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Lycopene detection in cherry tomatoes with feature enhancement and data fusion
Food Chemistry ( IF 8.5 ) Pub Date : 2024-09-12 , DOI: 10.1016/j.foodchem.2024.141183 Yuanhao Zheng , Xuan Luo , Yuan Gao , Zhizhong Sun , Kang Huang , Weilu Gao , Huirong Xu , Lijuan Xie
Food Chemistry ( IF 8.5 ) Pub Date : 2024-09-12 , DOI: 10.1016/j.foodchem.2024.141183 Yuanhao Zheng , Xuan Luo , Yuan Gao , Zhizhong Sun , Kang Huang , Weilu Gao , Huirong Xu , Lijuan Xie
Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving R P 2 , RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction.
中文翻译:
利用特征增强和数据融合的樱桃番茄红素检测
番茄红素是一种对健康有益的生物活性植物化学物质,是樱桃番茄的关键质量指标。虽然紫外/可见光/近红外 (UV/Vis/NIR) 光谱有望实现大规模在线番茄红素检测,但由于樱桃番茄中的番茄红素浓度较低,因此捕获其特征信号具有挑战性。本研究通过光谱特征增强和光谱-图像融合,用图像信息补充光谱数据,提高了番茄红素的预测精度。验证了使用 UV/Vis/NIR 光谱和图像特征预测番茄红素含量的可行性。通过增强与番茄红素相关的颜色相对应的光谱带,光谱模型的性能得到了改善。此外,直接光谱-图像融合进一步提高了预测精度,RP2 、 RMSEP 和 RPD 分别为 0.95 、 8.96 mg/kg 和 4.25。总体而言,本研究为用图像信息补充光谱数据以提高无损番茄红素检测的准确性提供了有价值的见解,为在线水果品质预测提供了实际意义。
更新日期:2024-09-12
中文翻译:
利用特征增强和数据融合的樱桃番茄红素检测
番茄红素是一种对健康有益的生物活性植物化学物质,是樱桃番茄的关键质量指标。虽然紫外/可见光/近红外 (UV/Vis/NIR) 光谱有望实现大规模在线番茄红素检测,但由于樱桃番茄中的番茄红素浓度较低,因此捕获其特征信号具有挑战性。本研究通过光谱特征增强和光谱-图像融合,用图像信息补充光谱数据,提高了番茄红素的预测精度。验证了使用 UV/Vis/NIR 光谱和图像特征预测番茄红素含量的可行性。通过增强与番茄红素相关的颜色相对应的光谱带,光谱模型的性能得到了改善。此外,直接光谱-图像融合进一步提高了预测精度,RP2 、 RMSEP 和 RPD 分别为 0.95 、 8.96 mg/kg 和 4.25。总体而言,本研究为用图像信息补充光谱数据以提高无损番茄红素检测的准确性提供了有价值的见解,为在线水果品质预测提供了实际意义。