A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit

Mishra, Puneet; Passos, Dário

doi:10.1016/j.chemolab.2021.104287

Cited by 82 publications

(99 citation statements)

References 29 publications

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“…26 The final data set has 9914 samples for model training and validation and 1448 samples for an independent test of the model. However, after the data augmentation suggested in Mishra and Passos, 26 the total number of variables were 618 by the stacking of five pre-processed data blocks to the original absorbance data block. The five different pre-processing's were 1st derivative and 2nd derivative with Savitzky-Golay 29 filter with window size 13 and polynomial order of 2, standard normal variate (SNV), 30 SNV + 1st derivative and SNV + 2nd derivative.…”

Section: Open-access Mango Data Setmentioning

confidence: 99%

“…In this study, there were three main directions for data modelling. The first was the direct application of the DL and PLS 26 26 The DL model was applied using the "model.predict()" function from TensorFlow/Keras. The application of PLS included the multiplication of the regression coefficients with the spectra from the new independent test set.…”

Section: Data Modellingmentioning

confidence: 99%

“…In the DL model presented in Mishra and Passos, 26 26 and first proposed in Cui and Fearn, 31 was implemented. The architecture is composed of a five-layer convolutional neural network.…”

Section: New Deep Learning Model Developmentmentioning

confidence: 99%

“…In the case of DL modelling, calibration transfer can be performed by fine-tuning the weights of the old DL model using some new samples measured on the new instrument. 26 This recent article explains more broadly the TL approach as used in this study for calibration transfer. 27 The calibration transfer with TL is a standard free procedure to transfer DL models between instruments; hence, it does not require any measurements from the primary instrument and is well suited for this study as the primary instrument used to generate the open-access mango data was not available.…”

Section: Standard Free Deep Calibration Transfermentioning

confidence: 99%

“…Hence, it was a classic case of calibration transfer but without standards as the two spectrometers belongs to different scientific research groups based on different continents. All models were implemented using the Python (3.6) language and the Tensorflow 2.1 framework.2.2.1 | Direct application of old models on a new independent test setIn Mishra and Passos,26 DL and PLS models based on open-access mango data set were proposed. The models were based on the calibration set of the open-access mango data set.…”

mentioning

confidence: 99%

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

Mishra

Passos

2021

Journal of Chemometrics

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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.

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Section: Open-access Mango Data Setmentioning

confidence: 99%

Section: Data Modellingmentioning

confidence: 99%

“…In the DL model presented in Mishra and Passos, 26 26 and first proposed in Cui and Fearn, 31 was implemented. The architecture is composed of a five-layer convolutional neural network.…”

Section: New Deep Learning Model Developmentmentioning

confidence: 99%

Section: Standard Free Deep Calibration Transfermentioning

confidence: 99%

mentioning

confidence: 99%

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

Mishra

Passos

2021

Journal of Chemometrics

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Explainable deep learning enhances robust and reliable real‐time monitoring of a chromatographic protein A capture step

Medl,

Leisch,

Dürauer

et al. 2024

Biotechnology Journal

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The application of model‐based real‐time monitoring in biopharmaceutical production is a major step toward quality‐by‐design and the fundament for model predictive control. Data‐driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state‐of‐the‐art modeling algorithms are employed, and (iii) the input‐output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data‐driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high‐quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion‐based methods can be used to gain an understanding of dependencies learned by one of the most complex data‐driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data‐driven models in biopharmaceutical manufacturing.

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NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Banerjee,

Mandal,

Jesubalan

et al. 2024

Biotech & Bioengineering

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As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near‐infrared (NIR) spectroscopy, are finding increasing use for real‐time quantitative analysis. Yet detection of multiple low‐concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module‐based two‐dimensional (2D) CNN approach (I‐CNN) for calibrating multiple analytes using NIR spectral data. The I‐CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I‐CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I‐CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real‐time process monitoring and comparison with offline analytics. The proposed I‐CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

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A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit

Cited by 82 publications

References 29 publications

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

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

Explainable deep learning enhances robust and reliable real‐time monitoring of a chromatographic protein A capture step

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

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