Guo Zhiming, Chen Xuan, Zhang Yiyin, Sun Chanjun, Jayan Heera, Majeed Usman, Watson Nicholas J, Zou Xiaobo
China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China.
Foods. 2024 May 28;13(11):1698. doi: 10.3390/foods13111698.
Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.
苹果在生长过程中通常会套袋，这可以有效提高品质。建立树上苹果的原位无损检测模型对于水果公司选择用于评估的苹果原料非常重要。通过我们团队开发的用于苹果内部品质评估的手持式测试仪分别获取低成熟度苹果和高成熟度苹果，并比较了这两个成熟度水平对苹果可溶性固形物含量（SSC）检测的影响。使用了四种特征选择算法，如蚁群优化（ACO）算法，来降低光谱复杂度并提高苹果SSC检测精度。比较结果表明，高成熟度苹果的漫反射光谱能更好地反映苹果内部的SSC信息。高成熟度苹果的漫反射光谱结合ACO算法在SSC预测方面取得了最佳结果，预测相关系数（Rp）为0.88，预测均方根误差（RMSEP）为0.5678°Brix，剩余预测偏差（RPD）值为2.466。此外，基于颜色数据使用PLS-LDA预测果实成熟度，低成熟度和高成熟度果实的预测准确率分别达到99.03%和99.35%。这些结果表明，树上苹果原位检测在提高苹果品质建模的稳健性和准确性方面具有巨大潜力。
