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Overview
Screening for the presence of an adulterant in a material is an essential task for numerous businesses. Screening methods for a certain adulterant could fail as new adulterants could be used to pass the established screening method. Detecting unknown adulterants in a known material is a real challenge. The spectral fingerprint of a material is a strong tool that can be used in this application. NeoSpectra offers a quick, accurate, and on-spot analysis to verify the purity of the receiving materials. In this application note, we demonstrate the use of NeoSpectra handheld NIR Spectrometer for quick detection of the presence of adulterants in milk powders.
Introduction
Dairy products are among the most widely consumed goods in the world, making milk one of the most adulterated foods. Urea (Carbamide - CO(NH2)2) is a natural constituent of milk and is rich in Nitrogen. In typical milk testing practices, the protein content of milk is estimated by analyzing the nitrogen content. Therefore, to increase the detectable amount of nitrogen in milk, urea is often used as a milk adulterant. This practice allows adulterators to falsely mark their milk as higher quality milk with higher protein content and charge higher prices.
This experiment aims to identify the presence of urea in milk powder samples by detecting its spectral response to Near InfraRed (NIR) light. This provides a powerful analysis tool to detect if any adulterants have been added to the sample. This demonstrates that NeoSpectra Scanner, characterized by its portability, compact size and cost-effectiveness, enables the screening of pure materials on-site.
How NIR works for conformity testing
Conformity means ensuring that a product meets specific quality standards and requirements. Near-Infrared (NIR) spectroscopy helps in this process by using light. The NeoSpectra Scanner shines light on a sample and analyzes the response of the reflected light. Each material has a unique reflection pattern, like a fingerprint. By comparing this pattern to those of standard samples, the solution can quickly tell if the sample meets the required standards. Sophisticated algorithms calculate a Conformity Index (CI), which is a number that shows how well the sample matches the standard.
Experiment design
Sample set:
A total of 5 milk powder samples were collected from different commercial sources.
The samples were divided into two main groups:
- Reference samples: Unadulterated milk powder samples
- Test samples: Powder milk samples adulterated with urea at various concentrations: 1.25%, 2.5%, and 5%.
To ensure the reusability of the models across different devices, a total of 5 instruments were used to collect spectra. One instrument was used to collect the reference samples and the remaining 4 instruments were used to collect spectra of the test samples.
Measurement conditions:
- Setup: Reflection using powder kit
- Spectral range: 1,350 – 2,550 nm
- Scan time: of 3s
- Measurements: Each sample was measured 4 times with the NeoSpectra Scanner and averaged for the analysis.
- Interval time: 12 s
- Temperature: Room temperature
Screening algorithm
An envelope around the mean of the reference spectra is used as an acceptance criterion. If the test spectrum falls fully inside this envelope, then this sample passes this screening algorithm, otherwise, this sample doesn’t match the reference samples. The envelope is based on the standard deviation of the reference spectra at every wavelength as shown in Figure 1.
While testing, the deviation of the test spectrum from the mean reference spectrum is divided by the standard deviation of the reference spectra at every wavelength.
The evaluation metric is the maximum normalized deviation from all wavelengths, called the conformity index.
CI= max[ ( test_spectrumi-meani )/ stdi ]
Where test_spectrumi is the spectral response of the test sample at wavelength i, meani is the mean of spectral responses of the reference samples at wavelength i, and stdi is the standard deviation of spectral responses of the reference samples at wavelength i.
The screening algorithm is built-in in the NeoSpectra Conformity app that enables users to self-develop quick pass/fail testing models.
Results and Discussion
3 milk powder samples adulterated with different concentrations of Urea are tested with 4 scanners, beside an unadulterated sample. All adulterated samples were detectable across all 4 NeoSpectra scanners as shown in Figure 2.
A threshold of 5 for the conformity index was found to be the most suitable threshold for this application.
Conclusion
NeoSpectra Scanner, when used in conjunction with the Conformity app, has demonstrated exceptional performance in detecting urea-adulterated milk powders. The straightforward sample measurement process and the ease of model development, which requires no prior modeling expertise, facilitate the rapid implementation of this tool in various settings with minimal time investment. This comprehensive solution effectively identifies economically motivated adulterations (EMA) and subsequently enables quick decision making whenever and wherever adulteration is detected.
