Intelligent spectral analysis equipment for nondestructive rapid detection of aroma components of peach fruit

Abstract

The application discloses an intelligent spectral analysis equipment and method for lossless and rapid detection of peach fruit aroma components, and the equipment comprises seven units of aroma enrichment construction, multi-mode coupled spectral collection, signal modulation, environmental factor sensing, aroma decoupling calculation, fingerprint reconstruction processing and quality evaluation output. A micro air film layer is formed by constructing a ring-shaped micro air flow cavity to enrich the aroma, solid-gas two-phase coupled spectral signals are collected, environmental parameters are synchronously collected, and an algorithm is used for decoupling, a deep neural network is used to generate an aroma fingerprint spectrum, a mapping relationship between the aroma fingerprint spectrum and a quality grade is established, and an evaluation result is output. The method comprises steps of aroma enrichment, spectral collection, signal modulation and the like. The application realizes lossless and rapid detection of peach fruit aroma components, can accurately guide postharvest grading and picking time determination, and improves detection efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of non-destructive testing technology for fruits, specifically to intelligent spectral analysis equipment for the non-destructive and rapid detection of aroma components in peaches. Background Technology

[0002] Aroma components of peaches are one of the core indicators for evaluating their quality, directly affecting their commercial value and market competitiveness. Traditional methods for detecting peach aroma components mostly involve gas chromatography-mass spectrometry (GC-MS), which requires destructive sampling of the fruit and is cumbersome and time-consuming, failing to meet the needs of rapid on-site testing in orchards and large-scale grading and sorting after harvest.

[0003] Meanwhile, some existing non-destructive testing technologies only target the appearance of the fruit or a single physicochemical indicator, making it difficult to accurately capture the complex characteristics of aroma components; moreover, the testing process is easily affected by environmental factors such as temperature and humidity, leading to insufficient accuracy of the test results. Therefore, there is an urgent need to develop equipment that can achieve non-destructive, rapid, and accurate detection of aroma components in peaches. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent spectroscopic analysis equipment for the non-destructive and rapid detection of aroma components in peaches, so as to solve the problems of existing detection methods being highly destructive, inefficient, and having weak anti-interference capabilities.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: An intelligent spectral analysis equipment for non-destructive and rapid detection of aroma components in peach fruit includes a fruit surface aroma enrichment construction unit, a multi-mode coupled spectral acquisition unit, an interface coupled signal modulation unit, an environmental factor synchronous sensing unit, an aroma dynamic decoupling calculation unit, an aroma fingerprint reconstruction intelligent processing unit, and a quality evaluation output unit. The fruit surface aroma enrichment construction unit forms a closed or semi-closed annular micro-airflow cavity structure around the periphery of the peach fruit skin to be tested. A micro-air film layer for enriching volatile aroma molecules is constructed between the fruit surface and the detection cavity through controllable micro-circulation airflow. The multi-mode coupled spectral acquisition unit is correspondingly set to the micro-air film layer, performing synchronous or temporal composite spectral irradiation and signal acquisition on the fruit surface-gas phase micro-air film interface region to obtain the interface coupled composite spectral signal. The interface coupled signal modulation unit performs temporal modulation, phase matching, and signal synchronization on different modal spectral signals, unifying the sampling window of the multi-spectral channels. The environmental factor synchronous sensing unit acquires detection data during... The system calculates environmental and fruit state parameters and synchronizes these parameters with the spectral signal in time. The aroma dynamic decoupling calculation unit performs physical mechanism decoupling of the interface-coupled spectral signal based on the aroma volatility dynamic model to construct an effective aroma concentration characterization quantity. The aroma fingerprint reconstruction intelligent processing unit performs deep fusion processing on the decoupled aroma effective feature quantity and multi-mode spectral features, constructs an aroma component feature embedding space through a neural network, generates the corresponding volatile aroma fingerprint spectrum of peach fruit, and establishes a mapping relationship between aroma fingerprint and quality grade. The quality evaluation output unit outputs the aroma-related evaluation results of peach fruit based on the aroma fingerprint spectrum.

[0006] In a further embodiment, the aroma enrichment construction unit on the fruit surface includes a micro airflow guiding cavity annularly surrounding the periphery of the peach fruit. A micro-flow circulation driving device is installed inside the cavity. The gas flow velocity inside the micro airflow guiding cavity is maintained within the range of 0.01-0.2 m / s, forming an aroma molecule enrichment micro air film layer with a thickness of 0.5-5 mm on the fruit surface.

