A Review of the Use of Near-Infrared Hyperspectral Imaging (NIR-HSI) Techniques for the Non-Destructive Quality Assessment of Root and Tuber Crops

Abstract

Hyperspectral imaging (HSI) is one of the most often used techniques for rapid quality evaluation for various applications. It is a non-destructive technique that effectively evaluates the quality attributes of root and tuber crops, including yam and cassava, and their food products. Hyperspectral imaging technology, which combines spectroscopy and imaging principles, has an advantage over conventional spectroscopy due to its ability to simultaneously evaluate the physical characteristics and chemical components of various food products and specify their spatial distributions. HSI has demonstrated significant potential for obtaining quick information regarding the chemical composition of the root and tuber, such as starch, protein, dry matter, amylose, and soluble sugars, as well as physical characteristics such as textural properties and water binding capacity. This review highlights the principles of near-infrared hyperspectral imaging (NIR-HSI) techniques combined with relevant image processing tools. It then provides cases of its application in determining crucial biochemical quality traits and textural attributes of roots and tuber crops, focusing on cassava and yam. The need for more information on using NIR-HSI in the quality evaluation of yam and cassava was underscored. It also presents the challenges and prospects of this technology.

Advanced Hyperspectral Image Analysis: Superpixelwise Multiscale Adaptive T-HOSVD for 3D Feature Extraction

“…Therefore, in our experiments, we determined the optimal number of superpixel blocks using a continuous adjustment method to adapt to the specific characteristics of each dataset. In fact, we can determine the rank based on the percentage of total energy, as implemented in (8) and (9). The process of determining the optimal rank can be likened to setting a threshold for a given HSI.…”

“…Unlike conventional images, HSI extends beyond spatial features, offering detailed one-dimensional spectral profiles per pixel [4]. At the same time, hyperspectral imaging has a wide range of potential in various electromagnetic radiation frequency ranges, such as X-ray, ultraviolet, visible and near-infrared, and terahertz [5][6][7][8][9]. With applications spanning agriculture, geology, ecology, and mineralogy [10][11][12][13], HSI faces challenges in classification due to its high spectral resolution and noise, potentially leading to dimensionality disaster and reduced classification accuracy, especially with limited training samples [14,15].…”

Light and Shadow in Near-Infrared Spectroscopy: A Powerful Tool for Cannabis sativa L. Analysis

“…Near-infrared hyperspectral imaging (NIR-HSI) is a highly advanced methodology, which can capture up to several hundred images of different wavelength, offering a detailed spectral response of target features [97]. This technique is particularly adept at discerning even the most subtle variations in ground covers, as well as tracking changes over time.…”

“…Analyzing and processing such voluminous data can be computationally intensive and time-consuming [102]. On the other hand, the spectral interference and the environmental sensitivity makes it difficult to obtain good predictions to be used in quality control or process control evaluation [97].…”