable coefficients of the functions are shown in a table 8.
TABLE 8 Gardenia mathematical discriminant function results
Establishing Bayes discrimination functions of different processing degrees (fire degrees) of gardenia according to coefficients of the fire discrimination functions of gardenia decoction pieces in the table 8 as follows:
raw gardenia "fire" function:
F 1 ＝175.9SR LY2/LG -12701.9SR LY2/G +13775.6SR LY2/gCTl -9777.4SR T30/1 -2684.5SR PA/2 +9908.0SR P30/1 +3873.3SR T40/1 +15272.5SR TA/2 -4007.3
stir-fried gardenia "duration" function:
F 2 ＝301.7SR LY2/LG -12571.5SR LY2/G +13779.1SR LY2/gCTl -10659.4SR T30/1 -1898.8SR PA/2 +10019.2SR P30/ 1+3539.7SR T40/1 +15667.0SR TA/2 -4131.4
fructus Gardeniae "fire" function:
F 3 ＝367.7SR LY2/LG -12050.5SR LY2/G +13224.5SR LY2/gCTl -11336.2SR T30/1 -613.4SR PA/2 +9634.9SR P30/1 +3192.1SR T40/1 +15123.8SR TA/2 -3930.5
cape jasmine charcoal "duration" function:
F 4 ＝538.1SR LY2/LG -12128.8SR LY2/G +13518.9SR LY2/gCTl -10888.0SR T30/1 -908.6SR PA/2 +9472.6SR P30/1 +3385.9SR T40/1 +15052.4SR TA/2 -3885.9
note: SR is the response value of the sample on each sensor.
Meanwhile, the function can also be used as a method for identifying the gardenia decoction pieces: and substituting the number of the gas values of the decoction pieces which are unknown to each person into the 4 mathematical discrimination functions to calculate and compare the values F1, F2, F3 and F4, wherein the value F1 with the highest value is judged as the raw gardenia, the value F2 with the highest value is judged as the fried gardenia, the value F3 with the highest value is judged as the scorched gardenia, and the value F4 with the highest value is judged as the gardenia charcoal.
4.2 Interactive verification results
In order to judge the rationality of the discrimination function, the specific odor data of each sample of the gardenia raw product, the fried product, the scorched product and the charcoal product with determined specifications are substituted into the established discrimination function, and the discrimination accuracy of the established discrimination function is verified by adopting a common method (Original) and a Cross-validated method, and the result is shown in table 9.
TABLE 9 Gardenia jasminoides discriminant analysis and verification results
Note: 1, 2,3 and 4 in the column in the table represent the original attribution of the gardenia sample, namely, a raw product, a fried product, a scorched product and a charcoal product; the horizontal lines 1, 2,3 and 4 respectively represent the attributions of the odor data of the gardenia samples determined by the calculation of the discriminant function, namely raw products, fried products, burnt products and charcoal products.
As shown in the table, the discrimination accuracy of each record in 4 groups of gardenia samples (raw product, fried product, burnt product and charcoal) is 99.1%, 96.9%, 82.4% and 100.0% respectively by using a common verification method; the interactive verification method respectively judges the correctness rates of 99.1%, 93.8%, 79.4% and 100%. The misjudgment of the sample mainly occurs in the gardenia coke, and a small part of coke is judged as a charcoal possibly due to misjudgment caused by the approach of the coke and the charcoal. The result shows that the established discrimination function for predicting the processing degree of the gardenia has very little misjudgment on the sample, so the established discrimination function for the processing duration of the gardenia decoction pieces is stable and reasonable.
Example 5 determination of odor characteristics of cape jasmine pieces
Processing raw gardenia decoction pieces according to processing standard operation procedures, respectively taking samples at different processing time points for odor detection, selecting 16 batches of samples with obvious odor change according to detection results for GC-MS fingerprint analysis, and establishing matched chromatographic peak data of the decoction pieces at each frying time point; and respectively carrying out correlation degree analysis on matching data of multiple components in the decoction pieces at different processing time points and corresponding decoction piece odor data, searching for a matching chromatographic peak which has correlation with the change of the response value of the electronic nose sensor, and determining the component or component group causing the odor change in the decoction piece processing process.
