CN111650347B - Method for controlling processing production degree and evaluating quality of hawthorn - Google Patents

Method for controlling processing production degree and evaluating quality of hawthorn Download PDF

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CN111650347B
CN111650347B CN202010433350.2A CN202010433350A CN111650347B CN 111650347 B CN111650347 B CN 111650347B CN 202010433350 A CN202010433350 A CN 202010433350A CN 111650347 B CN111650347 B CN 111650347B
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hawthorn
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odor
charred
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殷放宙
殷武
费程浩
李伟东
李林
吴丽
刘晓
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Nanjing University of Chinese Medicine
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Abstract

Aiming at the control method which is adopted in the processing production process of the traditional Chinese medicine hawthorn in the prior art and takes personal experience to judge color as a unique index, the invention introduces a novel hawthorn processing control method based on smell. The method has the advantages that the sensor response value changing in the hawthorn processing process is acquired by using an electronic nose technology capable of accurately quantifying the smell value, the technical problem that the sensor response value lacks objectivity in the evaluation process is effectively solved, and the digitalized standard, the modeled standard and the 'hot weather' discrimination function based on the smell value as an index are determined through statistical calculation and are used for online control in the hawthorn decoction piece processing process. And the index components causing the change of the processed smell of the hawthorns are determined by means of the correlation analysis of the electronic nose and the gas chromatography-mass spectrometry, and the limit is established. The method is used in the processing production of the hawthorn, can ensure the stability of the quality of hawthorn decoction pieces, has the advantages of objectivity, rapidness and reliability, and solves the technical problem that the manual judgment in the prior art is lack of objectivity.

Description

Method for controlling processing production degree and evaluating quality of hawthorn
Technical Field
The invention relates to the field of processing technology of hawthorn Chinese medicinal decoction pieces and quality control thereof, in particular to a discrimination method for controlling the processing degree and quality of hawthorn decoction pieces by taking smell data as a new important index.
Background
At present, the quality evaluation of traditional Chinese medicine decoction pieces mainly centers on the determination of the content of internal components, and with the progress of research, people realize that the quality of the traditional Chinese medicine decoction pieces can be comprehensively and effectively controlled only by combining the overall indexes of appearance properties. In the appearance and properties of the decoction pieces of traditional Chinese medicine, the smell is one of the most critical factors, because the decoction pieces of traditional Chinese medicine have respective inherent special smell, the smell is one of the means for judging the processing degree of the traditional Chinese medicine, and improper conditions, time and the like in the storage process easily cause the taste change of the medicine. Meanwhile, the smell of the traditional Chinese medicine is directly related to the types and the contents of chemical components contained in the traditional Chinese medicine. However, the description of the related smell in the properties of the traditional Chinese medicine decoction pieces adopts subjective character expression, such as aroma, burnt aroma or spicy aroma and the like, which are still fuzzy qualitative indexes, and the empirical description causes great difficulty in the actual implementation process due to the fact that the empirical description does not reach the measurement standards of datamation and standardization, so that the final judgment result varies from person to person and varies from time to time, and thus the related standards of the smell in the traditional Chinese medicine decoction pieces listed in various standards of China, province and city and the like become a paper space. The processing method mainly uses the change of appearance character to mark the processing degree (fire degree) of traditional Chinese medicine decoction pieces, wherein the change of smell in the processing process is the most important, but because the smell detection standard has no quantitative data, the execution of the smell detection standard as an index for evaluating the fire degree is unfavorable, so that the key factor 'fire degree' related to the processing process of traditional Chinese medicine is still a fuzzy concept so far, and the scientific connotation is not clarified yet.
Therefore, a technique capable of accurately quantifying the smell of the herbal pieces is required to be introduced, so as to realize objective expression of quality control indexes of the smell of the herbal pieces, and simultaneously consider the parameter values of smell change introduced in the processing process of the herbal pieces to participate in judging the processing degree, and discuss the correlation between the appearance (smell) and the internal components of the herbal pieces, so as to establish a unified quantitative quality evaluation mode of the two.
The sniffing technology has been developed rapidly at present. The machine olfaction technique, namely the electronic nose system, is a nondestructive testing technique which is based on gas sensor arrays and pattern recognition and simulates the olfaction systems of people and animals to analyze, recognize and detect complex components. As a novel artificial intelligence olfaction device, the device generates a fingerprint spectrum according to the overall information of a measured sample, has high sensitivity, good correlation between measured data and human sensory evaluation, and has intelligent identification effect on unknown samples. The electronic nose system mainly comprises a sample introduction system, a gas sensor array and a mode identification unit, wherein the sensors are key parts of the whole instrument.
The raw data curve detected by the electronic nose represents the change process of the response intensity of each sensor along with time. When volatile gas arrives to measureWhen the electronic nose sensor is in a room, 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 a sample, and is also called as the odor response intensity, and the calculation process is that R is (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.
