Near-infrared spectroscopy and machine learning for classification of food powders under moving conditions

Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R2) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively.

https://www.mdpi.com/2304-8158/10/4/785/pdf

Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy.

Quantification of metabolites from magnetic resonance spectra (MRS) has many applications in medicine and psychology, but remains a challenging task despite considerable research efforts. For example, the neurotransmitter $\gamma$-aminobutyric acid (GABA), present in very low concentration in vivo, regulates inhibitory neurotransmission in the brain and is involved in several processes outside the brain. Reliable quantification is required to determine its role in various physiological and pathological conditions. We present a novel approach to quantification of metabolites from MRS with convolutional neural networks --- MRSNet. MRSNet is trained to perform the multi-class regression problem of identifying relative metabolite concentrations from given input spectra, focusing specifically on the quantification of GABA, which is particularly difficult to resolve. Typically it can only be detected at all using special editing acquisition sequences such as MEGA-PRESS. A large range of network structures, data representations and automatic processing methods are investigated. Results are benchmarked using experimental datasets from test objects of known composition and compared to state-of-the-art quantification methods: LCModel, jMRUI (AQUES, QUEST), TARQUIN, VeSPA and Gannet. The results show that the overall accuracy and precision of metabolite quantification is improved using convolutional neural networks.

MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks

The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution.

/pdf/quantification-of-mr-spectra-by-deep-learning-in-an-3pbko7u3.pdf

Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias

To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning–based proton MRS of the brain.

Bayesian deep learning–based 1H‐MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout

Purpose: Many tools exist for quantifying magnetic resonance spectroscopy (MRS) data. Literature comparing them is sparse but indicates potential methodological bias. We benchmark MRS analysis tools to elucidate this. 
Methods: Four series of phantom experiments, including both solutions and tissue-mimicking gels, with constant concentrations of NAA, Creatine, Glutamine and Glutamate, and iteratively increased concentrations of GABA are performed. MEGA-PRESS spectra are acquired and quantified with several state-of-the-art MRS analysis tools (LCModel, TARQUIN, JMRUI, GANNET) and in-house code (LWFIT). GABA-to-NAA ratios for reported metabolite amplitudes are compared to the ground truth of known concentration ratios. Overall estimation accuracy is assessed by linear fits of reported vs. actual ratios and coefficients of determination. Simulations further elucidate the experimental results. 
Results: Significant differences in reported GABA-to-NAA amplitude ratios are observed. TARQUIN consistently overestimates, while most tools underestimate ratios to varying degrees compared to the ground truth. Underestimationdue to reduced editing efficiency is predicted by simulations. LCModel performs comparatively well for well-resolved solution spectra but struggles for intentionally miscalibrated spectra and gel spectra mimicking in-vivo conditions. GANNET shows better consistency and robustness to calibration errors but greater underestimation related to how the 3 ppm GABA peak is fitted. Surprisingly, simple peak integration with minimal pre-processing yields the most consistent and accurate results compared to the ground truth. 
Conclusions: A methodological dependence is observed not only in the quantification results for individual spectra, but in GABA-to-NAA gradients across experimental series, systematic offsets and coefficients of determination.

Seeking Ground Truth for GABA Quantification by Edited Magnetic Resonance Spectroscopy:Comparative Analysis of TARQUIN, LCModel, JMRUI and GANNET

Many tools exist for the quantification of GABA-edited magnetic resonance spectroscopy (MRS) data. Despite a recent consensus effort by the MRS community, literature comparing them is sparse but indicates a methodological bias. While invivo data sets can ascertain the level of agreement between tools, ground-truth is required to establish accuracy, and investigate the sources of discrepancy. We present a novel approach to benchmarking GABA quantification tools, using several series of phantom experiments with iterated GABA concentration. Each series presents a different set of background metabolites and environmental conditions allowing comparison of not only individual estimates, but the ability of tools to characterise changes in GABA across a range of potential confounds. The methodology of the phantom experiments is presented, as well as characterisation of the data. We also perform an initial comparative analysis of several common MRS quantification tools and in-house code, to illustrate utility of the dataset and the potential bias introduced by different quantification methods. The GABA-to-NAA ratios reported by each tool are compared to the ground-truth, and estimation accuracy is assessed by linear regression of this relationship. While the linearity of GABA-to-NAA gradients is generally captured by all of the tools, a large variation in the slope, offset and environmental stability of the gradient is observed. The primary driver of differences in linear combination modelling is the choice of basis function. However, tools employing a common basis and pre-processing still produce differences on the order of 4%. Less-strictly parametrised fitting approaches appear to improve the robustness of quantification, but accurate modelling of edit efficiency calculations is still necessary to avoid systematic offsets. In general, the level of variation suggests that comparisons across...

Benchmarking GABA Quantification: A Ground Truth Data Set and Comparative Analysis of TARQUIN, LCModel, jMRUI and Gannet.

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MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Networks

Seeking Ground Truth for GABA Quantification by Edited Magnetic Resonance Spectroscopy:Comparative Analysis of TARQUIN, LCModel, JMRUI and GANNET

Benchmarking GABA Quantification: A Ground Truth Data Set and Comparative Analysis of TARQUIN, LCModel, jMRUI and Gannet.