干果的外观明显影响消费者对产品质量的感知,但这是一个微妙而微妙的特征,难以定量测量,尤其是在线。本文介绍了一种方法,该方法结合了几种简单的策略来评估使用成像技术难以捉摸的质量的合适替代方法。结合多元统计和机器学习。有了这样一个方便的工具,这项研究还显示了如何可以改变预处理和干燥条件,以优化所得产品质量。具体来说,开发了一种图像批处理方法来提取颜色(色调,饱和度,和价值)和形态(面积,周边,和紧凑性)特征。使用热风干燥猕猴桃片预处理的案例研究实验数据验证了该方法的准确性。基于提取的图像特征,建立了偏最小二乘和随机森林模型,以令人满意地预测干燥过程中的水分比(MR)。在不使用任何称重装置的情况下,可以从外观变化准确预测猕猴桃片干燥过程中的MR。本研究还探讨了使用主成分分析确定基于外观质量的最佳干燥策略。在超声预处理下用4mm厚的切片在60°C下实现最佳干燥。对于70°C,6mm样品组,柠檬酸表现良好。
The appearance of dried fruit clearly influences the consumer\'s perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a
case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.