[0007] In a further embodiment, the multimode coupled spectral acquisition unit includes a near-infrared spectral emission / reception module, a terahertz band emission / reception module, and a Raman spectral excitation / acquisition module. The detection focus of the multimode coupled spectral acquisition unit is set at the interface region 0-3 mm from the fruit surface. The acquired interface coupled composite spectral signal simultaneously includes the spectral response of the fruit peel tissue, the absorption response of volatile gas phase molecules, and the Raman fingerprint response of molecular vibration, constituting a solid-gas two-phase coupled spectral response.

[0008] In a further embodiment, the interface coupling signal modulation unit maintains the same temporal modulation period for different modal spectral signals as the dynamic release period of aroma molecules. By using a unified sampling window for the multispectral channel, the acquired signals correspond to the interface response under the same aroma dynamic release state.

[0009] In a further embodiment, the environmental and fruit state parameters collected by the environmental factor synchronous sensing unit include environmental temperature, environmental humidity, fruit surface temperature, and fruit maturity characterization parameters.

[0010] In a further embodiment, the environmental factor synchronous sensing unit synchronizes the collected environmental and fruit state parameters with the interface coupled composite spectral signal acquired by the multimode coupled spectral acquisition unit using a timestamp marking method.

[0011] In a further embodiment, the formula for calculating the effective aroma concentration characterization quantity constructed by the aroma dynamic decoupling calculation unit is as follows: In the formula, for Time-equivalent effective aroma concentration parameter; The characteristic quantity of the interface coupling spectral response is obtained by weighted summation of the spectral response intensity of the pericarp tissue, the absorption response intensity of volatile gas phase molecules, and the Raman fingerprint response intensity of molecular vibration; T is the ambient temperature parameter. This refers to the ambient humidity parameter. Parameters for characterizing fruit maturity; These are the weighting coefficients for the spectral response characteristic quantities. γ is the environmental temperature weighting coefficient, and γ is the environmental humidity weighting coefficient. Let be the weighting coefficient for fruit maturity, and satisfy . + + + =1; The model achieves the separation of environmental disturbance factors from the actual aroma spectral response.

[0012] In a further embodiment, the aroma fingerprint reconstruction intelligent processing unit adopts a deep neural network structure, which includes a multi-modal feature fusion layer, an aroma feature embedding layer, and a fingerprint spectrum generation layer. The aroma feature embedding layer constructs a low-dimensional aroma component representation space to achieve the separable expression of aroma features among different peach fruits.

[0013] In a further embodiment, the feature fusion algorithm formula for the multi-modal feature fusion layer is as follows: In the formula, The fused feature vector; The near-infrared spectral feature vector, This represents the spectral eigenvectors in the terahertz band. These are the eigenvectors of the Raman spectrum; Let be the effective aroma concentration parameter at time t; , , , where are the weight coefficients of each feature vector, and satisfy . + + =1; The low-dimensional representation algorithm formula for the aroma feature embedding layer is as follows: In the formula, W is the low-dimensional embedding feature vector; W is the feature transformation weight matrix; b is the bias vector; σ is the nonlinear activation function. The fingerprint generation layer is based on Generate volatile aroma fingerprint spectrum and use formula Establish a mapping relationship between fingerprint patterns and quality grades, where... The quality grade of peach aroma. For the preset quality level category, the probability values ​​of different quality levels corresponding to the low-dimensional embedded feature vectors are:

[0014] In a further embodiment, the output content of the quality evaluation output unit is at least one of aroma quality grade, aroma intensity index, and aroma component relative proportion distribution map. The equipment is applied to post-harvest grading and sorting of peach fruits or field harvesting timing determination.