The hawthorn has the functions of promoting digestion, invigorating stomach, promoting qi circulation and removing blood stasis, and is clinically used for promoting digestion, removing stagnation, promoting blood circulation and removing blood stasis. The fructus crataegi mainly contains flavonoids (vitexin glucoside, vitexin rhamnoside, quercetin, hyperoside, isoquercitrin, etc.), anthocyanins (procyanidin B2, epicatechin, etc.), and organic acids (chlorogenic acid, etc.). Modern pharmacological research shows that the hawthorn has various pharmacological effects of reducing blood pressure, reducing blood fat, improving myocardial ischemia, reducing cholesterol content, reducing platelet aggregation rate, resisting lipid peroxidation, resisting arrhythmia, resisting cancer, inducing diuresis and the like. Common decoction pieces of fructus crataegi include raw fructus crataegi, parched fructus crataegi, charred fructus crataegi, etc. The description about the property items of each decoction piece of hawthorn in the current Chinese pharmacopoeia, national Chinese traditional medicine processing standard, each province and city Chinese traditional medicine decoction piece processing standard, and Chinese traditional medicine processing teaching materials and the like mainly comprises the following steps: the raw fruit has red or deep red peel and dark yellow to light brown pulp; the surface of the fried yellow product is yellowish, and the pulp is yellow brown; the surface of the scorched product is scorched and the interior is yellowish brown; the surface of the charcoal is scorched black and the interior is scorched brown. However, in the actual processing process, the hawthorn flavor changes significantly with the increase of processing temperature and processing time, such as generating a significant scorched aroma. However, the index of the smell is difficult to control in actual evaluation and is not always listed in the standards for detecting the processing degree and quality of the hawthorn, but the addition of the index can ensure that the processing control of the hawthorn decoction pieces and the quality standard of the decoction pieces are more reasonable and stable.
Aiming at the characteristics that the quality control of hawthorn decoction pieces does not have odor index, namely the air and odor are strong in subjectivity and difficult to control, the invention introduces a technology capable of accurately quantifying the odor of hawthorn, namely a method for measuring the odor of each hawthorn 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 hawthorn decoction piece; and the material components causing the smell change of the hawthorn are determined by combining the data of the electronic nose and are used as indexes for evaluating the quality of the decoction pieces. The method is applied to control in the processing process of the hawthorn decoction pieces, ensures the objective, rapid and reliable detection and ensures the stable processing quality of the hawthorn decoction pieces.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a method for controlling the processing production degree and evaluating the quality of hawthorn based on an 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 odor data obtained after the electronic nose detection of 4 different hawthorn 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 hawthorn 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 hawthorn, which comprises the following steps: measuring the smell value of the hawthorn by using an electronic nose, wherein the smell value completely accords with a specified range established by a percentile method, or accords with a 'fire' scientific expression function established by a Bayes discrimination method, or accords with a discrimination model established by discrimination factor analysis to control the degree of the hawthorn processing process and the quality of hawthorn processed 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 crataegi processing process and quality of fructus crataegi processed product.
The method comprises the following steps: measuring odor value of fructus crataegi 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 crataegi processing process and quality of fructus crataegi processed product
The hawthorn odor value is a sensor response value of the electronic nose, and the measurement 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 hawthorn and the quality control of the hawthorn processed 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 10 sensors which all accord with the specified range established by a percentile method or the response values of 10 sensors which accord with the scientific expression function of 'fire' established by a Bayesian discrimination method.
Further, the hawthorn odor value is a sensor response value of the electronic nose, and the measurement 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 hawthorn and the quality control of the hawthorn processed 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 10 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 range of the odor value of the raw hawthorn simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.034-0.237, the odor value of LY2/G is-0.296-0.109, the odor value of LY2/AA is-0.218-0.084, the odor value of LY2/GH is-0.430-0.146, the odor value of LY2/gCTl is-0.364-0.116, the odor value of LY2/gCT is-0.080-0.033, the odor value of T30/1 is 0.479-0.721, the odor value of P10/1 is 0.572-0.762, the odor value of P10/2 is 0.360-0.486, the odor value of P40/1 is 0.446-0.610, the odor value of T70/2 is 0.496-0.750, the odor value of PA/2 is 0.710-0.710, the odor value of P40/1 is 0.75/1, the odor value of P581 is 0.11-0.85, the odor value of TA 2/95-0.95, the odor value of P582 is 0.26-0.11/2, the odor value of TA 2 is 0.95-0.26, the odor value of TA 2 is 0.95-0.2/2;
