Intelligent monitoring method for spice drying process based on complementary optical sensing strategy

Abstract

The invention discloses an intelligent monitoring method for a spice drying process based on a complementary optical sensing strategy. The intelligent monitoring method comprises the following steps: determining volatile substances in the chili drying process by utilizing an HS-SMPE-GC-MS technology; preparing an intelligent silica gel based porphyrin-PH sensing plate; preparing a monitoring cavity in a drying stage; after the reaction, near infrared spectrum data and Raman spectrum data of the silica gel based porphyrin-PH sensing plate are acquired; and establishing an intelligent discrimination model for the chili drying process. By adopting the technical scheme provided by the invention, the rapid discrimination of the fragrance change stage in the chili drying process is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of food quality detection, and in particular relates to an intelligent monitoring method for a spice drying process based on a complementary optical sensing strategy. Background Art

[0002] Generally speaking, the roots, stems, leaves, buds, and seeds from plants that have aromatic or pungent smells, can give food flavor, increase appetite, and help digestion and absorption are called spices. They have typical characteristic smells or aromas. In addition, most spices belong to what Chinese medicine calls pungent and warm medicinal materials, which can improve body functions and have antioxidant, antibacterial and antiseptic functions. Chili pepper is one of the spicy spices. Chili pepper has a high moisture content and a short shelf life, so drying is the most common processing method for chili pepper. At present, the drying process is mainly controlled by human sensory experience, and it is difficult to ensure the stability of the quality of dried chili pepper. Volatile organic compounds (VOCs) from food have received widespread attention in recent years as an important indicator of food quality and safety. The aroma of chili pepper during drying generally comes from VOCs, and VOCs in chili pepper show a dynamic change process during drying. Therefore, real-time perception of the change of odor information during the drying process of chili pepper is an effective method to evaluate the quality of chili pepper during drying, and provides a theoretical basis for the intelligent regulation of chili pepper drying process parameters.

[0003] The existing detection methods for volatile organic gases are mainly liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), gas chromatography-olfactometry (GC-O), etc. The existing detection methods are cumbersome, laborious, time-consuming, susceptible to influence and impossible to implement online, which has a certain impact on sensitivity and accuracy. In addition, LC-MS and GC-MS are large instruments, expensive and require professionals to perform complex operations. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent monitoring method for the spice drying process based on a complementary optical sensing strategy.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] An intelligent monitoring method for spice drying process based on complementary optical sensing strategy, comprising:

[0007] Step 1: Determine the volatile substances in the pepper drying process using HS-SMPE-GC-MS technology;

[0008] Step 2: Screen out the odor recognition materials that react significantly with VOCs, prepare an odor recognition solution, use a capillary to suck a part of the odor recognition solution and spot it on a reverse silica gel plate, and use an array module to assist in spotting to make a silica-based porphyrin-PH sensor plate;

[0009] Step 3: After the silica-based porphyrin-PH sensor plate is prepared, place peppers at different drying stages in an aluminum box, attach the sensor plate to the lid of the aluminum box, cover the aluminum box to form a reaction chamber, and immediately put it into an oven to allow the sensor plate to fully react with the peppers during the drying process. Immediately take out the silica-based porphyrin-PH sensor plate after the reaction ends;

[0010] Step 4: Collect the near-infrared spectral data and Raman spectral data of the silica-based porphyrin-PH sensor plate after the reaction;

[0011] Step 5: According to the near-infrared spectral data and Raman spectral data, obtain an intelligent discrimination model for the drying process of peppers for intelligent detection of the drying process of spices.

[0012] Preferably, in Step 2, the odor recognition materials that react significantly with VOCs are porphyrin solution and PH indicator.

[0013] Preferably, the variables are screened by BOOS, CARS, and IVSO algorithms, and the selected Raman spectral variable subset and near-infrared spectral variable subset are spliced into a new feature matrix. Using the feature matrix as the input and the aroma change stage during the drying process of peppers as the output, an intelligent discrimination model for the drying process of peppers for intelligent detection of the drying process of spices is constructed.

[0014] Preferably, in Step 1, a triple quadrupole gas chromatography-mass spectrometry is used for volatile substance analysis.

