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Chemometrics and Intelligent Laboratory Systems 212 (2021) 104287 Contents lists available at ScienceDirect Chemometrics and Intelligent Laboratory Systems journal homepage: www.elsevier.com/locate/chemometrics A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit Puneet Mishra a,*,Dario Passos b a Wageningen Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, 6700AA, Wageningen, the Netherlands b CEOT, Universidade do Algarve, Campus de Gambelas, FCT Ed.2, 8005-189 Faro, Portugal ARTICLE INFO ABSTRACT Keywords: This study provides an innovative approach to improve deep learning (DL) models for spectral data processing 1D-CNN with the use of chemometrics knowledge. The technique proposes pre-filtering the outliers using the Hotelling’s Neural networks T2 and Q statistics obtained with partial least-square (PLS) analysis and spectral data augmentation in the variable Fruit quality domain to improve the predictive performance of DL models made on spectral data. The data augmentation is Artificial intelligence carried out by stacking the same data pre-processed with several pre-processing techniques such as standard Ensemble pre-processing normal variate, 1st derivatives, 2nd derivatives and their combinations. The performance of the approach is demonstrated on a real near-infrared (NIR) data set related to dry matter (DM) prediction in mango fruit. The data set consisted of a total 11,961 spectra and reference DM measurements. The results showed that removing the outliers and augmenting spectral data improved the predictive performance of DL models. Furthermore, this innovative approach not only improved DL models but attained the lowest root mean squared error of prediction (RMSEP) on the mango data set i.e., 0.79% compared to the best known RMSEP of 0.84%. Further, by removing outliers from the test set the RMSEP decreased to 0.75%. Several chemometrics approaches can complement DL models and should be widely explored in conjunction. 1. Introduction rectified units (GRU) are mostly used for sequence data such as time series and natural language processing (NLP). Deep learning (DL) after successfully solving several challenges in the DL in spectral data modelling is currently emerging and a few works domain of computer vision is now expanding in the chemometrics can be found related to regression modelling [1,3,4]. Basically, two main domain. Several primary applications of DL can be found to deal with approaches to spectral data processing are available, first is the use of chemometrics challenges such as spectral modelling [1], hyperspectral methods that are completely supervised and jointly perform feature image processing [2–4], data clustering [5], molecular generative extraction and learning, such as, 1-Dimensional (1D) CNNs with fully modelling [6], soft sensor modelling [7] and peaks identification in connected layers (FC) [1], and second is the combination of unsupervised chromatographic data [8]. The basic foundations of DL are artificial feature extraction with the supervised regression modelling such as the neural network (NN) algorithms that enable the learning process of use of auto-encoders for feature extraction as a first step and later complex hidden non-linear patterns in the data, otherwise unachievable training using support vector machines (SVM) to map the extracted with classical machine learning and chemometrics techniques. The major features with the property of interest [4]. The first approach involving role of DL comes into play in the availability of huge data sets, while in joint feature extraction and learning has the advantage that the features the case of small data sets, classical machine learning and chemometrics extracted are related to the property of interest whereas the in the second techniques can perform equally well. Further, based on the type of data, approach the feature extracted in an unsupervised way may not relate to different approaches to creating DL models are available such as con- the property of interest. volutional neural networks (CNNs) in the case of image processing and Apart from advancement in DL modelling approaches, in recent years, 1-D spectral data, whereas long-short term memory (LSTM) and gated major developments have taken place in the development of miniature * Corresponding author. E-mail address: [email protected] (P. Mishra). https://doi.org/10.1016/j.chemolab.2021.104287 Received 14 December 2020; Received in revised form 23 February 2021; Accepted 7 March 2021 Available online 11 March 2021 0169-7439/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/). P. Mishra, D. Passos Chemometrics and Intelligent Laboratory Systems 212 (2021) 104287 spectrometers and several low-cost spectrometers are now available in consisted of a total 11,961 spectra and reference DM measurements. The the market [9,10]. These low-cost spectrometers are easy to use; hence, performance of the DL model was compared