Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions. Issue 2 (26th December 2017)

• Record Type: Journal Article
• Authors: Feng, Chao‐Hui; Makino, Yoshio; Yoshimura, Masatoshi; Thuyet, Dang Quoc; García‐Martín, Juan Francisco
• Is Part Of: Journal of food science. Volume 83:Issue 2(2018)
• Page Start: 358
• Page End: 366
• Publication Date: 2017-12-26
• Subjects: chemometrics -- image analysis -- physical preservation methods -- sausage; Food -- Periodicals; Food -- Research -- Periodicals; Food -- Periodicals; Research -- Periodicals; Levensmiddelen; Voeding; 664
• DOI: 10.1111/1750-3841.14024
• Languages: English
• ISSNs: 0022-1147

Abstract

Abstract : Abstract: The potential of hyperspectral imaging with wavelengths of 380 to 1000 nm was used to determine the pH of cooked sausages after different storage conditions (4 °C for 1 d, 35 °C for 1, 3, and 5 d). The mean spectra of the sausages were extracted from the hyperspectral images and partial least squares regression (PLSR) model was developed to relate spectral profiles with the pH of the cooked sausages. Eleven important wavelengths were selected based on the regression coefficient values. The PLSR model established using the optimal wavelengths showed good precision being the prediction coefficient of determination ( R p 2 ) 0.909 and the root mean square error of prediction 0.035. The prediction map for illustrating pH indices in sausages was for the first time developed by R statistics. The overall results suggested that hyperspectral imaging combined with PLSR and R statistics are capable to quantify and visualize the sausages pH evolution under different storage conditions. Practical Application: In this paper, hyperspectral imaging is for the first time used to detect pH in cooked sausages using R statistics, which provides another useful information for the researchers who do not have the access to Matlab. Eleven optimal wavelengths were successfully selected, which were used for simplifying the PLSR model established based on the full wavelengths. This simplified model achieved a high R p 2 (0.909) and a low root mean square error of prediction (0.035), which can be useful for the design of multispectral imaging systems.