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An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis

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Abstract

Near-infrared (NIR) spectroscopy as a low-cost technique with its non-destructive fast nature, precision, control, accuracy, repeatability, and reproducibility has been extensively employed in most industries for food quality measurements. Its coupling to different modeling techniques has been identified as a way of improving the accuracy and robustness of non-destructive measurement of foodstuffs. This review provides an overview of the application of non-linear algorithms in food quality and safety specific to NIR spectroscopy. The review also provides in-depth knowledge about the principle of NIR spectroscopy along with different non-linear models such as artificial neural network (ANN), AdaBoost, local algorithm (LA), support vector machine (SVM), and extreme learning machine (ELM). Moreover, non-linear algorithms coupled with NIR spectroscopy for ensuring food quality and their future perspective has been discussed.

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Abbreviations

NIRS:

Near-infrared spectroscopy

ANN:

Artificial neural network

BP-ANN:

Backpropagation artificial neural network

BP:

Backpropagation

LA:

Local algorithm

SVM:

Support vector machine

SVR:

Support vector regression

ELM:

Extreme learning machine

PCs:

Principal components

LS-SVM:

Least square support vector machine

LS-SVR:

Least square support vector regression

LED:

Laser emission diode

PCA:

Principal component analysis

SIMCA:

Soft independent modeling of class analogy

PE:

Processing element

GA:

Genetic algorithm

GA-ANN:

Genetic algorithm artificial neural network

RBF:

Radial basis function

RBFNN:

Radial basis function neural networks

PLS:

Partial least square

PCR:

Principal component regressions

MLR:

Multiple linear regressions

APaRP:

Mallows augmented partial residual plot

PRP:

Partial residual plot

RP:

Residual plot

e-PC:

Residual versus PC plot

AVP/PaRP:

Added variable plot/partial residual plot

LOF:

Lack of fit

Map:

Mean average precision

SNV:

Standard normal variate transformation

MSC:

Multiplicative scatter correction smoothing

WT:

Wavelet transforms

OSC:

Orthogonal signal correction

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Acknowledgments

We would like to acknowledge our deep appreciation to all the members of the non-destructive research team of Jiangsu University for their diverse assistance in the course of this research.

Funding

This work has been financially supported by National Key Research and Development Program of China (2017YFC1600801), Natural Science Foundation of Jiangsu Province (BK20190100), the Key R&D Project of Jiangsu Province (BE2017357), and the Project of Faculty of Agricultural Equipment of Jiangsu University.

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Authors and Affiliations

  1. School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 213013, People’s Republic of China

    Muhammad Zareef, Quansheng Chen, Md Mehedi Hassan, Muhammad Arslan, Waqas Ahmad, Felix Y. H. Kutsanedzie & Akwasi A. Agyekum

  2. Department of Food Science and Technology, Gomal University, D.I. Khan, Pakistan

    Malik Muhammad Hashim

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  1. Muhammad Zareef
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  2. Quansheng Chen
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  3. Md Mehedi Hassan
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  4. Muhammad Arslan
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  5. Malik Muhammad Hashim
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  7. Felix Y. H. Kutsanedzie
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  8. Akwasi A. Agyekum
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Correspondence to Quansheng Chen.

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Zareef, M., Chen, Q., Hassan, M.M. et al. An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis. Food Eng Rev 12, 173–190 (2020). https://doi.org/10.1007/s12393-020-09210-7

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  • DOI: https://doi.org/10.1007/s12393-020-09210-7

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