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.

近红外（NIR）光谱技术是一种低成本技术，具有无损的快速特性，精度，控制，准确性，可重复性和可再现性，已在大多数行业中广泛用于食品质量测量。已将其与不同的建模技术相结合，被认为是提高食品非破坏性测量准确性和鲁棒性的一种方式。这篇综述概述了非线性算法在近红外光谱技术特定的食品质量和安全中的应用。该评论还提供了有关NIR光谱原理的深入知识以及不同的非线性模型，例如人工神经网络（ANN），AdaBoost，局部算法（LA），支持向量机（SVM）和极限学习机（榆树）。此外，