[0015] A non-destructive and rapid method for detecting aroma components in peach fruit includes the following steps: Step 1, aroma enrichment: A ring-shaped micro-airflow cavity structure is formed on the periphery of the peach fruit skin by constructing aroma enrichment building units on the fruit surface. The gas flow rate in the cavity is controlled to construct a micro-air film layer for enriching volatile aroma molecules on the fruit surface. Step 2, Multimode Spectral Acquisition: The multimode coupled spectral acquisition unit focuses on the fruit surface-gas phase micro-film interface region, and performs synchronous or time-series composite spectral irradiation and signal acquisition to obtain the interface coupled composite spectral signal, which includes the spectral response of fruit peel tissue, the absorption response of volatile gas phase molecules, and the Raman fingerprint response of molecular vibration. Step 3, Signal Modulation: The interface-coupled signal modulation unit performs time-series modulation, phase matching, and signal synchronization on different modal spectral signals to unify the sampling window of the multispectral channels; Step 4, Environmental parameter synchronization: The environmental temperature, environmental humidity, fruit surface temperature, and fruit maturity characterization parameters are collected through the environmental factor synchronization sensing unit, and the parameters are synchronized with the interface coupled composite spectral signal in time. Step 5, Aroma Concentration Decoupling: Based on the formula, the aroma dynamic decoupling calculation unit is used. Physical decoupling of the interface-coupled spectral signals was performed to obtain the equivalent effective aroma concentration parameter at time t; Step 6, fingerprint spectrum reconstruction: The aroma fingerprint reconstruction intelligent processing unit uses a deep neural network to fuse, embed in low dimension and generate fingerprints of the decoupled aroma effective features and multi-mode spectral features, and establishes a mapping relationship between aroma fingerprint spectrum and quality grade. Step 7, Quality Evaluation Output: The quality evaluation output unit outputs at least one of the following based on the aroma fingerprint spectrum: peach fruit aroma quality grade, aroma intensity index, and aroma component relative proportion distribution spectrum.

[0016] In a further embodiment, the gas flow rate inside the cavity in step 1 is controlled within the range of 0.01-0.2 m / s, and the thickness of the constructed micro-gas film layer is 0.5-5 mm; the detection focus of the multimode coupled spectral acquisition unit in step 2 is set at the interface region 0-3 mm away from the fruit surface.

[0017] In a further embodiment, the multi-modal feature fusion layer of the deep neural network in step 6 adopts the formula... Feature fusion is performed, and the aroma feature embedding layer uses a formula. Low-dimensional characterization is performed, and the fingerprint generation layer uses a formula. Establish a quality grade mapping relationship.

[0018] The present invention has the following beneficial effects: This invention enriches fruit aroma by constructing a micro-air film layer and acquires solid-gas two-phase coupled response signals using multi-mode coupled spectral acquisition technology, effectively solving the problem that traditional non-destructive testing techniques are unable to accurately capture aroma component characteristics. By synchronously sensing environmental factors and dynamically decoupling aroma calculation, the interference of environmental and fruit state parameters on the detection results is eliminated, improving the accuracy and stability of the detection results. By constructing an aroma fingerprint spectrum through a deep neural network and establishing a mapping relationship with quality grades, intelligent detection and evaluation of peach fruit aroma components are realized.

[0019] The entire testing process requires no destructive treatment of the fruit, is fast, and can meet the needs of on-site testing in orchards and large-scale grading and sorting after harvest. It provides reliable technical support for the quality control of peaches and helps to improve the commercial value and market competitiveness of peaches.

[0020] In this invention, the "micro-air film layer" refers to the enriched gaseous boundary layer formed by controllable micro-circulation airflow near the fruit surface within the annular micro-airflow cavity structure. Its thickness can be determined by the cavity geometry parameters, flow rate settings, and calibration relationships. The "solid-gas two-phase coupled spectral response" refers to the composite signal that simultaneously contains the reflection / absorption response of the pericarp tissue and the absorption / scattering (and its Raman fingerprint) response of volatile molecules in the gas phase within the interface region. The "effective aroma concentration characterization quantity Ce(t)" is an equivalent quantitative index used to characterize the differences in aroma intensity and composition at the detection time. The "aroma fingerprint data / map" is structured data that can distinguish the differences in different aroma compositions. Its form includes, but is not limited to, peak position-peak intensity vectors, unified wavenumber axis spectral feature vectors, or d-dimensional embedding vectors output by the network. Attached Figure Description

[0021] Figure 1 This is a structural block diagram of the present invention.