5.1 GC-MS analysis of Gardenia jasminoides Ellis decoction pieces
5.1.1 instruments
Agilent 7890B-7000C GC-MS combination with Agilent 7697A headspace autosampler.
5.1.2 methods
Preparing a test sample: pulverizing fructus Gardeniae decoction pieces during processing, sieving with a third sieve, precisely weighing 1.5g of each decoction piece powder, and bottling in 10mL headspace.
GC-MS conditions of Gardenia jasminoides ellis:
headspace conditions: the equilibrium temperature is 90 ℃, the quantitative ring is 105 ℃, the transmission line is 120 ℃, the equilibrium time is 15min, the carrier gas is He, the oscillation frequency is 250 times/min, the filling pressure is 15psi, the pressurization time is 0.1min, and the sample injection time is 0.5 min.
Chromatographic conditions are as follows: HP-INNOWAX column (30 m.times.250 μm,0.25 μm); the temperature of a sample inlet is 220 ℃; the programmed temperature rise condition is that the initial temperature is 45 ℃, the temperature is raised to 110 ℃ at 3 ℃/min and kept for 10min, then the temperature is raised to 150 ℃ at 5 ℃/min and kept for 5min, and then the temperature is raised to 260 ℃ at 20 ℃/min, and the total analysis time is 50 min; the carrier gas is He, the constant flow mode is adopted, and the flow rate is 1.0 ml/min; the split ratio is 5: 1; the sample volume is 1 mL.
Mass spectrum conditions: an EI ion source, wherein the electron energy is 70eV, and the ion source temperature is 230 ℃; the temperature of the four-level bar is 150 ℃, and the temperature of the interface is 270 ℃; the scanning range is 35-500 amu; the electron multiplier voltage was 1557.3V; the solvent delay was 3.15 min.
5.1.3 results
GC-MS detection is carried out on the gardenia decoction pieces in the stir-frying process according to the conditions, a GC-MS superposition graph of a sample is shown in figure 4(A), GC-MS data are introduced into Chinese medicine chromatography fingerprint similarity evaluation software (2012 edition of the national pharmacopoeia committee), and a common mode of the fingerprint of the produced gardenia decoction pieces is shown in figure 4 (B). By comparison, 333 chromatographic peaks are detected in the gardenia group, wherein 30 chromatographic peaks are detected in the gardenia group.
5.2 correlation analysis and composition confirmation of GC-MS and electronic nose data
Establishing a data matrix of the gardenia matching chromatographic peaks and the number of the processed samples and a data matrix of the sensor response values and the number of the processed samples, carrying out grey correlation degree analysis on the data matrix and the data matrix, and determining that the 52 gas chromatographic peaks have stronger correlation degrees with the data of the gardenia decoction piece electronic nose by taking the grey correlation degree r larger than 0.9 as an inclusion standard.
By checking standard maps in the database NIST 14.0 of the on-line search workstation and by qualitative reference, 36 compounds were identified in the 52 peaks with higher correlation, and the specific results are shown in Table 10, including 1 alcohol (ethanol), 7 ketones (2-butanone, 2, 3-pentanedione, 3-hydroxy-2-butanone, 1-hydroxy-2-acetone, furylhydroxymethyl ketone, isophorone, 4-methylideneisophorone), 7 aldehydes (acetaldehyde, propionaldehyde, furfural, benzaldehyde, 5-methylfuran aldehyde, 2, 3-dihydro-2, 2, 6-trimethylbenzaldehyde, n-undecyl), 4 esters (methyl formate, methyl acetate, vinyl acetate, furfuryl formate), 1 alkane (1-hexadecane), 2 aromatic hydrocarbons (toluene, and isopropyl alcohol), 2 esters (methyl acetate, vinyl acetate, furfuryl formate), and the like, 1,3, 5-trimethylbenzene), 1 acid (acetic acid), 3 furans (2-methylfuran, 2-ethylfuran, 2-N-pentylfuran), 1 thiophene (thiophene), 4 pyrroles (N-methylpyrrole, pyrrole, 1-furfurylpyrrole, 2-acetylpyrrole), 1 pyridine (pyridine), 2 pyrazines (2, 5-dimethylpyrazine, 2, 6-dimethylpyrazine), 1 amide (formamide), and 1 isocyanide (methyl cyanide).
