Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Abstract

Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

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

“…Through the use of analytical procedures and the analysis of the resulting data, relevant information about food composition [2], its chemical composition and structure [3], physicochemical properties [4], and sensory characteristics [5] can be obtained. Due to the dynamism of a modern food industry and increasing consumer self-awareness, even established analytical techniques are constantly challenged.…”

“…More recently, a diverse range of hyperspectral devices, including cameras and spectral imaging devices, are now readily available providing exciting new possibilities in food analysis and processing. This technology can acquire either single or multiple images at discrete wavelengths, presenting the potential for the detection of specific attributes relating to quality in an extensive range of raw materials and products using in the manufacture of foods [1][2][3][4][5][6][7][8]. Overall, the availability of hyperspectral (HSI) and multispectral imaging (MSI) systems allowed for obtaining spatial, spectral, and multi-constituent information about the sample being analysed [1][2][3][4][5][6][7][8].…”

“…Efforts have therefore been exerted towards the introduction and advancements of innovative imaging technologies for fast, non-invasive, and non-destructive monitoring of ripening and maturity stages of fresh produce in the farm and packing shed [1][2][3][4][5][6][7][8]. In the last decade, vibrational spectroscopy, hyperspectral/multispectral imaging, and biomimetic sensors have started to feature with increasing prominence as rapid and efficient methods for the assessment of food quality, safety, and authentication [1][2][3][4][5][6][7][8]. Moreover, these methods present useful alternatives to traditional expensive and time-consuming techniques, such as those on a microbiological platform.…”

“…Multispectral imaging involves the acquisition of spectral images at few discrete and narrow wavebands (bandwidths of between 5 and 50 nm) and it is considered to be an improvement of hyperspectral imaging as this technology is cost effective [1][2][3][4][5][6][7][8]. The result is the ability of MSI to simultaneously predict multiple components, providing a key advantage and rendering a promising future outlook with a single, automated image acquisition [1][2][3][4][5][6][7][8]. Hyperspectral imaging relies on one of two sensing modes as in-line scanning (push broom) mode or as filter-based imaging mode [1][2][3][4][5][6][7][8].…”

“…This results in three-dimensional (3D) hyperspectral images, also called hypercubes [1][2][3][4][5][6][7][8]. In filter-based imaging mode, spectral images are obtained from stationary targets for a waveband sequence using either a liquid crystal tuneable filter (LCTF) or an acousto-optical tunable filter (AOTF) [1][2][3][4][5][6][7][8]. In-line scanning is the most common as it is relatively easy to implement, particularly for real-time and online applications.…”