Evaluation of a mobile NIR spectrometer and cloud data analysis system for food quality rapid assessment

Near Infrared spectroscopy (NIR) is one of main techniques used in industry to determine food quality parameters in a rapid and nondestructive way. Recently, some simple and cheaper NIR spectrometers are becoming more common. The aim of this research was to evaluate the performance of the miniaturized NIR SCiO for the determination of quality parameters of some common foodstuff. Vegetable (apples, peaches, pistachio and tomato paste), bakery (bread), diary (milk and cheese) and confectionary (chocolate) products were analyzed by using SCiO sensor and a common NIR tool (MATRIX TM-F, Bruker Optics). To estimate specific qualitative parameters classification and predictive statistical models were developed by using SCiO Lab tool and a commercial multivariate statistical software. Similar results were achieved by using SCiO and classical NIR. In general, good models were obtained for all food products with determination coefficients (R2) range from 0.765 to 0.991. Considering the final consumer demands, the SCiO solution appeared a suitable instrument for a rapid evaluation of many food quality indexes.