Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks

Abstract

Dry grains from leguminous species, such as soybeans (Glycine max L.), common beans (Phaseolus vulgaris L.), chickpeas (Cicer arietinum L.) and corn (Zea mays L.), are regularly consumed for human nutrition. This paper showed the possibility of estimating bulk density as quality parameter of 4 different dry grains (soybeans, common beans, chickpeas and corn) in a same model using the average values of color descriptors from digital images combined with an artificial neural network, with low computational costs. These food products are good sources of carbohydrates, protein and dietary fiber, and they possess significant amounts of vitamins and minerals and a high energetic value. Estimation of the physicochemical properties of grains is challenging due to variations in shape, texture, and size and because the grain colors appear similar to the naked eye. In this work, an analytical method was developed based on digital images converted into ten color scale descriptors combined with a neural model to provide an accurate parameter for grain quality control with a low computational cost. The bulk densities of four type of grains, i.e., soybeans, beans, chickpeas and corn, were predicted using numerical data represented by the average values of color histograms of a ten color scale (red - R, green - G, blue - B, hue - H, saturation - S, value - V, relative RGB and luminosity - L) from digital images combined with artificial neural networks (ANNs). The reference bulk densities were empirically measured. A very good correlation between the reference values and values predicted by the ANN was achieved, and with a single ANN developed for the four grains, a correlation coefficient of 0.98 was observed for the test set. Moreover, the relative errors were between 0.01 and 5.6% for the test set.

Impact of storage technologies and duration on insect pest population, post‐harvest losses, and seed quality of stored chickpea in Ethiopia

“…The bulk density of chickpea seeds was also reported to range from 630 to 680 kg/m 3 . 48 However, previous results indicated that the long-term alteration in bulk density reported in previous study regarding bulked grains can vary depending on the specific grain types, storage conditions, and duration of storage. [49][50][51][52][53] Over time, bulk density has been observed to increase in some grain types due to factors such as moisture loss, particle settling, and compaction.…”

“…Hermetic storage units, such as PICS and SGP bags, are better for maintaining seed quality in terms of germination and bulk density compared to non‐hermetic storage units such as jute and PP bags and metal silos. The bulk density of chickpea seeds was also reported to range from 630 to 680 kg/m 3 48 . However, previous results indicated that the long‐term alteration in bulk density reported in previous study regarding bulked grains can vary depending on the specific grain types, storage conditions, and duration of storage 49‐53 .…”

Color approach to the analysis of white crystal cane sugar for the detection of solid impurities

“…For instance, Camargo et al 16 . and Floriam et al 17 . estimated the bulk density of grains using ten color descriptors, red (R), green (G), blue (B), hue (H), saturation (S), value (V), relative colors of red (Rr), green (Rg) and (Rb), and luminosity (L), using multivariate methods 18 .…”

“…Analytical methods involving digital images have been widely applied in many types of samples, such as cold meats, 10 sugarcane spirits, 11 citrus, 12,13 wine, 14 grain, 14 sugarcane, 4 and endoglucanase activity, 15 as far as the authors are aware. For instance, Camargo et al 16 and Floriam et al 17 estimated the bulk density of grains using ten color descriptors, red (R), green (G), blue (B), hue (H), saturation (S), value (V), relative colors of red (Rr), green (Rg) and (Rb), and luminosity (L), using multivariate methods. 18 Those previous studies reported a non-invasive, high-throughput, low-cost, and suitably sensitive method that demanded minimal or no chemical treatment and that could be a strong alternative technique for food analysis.…”