Development of a Quartz Crystal Microbalance (QCM) Sensor for the Identification of β-Pinene in Cumin

Various spices are produced in India because of various climatic conditions, and India leads the world in spice production as the largest producer. Cumin is one of the important spices which is used mainly during preparation of food dish. From consumers’ point of view, proper quality evaluation is required for cumin as this spice is used in large scale. There are few important volatiles such as β-Pinene, p-Cymene, γ-Terpinene, Cumin Aldehyde, and Carvone which are repeatedly produced through GCMS analysis for the significant essence of cumin. This study introduces a quartz crystal microbalance (QCM) sensor designed for the detection of the key volatile organic compound, β-pinene, in Indian cumin, using dietary supplements Vitamin D and Vitamin E. These vitamins possess specific chemical characteristics and functional groups that render them well-suited for either adsorbing or reacting with the target compound. The linear regression models formulated for QCM sensors coated with Vitamin D and Vitamin E are demonstrating promising outcomes in their ability to predict the β-pinene content within Indian cumin samples. At the same time, an extensive examination of authentic cumin samples was conducted employing three different models: logistic regression, support vector machine, and random forest classifier. In this regard, the results reveal that the logistic regression model stands out as particularly reliable and effective in achieving accurate predictions. Its satisfactory performance becomes evident when compared to the other models, affirming its capability to yield dependable results for β-pinene content prediction in real cumin samples.