Abstract：
Current technologies as correlation analysis, regression analysis and classification model, exhibited various limitations in the evaluation of soybean possessing potentials, including single, vague evaluation and failure of quantitative prediction, and thereby hindering more efficient and profitable soymilk industry. To solve this problem, 54 soybean cultivars and their corresponding soymilks were subjected to chemical, textural, and sensory analyses to obtain the soybean physicochemical nature (PN) and the soymilk profit and quality attribute (PQA) datasets. A deep-learning based model was established to quantitatively predict PQA data using PN data. Through 45 rounds of training with the stochastic gradient descent optimization, 9 remaining pairs of PN and PQA data were used for model validation. Results suggested that the overall prediction performance of the model showed significant improvements through iterative training, and the trained model eventually reached satisfying predictions (|relative error| ≤ 20%, standard deviation of relative error ≤ 40%) on 78% key soymilk PQAs. Future model training using big data may facilitate better prediction on soymilk odor qualities.
摘要：
当前的技术作为相关性分析，回归分析和分类模型，在具有潜力的大豆评估中表现出各种局限性，包括单身,模糊的评价和定量预测的失败，从而阻碍了豆浆行业的效率和利润。为了解决这个问题,对54个大豆品种及其相应的豆浆进行了化学处理，纹理，和感官分析，以获得大豆理化性质（PN）和豆浆利润和质量属性（PQA）数据集。建立了基于深度学习的模型,利用PN数据对PQA数据进行定量预测。通过随机梯度下降优化的45轮训练，其余9对PN和PQA数据用于模型验证。结果表明，通过迭代训练，模型的整体预测性能显着提高，训练后的模型最终达到了令人满意的预测(|相对误差|≤20%，78%关键豆浆PQA的相对误差标准偏差≤40%)。未来使用大数据的模型训练可能有助于更好地预测豆浆气味质量。
