Food quality analysis and detection device

[0031] Example 1:

[0032] This embodiment introduces the device and its functions of the present invention.

[0033] A food quality analysis and detection device, including a total control module 11, an image analysis module, a hardness analysis module, a moisture content analysis module, an infrared spectrum analysis module, and a mobile terminal 10;

[0034] The overall control module 11 is electrically connected to the image analysis module, the hardness analysis module, the moisture content analysis module, and the infrared spectrum analysis module, and the overall control module 11 is wirelessly connected to the mobile terminal 10; the mobile terminal 10 is used to take overall image shooting of the food to be analyzed, and Send the captured image to the general control module 11 to identify the type of food to be analyzed;

[0035] The image analysis module is used to collect images of the food sample to be analyzed, analyze the collected images to obtain the first freshness of the food to be analyzed, and send the analysis result to the overall control module 11; the hardness analysis module is used to analyze the food The sample block performs the first hardness analysis and sends the analysis result to the overall control module 11; the moisture content analysis module is used to perform the first moisture content analysis on the food sample block to be analyzed, and sends the analysis result to the overall control module 11; infrared spectrum The analysis module is used to analyze the second freshness, second hardness, and second moisture content of the food sample to be analyzed, and send the analysis result to the master control module 11;

[0036] The master control module 11 collects the first freshness, the first hardness coefficient, the first moisture content, the second freshness, the second hardness coefficient, and the second moisture content, and comprehensively calculates the quality level of the food to be analyzed based on this, and will The quality level of the analyzed food is displayed on the mobile terminal 10.

[0037] It also includes a cloud server, which is wirelessly connected to the master control module 11, and is used to provide cloud computing services for the master control module 11 to identify food types and calculate food quality levels.

[0038] The analysis and detection device includes a detection cabinet 01, a general control module 11, an image analysis module, a hardness analysis module, a moisture content analysis module, and an infrared spectrum analysis module are set in the detection cabinet 01;

[0039] A sample stage 02 is set in the detection cabinet 01, a sample pan 03 is set on the sample stage 02, and a hardness analysis module is set directly above the sample pan 03; an image analysis module and an infrared spectrum analysis module are set between the sample pan 03 and the hardness analysis module; images The analysis module is provided with an image acquisition camera 09;

[0040] The outside of the sample pan 03 is circular, and the inside is provided with a square detection station 04 for placing the food pieces to be analyzed; the sample pan 03 has heating and weighing functions. When the moisture content is analyzed, the heating module heats the food pieces to be analyzed and weighs The heavy module weighs the food block to be analyzed, and when the weight reaches a constant weight, the first moisture content of the food block to be analyzed can be obtained;

[0041] There are 64 color blocks 05 of 8×8 on the central bottom of the sample tray 03, 64 color blocks 05 are standard color blocks of multiple different colors; the image analysis module simultaneously obtains the image of the food block to be analyzed and 64 colors The image of block 05, the acquired image is color calibrated according to 64 standard color blocks to obtain an accurate color image of the food to be analyzed, and then the color image is sent to the cloud server, and the cloud server inputs the image into the freshness analysis model to obtain The first freshness of the food piece to be analyzed;

[0042] The hardness analysis module is equipped with three pins 06 with different diameters, and the tip of the pin 06 is flat; when performing hardness analysis, the pin 06 moves down and presses on the food block to be analyzed, and then the hardness analysis module records the pin 06 The rebound force received from the food piece to be analyzed changes with the displacement of the pin 06. When the rebound force changes suddenly, the rebound force immediately before the mutation is recorded. The sum of the three rebound forces is recorded as the first food piece to be analyzed. Hardness factor.

[0043] The infrared spectrum analysis module includes a light-emitting head 07 and a receiving head 08. The light-emitting head 07 is connected to the light source module 12 and emits full-spectrum laser light to the food block to be analyzed; the receiving head 08 receives the light reflected from the food block to be analyzed; the light received by the receiving head 08 After passing through the grating spectroscopic module 13, the infrared reflection absorption spectrum of the food block to be analyzed is obtained; the infrared spectrum analysis module sends the infrared reflection absorption spectrum of the food block to be analyzed to the cloud server, and the cloud server inputs the infrared reflection absorption spectrum of the food block to be analyzed into it In the spectral analysis model of the corresponding type, the second freshness, the second hardness coefficient, and the second water content are obtained, and the second freshness, the second hardness coefficient, and the second water content are sent to the overall control module 11.

[0044] After the master control module 11 obtains the first freshness, the first hardness coefficient, the first water content, the second freshness, the second hardness coefficient, and the second water content, it first excludes the analysis results; if the first freshness and the first freshness If the difference between the second freshness or the first hardness coefficient and the second hardness coefficient or the first water content and the second water content is greater than its corresponding judgment threshold, it is considered that the judgment of the freshness or the hardness coefficient or the water content is abnormal, then Do not use abnormal data for quality analysis;

[0045] The general control module 11 sends the non-abnormal data to the cloud server, and the cloud server inputs the non-abnormal data into the quality level calculation model of the corresponding food type to obtain the quality level of the food block to be analyzed.

[0046] Input the non-abnormal data into the quality level calculation model of the corresponding food type, and the specific way to obtain the quality level of the food block to be analyzed is to first perform freshness level A, hardness coefficient level B, and content according to freshness, hardness coefficient, and water content. Judgment of water level C; each food has its own range of freshness level, hardness coefficient level, and water content level; then calculate the food quality level D=A*B*C.

[0047] Example 2:

[0048] This embodiment introduces the analysis method of the present invention. The method of use is as follows:

[0049] First, use the mobile terminal to take the overall image of the food to be analyzed, and then upload it to the cloud server. The cloud server analyzes the image to obtain the type of food to be analyzed, such as cake, cucumber, tomato, apple, etc.; further details can be obtained. Types, such as red Fuji apple, yellow marshal apple, Aksu pear, etc.;

[0050] Then cut a piece of the food to be analyzed and put it on the detection station of the sample tray, perform image detection to obtain the first freshness; then perform infrared spectrum detection to obtain the second freshness, the second hardness coefficient, and the second moisture content. Then perform a hardness test to obtain the first hardness coefficient; after the hardness test, perform a moisture content test to obtain the first moisture content;

[0051] After the master control module 11 obtains the first freshness, the first hardness coefficient, the first water content, the second freshness, the second hardness coefficient, and the second water content, it first excludes the analysis results; if the first freshness and the first freshness If the difference between the second freshness or the first hardness coefficient and the second hardness coefficient or the first water content and the second water content is greater than its corresponding judgment threshold, it is considered that the judgment of the freshness or the hardness coefficient or the water content is abnormal, then Do not use abnormal data for quality analysis;

[0052] The general control module 11 sends the non-abnormal data to the cloud server, and the cloud server inputs the non-abnormal data into the quality level calculation model of the corresponding food type to obtain the quality level of the food block to be analyzed.

[0053] Input the non-abnormal data into the quality level calculation model of the corresponding food type, and the specific way to obtain the quality level of the food block to be analyzed is to first perform freshness level A, hardness coefficient level B, and content according to freshness, hardness coefficient, and water content. Judgment of water level C; each food has its own range of freshness level, hardness coefficient level, and water content level; then calculate the food quality level D=A*B*C.

[0054] Example 3:

[0055] The device of the present invention can also comprehensively test the hardness coefficient and water content to obtain a water loss-hardness coefficient curve;

[0056] When judging the hardness coefficient level, first select the hardness coefficient under a fixed moisture content for judgment, so as to ensure that the hardness is carried out under the same moisture content condition, thereby improving the detection accuracy of the hardness level.