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Review
. 2022 Oct 12:9:973457.
doi: 10.3389/fnut.2022.973457. eCollection 2022.

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

R Pandiselvam  1 , V Prithviraj  2 , M R Manikantan  1 , Anjineyulu Kothakota  3 , Alexandru Vasile Rusu  4   5 , Monica Trif  6 , Amin Mousavi Khaneghah  7
Affiliations
Review

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

R Pandiselvam et al. Front Nutr. .

Abstract

The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.

Keywords: cultivar authentications; damage detection; maturity; rapid method; spectroscopy; texture.

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Conflict of interest statement

Author MT was employed by Centiv. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Spectral data collection unit.
Figure 2
Figure 2
Flow chart depicting the NIRS modeling process.
Figure 3
Figure 3
The spectral patterns of products at various stages of maturity.
See this image and copyright information in PMC

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