the odor value range of the fried hawthorn simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.024-0.265, the odor value of LY2/G is-0.309-0.087, the odor value of LY2/AA is-0.228-0.066, the odor value of LY2/GH is-0.454-0.115, the odor value of LY2/gCTl is-0.388-0.090, the odor value of LY2/gCT is-0.085-0.026, the odor value of T30/1 is 0.383-0.741, the odor value of P10/1 is 0.500-0.777, the odor value of P10/2 is 0.328-0.501, the odor value of P40/1 is 0.401-0.626, the odor value of T70/2 is 0.399-0.7, the odor value of PA/2 is 0.439-0.469, the odor value of P40/1 is 0.401-0.626, the odor value of T70/2 is 0.68-0.68, the odor value of PA/2 is 0.68-0.68, the odor value of TA 2 is 0.68-0.68, the odor value of TA 2/1, the odor value of TA 2 is 0.68-0.68, the odor value of the odor value;
the odor value range of the scorched hawthorn simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.031-0.076, the odor value of LY2/G is-0.156-0.100, the odor value of LY2/AA is-0.118-0.075, the odor value of LY2/GH is-0.215-0.129, the odor value of LY2/gCTl is-0.177-0.107, the odor value of LY2/gCT is-0.047-0.027, the odor value of T30/1 is 0.342-0.486, the odor value of P10/1 is 0.478-0.614, the odor value of P10/2 is 0.335-0.412, the odor value of P40/1 is 0.405-0.496, the odor value of T70/2 is 0.378-0.529, the odor value of PA/2 is 0.523-0.544, the odor value of P30/1 is 0.262-0.528, the odor value of P462/1 is 0.850.62/1, the odor value of TA 2 is 0.36-0.349-0.18/1, the odor value of TA 2 is 0.18-0.758, the odor value of TA 2 is 0.18-0.18;
the odor value range of the hawthorn carbon simultaneously meets the following requirements: the odor value of a sensor LY2/LG is 0.017-0.227, the odor value of LY2/G is-0.262-0.084, the odor value of LY2/AA is-0.191-0.064, the odor value of LY2/GH is-0.382-0.110, the odor value of LY2/gCTl is-0.320-0.088, the odor value of LY2/gCT is-0.077-0.023, the odor value of T30/1 is 0.288-0.618, the odor value of P10/1 is 0.431-0.721, the odor value of P10/2 is 0.315-0.479, the odor value of P40/1 is 0.375-0.375, the odor value of T70/2 is 0.321-0.321, the odor value of PA/2 is 0.348-0.632, the odor value of P461/1 is 0.84/1, the odor value of P462 is 0.632-0.68/1, the odor value of P8259-0.95/1, the odor value of P8259/2 is 0.42/1, the odor value of P8295-0.42/2 is 0.68-0.95, the odor value of P8259/2 is 0.95-0.200/1, the odor value of TA 2 is 0.68-0.200/95/2, the odor value of TA 2 is 0.95/95-0.200/95, the odor value of TA-0.200/95/1, the odor value of TA-0.95/95/1, the odor value of TA-0.21/95, the odor value of TA-0.200/95/2, the odor value of TA-0.200/95, the odor value of TA-0.95/1, the odor value of TA-0.200/95, the odor value of TA-0.200, the odor value of TA-0.95/95, the odor value of TA-0.200, the TA-0.200/95, the odor value of TA-0.200/95, and the odor value of TA-0.200/95, the odor value of TA-0.200/95, the odor value of TA/95, the odor value of TA-0.200, the TA of TA-0.200, the TA/95, the TA-0.200/95/1, the TA of TA-0.200/95/.
Further, the number of the sensors is 10, and the judgment is carried out by using a scientific expression function of a specified range or 'duration of fire' 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 range of the odor value of the raw hawthorn simultaneously meets the following requirements: the odor value of a sensor LY2/AA is-0.218 to-0.084, the odor value of LY2/gCTl is-0.364 to-0.116, the odor value of T30/1 is 0.479 to 0.721, the odor value of P10/2 is 0.360 to 0.486, the odor value of PA/2 is 0.514 to 0.710, the odor value of P30/1 is 0.685 to 0.806, the odor value of P40/2 is 0.643 to 0.801, the odor value of P30/2 is 0.721 to 0.881, the odor value of T40/2 is 0.293 to 0.424, and the odor value of T40/1 is 0.206 to 0.315;
the odor value range of the fried hawthorn simultaneously meets the following requirements: the sensor LY2/AA has an odor value of-0.228 to-0.066, LY2/gCTl has an odor value of-0.388 to-0.090, T30/1 has an odor value of 0.383 to 0.741, P10/2 has an odor value of 0.328 to 0.501, PA/2 has an odor value of 0.439 to 0.729, P30/1 has an odor value of 0.642 to 0.836, P40/2 has an odor value of 0.590 to 0.814, P30/2 has an odor value of 0.625 to 0.891, T40/2 has an odor value of 0.252 to 0.439, and T40/1 has an odor value of 0.198 to 0.330;
the odor value range of the scorched hawthorn simultaneously meets the following requirements: the odor value of a sensor LY2/AA is-0.118 to-0.075, the odor value of LY2/gCTl is-0.177 to-0.107, the odor value of T30/1 is 0.342 to 0.486, the odor value of P10/2 is 0.335 to 0.412, the odor value of PA/2 is 0.393 to 0.523, the odor value of P30/1 is 0.528 to 0.651, the odor value of P40/2 is 0.544 to 0.644, the odor value of P30/2 is 0.614 to 0.758, the odor value of T40/2 is 0.262 to 0.339, and the odor value of T40/1 is 0.214 to 0.242;
the odor value range of the hawthorn carbon simultaneously meets the following requirements: the odor value of a sensor LY2/AA is-0.191 to-0.064, the odor value of LY2/gCTl is-0.320 to-0.088, the odor value of T30/1 is 0.288 to 0.618, the odor value of P10/2 is 0.315 to 0.479, the odor value of PA/2 is 0.348 to 0.632, the odor value of P30/1 is 0.493 to 0.746, the odor value of P40/2 is 0.502 to 0.736, the odor value of P30/2 is 0.535 to 0.854, the odor value of T40/2 is 0.231 to 0.410, and the odor value of T40/1 is 0.212 to 0.284.
