Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions

Abstract

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 (0.909) and a low root mean square error of prediction (0.035), which can be useful for the design of multispectral imaging systems.

Hyperspectral Imaging Coupled with Multivariate Analysis and Image Processing for Detection and Visualisation of Colour in Cooked Sausages Stuffed in Different Modified Casings

“…A visible near-infrared hyperspectral imaging equipment (JFE, Techno-Research Corporation, Tokyo, Japan) working in the range 380-1000 nm was utilised to line scan (push-broom) the core of the sausage. The specific system description can be found elsewhere [4][5][6]. As the white reference, a uniform, stable, and high reflectance white ceramic tile (about 99% reflectance) was used for calibrating the HSI system.…”

“…The predictive ability of the model using these wavelengths was compared to the predictive ability obtained using the full spectrum. The purpose of doing this was to simplify the model and to improve its accuracy [4][5][6]45]. If the model accuracy established by important optimum wavelengths performs comparably to the accuracy obtained when using the full spectrum, then the simplified model using the IOW could be proposed for an efficient online multispectral imaging system.…”

“…Hyperspectral imaging (HSI), which can provide the spectral information along with the spatial distribution from a subject, is superior to the traditional spectroscopic methods [1]. The prediction map, as the most attractive part of HSI, enables the different attributes to be displayed from spot to spot in samples [2][3][4][5][6][7][8]. It has been intensively applied for meat adulteration detection [9][10][11][12]; monounsaturated and polyunsaturated fatty acid prediction in processed pork meats [13]; textural feature assessments of normal and white striping broiler breast meat [14]; and monitoring bacterial contaminations in chicken meat [15], the shelf-life of packaged bratwurst [16], and the pH [4], colour, [5] and triphosphate content [6] in ready-to-eat sausages.…”

“…Previous studies addressed the microbial attributes [35,36], volatile composition [37], and physicochemical properties of sausages stuffed in modified casings [29,35,37]. HSI has showed its potential to monitor the evolution of surface sausage colour over time [2] and to determine pH, colour, and adenosine triphosphate in big Japanese sausage slices [4][5][6]. However, visualisation of the interior colour change related to hog casings with different modifications remains unexploited.…”

Nontargeted Analytical Methods as a Powerful Tool for the Authentication of Spices and Herbs: A Review

“…In relation to the availability of software for building chemical maps, distribution maps shown by HSI are commonly made using MATLAB (Roberts, Power, Chapman, Chandra, & Cozzolino, 2018); the free software R Statistics demonstrates an alternative to establish the distribution map (Feng, Makino, Yoshimura, & Rodríguez-Pulido, 2018). R Statistics is a versatile and user-friendly tool with many applications (Feng, Makino, Yoshimura, Thuyet Dang, & García-Martín, 2017), which can also be applied to generate chemical maps for spices.…”