[0015] Preferably, in Step 2, odor recognition solutions of zinc tetraphenylporphyrin, nickel tetraphenylporphyrin, cobalt tetraphenylporphyrin, cobalt tetra-p-methoxyphenylporphyrin, nickel octaethylporphyrin, 4-hydroxyphenylporphine, bromophenol red, chlorophenol red, and bromophenol blue with a concentration of 2 mg / mL are respectively prepared. Use a 100×0.3 mm capillary to suck 1 μL of the gas recognition solution and spot it on a 4×4 cm reverse silica gel plate, and use an array module to assist in spotting to make a 3×3 odor perception array. The odor perception array is a silica-based porphyrin-PH sensor plate.

[0016] Preferably, nine gas recognition solutions are prepared, which are composed of ethanol solutions of three pH indicators and dichloromethane and N,N-dimethylformamide solutions of six porphyrin solutions; among them, the nine odor recognition solutions are respectively denoted as odor recognition solution A, odor recognition solution B, odor recognition solution C, odor recognition solution D, odor recognition solution E, odor recognition solution F, odor recognition solution G, odor recognition solution H, and odor recognition solution I; odor recognition solution A is an ethanol solution of bromophenol red, and the dosage ratio of bromophenol red to ethanol is 20 mg:10 mL; odor recognition solution B is an ethanol solution of chlorophenol red, and the dosage ratio of chlorophenol red to ethanol is 20 mg:10 mL; odor recognition solution C is an ethanol solution of bromophenol blue, and the dosage ratio of bromophenol blue to ethanol is 20 mg:10 mL; odor recognition solution D is a dichloromethane solution of zinc tetraphenylporphyrin, and the dosage ratio of zinc tetraphenylporphyrin to dichloromethane is 20 mg:10 mL; odor recognition solution E is a dichloromethane solution of nickel tetraphenylporphyrin, and the dosage ratio of nickel octaethylporphyrin to dichloromethane is 20 mg:10 mL; odor recognition solution F is a dichloromethane solution of cobalt tetraphenylporphyrin, and the dosage ratio of manganese chloride tetraphenylporphyrin to dichloromethane is 20 mg:10 mL; odor recognition solution G is a dichloromethane solution of cobalt tetrakis(p-methoxyphenyl)porphyrin, and the dosage ratio of manganese chloride tetraphenylporphyrin to dichloromethane is 20 mg:10 mL; odor recognition solution H is a dichloromethane solution of nickel octaethylporphyrin, and the dosage ratio of manganese chloride tetraphenylporphyrin to dichloromethane is 20 mg:10 mL; odor recognition solution I is an N,N-dimethylformamide solution of 4-hydroxyphenylporphine, and the dosage ratio of manganese chloride tetraphenylporphyrin to N,N-dimethylformamide is 20 mg:10 mL; Use a microcapillary to aspirate 2 μL of the color-sensitive solution and fix it on the silica gel plate to obtain an intelligent silica-based porphyrin-PH sensor plate.

[0017] The present invention realizes the rapid discrimination of the aroma change stage in the chili drying process through the intelligent olfactory sensing detection technology that integrates Raman-near infrared spectroscopy; first, an odor perception array is constructed by odor recognition materials, and then the characteristic volatile organic compounds (VOCs) in the chili drying process are measured by collecting the odor perception array, and the near-infrared spectral characteristic information and Raman spectral characteristic information of the odor perception array after the reaction are extracted. The computer fuses the near-infrared spectral characteristic information and Raman spectral characteristic information into new spectral characteristic information to express the VOCs information in the chili drying process; establishing the relationship between the fused spectral characteristic information of the odor perception array and the aroma change stage in the chili drying process can effectively realize the rapid discrimination of the aroma change stage in the chili drying process.

[0018] Compared with the prior art, the present invention has the following technical effects:

[0019] 1. The present invention utilizes intelligent olfactory sensing detection technology integrating Raman-near infrared spectroscopy to achieve rapid and non-destructive detection of the aroma change stage during the pepper drying process.

[0020] 2. The present invention causes the color-sensitive material to react with the volatile components in the food through coordination to generate a complex, which further changes the molecular structure of the color-sensitive material and causes the color of the color-sensitive material to change.

[0021] 3. The present invention deeply mines multiple spectral information to improve the predictive performance of the dry chili quality detection model.

