The good fresh produce qualities at harvest must be maintained during the postharvest stages until consumption. Managers must make decisions for the orchards, packing, storage, and transport to optimize quality, which requires constant monitoring. Near-infrared spectroscopy is the most popular emerging non-destructive technology for pre- and post-harvest applications.
Fleshy fruits and vegetables provide fundamental nutrients to people but are highly perishable. Around 20–40% goes to waste between harvest and the consumers. So, production and handling processes focus on reducing losses and maintaining quality.
The conventional methods for assessing quality are destructive, time-consuming, and impractical for automation. In contrast, emerging non-destructive technologies, particularly Near-infrared spectroscopy (NIRS), have found wide commercial applications for quantitative analysis of chemical or physical attributes. They can be used without sample preparation and are suitable for analyzing packaged materials.
The NIRS technology measures light interaction with commodities in the 780–2500 nm spectrum, particularly the short-wave NIR region (750–1100 nm), and is based on the distinct NIR spectra that each material’s bio composition produces.
While fixed and portable systems exist, the recent trend is to develop portable devices, such as Felix Instruments quality meters (F750 and F751s) for pre- and post-harvest applications, aided by chemometrics.
NIRS is mainly used in the preharvest phases for assessing fruit maturity and precision agriculture.
Figure 1. Causes of postharvest losses along the supply chain, Palumbo et al. 2022. (Image credits: https://www.mdpi.com/2304-8158/11/23/3925
Managers make post-harvest decisions for sorting, grading, storage, ripening, and retailing depending on internal quality attributes estimated by NIRS.
Handheld NIRS tools are helpful in storage, ripening, repacking, distribution, and retail centers.
Food quality and yield can be enhanced by applying NIRS early in the ‘upstream’ for the best effects, as maturity attributes at harvest decide post-harvest quality, storage life, sorting, grading, handling, and marketing decisions.
Beghi, R., Buratti, S., Giovenzana, V., Benedetti, S. & Guidetti, R. (2017). Electronic nose and visible-near infrared spectroscopy in fruit and vegetable monitoring. Reviews in Analytical Chemistry, 36(4), 20160016. https://doi.org/10.1515/revac-2016-0016
Cattaneo, T.M.P., & Stellari, A. (2019). Review: NIR Spectroscopy as a Suitable Tool for the Investigation of the Horticultural Field. Agronomy, 9, 503. https://doi.org/10.3390/agronomy9090503
Habib, M. K, & Rizk, H. (2021). Pre-Harvest and Post-Harvest Techniques for Plant Disease Detections. IntechOpen. doi: 10.5772/intechopen.97612
Palumbo, M., Attolico, G., Capozzi, V., et al. (2022). Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview. Foods, 11, 3925. https://doi.org/10.3390/foods11233925
Pérez-Marín, D., Torres, I., Entrenas, J.-A., Vega, M., & Sánchez, M.-T. (2019). Pre-harvest screening on-vine of spinach quality and safety using NIRS technology. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 207, 242–250. https://doi.org/10.1016/j.saa.2018.09.035
Prananto, J. A., Minasny, B., & Weaver, T. (2021). Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS). PeerJ, 9, e11042. https://doi.org/10.7717/peerj.11042
Recena, R., Fernández-Cabanás, V. M., & Delgado, A. (2019). Soil fertility assessment by VIS-NIR spectroscopy: Predicting soil functioning rather than availability indices. Geoderma, 337, 368–374. https://doi.org/10.1016/j.geoderma.2018.09.049
Sohaib Ali Shah, S., Zeb, A., Qureshi, W. S., Arslan, M., Ullah Malik, A., Alasmary, W., & Alanazi, E. (2020). Towards fruit maturity estimation using NIR spectroscopy. Infrared Physics & Technology, 111, 103479. https://doi.org/10.1016/j.infrared.2020.103479
Walsh, K. B., McGlone, V. A., & Han, D. H. (2020). The uses of near infra-red spectroscopy in postharvest decision support: A Review. Postharvest Biology and Technology, 163, 111139. https://doi.org/10.1016/j.postharvbio.2020.111139