Or the judgment method II: the scientific expression function of the 'duration' is used for judging:
"duration" function of raw hawthorn:
F1＝-13496.6SR LY2/AA +13260.2SR LY2/gCT1 -8075.3SR T30/1 -6763.7SR P10/2 +2778.4SR PA/2 +4707.0SR P30/1 +35643.5SR P40/2 -8199.7SR P30/2 -17460.4SR T40/2 +11217.0SR T40/1 -6083.7
the "duration" function of parched hawthorn:
F2＝-12565.5SR LY2/AA +12884.6SR LY2/gCT1 -7845.7SR T30/1 -6459.7SR P10/2 +2284.2SR PA/2 +5309.1SR P30/1 +35956.8SR P40/2 -8742.9SR P30/2 -17431.1SR T40/2 +11158.2SR T40/1 -6241.2
burnt hawthorn "duration" function:
F3＝-24392.6SR LY2/AA +17991.9SR LY2/gCT1 -13444.9SR T30/1 -1499.5SR P10/2 +1137.7SR PA/2 +1866.6SR P30/1 +26850.5SR P40/2 -3876.8SR P30/2 -2848.3SR T40/2 +9219.1SR T40/1 -4880.0
the "duration" function of hawthorn-charcoal:
F4＝-22296.2SR LY2/AA +17047.6SR LY2/gCT1 -12995.5SR T30/1 -802.5SR P10/2 +847.5SR PA/2 +2432.9SR P30/1 +27539.0SR P40/2 -4836.8SR P30/2 -3366.6SR T40/2 +8792.8SR T40/1 -4961.1
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. Furthermore, the sensors are 10, and are respectively a sensor LY2/AA, a sensor LY2/gCTl, a sensor T30/1, a sensor P10/2, a sensor PA/2, a sensor P30/1, a sensor P40/2, a sensor P30/2, a sensor T40/2 and a sensor T40/1.
In still another improvement, the response value of the electronic nose sensor is calculated as the maximum response value, and the average value calculated after 2 times of measurement of each sample is used as the odor value of the sample.
The sample is sieved by a third sieve after being crushed, and 0.5g of the sample is weighed and loaded for analysis; the carrier gas type in the carrier gas parameters is synthetic dry air, and the flow rate is 150 mL/min; the hatching time in the headspace hatching parameters is 600s, the hatching temperature is 55 ℃, and the stirring speed is 500 rpm; the headspace injection parameters comprise an injection volume of 1200 mu L, an injection speed of 500 mu L/s, a total volume of 2.5mL of an injection needle and a temperature of 60 ℃ of the injection needle; 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 to control the degree of the fructus crataegi processing process and the quality of fructus crataegi processed product
Analyzing the component content by adopting a gas chromatography-mass spectrometry, and controlling the processing production degree of the hawthorn and the quality of the hawthorn processed product by using the component content to completely accord with a specified range, wherein the specified range takes the relative percentage content of three components, namely ethanethiol, furfural and 5-methylfuran aldehyde as evaluation indexes of the hawthorn processing: taking the relative percentage content of raw hawthorn as 1, calculating the ratio of the relative percentage content of the fried hawthorn, the charred hawthorn and the raw hawthorn, and respectively controlling the relative percentage content of the fried hawthorn, the charred hawthorn and the charred hawthorn to be 0.12-0.22, 0.06-0.08 and 0.02-0.04; calculating the ratio of the relative percentage contents of the fried hawthorn, the charred hawthorn and the raw hawthorn by taking the relative percentage content of the raw hawthorn as 1, wherein the relative percentage contents of the fried hawthorn, the charred hawthorn and the charred hawthorn are respectively controlled to be 31.53-46.22, 40.35-50.00 and 39.25-48.27; the content of 5-methylfuran aldehyde in raw hawthorn is 0, the relative percentage content of the fried hawthorn is 1, the relative percentage content ratio of the charred hawthorn, charred hawthorn and the fried hawthorn is calculated, and the content of 5-methylfuran aldehyde in the charred hawthorn and the charred hawthorn is controlled to be 1.69-2.41 and 2.61-3.75 respectively.
Advantageous effects
The invention discloses a method for monitoring change of smell in a processing process to realize control of the processing production degree and quality evaluation of hawthorn, and aims to measure the smell of a hawthorn sample by using an electronic nose, quantify the smell data of the hawthorn sample, combine a GC-MS method and realize the hawthorn smell represented by digitalization, modeling, functionalization and specific substance limitation through statistical calculation. The method is used in the processing on-line production of the hawthorn, can ensure the stability of the quality of hawthorn 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 hawthorn on the RSD value of the maximum response value of a 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 hawthorn decoction piece sensor array. A: original array, B: optimizing the array; 1: raw hawthorn, 2: stir-frying hawthorn, 3: charred hawthorn, 4: charred haw
Fig. 3 is a DFA discrimination model diagram of hawthorn decoction pieces. SSZ: raw hawthorn CSZ: and (3) frying hawthorn JSZ: charred hawthorn SZT: charred haw
FIG. 4 is a GC-MS diagram of decoction pieces of fructus crataegi. A: GC-MS overlay, B: GC-MS fingerprint spectrum common mode ( marked peaks 2, 22, 27, 31, 33, 34, 38, 54, 55, 59, 68, 90, 91, 92, 97, 99, 101, 104, 111, 112, 117, 119, 132, 140, 143, 150, 219, 257 and 376 in the figure are 29 common chromatographic peaks of hawthorn fruit.)
FIG. 5 is a graph showing the trend of the relative percentage change of various compounds during the parching process of fructus crataegi.
