A method and system for early warning of mold before dry food storage state

Abstract

This application provides a method and system for early warning of mold growth in the storage of dry food. The method includes the following steps: S1, measuring the changes in relative humidity, temperature, and volatile organic gases over a long period of time within the storage space of the dry food; S2, estimating risk indicators related to water activity based on the measured relative humidity, temperature, and the cumulative effect over time, wherein the risk indicators related to water activity are estimated based on the changing trends of relative humidity and time, and reflect the degree of risk of increased water activity inside the dry food; S3, analyzing the changing trends of the volatile organic gases, wherein the changing trends include the rising slope of gas concentration or the cumulative change; and S4, when the risk indicators related to water activity and the changing trends of the volatile organic gases simultaneously meet preset conditions, outputting early warning information on the storage risk of the dry food before mold growth.

Description

Technical Field

[0001] This application relates to the field of food preservation technology, and in particular to a method and system for early warning of mold growth in the storage of dry food. Background Technology

[0002] Dry food products (including pet food, nuts, grains, etc.) typically have a long shelf life due to their low moisture content. However, once opened, dry food is susceptible to moisture absorption from the environment, leading to an increase in its internal water activity (aw). When the water activity exceeds a certain critical value (usually aw > 0.60), mold can grow, ultimately causing the food to spoil. Therefore, real-time monitoring of the storage status of dry food and predicting the risk of mold growth is crucial for ensuring food safety.

[0003] Existing technologies mostly use single humidity or temperature sensors to monitor the food storage environment. For example, a humidity sensor monitors the relative humidity inside the packaging in real time and issues an alarm when the humidity exceeds a fixed threshold. However, relying solely on a single humidity or VOC parameter and lacking a multi-dimensional joint judgment mechanism, their accuracy is relatively low.

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for early warning of mold growth in dry food storage. By monitoring the relative humidity, temperature and volatile organic gases in the dry food storage space for a long time, the method estimates risk indicators related to water activity and analyzes the VOC change trend, and provides a storage risk warning before the dry food shows visible mold growth. This solves the technical problems of delayed warning, high false alarm rate, simplified model and infeasibility of deployment in the existing technology. Summary of the Invention

[0005] The purpose of this invention is to solve the above-mentioned problems by providing a method and system for early warning of mold growth in the storage state of dry food.

[0006] The technical solution of this application is implemented as follows: This invention provides a method for early warning of mold growth in dried food storage conditions, comprising the following steps: S1, within the dry food storage space, measures changes in relative humidity, temperature, and volatile organic gases over a long period of time; S2. Based on the measured relative humidity, temperature and time cumulative effect, estimate the risk indicators related to water activity, wherein the risk indicators related to water activity are estimated based on the changing trends of relative humidity and time, and reflect the degree of risk of increased water activity inside the dry food. S3, analyze the changing trend of the volatile organic gases, wherein the changing trend includes the rising slope or cumulative change in gas concentration; and S4: When the risk indicator related to water activity and the variation trend of the volatile organic gas simultaneously meet preset conditions, output storage risk early warning information for dry food before it goes moldy.

[0007] As a further improvement, the risk indicator related to water activity is estimated according to the following formula: , where g( ) is a weighting function reflecting the moisture absorption trend, RH_i is the relative humidity in the i-th time interval, T_i is the temperature in the i-th time interval, and Δt_i is the corresponding time interval.

[0008] As a further improvement, the weighting function g(RH_i, T_i) adopts a piecewise function form, specifically as follows: When RH_i ≤ RH_threshold, it satisfies: ; When RH_i>RH_threshold, it satisfies: ; Wherein, RH_threshold is the critical relative humidity value at which the internal water activity of the dry food inhibits mold growth; T_ref is the reference temperature; α is the moisture absorption coefficient in the low humidity zone; β is the temperature correction coefficient; γ is the accelerated moisture absorption coefficient in the high humidity zone, and γ>α.

[0009] As a further improvement, the risk indicator aw_proxy related to water activity is compared with at least three risk thresholds to determine the storage risk level of the dry food: When aw_proxy<aw_safe, it is determined as a safety level; When aw_safe ≤ aw_proxy<aw_warn, it is determined as an early warning level; When aw_warn ≤ aw_proxy<aw_danger, it is determined as a high risk level; When aw_proxy ≥ aw_danger, it is determined as a severe risk level; Wherein, aw_safe, aw_warn, and aw_danger respectively correspond to risk accumulation equivalent values when the internal water activity of the dry food is about 0.55, 0.62, and 0.70.