[0022] Figure 2 The diagram illustrates the specific steps of the working principle of this invention. Detailed Implementation

[0023] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0024] An intelligent spectral analysis equipment for non-destructive and rapid detection of aroma components in peach fruit includes a fruit surface aroma enrichment construction unit, a multi-mode coupled spectral acquisition unit, an interface coupled signal modulation unit, an environmental factor synchronous sensing unit, an aroma dynamic decoupling calculation unit, an aroma fingerprint reconstruction intelligent processing unit, and a quality evaluation output unit. Fruit surface aroma enrichment building blocks The unit's micro airflow guiding cavity is made of food-grade PP material. The cavity has a ring-shaped, split structure, with an elastic silicone gasket inside. This allows for adaptive adjustment within a 50-150mm range based on the diameter of the peach being tested, ensuring the cavity adheres to the fruit surface to form a closed or semi-closed space. An embedded micro DC fan serves as a micro-flow circulation drive device. The fan's speed is adjusted using PWM pulse width modulation technology. To ensure the repeatability of the enrichment process, the stability of the micro-air film layer can be determined using preset steady-state criteria. For example, a stable enrichment state is considered to have been entered when the relative fluctuation coefficient of the comprehensive response S(t) within the continuous sampling window does not exceed a preset threshold (e.g., 5%–15%). The thickness of the micro-air film layer can be estimated using the cavity's inner diameter, the cross-section of the guiding channel, the set flow rate, and the calibration curve, or through closed-loop correction using differential pressure / flow sensor feedback. This precisely controls the gas flow rate inside the cavity to remain stable within the set range.

[0025] During operation, the fan drives the gas inside the cavity to circulate at a low speed along the annular guide groove. This causes the volatile aroma molecules naturally released by the peach fruit to accumulate in the gap between the fruit surface and the cavity. After 3-5 minutes of enrichment, a stable micro-air film layer of a predetermined thickness is finally formed on the fruit surface. This micro-air film layer can increase the concentration of aroma molecules on the fruit surface to 5-10 times that under natural conditions, providing sufficient and stable detection targets for subsequent spectral analysis.

[0026] Multimode Coupled Spectral Acquisition Unit This unit integrates a near-infrared spectroscopy emission / reception module, a terahertz band emission / reception module, and a Raman spectroscopy excitation / acquisition module. The detection ends of the three modules are arranged in a triangle, all facing the micro-air film layer region on the fruit surface. The unit has a built-in high-precision displacement adjustment platform, which drives the spectral acquisition probe to move in a direction perpendicular to the fruit surface via a stepper motor, precisely positioning the detection focus at the interface region within a set distance from the fruit surface.

[0027] During operation, the near-infrared spectroscopy module emits near-infrared light with a wavelength range of 900-2500 nm to collect the reflection response signal of the fruit peel tissue to near-infrared light; the terahertz band module emits terahertz waves with a frequency of 0.1-3 THz to collect the characteristic absorption response signal of aroma molecules in the micro-air film layer to terahertz waves; and the Raman spectroscopy module emits a semiconductor laser with a wavelength of 785 nm to excite aroma molecules to produce Raman scattering effect, collecting the Raman fingerprint response signal of molecular vibration. The three modules are controlled by a timing trigger to achieve synchronous operation, ultimately acquiring an interface-coupled composite spectral signal containing the three types of response signals, and transmitting the signal to the subsequent processing unit.

[0028] Interface coupling signal modulation unit The unit incorporates a programmable logic controller (PLC) as a timing control module, and is also equipped with a high-precision phase matching circuit. The timing control module pre-programs the dynamic release cycle data of peach fruit aroma molecules, modulating the acquisition timing of different modal spectral signals to a frequency that is completely consistent with the aroma release cycle; the phase matching circuit compensates for the phase delay of different modal spectral signals during transmission, keeping the phases of the three types of spectral signals synchronized.

[0029] Meanwhile, the unit uniformly calibrates the sampling windows of the three spectral channels: near-infrared, terahertz, and Raman, controlling the sampling start time error of each channel within ±0.1ms and setting the sampling duration to 100ms / time. This ensures that the signals collected by each channel correspond to the interface response under the same aroma dynamic release state, effectively avoiding signal distortion caused by asynchronous sampling.