Detailed Description
The invention will be further described with reference to the following specific embodiments:
example 1 determination of the odor detection method for decoction pieces of hawthorn
In the experimental process, a batch of hawthorn decoction pieces are selected as experimental objects to carry out single-factor investigation. FOX-4000 electronic nose with Alpha Soft 11.0 version software (French Alpha MOS company) as the 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 hawthorn raw decoction pieces, sieving the five samples by a first sieve, a second sieve, a third sieve and a fourth sieve, respectively weighing 0.5g 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 1200 mu L, controlling the incubation temperature to be 40 ℃, the incubation time to be 150s and the like, wherein 6 parts are parallel to each other. The result shows that the maximum response value of each sensor gradually increases along with the increase of the crushed granularity of the decoction pieces, when the decoction piece powder passes through the third sieve, the odor response value reaches the maximum, and the response value of the sample crushed through the fourth sieve is close to the odor response value. The RSD of the response value of each sensor is combined with the change of the RSD of the maximum response value of each sensor, and the RSD of the response value of each sensor is the lowest and is more stable when the decoction pieces are crushed and pass through a third sieve. The granularity of the decoction pieces is determined by comprehensively considering, namely the decoction pieces are crushed and screened by 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 0.1g, 0.2g, 0.3g, 0.4g and 0.5g of hawthorn sample powder (sieved by a No. three sieve), respectively, filling into a headspace sample injection bottle, controlling the sample injection amount to be 500 mu L, and detecting under the conditions of 40 ℃ incubation temperature, 150s incubation time and the like, wherein each level is 3 parts in parallel. The results showed that the response value of each sensor increased with the increase in the sample amount, and the response value reached the maximum when the sample amount reached 0.5 g. At the same time, the RSD of the response value was found to be the lowest and more stable when each sensor weighed 0.5 g. The sample weight was determined to be 0.5g in combination. The RSD value results for the maximum response value are shown in fig. 1 (B).
1.1.3 optimization of sample size
Weighing 0.5g of hawthorn sample powder (screened by a third sieve), filling into a headspace sample feeding bottle, and setting the sample feeding amount to be 500 mu L, 800 mu L, L000 mu L, 1200 mu L and 1500 mu L respectively under the condition of controlling the incubation temperature to be 40 ℃ and the incubation time to be 150s, and the like, wherein each level is 3 parts in parallel. The result shows that the maximum response value of the sensor increases along with the increase of the sample amount, when the sample amount is 1200 mu L, the RSD of the maximum response value of most sensors is the lowest and is stable, and the sample amount is determined to be 1200 mu L by comprehensive consideration. The RSD value results for the maximum response value are shown in fig. 1 (C).
1.1.4 optimization of incubation temperature
Weighing 0.5g of hawthorn sample powder (sieved by a No. three sieve), filling into a headspace sample injection bottle, controlling the sample injection amount to be 1200 mu L, setting the incubation temperatures to be 40 ℃, 45 ℃, 50 ℃ and 55 ℃ respectively under the condition that the incubation time is 150s and the like are consistent, and horizontally paralleling 6 parts each. The results show that the maximum response value of each sensor gradually increases with the increase of the temperature, the data are close when the hatching temperature is 50 ℃ and 55 ℃, and the RSD value of the maximum response value gradually decreases to be most stable when the hatching temperature is 55 ℃. The incubation temperature was determined to be 55 ℃ in combination. The RSD value results for the maximum response value are shown in fig. 1 (D).
1.1.5 optimization of incubation time
Weighing 0.5g of hawthorn sample powder (sieved by a No. three sieve), filling into a headspace sample feeding bottle, controlling the sample feeding amount to be 1200 mu L, and setting the incubation time to be 100s, 150s, 200s, 250s, 300s and 600s under the condition of consistent incubation temperature of 55 ℃, wherein each level is parallel to 6 parts. The results show that the maximum response value of each sensor gradually rises and the RSD of the maximum value of the response value gradually decreases along with the extension of the hatching time, and when the hatching time is 600s, the response value of each sensor is maximum and the RSD of the maximum value is minimum and is most stable. The incubation time was determined to be 600s by considering the responses together. 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 hawthorn electronic nose test are shown in table 1.
TABLE 1 Hawthorn electronic nose test parameters
Figure GDA0003633042360000081
Figure GDA0003633042360000091
1.2 methodological examination
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 respectively put into headspace sampling bottles, the optimum detection conditions are adopted for continuous measurement, the result shows that the maximum RSD value of 18 sensors is 2.556%, the repeatability is good, and the specific result is shown in Table 2.
TABLE 2 repeatability test results of hawthorn electronic nose detection method
Figure GDA0003633042360000092
1.2.2 stability Studies
The same batch of sample powder is taken and filled 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, 10 hours, 14 hours and 16 hours, 3 parts of the sample powder are parallelly measured at each time point, the result shows that the maximum RSD value of the maximum response value of 18 sensors is 3.124 percent, the sample is stable within 16 hours, and the specific result is shown in Table 3.
TABLE 3 stability investigation result of hawthorn electronic nose detection method
Figure GDA0003633042360000101
1.3 sample odor detection
88 batches of samples (37 batches of raw hawthorns, 28 batches of fried hawthorns, 13 batches of scorched hawthorns and 10 batches of charred hawthorns) of hawthorn decoction pieces are detected under the optimal detection condition of an electronic nose, and each batch of samples are repeated for 2 times to obtain 176 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 hawthorn sample are subjected to stepwise discriminant analysis, and specific results are shown in table 4.
