Food Recognition using InfrarEd Spectroscopy(FRiES)

Food Adulteration is one of the major problems which affect the health of masses and also is responsible for some disease. The main aim of this project was to help people detect adulteration in food using Near Infrared (NIR) spectroscopy. The problem was that the traditional spectrometers were quite bulky and it was almost impossible to carry it around with you. What our aim was to develop a prototype design for the MEMS-FTIR spectrometer  so that it can be used for carrying around with us and also collect as many sample and study them so that any adulterated food could be detected.

In the first week we just spent our time to set-up the work place and make sure that all the conditions are perfect to test the sample and also learn how to use the spectrometer. Collection of samples from market and prepare the set-up for testing was our next aim.

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Our Mentor showing us how we to use the spectrometer.

IMAG3229 Milk samples

WP_20140605_12_10_12_ProThe Set-up

 

In the second week, we tested more samples and studied them. Some of the samples which we tested were : a) Tropicana Orange Juice b) Harvest Orange Juice c) Fresh Orange juice

The aim was to test it every day to determine consistency of data with only change of buying it from different shops every time.  The samples showed some consistency and so next started to mix some preservative ourselves and then test if we could tell the difference. Here are the results.

Graph shows Absorbance (Y axis) vs Wavelength in nm ( X axis)

pure_fruit_juice_spectrum_plot

The Raw data directly from sensor

TropicanaHarvestMalta_comparison

The smoothen graph using filters.

picGraph showing the significance of peak points

 

In the end of second week we started studying various hair oil in the same way we did with juice. And here are some of the data which we collected (This are data obtained after applying filter).

We plot a spectrum of all the oils to see if we can identify some peak points for differentiation.

Groundnut Oil Loose_Spectrum Figaro_Olive_Oil Coconut_Oil_Loose_Spectrum Palm Oil Loose_Spectrum

Now we started working on prototype so that it can be used as one whole device instead of having one IR-source, spectrometer and slide. Due to lack of material and hardware tools we had to make it with what was available to us. We first were planning to 3D print the prototype but due to high temperature of the IR source the ABS material could have melted, so we tried our hands on Aluminium.  We managed to find a drill from a nearby store, some waste aluminium and only 2 hours to make this. And this is what our final prototype looked like.

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Conclusion: One important conclusion we found was that the pure milk and similar looking adulterated milk with almost same physical properties looked very different under IR light. The adulterated milk had excess water ( to increase quantity), Palm oil ( for increasing density), Shampoo(to give foamy lather on top like fresh milk) but under light it looked like this! A important finding which proved that this method can be useful for detecting adulteration.

all comparison milkUnder the light.

 

Airflow sub-Team (team ārām)

The airflow measurement is one of the most important parameters to determine whether a person is undergoing an Apnea episode. The two main sensors used for Airflow measurement are:

  1. Thermistor – To measure the temperature range of the exhaled air.We have used standard thermistors of 10k ohm ratings. The voltage across it has been passed through an instrumentation amplifier AD620. This is done to increase the sensitivity. Appropriate filters are applied before giving the output to ADC pin of an Arduino. The right filters are necessary to remove ambient noise as also keep the response time in check.ad620 in-ampthermistor graphfrqeuency data of thermistor
  2. Pressure sensor – To gauge the pressure of the air coming out from nose / mouth.The sensor used is Freescale Silicon pressure sensor MPXV7002. The sensor has a good response time and senitivity. We have used it to directly with Arduino to convert analog to digital values. pressure sensor graph
  3. The logged values thermistor and pressure sensor  values are then processed (signal processing) on MATLAB to give out appropriate outputs. Serial oscilloscope is used to plot and log the values real time. Several functions like digital filters and down samplers are used to bring the values and sampling frequency to the range required by the machine learning team.

The Airflow sub-team includes Shalin Shah, Chaitrali Joshi and myself (Swadhin). Integration: We had a sensor integration work with the microphone team. Here’s what we did! 3 graphs

We thank the help form Vijay, Sabareesh, and the entire Team Ārām. It was a real good experience! We have a applied for a embedded systems competition from Intel. Also, we have good plans to take the project higher. An integrated PCB, better form factor and cloud service integration are some improvements.