[0030] Environmental Factor Synchronous Sensing Unit This unit integrates a DS18B20 digital temperature sensor, an SHT30 temperature and humidity sensor, a contact thermocouple temperature sensor, and a fruit firmness tester. The temperature and humidity sensors are deployed on the outside of the annular flow guide cavity to collect real-time temperature and humidity parameters of the detection environment; the contact thermocouple temperature sensor is in close contact with the fruit surface to collect the real-time temperature of the fruit surface; the fruit firmness tester contacts the fruit surface through a pressure probe to detect the fruit's firmness value, which is then converted into parameters characterizing fruit maturity.

[0031] This unit integrates a digital temperature sensor, a temperature and humidity sensor, a fruit surface temperature sensor, and a non-destructive maturity characterization module. The temperature and humidity sensors are deployed on the outside of the annular flow guide cavity to collect real-time temperature and humidity parameters of the detection environment. The fruit surface temperature sensor collects the fruit surface temperature Ts via attachment or infrared thermometry. The non-destructive maturity characterization module generates maturity characterization parameters M, which can be calculated from at least one of the following: color parameters, near-infrared maturity index or soluble solids prediction value, acoustic hardness parameters, and surface elasticity parameters. When contact measurement is required, the contact method is a non-puncture, light-touch measurement, and permanent indentations are prevented on the fruit surface by limiting contact pressure / displacement.

[0032] Aroma Dynamic Decoupling Calculation Unit This unit incorporates a high-performance STM32H743 computing chip, which pre-stores a dynamic aroma volatile model trained through extensive experiments. During operation, the computing chip reads time-synchronized interface-coupled spectral response characteristics and environmental and fruit state parameters from the cache module, and substitutes these parameters into a preset formula for calculation.

[0033] During the calculation, the chip first normalizes the spectral response features to eliminate the dimensional differences between different modal spectral signals. Then, it performs weighted calculations based on environmental temperature, humidity, and fruit maturity parameters to finally obtain the equivalent effective aroma concentration parameter at time t. Through this model, the influence of disturbances such as environmental temperature, humidity, and fruit maturity can be completely separated from the spectral response signal, extracting the features that truly reflect the fruit aroma concentration. Formula for characterizing effective aroma concentration The effective aroma concentration parameter at time t is the core quantitative indicator characterizing the aroma concentration of fruit. The interface-coupled spectral response characteristic quantity is obtained by weighting and summing the spectral response intensity of the pericarp tissue, the absorption response intensity of volatile gas phase molecules, and the Raman fingerprint response intensity of molecular vibrations in a 3:4:3 ratio. This ratio was determined based on experiments on the sensitivity of different spectral signals to aroma concentration. T is the ambient temperature parameter in degrees Celsius; H is the ambient humidity parameter in percentage; M is the fruit maturity characterization parameter, dimensionless, with a value ranging from 0 to 1; α is the weighting coefficient of the spectral response characteristic quantity. Let be the environmental temperature weighting coefficient, γ be the environmental humidity weighting coefficient, and δ be the fruit maturity weighting coefficient, and satisfy the following conditions: + + + =1. The above weight coefficients were obtained by training with 500 sets of detection data of peaches in different environments and at different maturity levels. The least squares method was used for parameter fitting during the training process.

[0034] Aroma Fingerprint Reconstruction Intelligent Processing Unit This unit is equipped with an NVIDIA Jetson Nano edge computing module, which pre-trains and deploys a deep neural network model, including a multi-modal feature fusion layer, an aroma feature embedding layer, and a fingerprint generation layer.

[0035] The multi-mode feature fusion layer reads high-dimensional feature vectors obtained from near-infrared, terahertz, and Raman spectral signals through feature extraction, as well as equivalent aroma effective concentration parameters. These are then substituted into the feature fusion algorithm formula for weighted fusion calculation to obtain a fused feature vector integrating multi-source information. The aroma feature embedding layer performs dimensionality reduction on the fused feature vector. Through a preset feature transformation weight matrix and bias vector, combined with a nonlinear activation function, the high-dimensional fused features are mapped to a 128-dimensional low-dimensional space to obtain a low-dimensional embedded feature vector. This vector can effectively realize the separable expression of aroma features among peaches of different varieties and ripeness. The fingerprint generation layer generates a visualized volatile aroma fingerprint spectrum of peaches based on the low-dimensional embedded feature vector. Different peak positions in the spectrum correspond to different aroma components, and peak height corresponds to the relative content of the components.