TABLE 4 progressive discriminant analysis results of maximum response values of 18 sensors of hawthorn samples
Figure GDA0003633042360000111
As shown in table 3, the statistics (F) for all 10 variables were 3.84 or more, and the P values for all the variables were less than 0.01. After 10 steps, the external and internal variables of the model are not input and not output, and the independent variable selection of gradual discriminant analysis is finished. Thus, the composition of the final optimized array of hawthorn sensors was: LY2/AA, LY2/gCTl, T30/1, P10/2, PA/2, P30/1, P40/2, P30/2, T40/2 and T40/1.
2.2 sensor optimization verification
In order to compare the classification effect of different processed products of hawthorn before and after the optimization of the sensor array, a Linear Discriminant Analysis (LDA) is adopted to output a visual classification result. The original sensor arrays of all the hawthorn samples and the optimized maximum response values of the sensor arrays are subjected to linear discriminant analysis, and LDA analysis before and after the optimization of the sensors is compared 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 hawthorn optimization is more than 99%, 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 hawthorn decoction pieces.
Example 3 establishment of Hawthorn odor standards
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 hawthorn 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 odor data of each electronic nose of the decoction pieces is established based on the original and optimized maximum response values of the sensor array. The acquired odor data of different hawthorn 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 of the hawthorn decoction pieces in different decoction pieces do not accord with normal distribution, so that the odor numerical range of 90% intervals on both sides of each sensor of the hawthorn different decoction pieces is established by P5 and P95 percentile indexes of percentile methods (Percentiles), and the odor numerical standard results of different hawthorn decoction pieces based on the original and optimized sensor arrays are respectively shown in tables 5 and 6.
TABLE 5 odor numerical standards of different decoction pieces of fructus crataegi based on raw sensor array
Figure GDA0003633042360000121
TABLE 6 odor numerical standard of different decoction pieces of fructus crataegi based on optimized sensor array
Figure GDA0003633042360000122
Figure GDA0003633042360000131
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 decoction pieces of the hawthorn are statistically analyzed by adopting independent sample test under a non-parameter test item, and the specific order and test results are shown in a table 7.
TABLE 7 nonparametric test results for Hawthorn
Figure GDA0003633042360000132
As shown in table 6, the P values in the test results of the response values of the sensors of the electronic noses of 4 groups of raw hawthorn, roasted hawthorn and charcoal decoction pieces are less than 0.01, which indicates that the differences among the numerical ranges of the odors of the 4 groups of different hawthorn decoction pieces are statistically significant, and the established numerical range of the odors can better distinguish the hawthorn decoction pieces with different processing specifications.
3.2 establishment of odor model Standard of decoction pieces
Based on the optimized array, a Discriminant Factor Analysis (DFA) is adopted to establish a Discriminant model for the electronic nose odor data of the hawthorn decoction pieces, and the model is evaluated through a cross-Validation score (Validation score). The DFA discrimination models and the verification scores of the different hawthorn processed products 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, the DFA can distinguish different processed product samples of the hawthorn, raw hawthorn products and fried hawthorn products are distributed in the positive direction of DF1, and coke products and charcoal products are distributed in the negative direction of DF 1; meanwhile, the interior of the raw product and the stir-fried product and the interior of the burnt product and the charcoal product are very clearly distributed on two sides of DF 2. The model cross-validation score was 93, indicating that the established discrimination model for hawthorn decoction pieces was better.
The DFA discrimination model established above can visually discriminate unknown samples in a visual mode. When an unknown sample enters a library established by the DFA discrimination model, the sample is projected to a corresponding region, and attribution can be confirmed, otherwise, the unknown sample is judged to be in a 'unrecognizable' state. The established discrimination library can be expanded along with the entry of the standard decoction pieces, and has adjustability.
Example 4 establishment of Hawthorn processing "duration" mathematical discriminant formula
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 (IBM company, USA) software is adopted, Bayesian discriminant analysis is carried out according to the optimized sensor array maximum response value of the hawthorn decoction pieces, a mathematical discriminant function formula of the hawthorn decoction piece processing fire degree represented by sensor data is established, and the variable coefficients of the functions are shown in a table 8.
TABLE 8 Hawthorn mathematical discriminant function results
Figure GDA0003633042360000141
According to the coefficient of the fire discrimination function of the haw decoction pieces in table 8, the bayesian discrimination functions of different degrees of processing (fire) of the hawthorns are established as follows:
"duration" function of raw hawthorn:
F1＝-13496.6SR LY2/AA +13260.2SR LY2/gCT -8075.3SR T30/1 -6763.7SR P10/2 +2778.4SR PA/2 +4707.0SR P30/1 +35643.5SR P40/2 -8199.7SR P30/2 -17460.4SR T40/2 +11217.0SR T40/1 -6083.7
the "duration" function of parched hawthorn:
F2＝-12565.5SR LY2/AA +12884.6SR LY2/gCT -7845.7SR T30/1 -6459.7SR P10/2 +2284.2SR PA/2 +5309.1SR P30/1 +35956.8SR P40/2 -8742.9SR P30/2 -17431.1SR T40/2 +11158.2SR T40/1 -6241.2
burnt hawthorn "duration" function:
F3＝-24392.6SR LY2/AA +17991.9SR LY2/gCT -13444.9SR T30/1 -1499.5SR P10/2 +1137.7SR PA/2 +1866.6SR P30/1 +26850.5SR P40/2 -3876.8SR P30/2 -2848.3SR T40/2 +9219.1SR T40/1 -4880.0
the "duration" function of hawthorn charcoal:
F4＝-22296.2SR LY2/AA +17047.6SR LY2/gCT -12995.5SR T30/1 -802.5SR P10/2 +847.5SR PA/2 +2432.9SR P30/1 +27539.0SR P40/2 -4836.8SR P30/2 -3366.6SR T40/2 +8792.8SR T40/1 -4961.1
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 hawthorn decoction pieces: and substituting the odor values of the decoction pieces of unknown attribution 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 raw hawthorn, the value F2 with the highest value is judged as fried hawthorn, the value F3 with the highest value is judged as scorched hawthorn, and the value F4 with the highest value is judged as charred hawthorn.
