Cherry freshness detection device based on volatile substance concentration proportion time sequence model

Abstract

The application discloses a cherry freshness detection device based on volatile substance concentration proportion time sequence model, which comprises a handheld detection instrument main body and a sampling bottle. The detection instrument main body comprises a micro air pump, a micro air chamber and a micro processing unit. The micro processing unit is integrated with a data acquisition module, an algorithm module and a calculation detection module. The algorithm module is integrated with concentration proportion time sequence models of various volatile substances. The calculation detection module compares the concentration data of various volatile substances and the concentration proportion time sequence models to determine the picking time and freshness of cherries. Compared with the traditional method, the application has the advantages of rapid determination, and the single test time is 4 min. Compared with artificial and single alcohol detection instrument, the application uses multiple characteristic volatile substance contents as test objects, and has higher precision and repeatability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of fresh degree detection of agricultural products, and particularly relates to a cherry freshness detection device based on a volatile substance concentration proportion time sequence model. BACKGROUND

[0002] Cherry has bright color and unique taste, and is rich in carbohydrates, proteins, vitamins, calcium, phosphorus, iron and other trace elements. In addition, cherry has the medical and health care effects of clearing heat and relieving summer-heat and regulating qi and promoting blood circulation, and is favored by consumers and has high commercial value.

[0003] From the global cherry yield, the global cherry yield shows an overall upward trend, and the growth trend is good.

[0004] However, cherry is a fresh product, has the characteristics of rapid post-harvest ripening, aging, corruption and quality deterioration, and has a considerable post-harvest loss. The loss caused by rotting is more than one billion yuan per year, which seriously restricts the circulation of cherry. Under room temperature conditions, the respiration intensity of cherry can reach 40-90 mgCO2kg -1 h -1 , and it is easy to appear phenomena such as wilting, softening of fruit flesh, loss of juice, deterioration and corruption, which seriously affect the nutritional quality and commodity value of cherry. Temperature is the main factor affecting the shelf life of cherry, so the whole cold chain is needed from the field to the table after cherry picking to ensure its quality, so there are many transportation environments for cherry, and it is easy to appear the situation of chain loss, which seriously affects the quality of cherry.

[0005] The fruit freshness detection is usually completed by manual work, and the detection personnel have high labor intensity. In addition, such subjective evaluation is affected by personal vision, color identification ability, experience, emotion, fatigue degree and the like, and has poor accuracy, poor consistency and low efficiency. In addition, the traditional physical and chemical detection procedure is complicated, time-consuming and destructive to samples.

[0006] The existing detection device can realize high sensitivity and selectivity of alcohol detection, but the cost of the device is high, and the real-time responsiveness is poor, and the precision of the detection result is not high for a single feature; these devices usually need to be prepared under high temperature, high pressure and other conditions, and the output signal cannot be directly observed by the naked eye, and needs to be converted by a calculator or other sensors, which increases the response time and cost. Although the artificial sensory evaluation method can distinguish the subtle changes of cherries during storage, the results of this method are poor in repeatability and reference due to individual differences, health status and other factors of the evaluators. Although the physicochemical test method can reflect the freshness of the cherry sample, the experimental operation is complicated, the time required is long, and it is difficult to meet the needs of rapid detection. Although the instrument analysis method such as gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) can accurately analyze the volatile substance composition information of cherry samples at different storage periods, the detection method is expensive and has a long detection period, and the obtained odor components are the products after the sample is separated; in addition, instrument analysis usually has a great dependence on the operation proficiency of the operator, so there is an urgent need for a convenient and fast detection technology for cherry freshness. SUMMARY

[0007] In view of the above, the present application provides a cherry freshness detection device based on a volatile substance concentration ratio time sequence model, which has the characteristics of convenient operation, rapid response and accurate quantification.