[0022] 4. The machine learning model for rapid discrimination of aroma change stages in the pepper drying process established by the present invention takes the near-infrared spectral information collected by the color-sensitive sensor matrix after the reaction and the Raman spectral information fused into a new matrix as input, and takes the aroma change stages in the pepper drying process as output. A model for rapid discrimination of aroma change stages in the pepper drying process is constructed, which has good versatility and improved efficiency, provides important characteristic information for the pepper drying process and provides a reference for evaluating the volatile flavor quality of dried peppers. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0024] Figure 1 The present invention is a flow chart of the intelligent monitoring method of the spice drying process based on the complementary optical sensing strategy;

[0025] Figure 2 It is a multivariate statistical analysis diagram of the mass spectrometry data set of the chili sample of the present invention; wherein A is a count diagram of the types of volatile flavor substances in the process of drying chili; B is a Venn diagram of the types of volatile flavor substances in the process of drying chili; C is a VIP value diagram; D is a PLS-DA score diagram;

[0026] Figure 3 is a trend diagram of characteristic volatile flavor substances of the present invention; wherein A is a trend diagram of 3-carene; B is a trend diagram of 4-methylvalerate;

[0027] Figure 4 This is a schematic diagram of the image of the silica-based porphyrin-PH sensor plate (odor sensing array) of the present invention;

[0028] Figure 5Schematic diagram of the rapid discrimination model for the aroma change stage during the drying process of chili peppers based on the near-infrared spectral information of the odor perception array of the present invention; wherein, A is the prediction score chart of the RF test set (accuracy = 100%); B is the prediction score chart of the RF training set (accuracy = 90%); C is the confusion matrix of the test set of the RF model; D is the confusion matrix of the training set of the RF model;

[0029] Figure 6 Schematic diagram of the rapid discrimination model for the aroma change stage during the drying process of chili peppers based on the Raman spectral information of the odor perception array of the present invention; wherein, A is the prediction score chart of the RF test set (accuracy = 100%); B is the prediction score chart of the RF training set (accuracy = 95%); C is the confusion matrix of the test set of the RF model; D is the confusion matrix of the training set of the RF model;

[0030] Figure 7 Schematic diagram of the rapid discrimination model for the aroma change stage during the drying process of chili peppers based on the fused spectral information of the odor perception array of the present invention; wherein, A is the prediction score chart of the RF test set (accuracy = 100%); B is the prediction score chart of the RF training set (accuracy = 100%); C is the confusion matrix of the test set of the RF model; D is the confusion matrix of the training set of the RF model. Detailed implementation manners

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0033] Embodiment 1:

[0034] As Figure 1 shown, the embodiment of the present invention provides an intelligent monitoring method for the drying process of spices based on a complementary optical sensing strategy, including:

[0035] Step S1: Use headspace solid-phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS) technology to determine the volatile substances during the drying process of chili peppers

[0036] The size of selecting is consistent, and the chili pepper of the same batch is put into a forced air drying oven and dried for 0H, 4H, 8H, 12H, 16H, and 20H. The chili pepper dried for 0H, 4H, 8H, 12H, 16H, and 20H is packed in a sealed bag and preserved. It is stored in a desiccator under normal temperature to avoid sample moisture absorption, affecting subsequent experiments. The chili pepper dried for 0H, 4H, 8H, 12H, 16H, and 20H (20H moisture content is less than 14%) is crushed using a crusher. Weigh the dried chili pepper pulverized by 2.0g in a 20mL sample bottle and seal with a silicone rubber pad lined with polytetrafluoroethylene. The mixture was preheated at 60°C for 20 min, and then a 100-m PDMS extraction head was inserted. Headspace sampling was performed for 30 min, and volatile substances were analyzed using a triple quadrupole gas chromatograph-mass spectrometer (Shimadzu). The chromatographic column model was HP-5MS (30 m × 250 μm × 0.25 μm). GC-MS analysis conditions were as follows: 40°C was maintained for 5 min, the temperature was increased to 120°C at a rate of 2°C·min-1, then to 210°C at a rate of 10°C·min-1, and finally to 280°C at a rate of 30°C·min-1 and maintained for 1 min. The carrier gas was helium, the carrier gas flow rate was 1 mL·min-1, the split ratio was 2:1, the injection port temperature was 250°C, and the injection volume was 1 μL; the mass spectrometry conditions were: EI ionization source, ionization energy 70 eV, interface temperature 220°C, ion source temperature 200°C, solvent delay time 10 min, mass-to-nuclear ratio scanning range 45-500 (m / z);

