Rapid, precise, and relevant methods for predicting the sensory quality of frozen peas were sought. Pea batches chosen to span many different types of quality variations were analyzed by a consumer test, sensory laboratory analysis, and traditional chemical and physical measurements as well as by near-infrared reflectance analysis (NIR). Partial least-squares (PLS) regression was used to reveal the relationships between the different types of measurements. A noise-compensated value, relative ability of prediction (RAP), was used to express the degree of prediction (1.0 = perfect prediction). NIR was found to predict the sensory texture variables (RAP = 0.79) better than the flavor variables (RAP = 0.67). Average consumer preference was less well predicted (RAP = 0.48) by NIR. This was interpretable since NIR gave a better description of the chemical and physical methods relevant for texture (e.g., dry matter (RAP = 0.93)) than the flavor-related variables (e.g., sucrose (RAP = 0.45)) that apparently determine the consumer preference. However, NIR was found to describe the average variation in sensory quality better than the traditional tenderometer value (TV). The highest prediction of sensory variables was obtained by a combination of NIR, TV, and chemical measurements (RAP = 0.87 and 0.80 for texture and flavor variables, respectively). We discuss the predictive validity and the meaning of the present predictive abilities in practice, leading to a conclusion that NIR has a potential for predicting the sensory quality of peas.

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