kutluhan_aktarThe bacteria utilized to produce yogurt are known as yogurt cultures (or starters). Fermentation of sugars in the milk by yogurt cultures yields lactic acid, which decomposes and coagulates proteins in the milk to give yogurt its texture and characteristic tangy flavor. Also, this process improves the digestibility of proteins in the milk and enhances the nutritional value of proteins. After the fermentation of the milk, yogurt culture could help the human intestinal tract to absorb the amino acids more efficiently[2].
Even though yogurt production and manufacturing look like a simple task, achieving precise yogurt texture (consistency) can be arduous and strenuous since various factors affect the fermentation process while processing yogurt, such as:
- Temperature
- Humidity
- Pressure
- Milk Temperature
- Yogurt Culture (Starter) Amount (Weight)
In this regard, most companies employ food (chemical) additives while mass-producing yogurt to maintain its freshness, taste, texture, and appearance. Depending on the production method, yogurt additives can include diluents, water, artificial flavorings, rehashed starch, sugar, and gelatine.
In recent years, due to the surge in food awareness and apposite health regulations, companies were coerced into changing their yogurt production methods or labeling them conspicuously on the packaging. Since people started to have a penchant for consuming more healthy and organic (natural) yogurt, it became a necessity to prepare prerequisites precisely for yogurt production without any additives. However, unfortunately, organic (natural) yogurt production besets some local dairies since following strict requirements can be expensive and demanding for small businesses trying to gain a foothold in the dairy industry.
After perusing recent research papers on yogurt production, I decided to utilize temperature, humidity, pressure, milk temperature, and culture weight measurements denoting yogurt consistency before fermentation so as to create an easy-to-use and budget-friendly device in the hope of assisting dairies in reducing total cost and improving product quality.
Even though the mentioned factors can provide insight into detecting yogurt consistency before fermentation, it is not possible to extrapolate and construe yogurt texture levels precisely by merely employing limited data without applying complex algorithms. Hence, I decided to build and train an artificial neural network model by utilizing the empirically assigned yogurt consistency classes to predict yogurt texture levels before fermentation based on temperature, humidity, pressure, milk temperature, and culture weight measurements.
Since XIAO ESP32C3 is an ultra-small size IoT development board that can easily collect data and run my neural network model after being trained to predict yogurt consistency levels, I decided to employ XIAO ESP32C3 in this project. To collect the required measurements to train my model, I used a temperature & humidity sensor (Grove), an integrated pressure sensor kit (Grove), an I2C weight sensor kit (Gravity), and a DS18B20 waterproof temperature sensor. Since the XIAO expansion board provides various prototyping options and built-in peripherals, such as an SSD1306 OLED display and a MicroSD card module, I used the expansion board to make rigid connections between XIAO ESP32C3 and the sensors.
Since the expansion board supports reading and writing information from/to files on an SD card, I stored the collected data in a CSV file on the SD card to create a data set. In this regard, I was able to save data records via XIAO ESP32C3 without requiring any additional procedures.
After completing my data set, I built my artificial neural network model (ANN) with Edge Impulse to make predictions on yogurt consistency levels (classes). Since Edge Impulse is nearly compatible with all microcontrollers and development boards, I had not encountered any issues while uploading and running my model on XIAO ESP32C3. As labels, I utilized the empirically assigned yogurt texture classes for each data record while collecting yogurt processing data:
- Thinner
- Optimum
- Curdling (Lumpy)
After training and testing my neural network model, I deployed and uploaded the model on XIAO ESP32C3. Therefore, the device is capable of detecting precise yogurt consistency levels (classes) by running the model independently.
Since I wanted to allow the user to get updates and control the device remotely, I decided to build a complementing Blynk application for this project: The Blynk dashboard displays the recent sensor readings transferred from XIAO ESP32C3, makes XIAO ESP32C3 run the neural network model, and shows the prediction result followingly.
Lastly, to make the device as sturdy and robust as possible while operating in a dairy, I designed a dairy-themed case with a sliding (removable) front cover (3D printable).
