Recent Advances in Nondestructive Analytical Techniques for Determining the Total Soluble Solids in Fruits: A Review

Comp Rev Food Sci Food Safe

2017

Self Cite

138

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.

Section: Introductionmentioning

confidence: 99%

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

Comp Rev Food Sci Food Safe

2017

Self Cite

138

“…In recent years, the nondestructive methodologies utilizing the electromagnetic spectrum technology have been emerged, including hyperspectral imaging, laser light backscattering imaging, magnetic resonance imaging, X‐ray computed tomography, near‐infrared spectroscopy, and Raman spectroscopy (Li, Sun, & Cheng, ). However, they are still time‐consuming, expensive and should be difficultly to be employed in fruit industry.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Firmness and pH for “Golden Delicious” Apple Based on Elasticity Index from Modal Analysis

Hou

Zhang

Journal of Food Science

et al. 2018

“…Hyperspectral images are those where each pixel forms an almost continuum spectrum. Each spatial location of a hyperspectral image could have hundreds of bands (Li and others ). Such huge size of data increases the calculation burden and costs of instruments, which is not possible for real‐time application (Dai and others ).…”

Section: Multispectral Imagingmentioning

confidence: 99%

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Comp Rev Food Sci Food Safe

2018

Self Cite

102

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.