[0010] As a further improvement, the volatile organic gas includes at least one gas generated by lipid oxidation or microbial metabolism, and the gas is selected from at least one of aldehydes, ketones, short-chain fatty acids, ammonia, amines, and sulfur-containing compounds.

[0011] As a further improvement, the preset conditions in step S4 include: the water activity-related risk indicators entering a preset risk range, and the slope of the increase in the concentration of the volatile organic gases exceeding a preset threshold.

[0012] As a further improvement, the method for early warning of mold growth in the storage condition of dry food also includes: The trend of volatile organic gases is determined by combining data from at least two different sources: The first volatile organic gas is produced by the oxidation of fats and oils, and is selected from at least one of aldehydes and ketones. Its concentration rise slope is denoted as Slope_1. The second volatile organic gas is produced in the early stage of microbial metabolism and is selected from at least one of ammonia and amines. Its concentration rise slope is denoted as Slope_2. The preset conditions further include: when Slope_1 exceeds a first preset threshold, and Slope_2 exceeds a second preset threshold, and the timing of the changes in Slope_1 and Slope_2 satisfies a preset timing relationship that lipid oxidation precedes microbial metabolism, the storage risk warning information is output. This invention provides an early warning system for the pre-molding state of dry food storage, comprising: The sensing subsystem includes a temperature and humidity sensing unit and a gas sensing unit, which are used to measure the relative humidity, temperature and volatile organic gases in the dry food storage space, respectively. The main control and computing subsystem includes a microprocessor controller and memory. The microprocessor controller receives measurement data from the sensing subsystem and performs estimation calculations of water-related risk indicators and trend analysis of volatile organic compounds. The memory stores historical trend data. The communication and human-machine interface subsystem is used to output and store risk warning information; The main control computing subsystem is configured to output a risk warning information on the storage of dry food before it becomes moldy when the risk indicators related to water activity and the changing trends of volatile organic gases simultaneously meet preset conditions.

[0013] The advantages or beneficial effects of the above technical solutions include at least the following: (i) The warning time was significantly advanced. By combining aw_proxy and VOC trends for joint determination, this invention can provide early warning information 3-5 days before dry food becomes moldy. Compared to existing "determination after mold has appeared" technology, the warning time is at least 72 hours earlier, providing consumers with ample time to take action (such as transferring to a sealed container, adding a desiccant, or prioritizing consumption).

[0014] (ii) The false alarm rate has been significantly reduced. The coupling logic of "simultaneous satisfaction" of the two dimensions realizes the cross-validation of "hygroscopic conditions + putrefaction biochemical reaction". The false alarm rate of the two-dimensional joint judgment is reduced to less than 1 / 5 of the original level compared with the false alarm rate of single-dimensional judgment (15%-30%).

[0015] (III) Improved accuracy of risk models The piecewise weighted function (γ>α) accurately characterizes the nonlinear feature of "slow accumulation in low humidity areas - accelerated accumulation in high humidity areas". The piecewise function calculation of aw_proxy (357) is in high agreement with the actual observation of mold growth on day 7, while the calculation result of the linear function (238) significantly underestimates the risk. The early warning accuracy under high humidity conditions increased from 62% to 91%, an improvement of 46.8%. Attached Figure Description

[0016] The accompanying drawings illustrate exemplary embodiments of the present application and, together with the description thereof, serve to explain the principles of the present application. These drawings are included to provide a further understanding of the present application and are incorporated in and constitute a part of this specification.

[0017] Figure 1 A flowchart of a method for early warning of mold growth in dry food storage provided by an embodiment of the present invention is shown. Detailed Implementation

[0018] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0019] It should be noted that, where there is no conflict, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0021] Reference Figure 1 This invention provides a method for early warning of mold growth in dried food storage conditions, comprising the following steps: S1, within the dry food storage space, measures changes in relative humidity, temperature, and volatile organic gases over a long period of time; S2. Based on the measured relative humidity, temperature and time cumulative effect, estimate the risk indicators related to water activity, wherein the risk indicators related to water activity are estimated based on the changing trends of relative humidity and time, and reflect the degree of risk of increased water activity inside the dry food. S3, analyze the changing trend of the volatile organic gases, wherein the changing trend includes the rising slope or cumulative change in gas concentration; and S4. When the risk indicators related to water activity and the trend of volatile organic gas change simultaneously meet the preset conditions, output the risk warning information of the dry food before it becomes moldy.