So, this is my project in a nutshell 😃
In the following steps, you can find more detailed information on coding, logging data on the SD card, communicating with a Blynk application, building a neural network model with Edge Impulse, and running it on XIAO ESP32C3.
🎁🎨 Huge thanks to Seeed Studio for sponsoring these products:
⭐ XIAO ESP32C3 | Inspect
⭐ XIAO Expansion Board | Inspect
⭐ Grove - Temperature & Humidity Sensor | Inspect
⭐ Grove - Integrated Pressure Sensor Kit | Inspect
🎁🎨 Huge thanks to DFRobot for sponsoring a Gravity: I2C 1Kg Weight Sensor Kit (HX711).
🎁🎨 Also, huge thanks to Creality for sending me a Creality Sonic Pad, a Creality Sermoon V1 3D Printer, and a Creality CR-200B 3D Printer.
Step 1: Designing and printing a dairy-themed case
Since I focused on building a budget-friendly and easy-to-use device that collects yogurt processing data and informs the user of the predicted yogurt consistency level before fermentation, I decided to design a robust and sturdy case allowing the user to access the SD card after logging data and weigh yogurt culture (starter) easily. To avoid overexposure to dust and prevent loose wire connections, I added a sliding front cover with a handle to the case. Also, I decided to emboss yogurt and milk icons on the sliding front cover so as to complement the dairy theme gloriously.
Since I needed to adjust the rubber tube length of the integrated pressure sensor, I added a hollow cylinder part to the main case to place the rubber tube. Then, I decided to fasten a small cow figure to the cylinder part because I thought it would make the case design align with the dairy theme.
I designed the main case and its sliding front cover in Autodesk Fusion 360. You can download their STL files below.
For the cow figure (replica) affixed to the top of the cylinder part of the main case, I utilized this model from Thingiverse:
Then, I sliced all 3D models (STL files) in Ultimaker Cura.
Since I wanted to create a solid structure for the main case with the sliding front cover representing dairy products, I utilized these PLA filaments:
- Beige
- ePLA-Matte Milky White
Finally, I printed all parts (models) with my Creality Sermoon V1 3D Printer and Creality CR-200B 3D Printer in combination with the Creality Sonic Pad. You can find more detailed information regarding the Sonic Pad in Step 1.1.
If you are a maker or hobbyist planning to print your 3D models to create more complex and detailed projects, I highly recommend the Sermoon V1. Since the Sermoon V1 is fully-enclosed, you can print high-resolution 3D models with PLA and ABS filaments. Also, it has a smart filament runout sensor and the resume printing option for power failures.
Furthermore, the Sermoon V1 provides a flexible metal magnetic suction platform on the heated bed. So, you can remove your prints without any struggle. Also, you can feed and remove filaments automatically (one-touch) due to its unique sprite extruder (hot end) design supporting dual-gear feeding. Most importantly, you can level the bed automatically due to its user-friendly and assisted bed leveling function.
#️⃣ Before the first use, remove unnecessary cable ties and apply grease to the rails.
#️⃣ Test the nozzle and hot bed temperatures.
#️⃣ Go to Print Setup ➡ Auto leveling and adjust five predefined points automatically with the assisted leveling function.
#️⃣ Finally, place the filament into the integrated spool holder and feed the extruder with the filament.
#️⃣ Since the Sermoon V1 is not officially supported by Cura, download the latest Creality Slicer version and copy the official printer settings provided by Creality, including Start G-code and End G-code, to a custom printer profile on Cura.
Step 1.1: Improving print quality and speed with the Creality Sonic Pad
Since I wanted to improve my print quality and speed with Klipper, I decided to upgrade my Creality CR-200B 3D Printer with the Creality Sonic Pad.
Creality Sonic Pad is a beginner-friendly device to control almost any FDM 3D printer on the market with the Klipper firmware. Since the Sonic Pad uses precision-oriented algorithms, it provides remarkable results with higher printing speeds. The built-in input shaper function mitigates oscillation during high-speed printing and smooths ringing to maintain high model quality. Also, it supports G-code model preview.