[0036] Meanwhile, the fingerprint generation layer is substituted into the quality grade mapping formula to calculate the probability value of the feature vector corresponding to different preset quality grades, and the quality grade with the highest probability value is selected as the aroma quality grade of the fruit. Multimodal feature fusion layer formula In the formula, The fused feature vector has a dimension of 256 and is a comprehensive feature index that integrates information from multiple sources. The near-infrared spectral feature vector, This represents the spectral eigenvectors in the terahertz band. These are Raman spectral feature vectors. All three vectors are 64-dimensional high-dimensional vectors obtained by extracting features from the original spectral signal through principal component analysis (PCA). Let be the effective aroma concentration parameter at time t; , , Let be the weight coefficients of each feature vector, and satisfy . + + =1, the weighting coefficients are determined iteratively by gradient descent based on the contribution of different features to the aroma fingerprint spectrum; Aroma Feature Embedding Layer Formula In the formula, The low-dimensional embedded feature vector has a dimension of 128, which is much lower than the dimension of the fused feature vector; W is the feature transformation weight matrix with a dimension of 128×256, which is obtained through training with a large number of peach fruit aroma detection samples; b is the bias vector with a dimension of 128; σ is the non-linear activation function, and the ReLU function is used in this embodiment. This function can effectively solve the gradient vanishing problem in the neural network training process and improve the non-linear fitting ability of the model.

[0037] Quality evaluation output unit The unit has a built-in LCD display module and a 4G wireless transmission module. The LCD display module uses a 5-inch high-definition touch screen, which can display at least one of the following in real time: aroma quality grade, aroma intensity index, and aroma component relative proportion distribution spectrum. Operators can switch the display interface through the touch screen. The 4G wireless transmission module supports uploading the test results to the cloud management platform in real time for remote devices to view and statistically analyze the data.

[0038] The output of this unit can be directly applied to the post-harvest grading and sorting of peaches, allowing sorters to classify the fruits into different grades based on their aroma quality; it can also be used to determine the timing of harvesting in the field, allowing growers to determine the best time to harvest the fruits based on the aroma intensity index and component distribution. Quality grade mapping formula In the formula, The aroma quality grade of the peach fruit is represented by three preset quality grade categories: Grade 1, Grade 2, and Grade 3; k is the preset quality grade category. The probability values ​​corresponding to different quality grades for low-dimensional embedded feature vectors are obtained by training a deep neural network with 1000 sets of peach aroma samples labeled with quality grades. After training, the model's recognition accuracy can reach over 95%. The deep neural network is trained with aroma samples labeled with quality grades. Preferably, under the sample size, variety and maturity stratification method, and evaluation index definition (e.g., overall accuracy and / or macro-average F1) described in the embodiment, the model's recognition index on the independent test set reaches a preset level (e.g., accuracy ≥ 0.85, or F1 ≥ 0.85). The specific values ​​of the above indicators can vary with sensor configuration, sample distribution, and labeling standards.