[0022] As a further improvement, the water-activity-related risk indicator is estimated based on the following formula: , where g() is a weighted function reflecting the moisture absorption trend, RH_i is the relative humidity of the i-th time interval, T_i is the temperature of the i-th time interval, and Δt_i is the corresponding time interval.

[0023] Example 2: Take 500g of commercially available dry pet food (moisture content approximately 8%) (three groups: A, B, and C) and place them in a standard dry pet food bag (approximately 2L in volume). Place an integrated sensing module (30mm × 20mm × 5mm, including an SHT30 temperature and humidity sensor and an SGP30 VOC sensor) inside the bag and connect it to a mobile app via Bluetooth Low Energy. Set the sampling period to once every 60 minutes and continue monitoring until mold appears.

[0024] During the monitoring period, the conditions are shown in Table 1. The system records RH, T and total VOC (TVOC) data.

[0025] Table 1 shows the test conditions for different groups in Example 1.

[0026] The sampling period Δt_i = 1 hour, and g(RH_i, T_i) adopts the basic form: g=RH_i / 100 · (1 +0.05·(T_i-25)).

[0027] Calculation results: Group A: aw_proxy = Σ(0.65 × 1.0) × 336hr = 218.4; Group B: aw_proxy = Σ(0.65 × 1.25) × 240hr = 195.0 (but the temperature acceleration effect makes the actual equivalent risk higher); Group C: aw_proxy = Σ(0.75 × 1.0) × 168hr = 126.0.

[0028] Actual observation: Mold spots appeared in group C on day 7, in group B on day 10, and in group A on day 14. The aw_proxy value was negatively correlated with the time to mold growth, verifying the formula's effectiveness to some extent.

[0029] As a further improvement, the weighting function g(RH_i, T_i) is in piecewise form, specifically as follows: When RH_i ≤ RH_threshold, the following condition is met: ; When RH_i > RH_threshold, the following condition is met: ; Where RH_threshold is the critical relative humidity value at which the water activity inside the corresponding dry food inhibits mold growth; T_ref is the reference temperature; α is the moisture absorption coefficient in the low humidity zone; β is the temperature correction coefficient; γ is the accelerated moisture absorption coefficient in the high humidity zone, and γ>α.

[0030] The moisture absorption coefficient α in the low humidity region, the temperature correction coefficient β, and the accelerated moisture absorption coefficient γ in the high humidity region are determined through the following calibration procedure: (a) Take a standard dry food sample and place it in a controlled temperature and humidity environment. Under low humidity conditions of RH = RH_threshold - 10%, record the rate of change of mass Δm / Δt per unit time and calculate α = (Δm / Δt) / (m0· RH_threshold), where m0 is the initial dry basis mass of the sample; (b) Under the condition of fixed RH = RH_threshold, repeat step (a) at three temperatures: T_ref, T_ref + 5℃, and T_ref + 10℃, and record the mass change rate at different temperatures. Fit the β value using the least squares method to minimize the mean square error between g(RH_threshold,T)=α·RH_threshold ·(1+β·(T-T_ref)) and the actual observed value. (c) Under high humidity conditions of RH = RH_threshold + 15%, record the rate of mass change per unit time and calculate γ = [(Δm / Δt)_high / (m0·(RH_threshold+ 15%)) -α·RH_threshold] / (RH_threshold +15%-RH_threshold), ensuring that γ>α.

[0031] Example 2: Set RH_threshold = 60% (corresponding to the critical value of aw ≈ 0.6), T_ref=25°C, α=0.008, β=0.03, γ=0.025 (γ / α≈ 3.1).

[0032] During the monitoring period, the conditions are shown in Table 2, and the system records RH, T and total VOC (TVOC) data.

