Son Daesik, Lee Siun, Jeon Sehyeon, Kim Jae Joon, Chung Soo
Department of Biosystems Engineering, Seoul National University, Seoul 08826, Republic of Korea.
Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea.
Sensors (Basel). 2025 Apr 21;25(8):2627. doi: 10.3390/s25082627.
Mandarin ( L.) is consumed worldwide. Improper storage temperatures cause flavor loss and shorten shelf lives, reducing marketability. Mandarins' quality is difficult to assess visually, as they show no apparent changes during storage. Therefore, a simple, non-destructive method is needed to assess their freshness as affected by temperature. This work utilized non-invasive bioimpedance spectroscopy (BIS) on mandarins stored at different temperatures. Eight machine learning (ML) models were trained with bioimpedance data to classify storage temperature. Also, we confirmed whether integrating diameter and time-series changes into the bioimpedance could improve the ML models' accuracies by minimizing sample variations. Additionally, we evaluated the effectiveness of equivalent circuit (EC) parameters derived from bioimpedance data for ML training. Although slightly less accurate than using raw bioimpedance data, EC parameters can efficiently reduce data dimensionality. Among all models, the SVM model trained with changes in bioimpedance integrated with diameter data achieved the highest accuracy of 0.92. It was a significant improvement compared to the accuracy of 0.76 achieved when using only the raw bioimpedance data. Thus, this study suggests a novel method of integrating diameter and bioimpedance changes to assess the storage temperature of mandarins. This approach can also be applied to other fruits when utilizing BIS.
柑橘（L.）在全球范围内都有消费。不当的储存温度会导致风味丧失并缩短保质期，降低市场适销性。柑橘的品质很难通过视觉评估，因为它们在储存期间没有明显变化。因此，需要一种简单的非破坏性方法来评估其受温度影响的新鲜度。这项工作对储存在不同温度下的柑橘采用了非侵入式生物阻抗光谱法（BIS）。使用生物阻抗数据训练了八个机器学习（ML）模型来对储存温度进行分类。此外，我们通过最小化样本变化来确认将直径和时间序列变化整合到生物阻抗中是否可以提高ML模型的准确性。另外，我们评估了从生物阻抗数据导出的等效电路（EC）参数用于ML训练的有效性。尽管比使用原始生物阻抗数据的准确性略低，但EC参数可以有效降低数据维度。在所有模型中，结合直径数据的生物阻抗变化训练的支持向量机（SVM）模型达到了最高准确率0.92。与仅使用原始生物阻抗数据时达到的0.76准确率相比，这是一个显著的提高。因此，本研究提出了一种整合直径和生物阻抗变化来评估柑橘储存温度的新方法。当利用BIS时，这种方法也可以应用于其他水果。