[0039] A non-destructive and rapid method for detecting aroma components in peaches includes the following steps: Step 1 is aroma enrichment. By constructing an annular micro-airflow cavity structure for aroma enrichment on the fruit surface, adjusting the rotation speed of the micro-flow circulation drive device, controlling the gas flow rate inside the cavity, and constructing a micro-air film layer of a set thickness on the fruit surface. Step 2 is multimode spectral acquisition. The detection focus position of the multimode coupled spectral acquisition unit is adjusted by the displacement adjustment platform, and the three spectral modules are started to work synchronously to acquire the interface coupled composite spectral signal. Step 3 is signal modulation, which completes timing modulation, phase matching and sampling window unification through the interface-coupled signal modulation unit, and outputs the synchronized spectral signal; Step 4 is to synchronize environmental parameters. Environmental and fruit status parameters are collected by the environmental factor synchronization sensing unit and time synchronization with the spectral signal is completed by using timestamp marking. Step 5 is aroma concentration decoupling. The aroma dynamic decoupling calculation unit calls the preset model and substitutes it into the formula to calculate the equivalent effective aroma concentration parameters. Step 6 is fingerprint spectrum reconstruction. Through the deep neural network model of the aroma fingerprint reconstruction intelligent processing unit, feature fusion, low-dimensional embedding and fingerprint generation are completed to establish the mapping relationship between aroma fingerprint spectrum and quality grade. Step 7 is the quality evaluation output, which displays and transmits the test results through the quality evaluation output unit.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent spectroscopic analysis equipment for non-destructive and rapid detection of aroma components in peach fruit, characterized in that, include: Aroma enrichment building blocks on fruit surface; A closed or semi-closed annular micro-airflow cavity structure is formed on the outer periphery of the peach fruit to be tested. A micro-air film layer enriched with volatile aroma molecules is constructed between the fruit surface and the detection cavity through controllable micro-circulation airflow. A multimode coupled spectral acquisition unit is provided, corresponding to the micro-air film layer, to synchronously or sequentially perform composite spectral irradiation and signal acquisition on the fruit surface-gas phase micro-air film interface region, and to obtain the interface coupled composite spectral signal. Interface-coupled signal modulation unit; performs time-series modulation, phase matching, and signal synchronization on different modal spectral signals, and unifies the sampling window of multispectral channels; Environmental factor synchronous sensing unit; collects environmental and fruit state parameters at the detection time, and synchronizes the parameters with the spectral signal in time; Aroma dynamic decoupling calculation unit; based on the aroma volatilization dynamic model, physical mechanism decoupling of interface coupling spectral signals is performed to construct an effective aroma concentration characterization quantity; The aroma fingerprint reconstruction intelligent processing unit performs deep fusion processing on the decoupled aroma effective feature quantity and multi-mode spectral features, constructs the aroma component feature embedding space through neural network, generates the corresponding volatile aroma fingerprint spectrum of peach fruit, and establishes the mapping relationship between aroma fingerprint and quality grade. Quality evaluation output unit: The output unit outputs the evaluation results related to the aroma of peach fruit based on the aroma fingerprint spectrum.

2. The intelligent spectral analysis equipment according to claim 1, characterized in that, The aroma enrichment construction unit on the fruit surface includes a micro airflow guiding cavity annularly surrounding the periphery of the peach fruit, and a micro-flow circulation driving device is installed inside the cavity; the gas flow velocity inside the micro airflow guiding cavity is 0.01 to 0.2 m / s, so as to form an aroma molecule enrichment micro air film layer with a thickness of 0.5 to 5 mm on the fruit surface; the enrichment time of the micro air film layer is 1 to 10 min, preferably 3 to 5 min.

3. The intelligent spectroscopic analysis equipment for non-destructive and rapid detection of peach fruit aroma components according to claim 1, characterized in that, The multimode coupled spectral acquisition unit includes a near-infrared spectral emission / reception module, a terahertz band emission / reception module, and a Raman spectral excitation / acquisition module. The detection focus of the multimode coupled spectral acquisition unit is set at the interface region 0-3 mm from the fruit surface. The acquired interface coupled composite spectral signal simultaneously includes the spectral response of the fruit peel tissue, the absorption response of volatile gas phase molecules, and the Raman fingerprint response of molecular vibration, constituting a solid-gas two-phase coupled spectral response.

4. The intelligent spectroscopic analysis equipment for non-destructive and rapid detection of peach fruit aroma components according to claim 1, characterized in that, The interface coupling signal modulation unit keeps the timing modulation period of different modal spectral signals consistent with the dynamic release period of aroma molecules. Through a unified multispectral channel sampling window, the acquired signals correspond to the interface response under the same aroma dynamic release state.

5. The intelligent spectroscopic analysis equipment for non-destructive and rapid detection of peach fruit aroma components according to claim 1, characterized in that, The environmental and fruit status parameters collected by the synchronous sensing unit of environmental factors include environmental temperature, environmental humidity, fruit surface temperature, and fruit maturity characterization parameters. The environmental factor synchronization sensing unit synchronizes the collected environmental and fruit state parameters with the interface coupled composite spectral signal acquired by the multimode coupled spectral acquisition unit using a timestamp marking method.

6. The intelligent spectroscopic analysis equipment for non-destructive and rapid detection of peach fruit aroma components according to claim 1, characterized in that, The formula for calculating the effective aroma concentration characterization quantity constructed by the aroma dynamic decoupling calculation unit is as follows: In the formula, for Time-equivalent effective aroma concentration parameter; The characteristic quantity of the interface coupling spectral response is obtained by weighted summation of the spectral response intensity of the pericarp tissue, the absorption response intensity of volatile gas phase molecules, and the Raman fingerprint response intensity of molecular vibration; T is the ambient temperature parameter. This refers to the ambient humidity parameter. Parameters for characterizing fruit maturity; These are the weighting coefficients for the spectral response characteristic quantities. γ is the environmental temperature weighting coefficient, and γ is the environmental humidity weighting coefficient. Let be the weighting coefficient for fruit maturity, and satisfy . + + + =1; The model achieves the separation of environmental disturbance factors from the actual aroma spectral response.

