Estimation of the quality of food products is vital in determining the properties and validity of the food concerning the baking and other manufacturing processes. This article considers the quality estimation of the wheat bread that is baked under standard conditions. The sensory data are collected in real-time, and the obtained data are analysed using the efficient data analytics to predict the quality of the product. The dataset obtained consists of 300 bread samples prepared in 15 days whose vital physical, chemical, and rheological measures are sensed. The measures of the read are obtained through sensory tools and are gathered as a dataset. The obtained data are generally raw, and hence, the required features are obtained through dimensionality reduction using the Linear Discriminant Analysis (LDA). The processed data and the attributes are given as input to the classifier to obtain final estimation results. The efficient Fuzzy Weighted Relevance Vector Machine (FWRVM) classifier model is developed for this achieving this objective. The proposed quality estimation model is implemented using the MATLAB programming environment with the required setting for the FWRVM classifier. The model is trained and tested with the input dataset with data analysis steps. Some state-of-the-art classifiers are also implemented to compare the evaluated performance of the proposed model. The estimation accuracy is obtained by comparing the number of correctly detected bread classes with the wrongly classified breads. The results indicate that the proposed FWRVM-based classifier estimates the quality of the breads with 96.67% accuracy, 96.687% precision, 96.6% recall, and 96.6% F-measure within 8.96726 seconds processing time which is better than the compared Support vector machine (SVM), RVM, and Deep Neural Networks (DNN) classifiers.

中文翻译：

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使用模糊加权相关向量机分类器的面包质量熟练度估计模型

食品质量的评估对于确定食品在烘焙和其他制造过程中的特性和有效性至关重要。本文考虑了在标准条件下烘焙的小麦面包的质量评估。实时收集感官数据，并使用有效的数据分析对获得的数据进行分析，以预测产品的质量。获得的数据集由 15 天内制备的 300 个面包样本组成，这些样本的重要物理、化学和流变指标均被检测到。读取的测量结果是通过感官工具获得的，并收集为数据集。获得的数据通常是原始数据，因此需要使用线性判别分析（LDA）通过降维来获得所需的特征。处理后的数据和属性作为分类器的输入以获得最终的估计结果。高效的模糊加权相关向量机（FWRVM）分类器模型就是为了实现这一目标而开发的。所提出的质量估计模型是使用 MATLAB 编程环境以及 FWRVM 分类器所需的设置来实现的。该模型通过数据分析步骤使用输入数据集进行训练和测试。还实现了一些最先进的分类器来比较所提出模型的评估性能。通过比较正确检测到的面包类别与错误分类的面包的数量来获得估计精度。结果表明，所提出的基于 FWRVM 的分类器在 8.96726 秒的处理时间内以 96.67% 的准确度、96.687% 的精确度、96.6% 的召回率和 96.6% 的 F 测量来估计面包的质量，这优于比较的支持向量机（ SVM）、RVM 和深度神经网络 (DNN) 分类器。