Determination of quality and maturity of processing tomatoes using near-infrared hyperspectral imaging with interpretable machine learning methods

• Published in: Food science & technology 2023-06, Vol.183, p.114861, Article 114861
• Main Authors: Zhao, Mingrui, Cang, Hao, Chen, Huixin, Zhang, Chu, Yan, Tianying, Zhang, Yifan, Gao, Pan, Xu, Wei
• Format: Article
• Language: English
• Subjects: Maturity; Nondestructive; Processing tomato; Quality; Recurrent neural network
• ISSN: 0023-6438; 1096-1127
• DOI: 10.1016/j.lwt.2023.114861

Processing tomato (Lycopersicon esculentum Mill.) is rich in vitamins and lycopene, which is favored by consumers. In this study, near-infrared hyperspectral imaging (HSI) technology (980–1660 nm) was used to detect the firmness, soluble solids, lycopene, and titratable acid content of processing tomatoes and to classify fruits at three maturity stages. Savitzky-Golay (SG) smoothing was used to reduce the noise of hyperspectral images. The average spectrum of the tomato fruit was extracted for model development. Random forest (RF), partial least squares (PLS), and recurrent neural network (RNN) were used to develop models for predicting the four quality attributes and identifying the maturity level. Results showed that the RNN model had a classification accuracy of 40% higher than RF and 17% higher than PLS. In the prediction of quality parameters, RNN models had the highest R2 value (>0.87), followed by PLS and RF models. Important wavelengths were identified by calculating its contribution values and were used to interpret the model. The results illustrated that near-infrared hyperspectral imaging technology combined with deep learning could effectively predict the quality and maturity of processing tomatoes. The work can provide a perspective on the application of HSI as a nondestructive testing approach for other agricultural products. •Use of hyperspectral imaging to predict multi-quality of processing tomatoes.•Use of hyperspectral imaging to classify maturity of processing tomatoes.•Comparison of recurrent neural networks with traditional machine learning methods.•Identification of important wavelengths in contributing to model performance.