A portable NIR-system for mixture powdery food analysis using deep learning

Zhou, Lei; Tan, Lehao; Zhang, Chu; Zhao, Nan; He, Yong; Qiu, Zhengjun

doi:10.1016/j.lwt.2021.112456

Cited by 22 publications

(3 citation statements)

References 24 publications

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“…Zhou et al presented a handheld device (“NIR-spoon”) for simultaneous evaluation of multi-mixture powdery food. Each mixed powdered sample was analyzed by a “NIR-Spoon” and software was consequently dedicated to the “NIR-Spoon” that resulted in a good accuracy with the possibility of a mobile app ( Zhou et al, 2022b ). An OPLS-DA model, based on the data from the handheld NIR, showed 84.85% and 86.96% correct classification ( Srinuttrakul et al, 2021 ).…”

Section: Applications To Food Quality Characterization and Adulterationsmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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Section: Applications To Food Quality Characterization and Adulterationsmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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“…This results in researchers exploring new methods to improve NIR in this application. Other application 'hot spots' include grain, 28,37,[48][49][50][51][52] organic matter such as leaves, wood and beans, [53][54][55][56][57][58][59] food powders, 37,60,61 oil 62 and brain, 63 with 7, 7, 3, one and one papers, respectively.…”

Section: Cnn For Nir Spectroscopymentioning

confidence: 99%

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

Walsh

Neupane

Koirala

et al. 2023

Journal of Near Infrared Spectroscopy

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The Part 1 prequel to this review evaluated the evolution of modelling techniques used in evaluation of fruit quality over the past three decades and noted a progression towards the use of artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this review, Part 2, the use of CNNs for NIR fruit quality evaluation is explored, given the success of CNNs in various other fields, such as image, video, speech, and audio processing, and the availability of large (open source) datasets of fruit spectra and reference quality attribute, which is required for the training of CNN models. The review provides an overview of deep learning and the CNN architectures and techniques used in NIR spectroscopy for regression modelling, with advantages and disadvantages identified. Studies using CNN for NIR based fruit quality evaluation are then critically examined. Eight publications have presented on models using the same open-source mango dry matter calibration and test set, enabling inter-method comparisons. CNN models have been demonstrated to be accurate, precise and robust. Techniques of transfer learning for CNN models offer an alternative solution to model updating and calibration transfer methods applied in traditional chemometrics. The review has highlighted crucial areas that require resolution and exploration in this application through future research, including, (i) data requirements for training a CNN (ii) optimal spectral pre-processing for CNN (iii) CNN architecture and hyper-parameter selection and tuning for fruit quality evaluation (iv) CNN model interpretability and explainability. Future studies must conduct clearer comparison to partial least squares (PLS) regression and shallow ANNs to better assess the prospective benefit of using CNN, a more complex model. The potential for visualisation of spectra relevance to the CNN model using techniques such as GradCam, currently employed in visualising 2D-CNN models, remains to be explored.

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“…Application of image processing procedures for determining particle size distributions, morphological operations, filtering and 2D diffusion calculation in biological objects have been reported [ 8 , 9 , 10 , 11 ]. At the same time, Deep and Machine Learning approach was used to distinguish polysaccharides in raspberry powders [ 12 ], composition analysis of powder mixtures [ 13 ] and for predicting fishiness off-flavor and identifying compounds of lipid oxidation in dairy powders [ 14 ]. However, these approaches have not been extended to analyze water diffusion measurement in high-protein dairy powders.…”

Section: Introductionmentioning

confidence: 99%

Water Effective Diffusion Coefficient in Dairy Powder Calculated by Digital Image Processing and through Machine Learning Algorithms of CLSM Micrographs

Maidannyk,

Simonov,

McCarthy

et al. 2023

Foods

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Rehydration of dairy powders is a complex and essential process. A relatively new quantitative mechanism for monitoring powders’ rehydration process uses the effective diffusion coefficient. This research focused on modifying a previously used labor-intensive method that will be able to automatically measure the real-time water diffusion coefficient in dairy powders based on confocal microscopy techniques. Furthermore, morphological characteristics and local hydration of individual particles were identified using an imaging analysis procedure written in Matlab©—R2023b and image analysis through machine learning algorithms written in Python™-3.11. The first model includes segmentation into binary images and labeling particles during water diffusion. The second model includes the expansion of data set selection, neural network training and particle markup. For both models, the effective diffusion follows Fick’s second law for spherical geometry. The effective diffusion coefficient on each particle was computed from the dye intensity during the rehydration process. The results showed that effective diffusion coefficients for water increased linearly with increasing powder particle size and are in agreement with previously used methods. In summary, the models provide two independent machine measurements of effective diffusion coefficient based on the same set of micrographs and may be useful in a wide variety of high-protein powders.

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A portable NIR-system for mixture powdery food analysis using deep learning

Cited by 22 publications

References 24 publications

Portable NIR spectroscopy: the route to green analytical chemistry

Portable NIR spectroscopy: the route to green analytical chemistry

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

Water Effective Diffusion Coefficient in Dairy Powder Calculated by Digital Image Processing and through Machine Learning Algorithms of CLSM Micrographs

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