A review of the opportunities for spectral-based technologies in post-harvest testing of pulse grains

Abstract

Pulse grains are phenotypically diverse varying in colour, size, shape, and uniformity and have been integrated within many cultures and cuisines for several thousand years. Consumption of pulses within traditional dishes is still the dominant use for these grains, and therefore, the marketability is largely based on visual characteristics. There is also increasing interest into the utilisation of pulses in new processed food products because of their high protein content.

Pulse-quality assessment is critical within industry to determine marketability of the produce and remuneration for growers; however, the methods for assessment are largely subjective, completed by visual appraisal. Furthermore, targeted pulse-quality traits form part of the overall strategy of plant breeding programmes, but the grain-assessment methodologies are time consuming, constraining testing efficiency, and some destructive tests are reserved for advanced germplasm.

Recent advances in computing and spectral sensing technology have improved opportunities for development of non-destructive, high-throughput and accurate machine vision (MV) systems for product-quality evaluation. Algorithms based on digital image analysis have been developed to classify and quantify characteristics relating to the size, shape, colour and defects of grains and other agricultural products. Additionally, near-infrared-spectral processing has been successfully applied in the prediction of compositional constituents, such as protein and moisture, for some agricultural products.

This review describes the standard methodologies for the assessment of pulse-quality traits and developments in MV applications for grain quality assessment. Opportunities are identified, both within the pulse grain industry and plant breeding programmes, for objective and standardised post-harvest testing of pulse grains through MV.