利用改进的偏最小二乘法 (PLS) 通过荧光测定橘子中的糖分

Lei Liu, Chunzhong Li, Haiyi Bian, Ahmed N Abdalla, Hua Yao, Wen Li
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引用次数: 0

摘要

准确测定橘子中的含糖量对评估其质量、营养价值和适销性起着至关重要的作用。传统的糖分定量方法通常需要耗费大量时间和资源。本文介绍了一种利用荧光光谱和改进的偏最小二乘法 (iPLS) 算法测定橘子中糖含量的新方法。我们开发了一个稳健的测试模型,将已知糖分浓度的各种橘子样品数据集纳入其中。共采集了 80 个样品的荧光光谱,其中 37 个用于建立 iPLS 模型,并被视为训练数据集。其余 43 个样品作为验证数据集,用于显示模型的有效性。训练数据集通过交叉验证进行评估,并计算 F 值,以确定应使用多少个主要成分来构建模型。结果批准的验证数据集的R平方和均方根误差分别为0.9777和0.002992。这些发现为柑橘行业及其他领域的更广泛应用打开了大门,有可能实现分析过程自动化并改善整体质量控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of sugar in tangerines by fluorescence with an Improved partial least squares (PLS) algorithm
The accurate determination of sugar content in tangerines plays a pivotal role in assessing their quality, nutritional value, and marketability. Traditional methods for sugar quantification often involve time-consuming and resource-intensive processes. In this paper, we introduce a novel approach for sugar determination in tangerines utilizing fluorescence spectroscopy in conjunction with an improved Partial Least Squares (iPLS) algorithm. A robust testing model was developed, incorporating a diverse dataset of tangerine samples with known sugar concentrations. Fluorescence spectra were acquired for 80 samples, of which 37 were used to build the iPLS model and were considered as the training dataset. The remaining 43 samples served as the validation dataset and were used to show the model’s efficacy. The training dataset was evaluated using cross-validation, and F-values were computed to determine how many main components should be utilized to build the model. The result approved validation dataset’s R-square and root-mean-square error were 0.9777 and 0.002992, respectively. These findings open the door to broader applications in the citrus industry and beyond, with the potential for automating the analysis process and improving overall quality control.
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