[0033] Table 2 shows the test conditions of different groups in Example 2

[0034] Actual mold growth observation: Group D had no mold growth (no mold growth within 20 days), Group E had mold growth on the 15th day, Group F had mold growth on the 10th day, and Group G had mold growth on the 7th day. The moisture absorption process of dry grain is not linear. When RH is below the critical value, the moisture absorption rate is slow and contributes limitedly to the mold growth risk; when RH exceeds the critical value, the moisture absorption rate accelerates significantly, and the mold growth probability rises exponentially. If a single linear weighting function is adopted, the risk accumulation under high humidity conditions will be underestimated, leading to delayed early warning. The piecewise function accurately characterizes the non-linear feature of "slow accumulation in low humidity zone - accelerated accumulation in high humidity zone". In the experiment of Group G, the aw_proxy (357) calculated by the piecewise function is significantly higher than that of the linear function (238), which is highly consistent with the actual observation of mold growth on the 7th day. Compared with the linear function, the piecewise function improves the early warning accuracy under high humidity conditions to a certain extent.

[0035] As a further improvement, the water activity-related risk indicator aw_proxy is compared with at least three risk thresholds to determine the storage risk level of dry grain: When aw_proxy < aw_safe, it is determined as the safety level; When aw_safe ≤ aw_proxy < aw_warn, it is determined as the early warning level; When aw_warn ≤ aw_proxy < aw_danger, it is determined as the high risk level; When aw_proxy ≥ aw_danger, it is determined as the severe risk level; Wherein, aw_safe, aw_warn, and aw_danger respectively correspond to the risk accumulation equivalent values when the internal water activity of dry grain is about 0.55, 0.62, and 0.70.

[0036] The four-level threshold translates the abstract aw_proxy value into operational instructions that consumers can understand. When consumers receive a yellow warning, they can choose to transfer the dry food to a sealed container or add a desiccant, effectively slowing down the mold growth process. Compared to the control group without warnings, the dry food in the warning group has a longer safe consumption period.

[0037] As a further improvement, the volatile organic compounds include at least one gas produced by the oxidation of oils or microbial metabolism, wherein the gas is selected from at least one of aldehydes, ketones, short-chain fatty acids, ammonia, amines, and sulfur-containing compounds. The system's VOC sensor has the highest sensitivity to hexanal and ammonia, and these can be used as the primary gases for detection.

[0038] As a further improvement, the preset conditions in step S4 include: the water activity-related risk indicators entering a preset risk range, and the slope of the increase in the concentration of the volatile organic gases exceeding a preset threshold.

[0039] As a further improvement, the method for early warning of mold growth in the storage condition of dry food also includes: The trend of volatile organic gases is determined by combining data from at least two different sources: The first volatile organic gas is produced by the oxidation of fats and oils, and is selected from at least one of aldehydes and ketones. Its concentration rise slope is denoted as Slope_1. The second volatile organic gas is produced in the early stage of microbial metabolism and is selected from at least one of ammonia and amines. Its concentration rise slope is denoted as Slope_2. The preset conditions further include: when Slope_1 exceeds a first preset threshold, and Slope_2 exceeds a second preset threshold, and the change sequence of Slope_1 and Slope_2 satisfies the preset time relationship that lipid oxidation precedes microbial metabolism, the storage risk warning information is output.

[0040] Single-dimensional judgments are susceptible to environmental interference, leading to false alarms. For example, aw_proxy may falsely increase due to temporary humidity fluctuations, and VOC may falsely increase due to external odor sources. The key technology of this invention is to eliminate such interference and improve the specificity of the warning. In this invention, the dual-dimensional "simultaneous satisfaction" logic achieves cross-validation of "hygroscopic conditions + putrefaction biochemical reactions" to further improve accuracy.

[0041] This invention provides an early warning system for the pre-molding state of dry food storage, comprising: The sensing subsystem includes a temperature and humidity sensing unit and a gas sensing unit, which are used to measure the relative humidity, temperature and volatile organic gases in the dry food storage space, respectively. The main control and computing subsystem includes a microprocessor controller and memory. The microprocessor controller receives measurement data from the sensing subsystem and performs estimation calculations of water-related risk indicators and trend analysis of volatile organic compounds. The memory stores historical trend data. The communication and human-machine interface subsystem is used to output and store risk warning information; The main control computing subsystem is configured to output a risk warning information on the storage of dry food before it becomes moldy when the risk indicators related to water activity and the changing trends of volatile organic gases simultaneously meet preset conditions.

[0042] Those skilled in the art should understand that the above embodiments are merely for illustrative purposes and are not intended to limit the scope of this application. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of this application.