7. The intelligent spectroscopic analysis equipment for non-destructive and rapid detection of peach fruit aroma components according to claim 1, characterized in that, The aroma fingerprint reconstruction intelligent processing unit adopts a deep neural network structure, which includes a multi-modal feature fusion layer, an aroma feature embedding layer, and a fingerprint spectrum generation layer. The aroma feature embedding layer constructs a low-dimensional aroma component representation space to achieve the separable expression of aroma features among different peach fruits. The feature fusion algorithm formula for the multi-modal feature fusion layer is as follows: , , , where are the weight coefficients of each feature vector, and satisfy . + + =1; The low-dimensional representation algorithm formula for the aroma feature embedding layer is as follows: ; The fingerprint generation layer is based on Generate volatile aroma fingerprint spectrum and use formula Establish a mapping relationship between fingerprint patterns and quality grades.

8. The intelligent spectroscopic analysis equipment for non-destructive and rapid detection of peach fruit aroma components according to claim 1, characterized in that, The analytical methods for analyzing equipment specifically include the following steps: Step 1, aroma enrichment: A ring-shaped micro-airflow cavity structure is formed on the periphery of the peach fruit skin by constructing aroma enrichment building units on the fruit surface. The gas flow rate in the cavity is controlled to construct a micro-air film layer for enriching volatile aroma molecules on the fruit surface. Step 2, Multimode Spectral Acquisition: The multimode coupled spectral acquisition unit focuses on the fruit surface-gas phase micro-film interface region, and performs synchronous or time-series composite spectral irradiation and signal acquisition to obtain the interface coupled composite spectral signal, which includes the spectral response of fruit peel tissue, the absorption response of volatile gas phase molecules, and the Raman fingerprint response of molecular vibration. Step 3, Signal Modulation: The interface-coupled signal modulation unit performs time-series modulation, phase matching, and signal synchronization on different modal spectral signals to unify the sampling window of the multispectral channels; Step 4, Environmental parameter synchronization: The environmental temperature, environmental humidity, fruit surface temperature, and fruit maturity characterization parameters are collected through the environmental factor synchronization sensing unit, and the parameters are synchronized with the interface coupled composite spectral signal in time. Step 5, Aroma Concentration Decoupling: Based on the formula, the aroma dynamic decoupling calculation unit is used. Physical decoupling of the interface-coupled spectral signals yields the equivalent effective aroma concentration parameter at time t; where Ce(t) is the characterization of the equivalent effective aroma concentration at time t; S(t) is the comprehensive characteristic quantity of the interface-coupled spectral response, which can be obtained by weighted summation of the pericarp tissue spectral response intensity Inir(t), the volatile gas phase molecular absorption response intensity Ithz(t), and the molecular vibrational Raman fingerprint response intensity Iraman(t), for example, S(t) = w1·Inir(t) + w2·Ithz(t). t)+w3·Iraman(t), and w1+w2+w3=1 (the preferred ratio can be determined based on sensitivity experiments); T is the environmental temperature parameter; H is the environmental humidity parameter; M is the non-destructive maturity characterization parameter (preferably normalized to 0~1); α, β, γ, δ are the weight coefficients corresponding to S(t), T, H, and M respectively, and satisfy α+β+γ+δ=1; the weight coefficients can be obtained by fitting the calibration data using the least squares method or by training, so as to effectively suppress the influence of environmental disturbance factors. Step 6, fingerprint spectrum reconstruction: The aroma fingerprint reconstruction intelligent processing unit uses a deep neural network to fuse, embed in low dimension and generate fingerprints of the decoupled aroma effective features and multi-mode spectral features, and establishes a mapping relationship between aroma fingerprint spectrum and quality grade. Step 7, Quality Evaluation Output: The quality evaluation output unit outputs at least one of the following based on the aroma fingerprint spectrum: peach fruit aroma quality grade, aroma intensity index, and aroma component relative proportion distribution spectrum.

Images