Applicability of a Respiration-Based Ascorbic Acid Prediction Model to a Range of Selected Vegetables

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  • TECHAVUTHIPORN Chairat
    The Japanese Society of Agricultural Machinery The United Graduate School of Agricultural Science, Gifu University
  • NAKANO Kohei
    The Japanese Society of Agricultural Machinery The United Graduate School of Agricultural Science, Gifu University
  • MAEZAWA Shigenori
    The United Graduate School of Agricultural Science, Gifu University The Japanese Society of Agricultural Machinery

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  • 数種青果物に対する呼吸に基づくアスコルビン酸含量変化予測モデルの適用性

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Abstract

To confirm the applicability of the respiration-based total ascorbic acid (AA; L-ascorbic and dehydroascorbic acids) prediction model, described in our previous research, to a wide range of commodities, the relationship between AA change and the CO2 production rate in stored cabbage, cauliflower, spinach and green pepper was investigated in an experiment involving various temperature conditions (5, 10, 20 and 30°C). The percentage of relative AA (AArel), which was normalized against the initial value, was plotted against the accumulated amount of CO2 production (ARCO<sub>2</sub>). AArel decreased with increasing ARCO<sub>2</sub> in cabbage, cauliflower and spinach. The relationship between AArel and ARCO<sub>2</sub> was well expressed by the equation (AArel=100 exp (-β ARCO<sub>2</sub>)) in these products. On the other hand, this relationship could not be observed in green pepper (Capsicum), which was considered a limitation of the model. The statistical test for homogeneity of regression coefficients showed that there was no difference among the β parameters of broccoli (previous data), cabbage and cauliflower, all of which belong to the genus Brassica. The β parameter of spinach, belonging to the genus Spinacia, was significantly different from the other commodities. The results obtained in this study showed that our proposed model is applicable to not only for broccoli but also for other commodities. The model could be a useful tool in designing the optimum distribution chain for these products.

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