Detection of Pea and Spinach Adulteration in Pistachio Nuts using a Low-Cost Spectral Sensor
DOI:
https://doi.org/10.24925/turjaf.v12is2.2206-2215.6814Keywords:
adulteration, chemometric methods, PCA, deep learning, Artificial Neural NetworksAbstract
Pistachio is a product frequently used in many desserts, especially baklava. Especially ground pistachios are frequently subjected to adulteration due to their high demand and cost. Green peas and spinach are among the most counterfeited products by being mixed with pistachios due to their organoleptic properties, colour similarity and cheapness. However, when these products are mixed with ground pistachios at certain proportions, rapid and on-site detection of counterfeiting is often not possible by methods such as visual inspection. Therefore, the current study aims to detect the adulteration of green peas and spinach in pistachios using a low-cost spectral sensor technology and chemometric methods. In this context, green pea and spinach samples were mixed with pistachios at concentrations between 5-50% (w/w) in 5% increments. Reflectance spectra of pure pistachios and mixed samples were obtained between 410-940 nm. Deep learning method was used to train a model developed to detect fraud rates in samples. Principal Component Analysis was used for dimension reduction in the data fed to the model, and coefficient of determination (R2), root mean square error (RMSE), and residual predictive deviation (RPD) statistics were used to test the goodness of fit of the regression problem. For training, validation and test data, R2 was 0.85, 0.83, and 0.80; RMSE was 5.81, 6.13, and 6.71; RPD was 2.55, 2.44, and 2.21, respectively. As a result of the findings, it has been revealed that the low-cost spectral sensor and the developed chemometric method have the potential to be used as a non-destructive, rapid and simple method for determining the adulteration rate of green peas and spinach in pistachios, especially above 10%.
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