Claims

1. A method for early warning of mold growth in dried food storage conditions, characterized in that: comprising the following steps: S1, in a dry food storage space, measuring the changes of relative humidity, temperature and volatile organic gases for a long time; S2, estimating a water activity-related risk indicator according to the measured relative humidity, temperature and time cumulative effect, wherein the water activity-related risk indicator is estimated based on the variation trend of relative humidity and time, and reflects the risk degree of water activity rise inside the dry food; S3, analyzing the variation trend of the volatile organic gases, wherein the variation trend includes the rising slope or cumulative variation of gas concentration; and S4, when the water activity-related risk indicator and the variation trend of the volatile organic gases simultaneously meet preset conditions, outputting storage risk warning information for the dry food before mildew growth.

2. The method for early warning of mold growth in dry food storage conditions according to claim 1, characterized in that: wherein the water activity-related risk indicator is estimated according to the following formula: , where g() is a weighted function reflecting the moisture absorption trend, RH_i is the relative humidity of the i-th time interval, T_i is the temperature of the i-th time interval, and Δt_i is the corresponding time interval.

3. The method for early warning of mold growth in dry food storage conditions according to claim 2, characterized in that: the weighting function g(RH_i, T_i) is in the form of a piecewise function, specifically: when RH_i ≤ RH_threshold, it satisfies: ； when RH_i>RH_threshold, it satisfies: ； wherein, RH_threshold is the critical relative humidity value corresponding to the water activity inside the dry food that inhibits mold growth; T_ref is the reference temperature; α is the moisture absorption coefficient in the low humidity region; β is the temperature correction coefficient; γ is the accelerated moisture absorption coefficient in the high humidity region, and γ>α.

4. The method for early warning of mold growth in dry food storage conditions according to claim 2 or 3, characterized in that: The water activity-related risk indicator aw_proxy is compared with at least three risk thresholds to determine the storage risk level of the dry food: When aw_proxy<aw_safe, it is determined as a safety level; When aw_safe ≤ aw_proxy<aw_warn, it is determined as a warning level; When aw_warn ≤ aw_proxy<aw_danger, it is determined as a high risk level; When aw_proxy ≥ aw_danger, it is determined as a severe risk level; Wherein, aw_safe, aw_warn, and aw_danger respectively correspond to risk cumulative equivalent values where the water activity inside the dry food is about 0.55, 0.62, and 0.

70.

5. The method for early warning of mold growth in dry food storage conditions according to claim 1, characterized in that: The volatile organic gases include at least one gas generated by lipid oxidation or microbial metabolism, and the gas is selected from at least one of aldehydes, ketones, short-chain fatty acids, ammonia, amines and sulfur-containing compounds.

6. The method for early warning of mold growth in dry food storage conditions according to claim 5, characterized in that: Wherein the preset conditions in step S4 include: the water activity-related risk indicator enters a preset risk interval, and the rising slope of the concentration of the volatile organic gases exceeds a preset threshold.

7. The pre-mildew warning method for the storage state of dry food according to claim 6, characterized in that: in, The variation trend of the volatile organic gases is determined by integrating at least two volatile organic gases from different sources: The first volatile organic gas is generated by lipid oxidation, and is selected from at least one of aldehydes and ketones, and its concentration rising slope is denoted as Slope_1; The second volatile organic gas is generated in the initial stage of microbial metabolism, and is selected from at least one of ammonia and amines, and its concentration rising slope is denoted as Slope_2; The preset conditions further include: when Slope_1 exceeds a first preset threshold, and Slope_2 exceeds a second preset threshold, and the change sequence of Slope_1 and Slope_2 satisfies the preset time relationship that lipid oxidation precedes microbial metabolism, the storage risk warning information is output.

8. A pre-moldy warning system for the storage status of dry food, characterized in that, include: The sensing subsystem includes a temperature and humidity sensing unit and a gas sensing unit, which are used to measure the relative humidity, temperature and volatile organic gases in the dry food storage space, respectively. The main control and computing subsystem includes a microprocessor controller and memory. The microprocessor controller receives measurement data from the sensing subsystem and performs estimation calculations of water-related risk indicators and trend analysis of volatile organic compounds. The memory stores historical trend data. The communication and human-machine interface subsystem is used to output and store risk warning information; The main control computing subsystem is configured to output a risk warning information on the storage of dry food before it becomes moldy when the risk indicators related to water activity and the changing trends of volatile organic gases simultaneously meet preset conditions.

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