Liu Rui, Li Yuanpeng, Li Tinghui, Liu Ping, Huang Wenchang, Liu Lingli, Zeng Rui, Hua Yisheng, Tang Jian, Hu Junhui
Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.
School of Physical Science and Technology, Guangxi Normal University, Guilin, China.
J Food Sci. 2024 Dec;89(12):9354-9368. doi: 10.1111/1750-3841.17558. Epub 2024 Dec 10.
Citrus fruits are widely consumed for their nutritional value and taste; however, juice sac granulation during fruit storage poses a significant challenge to the citrus industry. This study used Raman spectroscopy coupled with machine learning algorithms to rapidly, non-destructively, and precisely detect citrus granulation. The investigation analyzed 969 Raman spectral data points, comprising 714 non-granulated and 255 granulated citrus samples. Following logistic regression, decision tree, and partial least squares discriminant analyses, the optimal model was refined using principal component analysis, a successive projection algorithm, and a competitive adaptive reweighted sampling algorithm (CARS). The identified characteristic Raman peaks at certain wavenumbers were used as input data for the classification model, revealing differences in the water, ferulic acid, and sugar contents between granulated and non-granulated samples. The partial least squares discriminant classification model achieved an accuracy rate of 0.997, recall rate of 0.994, and F-fraction of 0.996 after preprocessing the standard deviation data and selecting 22 optimal principal components. The critical peaks extracted from the citrus Raman spectra were those at wavenumbers of 1580 and 1661 cm. The classification model based on combined second derivative-CARS-partial least squares discriminant analysis exhibited the best performance, achieving 100% accuracy for all test sets. The proposed method provides a scientifically robust and reliable means of assessing the quality of an entire citrus crop. Reduced wastage and economic losses, and the related environmental effects of food waste. PRACTICAL APPLICATION: The proposed methods can determine if citrus fruit has become granulated during storage. Additionally, they provide technical support for screening granulated citrus in a pipeline, thereby providing a more scientific and reliable classification of the quality of a citrus crop.
柑橘类水果因其营养价值和口感而被广泛食用；然而，水果储存期间汁囊粒化给柑橘产业带来了重大挑战。本研究使用拉曼光谱结合机器学习算法来快速、无损且精确地检测柑橘粒化情况。该调查分析了969个拉曼光谱数据点，包括714个未粒化和255个粒化的柑橘样本。经过逻辑回归、决策树和偏最小二乘判别分析后，使用主成分分析、连续投影算法和竞争性自适应重加权采样算法（CARS）对最优模型进行了优化。在特定波数处识别出的特征拉曼峰被用作分类模型的输入数据，揭示了粒化和未粒化样本之间水分、阿魏酸和糖分含量的差异。在对标准差数据进行预处理并选择22个最优主成分后，偏最小二乘判别分类模型的准确率达到0.997，召回率达到0.994，F分数达到0.996。从柑橘拉曼光谱中提取的关键峰是波数为1580和1661 cm处的峰。基于二阶导数 - CARS - 偏最小二乘判别分析的组合分类模型表现最佳，所有测试集的准确率均达到100%。所提出的方法为评估整个柑橘作物的质量提供了科学稳健且可靠的手段。减少了浪费和经济损失，以及食物浪费的相关环境影响。实际应用：所提出的方法可以确定柑橘类水果在储存期间是否已粒化。此外，它们为在流水线上筛选粒化柑橘提供了技术支持，从而对柑橘作物的质量进行更科学可靠的分类。
