Rapid Determination of Nutritional Parameters of Pasta/Sauce Blends by Handheld Near-Infrared Spectroscopy

Nowadays, near infrared (NIR) spectroscopy has experienced a rapid progress in miniaturization (instruments < 100 g are presently available), and the price for handheld systems has reached the < $500 level for high lot sizes. Thus, the stage is set for NIR spectroscopy to become the technique of choice for food and beverage testing, not only in industry but also as a consumer application. Over the last years public health awareness has grown strongly, and the control of nutritional parameters of everyday life food is just one aspect of this issue. Beyond body weight control, nutritional parameters are directly related to quality of life and disease control, such as obesity, high cholesterol, gastritis, diabetes and high blood pressure. Thus, in the present study the quantitative analysis of nutritional parameters by handheld NIR spectroscopy is exemplary demonstrated in detail for different pasta/sauce blends in combination with a chemometric data evaluation. The present research will demonstrate realistic analytical data derived from the development of partial least squares (PLS) calibration models for six different nutritional parameters (energy, protein, fat, carbohydrates, sugar, and fiber). The prediction performance of the PLS calibration models for the individual parameters was double-checked by cross-validation (CV) and test-set validation. The predicted errors for energy, carbohydrate, protein, sugar, fat and fiber were 2.7, 6.4, 8.3, 11.4, 16.1 and 18.2% (w/w), respectively. The results obtained suggest that in the near future consumers will be able to predict the nutritional parameters of their meals by using handheld NIR spectroscopy under every-day life conditions. The objective of these investigations is to prove how feasible it will be for consumers in the near future to be able to predict the nutritional parameters of their meals by using handheld NIR spectroscopy.