Deep chemometrics: Validation and transfer of a global deep near‐infrared fruit model to use it on a new portable instrument

• Published in: Journal of chemometrics 2021-10, Vol.35 (10), p.n/a
• Main Authors: Mishra, Puneet, Passos, Dário
• Format: Article
• Language: English
• Subjects: artificial intelligence; calibration transfer; Chemometrics; Datasets; Deep learning; Fruits; fruit‐chemistry; Infrared spectroscopy; Machine learning; Mangoes; Near infrared radiation; Quality assessment; Seasonal variations; spectroscopy; Test sets
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.3367

Recently, a large near‐infrared spectroscopy data set for mango fruit quality assessment was made available online. Based on that data, a deep learning (DL) model outperformed all major chemometrics and machine learning approaches. However, in earlier studies, the model validation was limited to the test set from the same data set which was measured with the same instrument on samples from a similar origin. From a DL perspective, once a model is trained it is expected to generalise well when applied to a new batch of data. Hence, this study aims to validate the generalisability performance of the earlier developed DL model related to DM prediction in mango on a different test set measured in a local laboratory setting, with a different instrument. At first, the performance of the old DL model was presented. Later, a new DL model was crafted to cover the seasonal variability related to fruit harvest season. Finally, a DL model transfer method was performed to use the model on a new instrument. The direct application of the old DL model led to a higher error compared to the PLS model. However, the performance of the DL model was improved drastically when it was tuned to cover the seasonal variability. The updated DL model performed the best compared to the implementation of a new PLS model or updating the existing PLS model. A final root‐mean‐square error prediction (RMSEP) of 0.518% was reached. This result supports that, in the availability of large data sets, DL modelling can outperform chemometrics approaches. A validation of a global deep learning model based on near‐infrared spectroscopy for dry matter prediction in mango fruit is presented. Furthermore, the deep learning model was fine‐tuned to make them suitable to be used on the new instrument