Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Abstract

Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10−5 mg·g−1·d−1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.

Digitization of Broccoli Freshness Integrating External Color and Mass Loss

“…However, the colorimeter can only evaluate the color of the part where the sensor is attached, and it remains questionable whether the freshness of broccoli can be properly evaluated based on the spatial color change. Makino and Kousaka [ 32 ] evaluated the spatial distribution of yellowing in broccoli by hyperspectral imaging (HSI). However, since the HSI stores the spectral reflectance/absorbance for each pixel, it is necessary to analyze a huge amount of data, which may increase the time required for evaluation.…”

“…Weighing as well as image capturing is non-destructive measurement methods. Kasim et al [ 37 ], Makhlouf et al [ 30 ], Wang et al [ 25 ], Makino and Kousaka [ 32 ] also reported that mass loss or mass retention rate decreased as broccoli freshness declined. Although color and mass are the main freshness indicators of broccoli that can be measured nondestructively, they have been used as independent freshness indicators in the previous reports, and there are no studies on freshness evaluation using integrated evaluation values.…”

“…Makino et al [ 31 ] used a computer vision system (CVS) to visualize the temporal changes in the two-dimensional spatial distribution of green color retention associated with decreased freshness of broccoli flower buds. In addition to yellowing, a significant decrease in mass was also observed [ 32 ]. Similar to yellowing, mass loss was promoted as the respiration rate increased, and the O 2 consumption rate of broccoli stored under a low O 2 and high CO 2 environment was suppressed compared to the sample stored under normoxia, and the mass reduction was also suppressed accordingly [ 24 ].…”

Efficient preservation of sprouting vegetables under simulated microgravity conditions

“…However, it is quite difficult to store fresh vegetables and maintain their freshness after they have been harvested, and the loss/waste rate of vegetables during the distribution process is 18% in developed countries and 46% in developing nations [2]. The causes of vegetable loss are due to wilting, loss of L-ascorbic acid [3], discoloration [4], and spoilage [5]. Several methods for preserving vegetable freshness have been studied and analyzed.…”

Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

“…Artificial neural networks can be used in food science to model and optimize the extraction of cashew apple juice [19], to optimize an enzymatic approach to obtain modified artichoke pectin and pectic oligosaccharides [20] or to determine the broccoli buds loss green color velocity using hyperspectral camera combined with artificial neural networks [21].…”