Abstract

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.

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system

Yizhe Zhang, Jipeng Huang, Qiulei Zhang, Jinwei Liu, Yanli Meng, and Yan Yu
Appl. Opt. 61(12) 3419-3428 (2022)

Near Infrared Reflectance of Colored Fruits

Peter J. H. Sharpe and H. N. Barber
Appl. Opt. 11(12) 2902-2906 (1972)

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model

Lili Xu, Jinming Liu, Chunqi Wang, Zhijiang Li, and Dongjie Zhang
Appl. Opt. 62(11) 2756-2765 (2023)

Combination of near-infrared spectroscopy with Wasserstein generative adversarial networks for rapidly detecting raw material quality for formula products

Xiaowei Xin, Junhua Jia, Shunpeng Pang, Ruotong Hu, Huili Gong, Xiaoyan Gao, and Xiangqian Ding
Opt. Express 32(4) 5529-5549 (2024)

Sensory representation of surface reflectances: assessments with hyperspectral images

Hamed Karimipour, J. Kevin O’Regan, and Christoph Witzel
J. Opt. Soc. Am. A 40(3) A183-A189 (2023)