Also, all this wouldnt have been possible without Thanasi, Rohan, Dr.Maulik , Anshuman and of course Ramesh and John.

Thank You!

  • Swadhin Thakkar.

 

This is what currently happens!

orig Here we present ārām 1.0 to you! ours

WAND – Wound ANalysis Device

WAND-Wound Analysis Device is capable of analyzing wound using Skin Perfusion Photography. This device constitutes of 14 lasers of different wavelengths 650,785,850,980. The lasers are placed in a circular passion around the camera lens to create illumination pattern. The laser illuminates the area sequentially and the camera captures the image of each laser. From the acquired images, processing is done using a software to analyse the perfusion of that part.

The WAND is designed to resolve the hidden issues in healing process so as to enable the doctor for better analysis. It can be used for diagnosing various types of wounds like- Chronic Wound, Acute Wound, Traumatic Wound, Diabetic Ulcers etc.

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Mobile Spectrometer

Along with FTIR Spectroscopy, I also work on electronics on VIS-Spectrometer with Prashant and this one aims to bring the data directly on to a smartphone rather than a PC. We are working with the Hamamatsu VIS-Spectrometer and trying to find out the in and out of the Image sensor so that somehow we can get that same data from PC to Smartphone.

Prashant somehow figured out that the video pin of the image sensor directly gives the output when connected to a Oscilloscope.The below one is for Red light.

IMG_0055

 

The problem now was to get it on a smartphone. So we tried the 16-bit ADC on Teensy but somehow it failed to give the data as expected. Now we are trying to get the data to Phone directly via Audio Jack of phone.

final_bstSnapshot_61981

final_bstSnapshot_246081

 

Now we need to calibrate this reading properly.

 

-Sanket Shah

 

FoodScan

Hi,

Me, Ujjwal and Manu are working on testing of food samples with FTIR Spectroscopy and trying to find a pattern in the spectrum obtained of different food samples. The aim is to detect the adulteration just by looking at the spectrum of the sample. Till now we have collected data about different samples of orange juice with small changes to it like adding salt, sugar, salt+sugar, 100%pure juice, packet juice of various brands and trying to process the raw data by doing some Smoothing on the spectrum graph using MATLAB.

FTIR Set-up

 

The set-up for collecting the samples. There is a MEMS FTIR Spectrometer with a 3D printed glass slide stand which is used for sliding the microscope slides (for taking the readings)on it.

SamplesDifferent set of samples we tested.

Fresh Juice vs Tropicana

 

The results so far: Blue- Tropicana; Green-Fresh Orange Juice

 

-Sanket Shah

 

Project Updates: Accelerometer Team

Me and Aditya are working on accelerometer for tracking the body position during the sleep. Yesterday I calibrated the accelerometer sensor so that it gives proper values according to the position of the body, then finally attached the sensor to the frame and collected sample data wearing it.

Image

 

After mounting the sensor we plotted real time data using MATLAB. We successfully recorded the real time data of all 3 axes and also plot its magnitude in real time.

 Image Image 

The figure above on the left shows the real time data of all three axes which we plotted real time wearing the frame. The figure above on the right shows the resultant magnitude. The next thing will be to feed this data to machine learning algorithms for classifications.

-Anchit Srivastava

 

 

ELISA all the way

ELISA All The Way !

Hello World of Pathogens! Here we are, its Day 12 and yesterday we got our very first calibration curve for ELISA Test. We are capturing Fluorescence of the Dye(cooked the dye ourselves!) and got pretty good results. Now we are focusing on building a cooler device for the our plate reader. We are looking forward to pay a visit to Pathogen lab at Hinduja and get some real time images for further analysis and optimization.

Fluorescence Of Highlighter Ink

Fluorescence Of Highlighter Ink

 

 

 

 

 

 

 

Basic device for Testing

Basic device for Testing

Team ELISA! Diagnosing the World 🙂