[0008] A cherry freshness detection device based on a volatile substance concentration ratio time sequence model, comprising a handheld detection instrument main body and a sampling bottle, the sampling bottle is used to store the cherry sample to be detected, and is connected with the detection instrument main body through a conduit; the detection instrument main body contains a micro air pump, a micro air chamber and a micro processing unit, the micro air pump is used to draw the volatile substances in the sampling bottle into the micro air chamber for detection, the micro air chamber integrates concentration detection sensors of various volatile substances, and the micro processing unit integrates a data acquisition module, an algorithm module and a calculation detection module, wherein the data acquisition module is used to acquire concentration data generated by the sensor in the micro air chamber, the algorithm module integrates concentration ratio time sequence models of various volatile substances, and the calculation detection module determines the picking time and freshness of the cherry by comparison according to the concentration data of various volatile substances acquired and the concentration ratio time sequence model.

[0009] Further, the volatile substances include alcohol, ethylene, SO2 and organic vapor.

[0010] Further, the micro air chamber further integrates a temperature sensor and a humidity sensor to calibrate the concentration data output by the concentration detection sensor by using temperature and humidity.

[0011] Further, the concentration ratio time sequence model is constructed as follows:

[0012] (1) In different temperature and humidity environments, the concentration of various volatile substances generated after the cherry sample is picked is monitored for a long time;

[0013] (2) For any type of volatile substance, an S-shaped relationship curve between its concentration and the time after picking is established;

[0014] (3) According to the concentration data obtained by monitoring, the least square method and the minimum error square sum fitting are used to determine the coefficients in the S-shaped relationship curve;

[0015] (4) According to the fitted S-shaped relationship curve, the concentration ratio time sequence model of the volatile substance is calculated and determined.

[0016] Further, the expression of the S-shaped relationship curve is as follows:

[0017]

[0018] Wherein: ppm(t) represents the concentration of volatile substance at t hours after picking in the S-shaped relationship curve, t is a natural number, a, b, c are coefficients to be fitted.

[0019] Further, the expression of the minimum error square sum is as follows:

[0020]

[0021] Wherein: ppm(t) represents the concentration of volatile substance at t hours after picking in the S-shaped relationship curve, t is a natural number, S(t) is the actual concentration of volatile substance monitored at t hours after picking, and T is the monitoring time.

[0022] Further, the expression of the concentration ratio time sequence model is as follows:

[0023]

[0024] Wherein: P i (t) represents the concentration ratio of the i-th type of volatile substance at t hours after picking, ppm i (t) is the concentration of the i-th type of volatile substance at t hours after picking, t is a natural number, and n is the number of types of volatile substances.

[0025] Further, the calculation and detection module calculates the concentration ratio of each type of volatile substance at the current time according to the concentration data of each type of volatile substance collected, and compares it with the concentration ratio time sequence model of each type of volatile substance, so as to determine the picking time and freshness of the cherry.

[0026] Compared with the prior art, the present application has the following beneficial technical effects:

[0027] 1. The test procedure of the present application has the advantage of rapid determination compared with traditional physicochemical test methods and gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS) and other methods, and the single test time is 4 min.

[0028] 2. Compared with manual and single alcohol detection instruments, the present application uses the content of multiple characteristic volatile substances as the test object, and has higher precision and repeatability.

[0029] 3. In conventional volatile substance determination, even if the same batch is under the same temperature and humidity, the accurate quantitative analysis cannot be achieved due to the influence of operators and sample processing procedures; the time sequence proportion model of characteristic volatile substances proposed in the present application determines the freshness by the proportion change of the content of characteristic volatile substances in the process of cherry picking to spoilage, thereby avoiding the problem that accurate quantitative test cannot be performed in the conventional method. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 It is a structural schematic diagram of the cherry freshness detection device of the present application.

[0031] Figure 2 It is a process schematic diagram of the standard modeling and detection method of the present application.

[0032] Figure 3 It is a response schematic diagram of the concentration detection data of various volatile substances under normal temperature and humidity.