[0037] A total of 132 volatile flavor substances were identified in the hot-drying process of chili pepper samples, including 38 esters, 35 alkenes, 24 alkanes, 10 alcohols, 9 aldehydes, 3 ketones, 3 acids, and 10 other volatile compounds. The statistical analysis of the detected volatile flavor substances was as follows: Figure 2 As shown in A and B, with the increase of drying time, the number of volatile flavor substances showed a trend of first increasing and then decreasing. In the early stage of hot drying, the types of volatile compounds increased. This may be due to the high temperature causing the compounds to undergo oxidation, degradation and Maillard reaction to generate additional compounds. After that, a difference analysis was performed. The overlap in the Venn diagram showed that a total of 17 volatile compounds were detected in each heating stage. These 17 compounds were shared in multiple drying stages. PLS-DA was used to analyze the volatile substances at different drying times, as shown in Figure 1. Figure 2 As shown in D, PC1 and PC2 accounted for 87.7% and 3.4% respectively, with a cumulative variance of 91.1%. There was also a clear separation between the six different samples. The results showed that 0H, 4H, 8H, 12H, 16H, and 20H were the stages of aroma change during the drying process of peppers. This coupling is a valuable tool for evaluating the changes in volatile compounds during the drying process of chili peppers and provides a new strategy for quality evaluation.

[0038] The VIP values ​​of volatile flavor substances were calculated, and 19 substances had VIP values ​​greater than 1 (VIP values ​​reflect the contribution of variables to the overall fit and classification ability of the model). The multivariate analysis results of the mass spectrometry data set of dried chili pepper samples are as follows: Figure 2 As shown in C. Among the 19 substances with VIP values ​​greater than 1, 4-methylvalerate and 2-carene have a strong linear relationship with the pepper drying process, so they are considered to be the main characteristic volatile flavor substances. The change trend results of the two characteristic volatile flavor substances are shown in Figure 3 As shown in A and B.

[0039] Step 2: Preparation of smart silica-based porphyrin-PH sensor plate (odor sensing array)

[0040] Prepare odor recognition solutions of 2 mg / mL tetraphenylporphyrin zinc, tetraphenylporphyrin nickel, tetraphenylporphyrin cobalt, tetramethoxyporphyrin cobalt, octaethylporphyrin nickel, 4-hydroxyphenylporphine, bromophenol red, chlorophenol red, and bromophenol blue, respectively. Use a 100×0.3 mm capillary to draw 1 uL of the gas recognition solution and spot it on a 4×4 cm reverse silica gel plate and use an array module to assist in spotting to make a 3×3 odor sensing array. Figure 4 shown.

[0041] Nine gas identification solutions were prepared, consisting of three pH indicator solutions in ethanol and six porphyrin solutions in dichloromethane and N,N-dimethylformamide;

[0042] The nine odor recognition solutions are respectively denoted as odor recognition solution A (S1), odor recognition solution B (S2), odor recognition solution C (S3), odor recognition solution D (S4), odor recognition solution E (S5), odor recognition solution F (S6), odor recognition solution G (S7), odor recognition solution H (S8), and odor recognition solution I (S9);

[0043] Odor identification solution A is an ethanol solution of bromophenol red, and the dosage ratio of bromophenol red to ethanol is 20 mg:10 mL;

[0044] Odor identification solution B is an ethanol solution of chlorophenol red, and the dosage ratio of chlorophenol red to ethanol is 20 mg:10 mL;

[0045] Odor identification solution C is an ethanol solution of bromophenol blue, and the dosage ratio of bromophenol blue to ethanol is 20 mg:10 mL;

[0046] The odor identification solution D is a dichloromethane solution of zinc tetraphenylporphyrin, and the dosage ratio of zinc tetraphenylporphyrin to dichloromethane is 20 mg:10 mL;

[0047] The odor identification solution E is a dichloromethane solution of nickel tetraphenylporphyrin, and the dosage ratio of nickel octaethylporphyrin to dichloromethane is 20 mg:10 mL;

[0048] The odor identification solution F is a dichloromethane solution of tetraphenylporphyrin cobalt, and the dosage ratio of tetraphenylporphyrin manganese chloride and dichloromethane is 20 mg:10 mL;

[0049] The odor identification solution G is a dichloromethane solution of tetramethoxyporphyrin cobalt, and the dosage ratio of tetraphenylporphyrin manganese chloride and dichloromethane is 20 mg:10 mL;

[0050] The odor identification solution H is a dichloromethane solution of nickel octaethylporphyrin, and the dosage ratio of tetraphenylporphyrin manganese chloride and dichloromethane is 20 mg:10 mL;

[0051] Odor identification solution I is a solution of 4-hydroxyphenylporphine in N,N-dimethylformamide, and the dosage ratio of tetraphenylporphyrin manganese chloride and N,N-dimethylformamide is 20 mg:10 mL;

[0052] Finally, 2 μL of the color-sensitive solution was aspirated using a microcapillary and fixed on a silica gel plate to obtain an intelligent silica-based porphyrin-PH sensor plate.

