Meng Qinglong, Feng Shunan, Tan Tao, Wen Qingchun, Shang Jing
School of Food Science and Engineering Guiyang University Guiyang China.
Research Center of Nondestructive Testing for Agricultural Products of Guizhou Province Guiyang China.
Food Sci Nutr. 2024 Apr 14;12(7):4819-4830. doi: 10.1002/fsn3.4130. eCollection 2024 Jul.
Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.
检测枇杷的水分含量（MC）和新鲜度对于实现最佳口感和经济效益至关重要。传统的评估枇杷MC和新鲜度的方法存在诸如破坏性采样和耗时等缺点。为了研究快速无损检测枇杷MC和新鲜度的可行性，本研究使用了200 - 1000 nm范围内的光纤光谱。对全光谱采用标准正态变量法进行预处理，然后使用竞争性自适应加权采样（CARS）和随机蛙跳算法选择有效波长。基于所选的有效波长，使用偏最小二乘回归（PLSR）、多元线性回归、极限学习机和反向传播神经网络建立了MC的预测模型。此外，使用简化的k近邻、支持向量机（SVM）和偏最小二乘判别分析建立了新鲜度等级判别模型。对于预测模型，CARS - PLSR模型在预测MC方面比其他模型表现相对更好，其 和RPD值分别为0.84和2.51。此外，CARS - SVM模型获得了卓越的判别性能，校准集和预测集的准确率均为100%。结果表明，光纤光谱技术是快速检测枇杷MC和新鲜度的有效工具。
