Using new technologies to know the nutrition contents of food is the new emerging area of research. Predicting protein content from the image of food is one such area that will be most useful to the human beings because monitoring the nutrition intake has many health benefits. Patients with rare diseases like maple syrup urine disease need to be in good diet practices in order to survive. Protein intake has to be monitored for those individuals. In this paper, protein measurement of health drink powder is performed using image processing techniques. In this work food images are captured and a new database with 990 images of 9 health drink powders is created. Protein content is predicted using deep learning convolutional neural network and also using image features with linear regression. Image features like first order statistics, histogram-oriented gradient, gray level co-occurrence matrix, local binary pattern and gradient magnitude and gradient direction features obtained by applying Prewitt, Sobel and Kirsch are used to estimate the protein content. Training and testing are done using linear regression model which uses support vector machine to obtain the optimal hyper plane. A tenfold cross validation is used to improve the statistical significance of the results. Experimental results show that the protein contents are predicted with an average error of ± 2.71. Deep learning improves the prediction with an average error of ± 1.96.

中文翻译：

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从健康饮料粉的图像中估算蛋白质。

使用新技术来了解食物的营养成分是新出现的研究领域。从食物图像中预测蛋白质含量是对人类最有用的领域之一，因为监测营养摄入具有许多健康益处。患有枫糖浆尿病等罕见疾病的患者必须保持良好的饮食习惯才能生存。必须监测那些个体的蛋白质摄入量。在本文中，保健饮料粉的蛋白质测量是使用图像处理技术进行的。在这项工作中，将捕获食物图像，并创建一个包含990种9种健康饮料粉图像的新数据库。使用深度学习卷积神经网络以及线性回归的图像特征来预测蛋白质含量。图片功能，例如一阶统计信息，通过应用Prewitt，Sobel和Kirsch获得的面向直方图的梯度，灰度共生矩阵，局部二进制模式以及梯度幅度和梯度方向特征可用于估计蛋白质含量。训练和测试使用线性回归模型完成，该模型使用支持向量机获得最佳超平面。十倍交叉验证用于提高结果的统计显着性。实验结果表明，预测的蛋白质含量平均误差为±2.71。深度学习改善了预测，平均误差为±1.96。训练和测试使用线性回归模型完成，该模型使用支持向量机获得最佳超平面。十倍交叉验证用于提高结果的统计显着性。实验结果表明，预测的蛋白质含量平均误差为±2.71。深度学习改善了预测，平均误差为±1.96。训练和测试使用线性回归模型完成，该模型使用支持向量机获得最佳超平面。十倍交叉验证用于提高结果的统计显着性。实验结果表明，预测的蛋白质含量平均误差为±2.71。深度学习改善了预测，平均误差为±1.96。