Machine Learning and Taguchi DOE Combined Approach for Modeling Dynamic Ultrasound-Assisted Fresh-Cut Leafy Green Sanitation

Chlorine-based fresh produce sanitation is a dynamic process, and sanitation efficiency is limited due to chlorine degradation. Here, ultrasound was coupled with a benchtop sanitation system to enhance chlorine sanitizer efficiency in fresh-cut leafy green sanitation. Taguchi design of experiments (DOE) and machine learning (ML) were combined to model the relationship between sanitation condition parameters and sanitation outcomes. Multiple ML algorithms were fitted, tuned, and compared for performance using 127 experimental trials (training-to-validation ratio = 3:1). Gaussian process regression (GPR) models showed the best performance in predicting sanitation outcomes of chemical oxygen demand (COD, R2 = 0.73), remaining Escherichia coli O157:H7 on the leaf surface (“Surface Microbe”, R2 = 0.88), and E. coli O157:H7 concentration in sanitation water (“Water Microbe”, R2 = 1.00). Cut size and agitation speed were identified as the most critical input parameters. An initial free chlorine concentration over 20 mg/L was recommended to minimize the E. coli O157:H7 concentration in sanitation water. This work showcases the combined approach of ML and DOE in optimizing fresh-cut produce sanitation. Moreover, it provides a solution for overcoming the difficulties of modeling multiple controllable and uncontrollable factors with reduced experimental runs

Enhancing Contents of γ‑Aminobutyric Acid (GABA) and Other Micronutrients in Dehulled Rice during Germination under Normoxic and Hypoxic Conditions

Biofortification of staple grains with high contents of essential micronutrients is an important strategy to overcome micronutrient malnutrition. However, few attempts have targeted at γ-aminobutyric acid (GABA), a functional nutrient for aging populations. In this study, two rice cultivars, Heinuo and Xianhui 207, were used to investigate changes in GABA and other nutritional compounds of dehulled rice after germination under normoxic and hypoxic conditions. Forty-one metabolites were identified in both cultivars treated by normoxic germination, whereas the germinated dehulled rice of Heinuo and Xianhui 207 under hypoxic treatment had 43 and 41 metabolites identified, respectively. GABA increased in dehulled rice after germination, especially under hypoxia. Meanwhile, a number of other health-beneficial and/or flavor-related compounds such as lysine and d-mannose increased after the hypoxic treatment. The accumulation of GABA exhibited genotype-specific modes in both normoxic and hypoxic treatments. With regard to GABA production, Xianhui 207 was more responsive to the germination process than Heinuo, whereas Heinuo was more responsive to hypoxia than Xianhui 207. This study provides a promising approach to biofortify dehulled rice with increased GABA and other nutrients through metabolomic-based regulation