Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy

Barbon, Sylvio; Mastelini, Saulo Martiello; Barbon, Ana Paula A. C.; Barbin, Douglas Fernandes; Calvini, Rosalba; Lopes, Jessica Fernandes; Ulrici, Alessandro

doi:10.1016/j.inpa.2019.07.001

Cited by 31 publications

(11 citation statements)

References 47 publications

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“…However, the complexity of using deep learning models has significantly reduced its use for smaller models. Junior et al [ 22 ] proposed a multitarget bread wheat quality prediction model using near-infrared spectroscopy and machine learning algorithms. The results showed that the proposed multitarget-based machine learning algorithms provided better results.…”

Section: Related Workmentioning

confidence: 99%

Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier

Ali

Askerzade

Abdulwahab

2021

Applied Bionics and Biomechanics

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Estimation of the quality of food products is vital in determining the properties and validity of the food concerning the baking and other manufacturing processes. This article considers the quality estimation of the wheat bread that is baked under standard conditions. The sensory data are collected in real-time, and the obtained data are analysed using the efficient data analytics to predict the quality of the product. The dataset obtained consists of 300 bread samples prepared in 15 days whose vital physical, chemical, and rheological measures are sensed. The measures of the read are obtained through sensory tools and are gathered as a dataset. The obtained data are generally raw, and hence, the required features are obtained through dimensionality reduction using the Linear Discriminant Analysis (LDA). The processed data and the attributes are given as input to the classifier to obtain final estimation results. The efficient Fuzzy Weighted Relevance Vector Machine (FWRVM) classifier model is developed for this achieving this objective. The proposed quality estimation model is implemented using the MATLAB programming environment with the required setting for the FWRVM classifier. The model is trained and tested with the input dataset with data analysis steps. Some state-of-the-art classifiers are also implemented to compare the evaluated performance of the proposed model. The estimation accuracy is obtained by comparing the number of correctly detected bread classes with the wrongly classified breads. The results indicate that the proposed FWRVM-based classifier estimates the quality of the breads with 96.67% accuracy, 96.687% precision, 96.6% recall, and 96.6% F-measure within 8.96726 seconds processing time which is better than the compared Support vector machine (SVM), RVM, and Deep Neural Networks (DNN) classifiers.

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Section: Related Workmentioning

confidence: 99%

Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier

Ali

Askerzade

Abdulwahab

2021

Applied Bionics and Biomechanics

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“…The authors concluded that the x-ray imaging technique helped to visualize the structure of the cake as well as to investigate the relationships between the cake's structural features and the critical quality attributes. Barbon et al (2020) used multi-target (MT) prediction approaches combined with machine learning algorithms to enhance the prediction accuracy of NIR data obtained from wheat flour samples. They analyzed a total of 391 white wheat bread flour samples using a Bruker MPA Multi-Purpose FT-NIR analyzer coupled with an integrating sphere (reflectance mode) and an RT-PbS detector in the range of 12,500−3600 per cm.…”

Section: Prediction Of Quality Characteristicsmentioning

confidence: 99%

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Olakanmi

Jayas

Paliwal

2023

Comp Rev Food Sci Food Safe

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One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods.These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

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“…The NIR instrument emits wavelengths across the whole sample, which is reflected back to the instrument (transmitted in the case of NIT (near-infrared transmittance)) in the form of the electromagnetic spectra (400-2500 nm) (Zhang et al, 2022b). Reflectance bands from fundamental vibrations of chemical bonds (C-H, N-H, O-H, S-H, C-C and C=O) are observed in the electromagnetic spectra (400-2500 nm) (Junior et al, 2020). Two NIR regions (1120-1350 and 1600-1850 nm) correspond to the protein content (Currà et al, 2022;Moraru et al, 2022).…”

Section: Protein Secondary Structure Analysis Using Nir Spectroscopymentioning

confidence: 99%

Wheat quality under a climate spell : a focus on protein, physico-chemical and growth characteristics evaluated by innovatively combined approaches

Lama¹

2023

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The quality of bread is largely determined by the gluten protein concentration and composition, both greatly influenced by environmental factors such as heat and drought. Future climate in Sweden is expected to fluctuate severely, affecting gluten proteins and the production of bread wheat, as well as future availability of food. The thesis aimed to enhance knowledge of the effect of varying climates on the gluten protein quality in Swedish wheat and to evaluate new methods for yield and gluten protein screening in order to assist in future wheat breeding programs. In this thesis, plant growth-yield traits and gluten protein quality in flour and dough were studied in Swedish wheat of varying genetic backgrounds and imported varieties, all grown in diverse environments in Sweden. Red-green-blue (RGB) imaging and analytical chromatography tools, such as size exclusion high performance liquid chromatography (SE-HPLC) and mass spectrometry (LC-MS/MS), as well as near infrared spectroscopy (NIR) were used to study wheat plants and flour materials. A mixograph was used to prepare dough in this study. Robust flour sedimentation methods, such as swelling index of glutenins (SIG) and solvent retention capacity (SRC), were used to examine the gluten protein characteristics of wheat flour from varying growing environments and were compared to industrial flour screening methods. The results show that the combined heat-drought stresses negatively affected biomass, yield and thousand-kernel weight (TKW) in the wheat studied. During extreme heat and prolonged drought, higher amounts of large polymeric gluten proteins (%UPP) were observed in the spring wheat flours in both field and controlled growth environments. Total extractable gluten protein (TOTE) was higher in the wheat genotypes grown in the cool climate in the field and combined heat-drought stress in the greenhouse. No difference in optimum dough mixing time in wheat from different years was observed. Dough mixing time, together with the gluten protein parameters (%UPP and TOTE) could be promising traits for gluten stability evaluation in varying climates. RGB imaging in combination with SE-HPLC can be useful in screening stable wheat genotypes for yield and gluten quality in varying climates. A combination of robust small-scale sedimentation tests to assess wheat flour suitability for bread-making, SIG in diluted lactic acid, SRC and SE-HPLC can be effectively used for efficient screening of wheat resilient to climate change. The new set of combined methods that include plant imaging, flour sedimentation, analytical chromatography and NIR, is of the greatest interest for both breeding and breadbaking industries to evaluate wheat in a changing climate.

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Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy

Cited by 31 publications

References 47 publications

Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier

Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Wheat quality under a climate spell : a focus on protein, physico-chemical and growth characteristics evaluated by innovatively combined approaches

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