From bench to worktop: Rapid evaluation of nutritional parameters in liquid foodstuffs by IR spectroscopy. (15th December 2021)

• Record Type: Journal Article
• Authors: Perez-Guaita, David; Richardson, Zack; Rajendra, Amrut; Byrne, Hugh J.; Wood, Bayden
• Is Part Of: Food chemistry. Volume 365(2021)
• Publication Date: 2021-12-15
• Subjects: Infrared spectroscopy -- Chemometrics -- Nutritional parameters -- Partial least squares -- Recipe analysis; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.130442
• Languages: English
• ISSNs: 0308-8146

Abstract

Graphical abstract: Highlights: Infrared was evaluated for the estimation of nutritional parameters in the industrial kitchen context. 1-minute measurements allowed prediction of nutritional parameters in a wide variety of food. Dry measurements predicted accurately fractions of nutrients relative to total solids. A dedicated Matlab App was created to facilitate implementation in a kitchen environment. The method offered a good alternative for predicting nutritional values in an industrial kitchen set-up. Abstract: We evaluated the use of attenuated total reflectance infrared spectroscopy for simultaneous in situ quantification of the nutritional composition of liquid food stuffs in the industrial kitchen context. Different methodologies were compared, including dry and wet acquisition along with instrument parameters and measurement times of 4 and 60 s. The most effective technique was 1-minute measurement, with prediction errors of 2.6, 0.7, 1.0, 2.2, 0.8, 2.4 g/100 mL and 150 Kcal, for carbohydrates, proteins, fat, sugars, saturated fat, water and energy values, respectively. The 4-second method resulted in larger errors but was more applicable for inline measurements. Dry measurements successfully predicted the fractions of proteins, fat, carbohydrates, and sugars, relative to total solids. An app was created to facilitate implementation in a kitchen environment. Compared with other techniques recommended by the FAO, the approach offered a simple alternative for simultaneous prediction of nutritional parameters in an industrial kitchen set-up.