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


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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Fig. 1
Fig. 2
Fig. 3
Fig. 4



Near-infrared spectroscopy


Artificial neural network


Backpropagation artificial neural network




Local algorithm


Support vector machine


Support vector regression


Extreme learning machine


Principal components


Least square support vector machine


Least square support vector regression


Laser emission diode


Principal component analysis


Soft independent modeling of class analogy


Processing element


Genetic algorithm


Genetic algorithm artificial neural network


Radial basis function


Radial basis function neural networks


Partial least square


Principal component regressions


Multiple linear regressions


Mallows augmented partial residual plot


Partial residual plot


Residual plot


Residual versus PC plot


Added variable plot/partial residual plot


Lack of fit


Mean average precision


Standard normal variate transformation


Multiplicative scatter correction smoothing


Wavelet transforms


Orthogonal signal correction


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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.


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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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).

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  • Non-linear algorithm
  • NIR spectroscopy
  • Non-linear applications
  • BP-ANN
  • AdaBoost