[0033] Figure 4 It is a time sequence change schematic diagram of the proportion of the concentration of various volatile substances under normal temperature and humidity. DETAILED DESCRIPTION

[0034] In order to more specifically describe the present application, the technical solutions of the present application are described in detail below in combination with the drawings and specific embodiments.

[0035] The cherry freshness detection device based on the volatile substance concentration proportion time sequence model of the present application includes a handheld detection instrument main body and a sampling bottle, and the overall structure of the device is shown in Figure 1 , wherein 101 is a standard glass sampling bottle for storing the sample to be tested, the sampling bottle is connected to the detection instrument by a rubber conduit, and the sampling bottle is additionally provided with a water vapor and solid-liquid filtering device; a miniature air pump 102 in the instrument is used to draw the volatile gas in the sampling bottle into a gas chamber for detection, and the flow rate of the air pump is constant at 400 ml / min; a miniature gas chamber 103 integrated in the instrument has temperature, humidity, alcohol concentration, ethylene concentration, SO2 concentration and organic vapor concentration detection functions, wherein the temperature and humidity are mainly used for sensor data calibration; the hardware circuit part 104 mainly includes a data acquisition analog circuit for sensor raw data acquisition, an algorithm model CPU circuit, a power supply circuit and other modules.

[0036] The cherry freshness detection device has the characteristics of convenient operation and rapid response (4 minutes for detection results), and the standard modeling and specific detection process of the device are as shown in Figure 2 The instrument is turned on to enter the self-checking and initialization stage 203, during which the instrument automatically runs each module, and in addition, tests whether the sampling bottle and the gas chamber are contaminated, and the instrument automatically performs the exhaust process; the cherries are placed in the sampling bottle to start the single freshness detection process 204; after sampling, the system automatically returns the freshness rating and picking time estimation result 205 (storage temperature 25℃, humidity 60%); steps 206-211 are the model establishment process of the time sequence algorithm of various characteristic volatile substances.

[0037] The standard algorithm model data collection process is as follows:

[0038] 1) Test the concentration response data of various characteristic volatile substances at-10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 degrees Celsius, respectively;

[0039] 2) Under the above temperature conditions, test the concentration response data of various characteristic volatile substances under the control of humidity of 30%, 60%, and 85%;

[0040] 3) Under all the above conditions, simultaneously perform 3 parallel tests, respectively select 100g of fresh picked mature cherries, perform 300 hours of continuous testing (test every 1 hour), and record the concentration response data of various characteristic volatile substances.

[0041] According to the previous experimental test, the characteristic volatile substances of cherries will slowly increase in volatility speed after picking, and the volatility speed will significantly increase after a certain time, and finally when the cherries completely rot, the growth speed of the volatility speed will slow down, so the model of each characteristic volatile substance and the picking time is an S-shaped curve as follows:

[0042]

[0043] According to the above collected large amount of concentration response data, the best function matching of the data is found by the method of minimizing the sum of squares of errors, and the standard of the fitting curve is to always minimize the error, and the formula of minimizing the sum of squares of errors is as follows:

[0044]

[0045] Where: t is different time in hours, ppm(t) is the concentration function expression to be fitted, S(t) represents the concentration of characteristic volatile gas of cherries picked at t hours, and T is the total monitoring time (300 hours).

[0046] Furthermore, the concentration-harvesting time functional relationships of alcohol, ethylene, SO2, and organic vapors were fitted using the least squares method, as shown in the following formulas. The single-sample concentration detection data responses of each characteristic volatile substance are as follows: Figure 3 As shown.

[0047]

[0048] Finally, the proportions of single-detection concentration data for alcohol, ethylene, SO2, and organic vapors are calculated using the following formula to obtain the time-series model of the concentration proportion of each volatile substance. The changes in the concentration proportion of each characteristic volatile substance over time are shown in the figure below. Figure 4 As shown.