TABLE 10 Compounds associated with electronic nose response changes during Gardenia frying
"-" indicates no detection.
The peak area changes of the compounds related to the electronic nose response changes during stir-frying gardenia jasminoides ellis are shown in table 11.
TABLE 11 Compounds associated with electronic nose response changes during Gardenia frying
"a" represents a consensus peak; "-" indicates no detection.
The relative percentage change of each compound in the stir-frying process of fructus Gardeniae is shown in FIG. 5. As shown in the figure, the odor change in the processing process of gardenia is a comprehensive effect of multiple components, and alcohols, ketones, aldehydes, esters, aromatic hydrocarbons, acids, furans, pyrroles, pyrazines and isocyanides have relatively high relative percentage in the whole stir-frying process of gardenia, and are ranked from large to small as esters (12.08%) > aldehydes (10.19%) > ketones (9.91%)) aromatic hydrocarbons (9.67%) > acids (9.28%) > alcohols (6.49%)) isocyanides (4.70%) > pyrazines (2.83%) > furans (2.79%) > pyrroles (1.54%) on average, and the following change trends are presented: along with the increase of the stir-frying time, the relative content of the ester substances gradually rises; the relative contents of the ketone, aldehyde, aromatic hydrocarbon, acid, pyrrole and pyrazine substances are increased and then decreased; the relative contents of alcohols and isocyanic substances are gradually reduced; the relative content of furans decreases and increases. Therefore, the 10 kinds of substances are probably main components which cause the odor change of gardenia in the processing process.
Further analysis of these compounds revealed that methyl acetate, 2-methylfuran, methyl isocyanide, in which the relative percentage was over 1% and the peak area change was on the rise during the stir-frying process; acetaldehyde, methyl formate, ethanol, toluene, 2-n-pentylfuran, 1-hydroxy-2-propanone, 2, 5-dimethylpyrazine, 1,3, 5-trimethylbenzene, acetic acid, furfural, pyrrole, isophorone and 4-methyleneisophorone which exhibit a tendency of rising first and falling second; propionaldehyde and 2-ethyl furan which show the tendency of descending first and then ascending second are the main characteristic components in the stir-frying process of gardenia. Wherein the relative percentage of 5 components of methyl acetate, 2, 5-dimethyl pyrazine, acetic acid, furfural and 4-methylene isophorone in the sample is higher. Modern researches show that isophorone has mint or camphor-like flavor, gardenia has spilled flavor after frying, the faint scent of the gardenia is converted into coke flavor after frying, and the change of the odor is consistent with the change of the relative content of isophorone. Similarly, the aroma of the coke deepens in the frying process of the gardenia is related to the Maillard reaction, for example, the relative content of furfural increases and then decreases along with the increase of frying time, and is consistent with the change of the smell of the gardenia.
The odor data of 16 samples in the stir-frying process are detected by an electronic nose, and according to the established odor numerical standard and DFA mode standard, the samples S1 are crude products, S7 and S8 are stir-fried products, the samples S11 and S12 are scorched products, the samples S14 and S15 are charcoal products, and the rest are processed too much or not too much. The results of the relative percentages of methyl acetate, 2, 5-dimethylpyrazine, acetic acid, furfural, 4-methylidene isophorone for each sample are shown in Table 11. The relative percentage content of raw gardenia S1 is 1 for each component, and the specific results are shown in Table 11 for calculating the relative percentage content ratio of corresponding substances of the rest samples. According to the change ratios of the substances shown in the table, the relative percentage change ratio limit of the 5 characteristic compounds is established according to the upper limit floating by 10 percent and the lower limit floating by 10 percent.