4.2 Interactive verification results
In order to judge the rationality of the discrimination function, the specific odor data of each sample of raw hawthorn, fried hawthorn, burnt hawthorn and charcoal 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 (origin) and a Cross-validated method (Cross-validated), and the result is shown in table 9.
TABLE 9 Hawthorn discriminant analysis and verification results
Figure GDA0003633042360000151
Note: 1, 2, 3 and 4 in the column in the table represent the original attribution of the hawthorn sample, namely, raw product, fried product, burnt product and charcoal product; the 1, 2, 3 and 4 in the horizontal row respectively represent the attribution of the odor data of the hawthorn 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 is carried out on each record in 4 groups of hawthorn samples (raw products, fried products, scorched products and charcoal products), and the discrimination accuracy rates of the common verification method are respectively 100.0%, 100.0%, 92.3% and 100.0%; the interactive verification method respectively judges the correctness rates of 97.3%, 100%, 88.5% and 95%. The misjudgment of the sample mainly occurs between the hawthorn burnt product and the charcoal product, and because the boundary of the hawthorn burnt product and the charcoal product on the odor is not very clear, certain difficulty is brought to distinguishing. The result shows that the established discrimination function for predicting the processing degree of the hawthorn has very few misjudgments on the sample, so the established discrimination function for the processing duration of the hawthorn decoction pieces is stable and reasonable.
Example 5 determination of odor characteristics of decoction pieces of Hawthorn fruit
Processing raw hawthorn decoction pieces according to processing standard operation procedures, respectively taking samples at different processing time points for odor detection, selecting 13 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 Hawthorn decoction pieces
5.1.1 instruments
Agilent 7890B-7000C GC-MS combined instrument, and Agilent 7697A headspace automatic sample injector.
5.1.2 methods
Preparing a test sample: crushing the haw decoction pieces in the processing process, sieving the crushed haw decoction pieces by a third sieve, precisely weighing 1.5g of each decoction piece powder respectively, and placing the powder into a 10mL headspace sample loading bottle.
GC-MS conditions of hawthorn:
and (3) headspace conditions: the equilibrium temperature is 80 ℃, the quantitative ring is 95 ℃, the transmission line is 110 ℃, the equilibrium time is 30min, 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 40 ℃, the temperature rises to 65 ℃ at 5 ℃/min, the temperature rises to 90 ℃ at 10 ℃/min, the temperature rises to 110 ℃ at 2 ℃/min, the temperature is kept for 10min, the temperature rises to 165 ℃ at 5 ℃/min, the temperature rises to 260 ℃ at 10 ℃/min, the temperature is kept for 5min, and the total analysis time is 53 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: 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 interface temperature is 270 ℃; the scanning range is 45-500 amu; the electron multiplier voltage was 1557.3V.
5.1.3 results
GC-MS detection is carried out on the hawthorn 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 led into 'traditional Chinese medicine chromatography fingerprint similarity evaluation software' (2012 edition of the national pharmacopoeia committee), and a common fingerprint pattern of the hawthorn decoction pieces is shown in figure 4 (B). Through comparison, 540 chromatographic peaks are detected in the hawthorn group, wherein 29 chromatographic peaks are detected in the hawthorn group.
5.2 correlation analysis and composition confirmation of GC-MS and electronic nose data
Establishing a data matrix of hawthorn matching chromatographic peaks and the number of processed samples and a data matrix of sensor response values and the number of processed samples, carrying out grey correlation degree analysis on the data matrix and the data matrix, and determining that 33 gas chromatographic peaks have stronger correlation degrees with hawthorn decoction piece electronic nose data by taking the grey correlation degree r larger than 0.9 as an inclusion standard.
By checking the standard spectra in the database NIST 14.0 of the on-line search workstation and by reference, 21 compounds were identified in the 33 peaks with higher correlation, the specific results are shown in Table 10, including 4 alcohols (ethanol, ethanethiol, 2-methyl-1-butanol); ketones 3 (acetone, 5-methyl-2 (3H) -furanone, dihydro-3-methylene-5-methyl-2-furanone); aldehydes 5 kinds (isovaleraldehyde, hexanal, nonanal, furfural, 5-methylfuran aldehyde); esters 1 (vinyl acetate); 5 alkanes (1-hexadecane, 2-carene, limonene, terpinolene); one of the aromatic hydrocarbons (o-isopropylbenzene); acids 1 (acetic acid); furans 1 species (2- (methoxymethyl) -furan).
TABLE 10 Compounds related to electronic nose response changes during Hawthorn parching
Figure GDA0003633042360000171
Figure GDA0003633042360000181
"-" indicates no detection.
The peak area changes of the compounds related to the electronic nose response changes during the stir-frying process of hawthorn are shown in table 11.
