Consumer-oriented sensory optimization of yogurt: An artificial intelligence approach. (August 2022) - Bi, Kexin; Zhang, Shuyuan; Zhang, Chen; Qiu, Tong
Abstract: With the emergence of the 'chain reversal' phenomenon, the design and development of food products has gradually tended to the consumer-oriented pattern. Catering to consumer preference for sensory attributes is a key factor for food enterprises striving for favor in the market. In this paper, a sensory optimization process is proposed for searching for optimal attributes and to provide further sensory improvement suggestions. An intelligent hybrid genetic algorithm (GA)-particle swarm optimization (PSO)-tabu search (TS) algorithm is used for hybrid deep convolutional auto-encoder and support vector machine (DCAE-SVM) grey-box model optimization in high-dimensional variable space. A comparison of GA and GA-PSO on the given grey-box models and dataset demonstrated that the performance of the proposed GA-PSO-TS was effective. The entire framework is also applicable to explain the influence of brands and storage conditions on sensory attributes and consumer preference. Highlights: An artificial intelligence approach achieves consumer-oriented sensory optimization. A new GA-PSO-TS intelligent optimization method is proposed and applied. Prediction and optimization results are interpretable in the case of yogurt sensory optimization. Product design and improvement suggestions are given for dairy enterprises.