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Multispectral Imaging for Plant Food Quality Analysis and Visualization.

Wen-Hao Su¹, Da-Wen Sun¹

¹Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD), National Univ. of Ireland, Belfield, Dublin 4, Ireland.

Comprehensive Reviews in Food Science and Food Safety

|December 22, 2020

Summary

Multispectral imaging offers a promising, noninvasive method for rapidly assessing plant-based food quality. This review highlights its application in determining physicochemical and microbiological aspects using advanced modeling and wavelength selection techniques.

Keywords:

chemometrics multispectral imaging plant foods quality safety

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Area of Science:

Food Science and Technology
Agricultural Engineering
Spectroscopy

Background:

Multispectral imaging (MSI) is an advancement over hyperspectral imaging for food quality evaluation.
Existing sensor-based techniques for food quality assessment are being surpassed by MSI's potential.
MSI provides noninvasive and rapid analysis capabilities crucial for the food industry.

Purpose of the Study:

To comprehensively review the application of multispectral sensors in assessing the quality of plant-based foods.
To summarize quality parameters (physicochemical, microbiological) determined by MSI in foods like cereals, legumes, tubers, fruits, and vegetables.
To identify effective modeling and wavelength selection methods used in MSI for food analysis.

Main Methods:

Literature review of studies employing multispectral imaging for plant food quality assessment.
Identification and summary of commonly used wavelength selection algorithms, including Successive Projection Algorithm (SPA) and Regression Coefficient (RC).
Analysis of prevalent chemometric models such as Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and their variants (LS-SVM, PLSDA), along with Multiple Linear Regression (MLR).

Main Results:

MSI effectively determines various quality parameters in diverse plant foods.
Successive Projection Algorithm (SPA) and Regression Coefficient (RC) are frequently utilized for feature wavelength selection.
Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and related models demonstrate high efficacy in analyzing plant food data.

Conclusions:

Multispectral imaging is a highly promising technique for the quality assessment of plant-based foods.
Challenges, future trends, and potential applications of MSI in the food sector are discussed.
The integration of MSI with advanced modeling techniques offers significant opportunities for innovation in food quality control.