Machine Learning Enabled Nanosensor Array for Monitoring Citrus Juice Adulteration
Christopher W. Smith, Mahera J. Kachwala, Rachel Cole, Mehmet V. Yigit
Abstract
Citrus fruit is a global commodity that is decreasing in production, leading to adulteration and fraud of citrus juices. Here, a biomarker-free detection assay was developed using an optical nanosensor array to aid in the food safety of citrus juices. Coupling the machine learning capability of our computational process named algorithmically guided optical nanosensor selector (AGONS) with the fluorescence data collected using our nanosensor array, we studied hundreds of citrus juice adulterations. Over 707 measurements of pure and adulterated citrus juices were collected for prediction. Overall, our approach achieved above 90% accuracy on three data sets in discriminating three pure citrus fruit juices, artificially sweetened tangerine juice with various concentrations of corn syrup, and juice-to-juice dilution of orange juice using apple juice. This machine learning-enabled nanosensor array can be applied toward combating food fraud worldwide in the rising threat to food security.