Efficacy study on the non-destructive determination of water fractions in infrared-dried Lentinus edodes using multispectral imaging

Younas, Shoaib; Mao, Yu; Liu, Changhong; Liu, Wei; Jin, Tao; Zheng, Lei

doi:10.1016/j.jfoodeng.2020.110226

Cited by 19 publications

(17 citation statements)

References 37 publications

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“…With the increase of drying time, the intensity of reflection spectra decreased due to the gradual loss of water and surface shrinkage 27 . Previous researchers found that the reflection intensity of the spectra in the 940–970 nm region increased with a decrease in water content, which was due to the OH third stretching overtone 17,24 . However, no OH third stretching overtone was detected in the spectra in this study.…”

Section: Resultscontrasting

confidence: 57%

“…It has been widely applied in pattern recognition, control optimization, and fault diagnosis because of its ability of learning, multi‐input parallel processing, dislocation mapping, fault tolerance, and adaptive ability through new knowledge. In propagation of data processing, the rules of the speediest gradient descent method are adopted and the parameters of the network can be adjusted to minimize the error of the network 17 . The structure of the BPNN consists of an input layer, a hidden layer, and an output layer.…”

Section: Methodsmentioning

confidence: 99%

“…Compared with the traditional regression and classification methods, LS‐SVM has the advantages of easy overfitting control and high reliability prediction, which simplifies the complexity of data processing and saves time to a certain extent. Moreover, LS‐SVM has strong generalization ability and is more suitable for quantitative and qualitative modeling analysis of small sample datasets 17 . The nonlinear function is used to map the input data to a high‐dimensional feature space and establish the relational model:

y (x) = wtφ (x) + b

where

φ (x)

is the input data,

y (x)

is the output data,

w \in R^{n}

is the weight vector, and b is the offset value.…”

Section: Methodsmentioning

confidence: 99%

“…As an analytical tool, multispectral imaging is rapid, nondestructive, and requires no sample pretreatment, which makes it suitable for food quality analysis and visualization. 12 Recently, this technology has been applied as a powerful analytical tool for rapid detection of water content in tea leaves, 13,14 carrot slices, 15 mangoes, 16 mushrooms, 17 Daqu, 18 and green coffee beans. 19 To the best of our knowledge, there are no published reports on multispectral imaging for detection of the water status and distribution in corn kernels.…”

Section: Introductionmentioning

confidence: 99%

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Nondestructive detection of water status and distribution in corn kernels during hot air drying using multispectral imaging

Ren

Liu

et al. 2023

J Sci Food Agric

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BACKGROUND The characteristics of corn kernels are strongly connected with the content of three statuses of water: bound water, immobilized water, and free water. Monitoring different water contents is very important to optimize the drying process, improve corn quality, and reduce energy consumption. The feasibility of nondestructive detection of water status and its distribution in corn kernels during the hot‐air drying process using multispectral imaging was investigated. RESULTS The chemometric methods used to develop prediction models were back propagation neural network, least‐squares support vector machine, and partial least squares. The back propagation neural network achieved the best prediction performance for total and free water contents, with correlation coefficient of prediction (Rp) of 0.9717 and 0.9782 respectively, root‐mean‐square error of prediction (RMSEP) of 4.48% and 2.54% respectively, and ratio of prediction to deviation (RPD) of 4.87 and 4.29 respectively. And partial least squares was better for the prediction of immobilized and bound water contents, with Rp of 0.9612 and 0.9798 respectively, RMSEP of 0.57% and 0.06% respectively, and RPD of 4.78 and 4.42 respectively. CONCLUSION It could be concluded that multispectral imaging combined with chemometric methods would be a promising technique for rapid and nondestructive detection of water status and its distribution in corn kernels. © 2023 Society of Chemical Industry.

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Section: Resultscontrasting

confidence: 57%

Section: Methodsmentioning

confidence: 99%

y (x) = wtφ (x) + b

where

φ (x)

is the input data,

y (x)

is the output data,

w \in R^{n}

is the weight vector, and b is the offset value.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nondestructive detection of water status and distribution in corn kernels during hot air drying using multispectral imaging

Ren

Liu

et al. 2023

J Sci Food Agric

Self Cite

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“…Data prediction is significant for operating state prediction. Least squares support vector machine (LSSVM) is a commonly used method in data prediction [5][6][7]. However, this method often only considers the trend of a single characteristic parameter with time and does not consider the interaction between various characteristic parameters.…”

Section: Introductionmentioning

confidence: 99%

Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO

Gu¹,

Ma²

2021

J. vibroeng.

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Aiming at the problem of operating state prediction of generator bearing, a prediction method based on quantum particle swarm optimization (QPSO) and united least squares support vector machine (ULSSVM) is proposed. Firstly, the time least squares support vector machine (TLSSVM) model is established in accordance with the change law of characteristic parameters over time. Space least squares support vector machine (SLSSVM) model is established in accordance with the law of mutual influence between characteristic parameters. Secondly, the QPSO algorithm is used to optimize the parameters of each least squares support vector machine (LSSVM) model. When the difference between the predicted value and the measured value reaches the minimum, the optimal LSSVM parameter set is output. Then the improved Dempster-Shafer (D-S) theory is used to determine the weights of TLSSVM and SLSSVM. A united model of time LSSVM and space LSSVM is established. The characteristic parameters are predicted. The prediction results and the reference matrix are fused and reduced in dimension. Finally, the generator bearing operating status is predicted based on the location of the prediction results. The results show that the proposed method is helpful to realize the operating state prediction of the wind turbine bearing.

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New preservation and detection technologies for edible mushrooms: A review

et al. 2023

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Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post‐harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post‐harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non‐destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 Society of Chemical Industry.

show abstract

Efficacy study on the non-destructive determination of water fractions in infrared-dried Lentinus edodes using multispectral imaging

Cited by 19 publications

References 37 publications

Nondestructive detection of water status and distribution in corn kernels during hot air drying using multispectral imaging

Nondestructive detection of water status and distribution in corn kernels during hot air drying using multispectral imaging

Operating state prediction for wind turbine generator bearing based on ULSSVM and QPSO

New preservation and detection technologies for edible mushrooms: A review

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