with the best-known RMSEP their usage range from high-end research facilities to an individual obtained with individual and ensemble models for the same data. non-expert user [9]. A side outcome of such a wide distributed usage of low-cost spectrometers is the generation of huge data sets, which for DL 2. Materials and method modelling is of high-interest. A practical example is the portable spec- troscopy of fresh fruit, where near-infrared is widely expanding with the 2.1. Data set availability of portable handheld spectrometers [11–18]. Further, a recent experiment using one of these handheld units was recently made The data set used in this study comprises a total of 11,691 NIR spectra available as an open access ‘mango data set’ comprising a total of 11,961 (684–990 nm in 3 nm sampling with a total 103 variables) and DM near-infrared (NIR) spectra and reference dry matter (DM) [17,19]. DM is measurements performed on 4675 mango fruit across 4 harvest seasons a key quality trait in fresh fruit and allows to judge its maturity [18]. NIR 2015, 2016, 2017 and 2018 [24]. Portable F750 Produce Quality Meter spectroscopy is of huge interest as it can replace the laboratory-based DM (Felix Instruments, Camas, USA) was used for the non-destructive NIR analysis with a rapid non-destructive on-site measurement [20]. In pre- measurements. DM was measured with oven drying (UltraFD1000, Ezi- vious works related to the analysis of this mango data set, several linear, dri, Beverley, Australia) on the samples extracted from the sample non-linear, local, global and ensemble methods have been tried [17,19]. location on fruit where the NIR measurements were done. The data set is The lowest reported root mean squared error of prediction (RMSEP) on associated with the publications [17,19] and can also be accessed in the the mango data set is 0.839% and was achieved using an ensemble of the supplementary file section of the manuscripts. Out of 11,691 spectra, 10, artificial neural network, Gaussian process regression and local PLS [19]. 243 spectra corresponding to harvest seasons 2015, 2016 and 2017 were For individual models, the lowest RMSEP achieved was 0.881% using a used for training and tuning, whereas the remaining 1448 spectra from Local Optimized by Variance Regression (LOVR) model [19]. However, season 2018 were used as an independent test set. This data partition was no study yet reported the use of DL on this mango data set, hence, for the used as it was previously used in Refs. [17,19] to compare the perfor- first time a DL analysis on mango data set is performed in this study. mance of the DL models to previously reported results. Even though the Most of the previous works on DL analysis of spectral data were authors of the mango dataset report that they eliminated a few outliers in focussed on optimising the model architecture and showing that this kind the data, in this study a more stringent outlier analysis was performed. of algorithms could achieve higher prediction accuracy than standard The presence of outliers in both the training and test sets was detected by chemometrics approaches [1,3,4]. However, none of the spectral DL using Hotelling’sT2 and Q statistics from PLS decomposition with modelling approaches focussed on finding a cooperative strategy be- NIPALS algorithm [25] leading to the removal of the abnormal samples tween chemometrics and DL. Initial works on DL spectral analysis suggest pertaining either high T2 or Q statistics. For outlier removal, at first a PLS that the first layers of certain NN architectures can perform data trans- model was built (on the training set with outliers) with 10-fold formations akin to classical pre-processing methods in an automated way cross-validation using the MBA-GUI [26], and later, the scores from the [1,30]. However, it is not clear how deep these NN need to be to achieve PLS decomposition were used to estimate the T2 and Q statistics. The this feature [31] and if this is indeed the best strategy for extracting in- computed T2 and Q statistics were plotted in a 2D plot, and later, formation from spectral data. Chemometrics has several approaches that manually by user, the outlying samples were eliminated by region of can directly benefit the DL such as pre-filtering outliers with the use of interest selection using MATLAB’s ‘roipoly’ function. After, the outlier Hotelling’sT2 and Q statistics and spectral data pre-processing to remove removal, a new PLS decomposition was performed and new T2 and Q the artefacts from spectral data. Furthermore, the recent concept of statistics were computed and plotted in a 2D plot. Once again based on ensemble data pre-processing can play a key role in spectral data the visual inspection of the samples, the user selects a region of interest augmentation where benefits of different pre-processing techniques such using MATLAB’s ‘roipoly’ function and remove the outlying samples.

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