Although the Sonic Pad is pre-configured for some Creality printers, it does not support the CR-200B officially yet. Therefore, I needed to add the CR-200B as a user-defined printer to the Sonic Pad. Since the Sonic Pad needs unsupported printers to be flashed with the self-compiled Klipper firmware before connection, I flashed my CR-200B with the required Klipper firmware settings via FluiddPI by following this YouTube tutorial.
If you do not know how to write a printer configuration file for Klipper, you can download the stock CR-200B configuration file from here.
#️⃣ After flashing the CR-200B with the Klipper firmware, copy the configuration file (printer.cfg) to a USB drive and connect the drive to the Sonic Pad.
#️⃣ After setting up the Sonic Pad, select Other models. Then, load the printer.cfg file.
#️⃣ After connecting the Sonic Pad to the CR-200B successfully via a USB cable, the Sonic Pad starts the self-testing procedure, which allows the user to test printer functions and level the bed.
#️⃣ After completing setting up the printer, the Sonic Pad lets the user control all functions provided by the Klipper firmware.
#️⃣ In Cura, export the sliced model in the ufp format. After uploading .ufp files to the Sonic Pad via the USB drive, it converts them to sliced G-code files automatically.
#️⃣ Also, the Sonic Pad can display model preview pictures generated by Cura with the Create Thumbnail script.
Step 1.2: Assembling the case and making connections & adjustments
// Connections// XIAO ESP32C3 : // Grove - Temperature & Humidity Sensor // A4 --------------------------- SDA // A5 --------------------------- SCL // Grove - Integrated Pressure Sensor // A0 --------------------------- S // Gravity: I2C 1Kg Weight Sensor Kit - HX711 // A4 --------------------------- SDA // A5 --------------------------- SCL // DS18B20 Waterproof Temperature Sensor // D6 --------------------------- Data // SSD1306 OLED Display (128x64) // A4 --------------------------- SDA // A5 --------------------------- SCL // MicroSD Card Module (Built-in on the XIAO Expansion board) // D10 --------------------------- MOSI // D9 --------------------------- MISO // D8 --------------------------- CLK (SCK) // D2 --------------------------- CS (SS) // Button (Built-in on the XIAO Expansion board) // D1 --------------------------- +
First of all, I attached XIAO ESP32C3 to the XIAO expansion board. Then, I connected the temperature & humidity sensor (Grove) and the integrated pressure sensor kit (Grove) to the expansion board via Grove connection cables.
Since the I2C weight sensor kit (Gravity) does not include a compatible connection cable for a Grove port, I connected the weight sensor to the expansion board via a 4-pin male jumper to Grove 4-pin conversion cable.
As shown in the schematic below, before connecting the DS18B20 waterproof temperature sensor to the expansion board, I attached a 4.7K resistor as a pull-up from the DATA line to the VCC line of the sensor to generate accurate temperature measurements.
To display the collected data, I utilized the built-in SSD1306 OLED screen on the expansion board. To assign yogurt consistency levels empirically while saving data records to a CSV file on the SD card, I used the built-in MicroSD card module and button on the expansion board.
After printing all parts (models), I fastened all components except the expansion board to their corresponding slots on the main case via a hot glue gun.
I attached the expansion board to the main case by utilizing M3 screws with hex nuts and placed the rubber tube of the integrated pressure sensor in the hollow cylinder part of the main case.
Then, I placed the sliding front cover via the dents on the main case.
Finally, I affixed the small cow figure to the top of the cylinder part of the main case via the hot glue gun.
Step 2: Creating a Blynk application and user interface for XIAO ESP32C3
Since I focused on building an accessible device, I decided to create a complementing Blynk application for allowing the user to display recent sensor readings, run the Edge Impulse neural network model, and get informed of the prediction result remotely.
The Blynk IoT Platform provides a free cloud service to communicate with supported microcontrollers and development boards, such as ESP32C3. Also, Blynk lets the user design unique web and mobile applications with drag-and-drop editors.
#️⃣ First of all, create an account on Blynk and open Blynk.Console.
