An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis

Zareef, Muhammad; Hassan, Mehedi; Arslan, Muhammad; Hashim, Malik Muhammad; Ahmad, Waqas; Kutsanedzie, Felix Y.H.; Agyekum, Akwasi Akomeah

doi:10.1007/s12393-020-09210-7

Cited by 109 publications

(52 citation statements)

References 111 publications

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“…ANNs have the ability to approximate any non‐linear mathematical function that is useful, especially when the relationship between the variables is unknown, or is complex 24 . ANNs imitate the structure and working of the human nervous system via computer programs in order to assemble information‐processing systems that represent a degree of intelligent behaviour 25,26 . Due to the ability to detect and solve complex non‐linear relationships between inputs (targeted) and outputs (investigated) variables, ANNs have been successfully applied in different areas, from computation to medicine.…”

Section: Introductionmentioning

confidence: 99%

Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Valinger

Jurina

Šain

et al. 2020

Phytochemical Analysis

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Objectives The aim of this study was to develop artificial neural network (ANNs) models for prediction of physical (total dissolved solids, extraction yield) and chemical (total polyphenolic content, antioxidant activity) properties of industrial hemp extracts, prepared by two different extraction methods (solid‐liquid extraction and microwave‐assisted extraction) based on combined UV‐VIS‐NIR spectra. Spectral data were gathered for 46 samples per extraction method. Results The PCA analysis ensured efficient separation of the samples based on the amount of ethanol in extraction solvent using NIR spectra for both conventional and microwave‐assisted extraction. Conclusions Results showed that reliable ANN models (R2>0.7000) for describing physical, chemical, and simultaneously physical and chemical characteristics can be developed based on combined UV‐VIS‐NIR spectra of industrial hemp extracts without spectra pre‐processing.

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Section: Introductionmentioning

confidence: 99%

Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Valinger

Jurina

Šain

et al. 2020

Phytochemical Analysis

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“…SVM is a well-known non-linear learning algorithm that uses a kernel function to transform the data into higher-dimensional spaces where a hyperplane can be constructed to separate the data with the maximal margin between the desired classes. They have also been intensively coupled with NIR spectroscopy in food analysis [46].…”

Section: ) Developing An Svm Classifiermentioning

confidence: 99%

Precise Sweetness Grading of Mangoes (Mangifera indica L.) Based on Random Forest Technique With Low-Cost Multispectral Sensors

et al. 2020

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Before being exported, mangoes generally undergo rigorous external and internal quality inspection processes in which near-infrared (NIR) spectral approaches are favorable for grading purposes. A successful NIR-based grading system depends largely on high-quality spectral sensors and the reliability of the classifier. Motivated by the high economic impact of Cat Hoa Loc mangoes (Mangifera indica L.), we demonstrated that the sweetness of that mangoes could be precisely graded based on a random forest (RF) classifier in a three-phase approach with a low-cost Visible-Near infrared (VIS-NIR) multispectral sensor chipset. This approach is so-called RPR because RF, Partial Least Squares regression, and RF were respectively applied to consecutively determine the significant VIS-NIR responses, the good features as input variables, and the reliable RF classifier via our formulated discriminant index (DI). The experimental results confirmed that higher classification accuracy was achieved by using the extracted latent features rather than the raw VIS-NIR data. The DI was effectively used as a reliability measure to select the optimal classifier among those of identical training and testing accuracies of 100% and 82.1%, respectively. Performance comparison between the optimal RF classifier with a Support Vector Machines classifier and a multinomial logistic regression showed that the developed RF classifier was superior in various performance indices. Therefore, it is promising to extend the proposed approach to more complicated fruit grading problems with sufficient VIS-NIR datasets that are acquired from low-cost multispectral sensors. INDEX TERMS Cost-effective, sweetness grading of Cat Hoa Loc mango, spectral response selection, VIS-NIR features extraction, discriminant index for random forest classifier.

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“…Traditional approaches include, for example, different linear regression models, such as principal components regression and partial least squares regression. More recently, neural networks and machine learning have also been successfully applied for the task (6).…”

Section: Applied Nir Spectroscopymentioning

confidence: 99%

Portable Near-Infrared Spectroscopy for Analysis of Crops

2020

CFW

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An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis

Cited by 109 publications

References 111 publications

Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

Precise Sweetness Grading of Mangoes (Mangifera indica L.) Based on Random Forest Technique With Low-Cost Multispectral Sensors

Portable Near-Infrared Spectroscopy for Analysis of Crops

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