Multispectral Imaging for Plant Food Quality Analysis and Visualization

Abstract

The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor‐based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant‐based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS‐SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.

Detecting Green Mold Pathogens on Lemons Using Hyperspectral Images

“…Sci. 2020, 10,1209 3 of 16 the bright yellow skin color becomes greener as the green mold infection gets deeper. To show these color changes, the pathogen infected lemon samples are prepared and the hyperspectral images of each infected sample are taken and their intensity distributions are found in the following ways.…”

“…For this purpose, a quick and accurate method of identifying the infected fruits, i.e., inspecting the large amount of fruits in real-time and quantifying the degree of the infections should be devised. In this regard, the spectral imaging, which photographs an object or scene with the use of a spectral filter, has been recognized as a very promising inspection method of the qualities of agricultural products and foods [10][11][12][13][14][15][16]. This is because the imaging can reveal physical, chemical, geometrical, and optical parameters of the objects being inspected, whereas the spectroscopic method can provide the optical parameter only, because it can measure only the spectral intensity.…”

A Short Update on the Advantages, Applications and Limitations of Hyperspectral and Chemical Imaging in Food Authentication

“…The use of HSI analysis has been expanded to the analysis of fruits and horticultural products [16][17][18][19][20]. To increase the classification accuracy and the stability of the prediction model, an approach to evaluate the quality of samples' HSI is needed, accordingly to these authors [16][17][18][19][20].…”

“…To increase the classification accuracy and the stability of the prediction model, an approach to evaluate the quality of samples' HSI is needed, accordingly to these authors [16][17][18][19][20]. The spectral correlation analysis of each pixel is used to determine the quality of the samples analysed using the hyperspectral image system [16].…”

Optimal modeling pattern of variables selection on analog complex using UVE-PLS regression

“…NIR has been proved to be a powerful analytical tool combined with partial least squares (PLS) which is a commonly used multivariate calibration [6,7]. However, full spectra have much redundant information in the spectral data such as noise, background and overlapping information that would influence the model development and prediction [8]. It is essential and important to compress the large amount of data and to choose the useful and relevant information before executing PLS modeling [9][10][11].…”