Pandiselvam R, Prithviraj V, Manikantan M R, Kothakota Anjineyulu, Rusu Alexandru Vasile, Trif Monica, Mousavi Khaneghah Amin
Physiology, Biochemistry and Post-Harvest Technology Division, ICAR -Central Plantation Crops Research Institute, Kasaragod, Kerala, India.
Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management, Sonipat, Haryana, India.
Front Nutr. 2022 Oct 12;9:973457. doi: 10.3389/fnut.2022.973457. eCollection 2022.
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.
农产品的定性和定量评估通常采用传统的即破坏性的技术来进行。由于其固有的缺点，使用近红外光谱（NIRS）结合化学计量学的非破坏性方法可能有助于评估各种农产品。计算能力、机器学习、回归模型、人工神经网络（ANN）以及其他预测工具的进步已应用于近红外光谱，提高了其成为破坏性测量可行替代方法的潜力。此外，合适的预处理技术和波长选择方法的纳入也证明了其实际可行性。本综述重点关注用于处理所收集光谱数据的各种计算方法，并讨论近红外光谱在评估农产品质量和安全性方面的潜在应用。还讨论了与该技术相关的挑战以及潜在的未来前景。我们得出结论，近红外光谱是快速评估农产品质量和安全性的潜在有用工具。
