Potato chips are the most important product among all the snack industry. Quality of potato chips is monitored by their moisture content and fat content. Traditional methods are reliable, but also time consuming and expensive. The feasibility of developing a methodology for a reliable, sensitive and fast quantification of moisture and fat in potato chips by using infrared spectroscopy combined with multivariate analysis was investigated
Commercial potato chips (16) were obtained from a local market. Samples were grinded and their spectra collected using an integrating sphere (NIR) or attenuated total reflectance (MIR). Calibration models were developed to predict the content of fat and moisture in an independent set of samples. The total fat content ranged from 18% to 45% and the moisture content ranged from 1.2% to 4%. The correlation coefficients (r) obtained for the prediction model of moisture were >0.97 and standard error of cross validation (SECV) 0.96 and SECV values of 1.29% and 1.65% for NIR and MIR, respectively. Classification models developed based on Soft Independent Modeling of Class Analogy were able to differentiate the potato chips according to type of oil in which the potatoes were fried. The MIR spectroscopy was able to identify samples containing -trans fats at 965 cm-1. A fast, simple and accurate method to evaluate potato chip quality was developed by infrared spectroscopy, providing the industry a convenient technique for routine analysis of the product