[0053] Step 3: Preparation of the detection chamber for the drying stage of chili pepper

[0054] After the sensor plate was prepared, the chili peppers dried in six stages were placed in an aluminum box, the sensor plate was attached to the lid of the aluminum box, and the aluminum box was closed to form a reaction chamber. The sensor was immediately placed in a 60°C oven to allow the sensor to fully react with the chili peppers in six drying stages for 20 minutes. After the reaction was completed, the sensor plate was immediately taken out. The experiment was repeated ten times, and a total of 60 samples were prepared.

[0055] Step 4: Collection of near infrared spectral data and Raman spectrum of the odor sensing array after reaction

[0056] Near-infrared spectral acquisition: The spectral data of the reacted odor perception array were collected by a spectral acquisition device. To eliminate the environmental noise of infrared information, such as the scattering effect caused by diffuse reflection, baseline shift, overlapping peaks, and the signal-to-noise ratio in the spectral data. The spectral detection module was encapsulated in the lighting box, and the computer was connected to a portable spectrometer with a spectral range of 340.886–1074.36 nm through an optical fiber cable. In the reflection mode, the integration time was set to 5 ms, the number of scans was 10 times, and the pixel smoothing was 5 times. The spectral data of nine dye spots on the silica gel plate were collected with an optical fiber probe, and each spot was collected three times to avoid errors. The average value represents the final spectral data. Raman spectral data acquisition: Raman measurements were performed using a portable Raman spectrometer (XploRA PLUS, HORIBA, France) equipped with a laser excitation with a central wavelength of 785.025 nm. A long working distance of 0.2 cm was used to focus on the object to be tested. To ensure consistent thickness and minimize light scattering, the reacted odor perception array was placed on the platform. The spectral features were recorded in the range of 400–3200 cm-1. Raman spectra were collected using a laser power of 150 mW and an integration time of 3 s. Each point was collected three times to avoid errors, and the average value represented the final spectral data.

[0057] Step 5: Establishment of intelligent discrimination model for aroma change stage during the drying process of chili pepper

[0058] Through centering (CENTER), second-order derivative (DX 2 ) The original spectral data (a total of 14553 variables) obtained through infrared information and Raman information collection were preprocessed to eliminate environmental noise and correct the data. The variables were screened by BOOS and IVSO variable screening methods, and 1400 infrared information variable subsets and 224 Raman information were obtained. With 1400 infrared information variables as input and the aroma change stages of the millet pepper drying process as output, a qualitative judgment RF model was constructed, and the accuracy of its prediction set reached 90%, such as Figure 5 A, B, C, and D are shown in Figure 1. With 224 Raman information variables as input and the aroma change stages of the chili pepper drying process as output, the RF model for qualitative judgment is constructed, and the accuracy of the prediction set reaches 95%, as shown in Figure 1. Figure 6 A, B, C, and D are shown in FIG. 1400 infrared information variable subsets and 224 Raman information variable subsets are spliced ​​into a matrix, and the CARS variable screening method is used again to screen out 53 optimal subset variables as a new feature matrix as input, and the aroma change stage of the chili pepper drying process as output. A qualitative judgment model (RF) of the aroma change stage of the chili pepper drying process with a prediction accuracy of 100% is constructed, which is the aroma quality prediction model of the chili pepper drying process, as shown in FIG. Figure 7 As shown in A, B, C, and D.

[0059] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. An intelligent monitoring method for spice drying process based on complementary optical sensing strategy, characterized in that: include: Step 1: Determine the volatile substances in the pepper drying process using HS-SMPE-GC-MS technology; Step 2: Screen out the odor recognition materials that react more obviously with VOCS, prepare them into odor recognition solutions, use capillaries to absorb part of the odor recognition solutions and spot them on the reverse silica gel plate, and use array modules to assist in spotting, so as to make silica-based porphyrin-PH sensor plates; Step 3: After the silica-based porphyrin-PH sensor plate is prepared, peppers at different drying stages are placed in an aluminum box, the sensor plate is attached to the aluminum box cover, the aluminum box is covered to form a reaction chamber, and the sensor plate is immediately placed in an oven to allow the sensor plate to fully react with the peppers in the drying process, and the silica-based porphyrin-PH sensor plate is immediately taken out after the reaction is completed; Step 4, collecting near infrared spectrum data and Raman spectrum data of the silica-based porphyrin-PH sensor plate after the reaction; Step 5: Based on the near-infrared spectral data and the Raman spectral data, an intelligent discrimination model of the chili drying process for intelligent detection of the spice drying process is obtained.