[0049]

[0050] After modeling is completed, the operator uses the detection device of this invention to detect the concentration of each characteristic volatile substance, and then estimates the similarity between the concentration ratio of each volatile substance at the current moment and the above concentration ratio time series model to calculate the estimated harvesting time and freshness.

[0051] Different temperatures, humidity levels, and storage environments play a decisive role in the rate of fruit spoilage. In this embodiment, freshness is defined by anchoring the cherry picking time under normal temperature and humidity conditions, as shown in Table 1:

[0052] Table 1

[0053]

[0054]

[0055] The above description of the embodiments is provided to enable those skilled in the art to understand and apply the present invention. Those skilled in the art can readily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made to the present invention by those skilled in the art based on the disclosure thereof should be within the scope of protection of the present invention.

Claims

1. A cherry freshness detection device based on a time-series model of volatile substance concentration ratio, comprising a handheld detection instrument body and a sampling bottle, characterized in that: The sampling bottle is used to store the cherry sample to be tested, and it is connected to the main body of the testing instrument through a conduit; The main body of the detection instrument includes a micro air pump, a micro air chamber and a microprocessor unit. The micro air pump is used to draw volatile substances in the sampling bottle into the micro air chamber for detection. The miniature chamber integrates a concentration sensor for detecting various volatile substances that are characteristic of cherry spoilage, including alcohol, ethylene, SO2, and organic vapors. The microprocessor unit integrates a data acquisition module, an algorithm module, and a calculation and detection module. The data acquisition module is used to acquire concentration data generated by the sensor in the miniature gas chamber. The algorithm module integrates time-series models of the concentration ratio of various volatile substances. The construction method of the concentration ratio time-series model is as follows: (1) Long-term concentration monitoring of various volatile substances generated after cherry samples are picked under different temperature and humidity conditions; (2) For any type of volatile substance, establish an S-shaped curve showing the relationship between its concentration and time after harvesting; (3) Based on the concentration data obtained from monitoring, the coefficients in the S-shaped relationship curve are determined by using the least squares method and minimizing the sum of squared errors; (4) Calculate and determine the time series model of the concentration ratio of volatile substances based on the fitted S-shaped relationship curve; After detecting the concentration of various volatile substances, the calculation and detection module estimates the similarity between the current concentration ratio of each volatile substance and the above-mentioned concentration ratio time series model, and calculates the estimated harvest time and freshness.

2. The cherry freshness detection device according to claim 1, characterized in that: The miniature chamber also integrates a temperature sensor and a humidity sensor to calibrate the concentration data output by the concentration detection sensor using temperature and humidity.

3. The cherry freshness detection device according to claim 1, characterized in that: The expression for the S-shaped relationship curve is as follows: ， Where: ppm(t) represents the concentration of volatile substances at hour t after harvesting in the S-shaped curve, t is a natural number, and a, b, and c are the coefficients to be fitted.

4. The cherry freshness detection device according to claim 1, characterized in that: The expression for minimizing the sum of squared errors is as follows: ， Where: ppm(t) represents the concentration of volatile substances at hour t after harvesting in the S-shaped curve, t is a natural number, S(t) is the actual concentration of volatile substances detected at hour t after harvesting, and T is the monitoring time.

5. The cherry freshness detection device according to claim 1, characterized in that: The expression for the concentration ratio time series model is as follows: ， Where Pi(t) represents the concentration percentage of the i-th type of volatile substance at hour t after harvesting, ppmi(t) is the concentration of the i-th type of volatile substance at hour t after harvesting, t is a natural number, and n is the number of volatile substance categories.

6. The cherry freshness detection device according to claim 1, characterized in that: The calculation and detection module calculates the concentration ratio of various volatile substances at the current moment based on the collected concentration data of various volatile substances, and compares it with the time series model of the concentration ratio of various volatile substances to determine the cherry picking time and freshness.

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