Ni Fupeng, Zhu Xiaowen, Gu Fang, Hu Yaohua
College of Mechanical and Electronic Engineering Northwest A&F University Yangling China.
Key Laboratory of Agricultural Internet of Things Ministry of Agriculture Yangling China.
Food Sci Nutr. 2019 Oct 3;7(11):3654-3663. doi: 10.1002/fsn3.1222. eCollection 2019 Nov.
Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x-loading weights (x-LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of "Fuji" and "Qinguan" apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for "Fuji" and "Qinguan" apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the "Fuji" apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x-LW for the "Qinguan" apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for "Fuji" and "Qinguan" apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both "Fuji" and "Qinguan" apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection.
脆度被视为苹果的一项重要品质指标。目前，破坏性感官评价是检测苹果脆度所采用的公认方法，因此开发一种能够以非破坏性方式检测苹果脆度的方法至关重要。在本研究中，提出将光谱学作为检测苹果脆度的非破坏性技术，最终获得了450纳米至1000纳米之间的光谱反射率曲线。为了简化并提高建模效率，采用连续投影算法（SPA）和x载荷权重（x-LW）方法来选择最有效的波长。使用偏最小二乘法（PLS）算法、径向基神经网络（RBNN）和多层感知器神经网络（MLPNN）方法来建立模型，并预测“富士”和“秦冠”苹果品种的脆度。基于全波长（FW），“富士”和“秦冠”苹果品种的PLS模型预测准确率分别为92.05%和95.87%。通过SPA为“富士”苹果品种选择的有效波长为450.41纳米、476.80纳米、677.75纳米和750.72纳米，通过x-LW为“秦冠”苹果品种选择的有效波长为542.51纳米、544.79纳米、676.96纳米和718.29纳米。基于有效波长的“富士”和“秦冠”苹果品种的PLS模型预测准确率分别达到91.31%和96.41%。与RBNN模型相比，MLPNN模型对“富士”和“秦冠”苹果均取得了更好的预测结果，预测准确率分别达到97.8%和99.9%。基于上述研究结果，有效波长选择和MLPNN建模能够以最高的准确率检测苹果脆度。总体而言，可以得出结论，不太有效的波长有利于开发脆度检测仪器。