Taking the relative percentage content of raw gardenia as 1, calculating the ratio of the relative percentage content of fried gardenia, charred gardenia and raw gardenia, and controlling the relative percentage content of fried gardenia, charred gardenia and charred gardenia to be 1.75-2.47, 1.74-2.71 and 2.10-3.70 respectively; calculating the ratio of the relative percentage of fried gardenia, charred gardenia and gardenia charcoal to the relative percentage of raw gardenia by taking the relative percentage of raw gardenia as 1, wherein the relative percentage of fried gardenia, charred gardenia and gardenia charcoal is respectively controlled to be 9.76-15.64, 4.19-8.15 and 1.86-3.30; calculating the relative percentage ratio of fructus Gardeniae preparata, and fructus Gardeniae charcoal to fructus Gardeniae with acetic acid as raw fructus Gardeniae relative percentage of 1, wherein fructus Gardeniae preparata, and fructus Gardeniae charcoal should be controlled at 5.13-7.40, 3.89-5.34, and 2.48-3.48, respectively; calculating the ratio of the relative percentage of fried gardenia, charred gardenia and gardenia charcoal to the relative percentage of raw gardenia by taking the relative percentage of raw gardenia as 1, wherein the relative percentage of fried gardenia, charred gardenia and gardenia charcoal is respectively controlled to be 246.60-397.65, 111.60-184.80 and 61.20-101.75; the relative percentage of the 4-methylene isophorone in raw gardenia is 1, the ratio of the relative percentage of fried gardenia, charred gardenia and raw gardenia is calculated, and the relative percentage of the fried gardenia, the charred gardenia and the charred gardenia is respectively controlled to be 12.11-18.21, 14.11-18.81 and 6.86-12.46. The specific results are shown in Table 12.
TABLE 12 changes in characteristic Compounds during Stir-frying
Claims (1)
1. A method for controlling the processing production degree and evaluating the quality of gardenia comprises the following steps: measuring odor value of fructus Gardeniae with electronic nose, analyzing volatile component content with gas chromatography-mass spectrometry, wherein the component content completely meets the specified range established by content limit method, and controlling the degree of fructus Gardeniae processing process and the quality of fructus Gardeniae processed product; the determination method comprises the following steps: the specified range is that the relative percentage content of methyl acetate, 2, 5-dimethyl pyrazine, acetic acid, furfural and 4-methylene isophorone is the evaluation index of processing gardenia: calculating the relative percentage ratio of parched fructus Gardeniae, fructus Gardeniae Preparata, and fructus Gardeniae charcoal to raw fructus Gardeniae by using methyl acetate as raw fructus Gardeniae relative percentage of 1, wherein the contents of parched fructus Gardeniae, fructus Gardeniae Preparata, and fructus Gardeniae charcoal should be respectively controlled at 1.75-2.47, 1.74-2.71, and 2.10-3.70; calculating the ratio of the relative percentage of fried gardenia, charred gardenia and gardenia charcoal to the relative percentage of raw gardenia by taking the relative percentage of raw gardenia as 1, wherein the relative percentage of fried gardenia, charred gardenia and gardenia charcoal is respectively controlled to be 9.76-15.64, 4.19-8.15 and 1.86-3.30; calculating the relative percentage ratio of fructus Gardeniae preparata, and fructus Gardeniae charcoal to fructus Gardeniae with acetic acid as raw fructus Gardeniae relative percentage of 1, wherein fructus Gardeniae preparata, and fructus Gardeniae charcoal should be controlled at 5.13-7.40, 3.89-5.34, and 2.48-3.48, respectively; calculating the ratio of the relative percentage of fried gardenia, charred gardenia and gardenia charcoal to the relative percentage of raw gardenia by taking the relative percentage of raw gardenia as 1, wherein the relative percentage of fried gardenia, charred gardenia and gardenia charcoal is respectively controlled to be 246.60-397.65, 111.60-184.80 and 61.20-101.75; the relative percentage of the 4-methylene isophorone in raw gardenia is 1, the ratio of the relative percentage of fried gardenia, charred gardenia and raw gardenia is calculated, and the relative percentage of the fried gardenia, the charred gardenia and the charred gardenia is respectively controlled to be 12.11-18.21, 14.11-18.81 and 6.86-12.46.
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