2. The intelligent monitoring method for spice drying process based on complementary optical sensing strategy according to claim 1, characterized in that: In step 2, the odor recognition materials that react more obviously with VOCS are screened out as porphyrin solution and pH indicator.

3. The intelligent monitoring method for spice drying process based on complementary optical sensing strategy as claimed in claim 2, characterized in that: The variables are screened through BOOS, CARS, and IVSO algorithms, and the screened Raman spectroscopy variable subsets and near-infrared spectroscopy variable subsets are spliced ​​into a new feature matrix. With the feature matrix as input and the aroma change stages during the pepper drying process as output, an intelligent discrimination model of the pepper drying process for intelligent detection of the spice drying process is constructed.

4. The intelligent monitoring method for spice drying process based on complementary optical sensing strategy as claimed in claim 3 is characterized in that: In step 1, triple quadrupole gas chromatography-mass spectrometry is used to analyze volatile substances.

5. The intelligent monitoring method for spice drying process based on complementary optical sensing strategy as claimed in claim 4, characterized in that: In step 2, odor recognition solutions of tetraphenylporphyrin zinc, tetraphenylporphyrin nickel, tetraphenylporphyrin cobalt, tetramethoxyporphyrin cobalt, octaethylporphyrin nickel, 4-hydroxyphenylporphine, bromophenol red, chlorophenol red, and bromophenol blue were prepared at a concentration of 2 mg / mL, and 1 uL of the gas recognition solution was absorbed by a 100×0.3 mm capillary and spotted on a 4×4 cm reverse silica gel plate and assisted with spotting using an array module to prepare a 3×3 odor sensing array, which is a silica-based porphyrin-PH sensor plate.

6. The intelligent monitoring method for spice drying process based on complementary optical sensing strategy as claimed in claim 5, characterized in that: Nine gas identification solutions were prepared, consisting of ethanol solutions of three pH indicators and dichloromethane and N,N-dimethylformamide solutions of six porphyrin solutions; wherein the nine odor identification solutions were respectively recorded as odor identification solution A, odor identification solution B, odor identification solution C, odor identification solution D, odor identification solution E, odor identification solution F, odor identification solution G, odor identification solution H, and odor identification solution I; odor identification solution A was an ethanol solution of bromophenol red, and the dosage ratio of bromophenol red to ethanol was 20 mg:10 mL; odor identification solution B was an ethanol solution of chlorophenol red, and the dosage ratio of chlorophenol red to ethanol was 20 mg:10 mL; odor identification solution C was an ethanol solution of bromophenol blue, and the dosage ratio of bromophenol blue to ethanol was 20 mg:10 mL; odor identification solution D was a dichloromethane solution of tetraphenylporphyrin zinc, and the dosage ratio of tetraphenylporphyrin zinc to dichloromethane was 20 mg:10 mL ; Odor identification solution E is a dichloromethane solution of nickel tetraphenylporphyrin, and the amount ratio of nickel octaethylporphyrin to dichloromethane is 20mg:10mL; Odor identification solution F is a dichloromethane solution of cobalt tetraphenylporphyrin, and the amount ratio of tetraphenylporphyrin manganese chloride and dichloromethane is 20mg:10mL; Odor identification solution G is a dichloromethane solution of cobalt tetraphenylporphyrin, and the amount ratio of tetraphenylporphyrin manganese chloride and dichloromethane is 20mg:10mL; Gas The odor recognition solution H is a dichloromethane solution of octaethylporphyrin nickel, and the dosage ratio of tetraphenylporphyrin manganese chloride and dichloromethane is 20 mg:10 mL; the odor recognition solution I is an N,N-dimethylformamide solution of 4-hydroxyphenylporphine, and the dosage ratio of tetraphenylporphyrin manganese chloride and N,N-dimethylformamide is 20 mg:10 mL; 2 μL of the color-sensitive solution is aspirated using a microcapillary and fixed on a silica gel plate to obtain an intelligent silica-based porphyrin-PH sensor plate.

Images