TABLE 11 Compounds associated with electronic nasal response changes during Hawthorn stir-frying
Figure GDA0003633042360000191
Figure GDA0003633042360000201
The relative percentage change of various compounds during the parching process of fructus crataegi is shown in FIG. 5. As shown in the figure, odor changes in the processing process of the hawthorn are comprehensive effects of multiple components, and alcohol, aldehyde, ketone, acid and alkane substances have higher relative percentage contents in the whole frying process of the hawthorn, and the average percentage contents are that the aldehyde (50.2%) > alcohol (15.85%) > ketone (3.83%) > acid (3.50%) alkane (1.42%) in a descending order, and the following change trends are presented: the relative content of alcohol substances gradually decreases along with the increase of the stir-frying time; the relative contents of aldehydes and alkanes increase first and then decrease; the relative content of the ketone substances gradually rises; the relative content of the acid substances is firstly reduced and then increased. It is presumed that these 5 kinds of substances are likely to be the main component groups causing the change of the hawthorn odor during the processing.
Further analyzing the compounds, the results show that in the frying process, the relative percentage content exceeds 1 percent, and the peak area changes to show a rising trend with ethanol and acetone; acetic acid, furfural and 5-methylfuran aldehyde which show a rising-first-then-falling trend; ethanethiol is presented with a downward trend, and the 6 compounds are main characteristic components in the hawthorn stir-frying process. Wherein the relative percentage of 3 components of ethanethiol, furfural and 5-methylfuran aldehyde in the sample is higher. Researches show that the Maillard reaction is one of mechanisms of smell change in the hawthorn processing process, furfural, 5-methylfurfural and other substances can be generated in the Maillard reaction process, and researches report that the two compounds have activity of invigorating spleen.
Detecting odor data of 13 samples in the stir-frying process by using an electronic nose, and determining that the sample S1 is a raw product, the samples S4 and S5 are stir-fried products, the samples S7 and S8 are scorched products, the samples S9 and S10 are charcoal products, and the rest are processed 'too-old or not-too-old' according to established odor numerical standards and DFA mode standards. The results for the relative percentages of ethanethiol, furfural, and 5-methylfurfural for each sample are shown in Table 11. Taking the relative percentage content of raw hawthorn S1 as 1, and calculating the relative percentage content ratio of corresponding substances of the rest samples; calculating the ratio of the relative percentage of corresponding substances of the rest samples by taking the average relative percentage of the fried hawthorn fruits S4 and S5 as 1; specific results are shown in Table 11. According to the change ratios of the substances shown in the table, the relative percentage change ratio limit of the 3 characteristic compounds is established according to the upper limit floating by 10 percent and the lower limit floating by 10 percent. The ethanethiol is calculated by taking the relative percentage content of raw hawthorn as 1, and calculating the relative percentage content ratio of the fried hawthorn, the scorched hawthorn, the charred hawthorn and the raw hawthorn, wherein the relative percentage content of the fried hawthorn, the scorched hawthorn and the charred hawthorn are respectively controlled to be 0.12-0.22, 0.06-0.08 and 0.02-0.04; calculating the ratio of the relative percentage of the fried hawthorn, the charred hawthorn and the raw hawthorn by taking the relative percentage of the raw hawthorn as 1, wherein the relative percentage of the fried hawthorn, the charred hawthorn and the charred hawthorn is respectively controlled to be 31.53-46.22, 40.35-50.00 and 39.25-48.27; the content of 5-methylfuran aldehyde in raw hawthorn is 0, the relative percentage content of the roasted hawthorn is 1, the relative percentage content of the roasted hawthorn, charred hawthorn and the roasted hawthorn is calculated, and the relative percentage content of the roasted hawthorn, charred hawthorn and charred hawthorn is controlled to be 1.69-2.41 and 2.61-3.75 respectively. The specific results are shown in Table 12.
TABLE 12 Change in characteristic Compounds during Stir-frying
Figure GDA0003633042360000221

Claims (1)

1. A method for controlling the processing production degree and evaluating the quality of hawthorn comprises the following steps: analyzing the content of volatile components by gas chromatography-mass spectrometry, wherein the content of the components completely accords with the specified range established by a content limitation method, and the degree of the hawthorn processing process and the quality of the hawthorn processed product are controlled as follows: the specified range takes the relative percentage contents of three components of ethanethiol, furfural and 5-methylfurfural as evaluation indexes of hawthorn processing: taking the relative percentage content of raw hawthorn as 1, calculating the ratio of the relative percentage content of the fried hawthorn, the charred hawthorn and the raw hawthorn, and respectively controlling the relative percentage content of the fried hawthorn, the charred hawthorn and the charred hawthorn to be 0.12-0.22, 0.06-0.08 and 0.02-0.04; calculating the ratio of the relative percentage contents of the fried hawthorn, the charred hawthorn and the raw hawthorn by taking the relative percentage content of the raw hawthorn as 1, wherein the relative percentage contents of the fried hawthorn, the charred hawthorn and the charred hawthorn are respectively controlled to be 31.53-46.22, 40.35-50.00 and 39.25-48.27; the content of 5-methylfuran aldehyde in raw hawthorn is 0, the relative percentage content of the fried hawthorn is 1, the relative percentage content ratio of the charred hawthorn, charred hawthorn and the fried hawthorn is calculated, and the content of 5-methylfuran aldehyde in the charred hawthorn and the charred hawthorn is controlled to be 1.69-2.41 and 2.61-3.75 respectively.
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