Digital technologies to assess yoghurt quality traits and consumers acceptability. (10th May 2022)

• Record Type: Journal Article
• Authors: Gupta, Mitali K; Viejo, Claudia Gonzalez; Fuentes, Sigfredo; Torrico, Damir D; Saturno, Patrizia Camille; Gras, Sally L; Dunshea, Frank R; Cottrell, Jeremy J
• Is Part Of: Journal of the science of food and agriculture. Volume 102:Number 13(2022)
• Page Start: 5642
• Page End: 5652
• Publication Date: 2022-05-10
• Subjects: biometrics -- machine learning -- physiological responses -- emotions -- near‐infrared; Food -- Periodicals; Agriculture -- Periodicals; 664
• DOI: 10.1002/jsfa.11911
• Languages: English
• ISSNs: 0022-5142

Abstract

Abstract: BACKGROUND: Sensory biometrics provide advantages for consumer tasting by quantifying physiological changes and the emotional response from participants, removing variability associated with self‐reported responses. The present study aimed to measure consumers' emotional and physiological responses towards different commercial yoghurts, including dairy and plant‐based yoghurts. The physiochemical properties of these products were also measured and linked with consumer responses. RESULTS: Six samples (Control, Coconut, Soy, Berry, Cookies and Drinkable) were evaluated for overall liking by n = 62 consumers using a nine‐point hedonic scale. Videos from participants were recorded using the Bio‐Sensory application during tasting to assess emotions and heart rate. Physicochemical parameters Brix, pH, density, color ( L, a and b ), firmness and near‐infrared (NIR) spectroscopy were also measured. Principal component analysis and a correlation matrix were used to assess relationships between the measured parameters. Heart rate was positively related to firmness, yaw head movement and overall liking, which were further associated with the Cookies sample. Two machine learning regression models were developed using (i) NIR absorbance values as inputs to predict the physicochemical parameters (Model 1) and (ii) the outputs from Model 1 as inputs to predict consumers overall liking (Model 2). Both models presented very high accuracy (Model 1: R = 0.98; Model 2: R = 0.99). CONCLUSION: The presented methods were shown to be highly accurate and reliable with respect to their potential use by the industry to assess yoghurt quality traits and acceptability. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.