Feasibility study of smartphone-based Near Infrared Spectroscopy (NIRS) for salted minced meat composition diagnostics at different temperatures. (25th April 2019)

• Record Type: Journal Article
• Authors: Kartakoullis, Andreas; Comaposada, Josep; Cruz-Carrión, Alvaro; Serra, Xavier; Gou, Pere
• Is Part Of: Food chemistry. Volume 278(2019)
• Page Start: 314
• Page End: 321
• Publication Date: 2019-04-25
• Subjects: NIR -- Smartphone-based spectrometer -- Meat composition -- Temperature -- PLS-models -- Random Forest regression; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2018.11.054
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Smartphone-based NIR was used to estimate salted meat composition. Accuracy of fat and moisture estimates are acceptable for quality control purposes. Predictive ability of smartphone-based NIR is similar to a benchtop spectrometer. Random Forest Predictive models are robust at temperatures from −14 °C to 25 °C. Abstract: This research work evaluates the feasibility of a smartphone-based spectrometer (740–1070 nm) for salted minced meat composition diagnostics at industrial scale. A commercially available smartphone-based spectrometer and a benchtop NIR spectrometer (940–1700 nm) were used for acquiring 1312 spectra from meat samples stored at four different temperatures ranging from −14 °C to 25 °C. Thereafter, for each spectrometer, PLS and Random Forest regression models specific for each temperature and global models were created to predict the fat, moisture and protein contents. Fat and moisture can be estimated with the global model in a wide range of temperatures by using the smartphone-based spectrometer, which has an acceptable accuracy for quality control purposes (RPD > 7) and comparable to the accuracy of a benchtop spectrometer.