#️⃣ Before designing the web application on Blynk.Console, install the Blynk library on the Arduino IDE to send and receive data packets via the Blynk cloud: Go to Sketch ➡ Include Library ➡ Manage Libraries… and search for Blynk.
#️⃣ Then, create a new device with the Quickstart Template, named XIAO ESP32C3. And, select the board type as ESP32.
#️⃣ After creating the device successfully, copy the Template ID, Device Name, and Auth Token variables required by the Blynk library.
#️⃣ Open the Web Dashboard and click the Edit button to change the web application design.
#️⃣ From the Widget Box, add the required widgets and assign each widget to a virtual pin as the datastream option.
Since Blynk allows the user to adjust the unit, data range, and color scheme for each widget, I was able to create a unique web user interface for the device.
- Temperature Gauge ➡ V4
- Humidity Gauge ➡ V12
- Pressure Gauge ➡ V6
- Milk Temperature Gauge ➡ V7
- Weight Gauge ➡ V8
- Switch Button ➡ V9
- Label ➡ V10
After completing designing the web user interface, I tested the virtual pin connection of each widget with XIAO ESP32C3.
Step 3: Setting up XIAO ESP32C3 on the Arduino IDE
Step 4: Logging yogurt processing information in a CSV file on the SD card w/ XIAO ESP32C3
Step 4.1: Collecting samples while producing yogurt to create a data set
After uploading and running the code for collecting yogurt processing data and for saving information to the given CSV file on the SD card on XIAO ESP32C3:
🐄🥛📲 The device shows the opening screen if the sensor and MicroSD card module connections with XIAO ESP32C3 are successful.
🐄🥛📲 Then, the device displays the collected yogurt processing data and the selected class number on the SSD1306 OLED screen:
- Temperature (°C)
- Humidity (%)
- Pressure (kPa)
- Milk Temperature (°C)
- Starter Weight (g)
- Selected Class
🐄🥛📲 If the button (built-in) is short-pressed, the device increments the selected class number in the range of 0-2:
- Thinner [0]
- Optimum [1]
- Curdling [2]
🐄🥛📲 If the button (built-in) is long-pressed, the device appends the recently created data record from the collected data to the yogurt_data.csv file on the SD card, including the selected yogurt consistency class number under the consistency_level data field.
🐄🥛📲 After successfully appending the data record, the device notifies the user via the SSD1306 OLED screen.
🐄🥛📲 If XIAO ESP32C3 throws an error while operating, the device shows the error message on the SSD1306 OLED screen and prints the error details on the serial monitor.
🐄🥛📲 Also, the device prints notifications and sensor measurements on the serial monitor for debugging.
To create a data set with eminent validity and veracity, I collected yogurt processing data from nearly 30 different batches. Since I focused on predicting yogurt texture precisely, I always used cow milk in my experiments but changed milk temperature, yogurt culture (starter) amount, and environmental factors while conducting my experiments.
🐄🥛📲 After completing logging the collected data in the yogurt_data.csv file on the SD card, I elicited my data set.
Step 5: Building a neural network model with Edge Impulse
Step 6: Setting up the Edge Impulse model on XIAO ESP32C3
Step 7: Running the model on XIAO ESP32C3 to predict yogurt texture levels
Videos and Conclusion
Further Discussions
By applying neural network models trained on temperature, humidity, pressure, milk temperature, and culture weight measurements in detecting yogurt consistency (texture) levels, we can achieve to:
🐄🥛📲 improve product quality without food additives,
🐄🥛📲 reduce the total cost for local dairies,
🐄🥛📲 incentivize small businesses to produce organic (natural) yogurt.
References
[1] Good Food team, Yogurt, BBC Good Food, https://www.bbcgoodfood.com/glossary/yogurt-glossary
[2] Metabolism Characteristics of Lactic Acid Bacteria and the Expanding Applications in Food Industry, Front. Bioeng. Biotechnol., 12 May 2021, Sec. Synthetic Biology, https://doi.org/10.3389/fbioe.2021.612285