Application of molecular image technology in the detection of fungal toxins in rice and identification of their producing bacteria

Abstract

Abstract Objective. To explore theoretical basis and feasibility of using computer image processing technology for rapid analysis of rice mold, to promote application of this technology for rice quality analysis, and to make a new exploration for safety of rice in China to realize sustainable development of rice resources in China. Methods. Four types of rice (Zhengdan 958, Xiangyu 335, Yu'an 13, and Jundan 20) were used as research materials to simulate process of rice mildew in a specific environment (temperature 25°C, humidity 60%). Then, a correlation analysis was performed with amount of bacteria and mycotoxins (aflatoxin B1, vomitoxin, rice gibberellin, ochratoxin) in rice and a discriminant model was established. A BP neural network was used to identify degree of moldiness of rice. Results. The amount of bacteria in rice samples tended to increase with time, and color of rice grains became darker and duller as mold deepened. On 41st day, sample was seriously deteriorated and experiment could not be conducted. According to amount of bacteria, four rice samples were judged to be normal on days 1-5, pre-mold on days 7-11, mid-mold on days 13-33, and post-mold on days 33. The correlation analysis showed that there was a good correlation between amount of moldy rice and some color characteristics parameters. Y=5020.67-41.661XRt+20.199X1 value, R2=0.934; modeling process of bacterial load of Yu'an 13 introduced color characteristic parameters of B, S, I, modeled as Y=-15602.569+463.54XBn+75209.492Xsm-367.105X1t, R2=0.96; Jundan 20 The modeling process of amount of bacteria carried was introduced with color characteristic parameter I, modeled as Y=2696.205-15.445X1 value, R2=0.823 .