Study on drying characteristics and moisture content prediction model of Panax notoginseng taproot by using segmented drying of microwave vacuum combined with hot air

Zhang, Fujie; Shi, Lei; Xin, Lidong; Li, Lixia; Tian, Liquan; Cui, Xiuming; Gao, Yongping

doi:10.1111/jfpe.14179

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…Support vector regression is well known kind of machine learning frequently have been used for regression problems. SVR has core‐oriented functionality and the features of kernel used have a significant impact on optimal development (Zhang et al, 2022). The results of using this technique on nonlinear problems have been very impressive.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

Kaveh,

Çetin,

Khalife

et al. 2023

J Food Process Engineering

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Drying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (Deff) were calculated in these dryers. Accordingly, the best result of the Deff (3.14 × 10−10 m2/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of Deff (2.09 × 10−11 m2/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical ApplicationsRecently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.

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Section: Methodsmentioning

confidence: 99%

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

Kaveh,

Çetin,

Khalife

et al. 2023

J Food Process Engineering

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“…Fresh O. raphanipes mushrooms were dried by using VMD equipment. The VMD equipment was developed by our laboratory [25]. A total of 300 g of fresh O. raphanipes mushrooms were laid flat on a tray at a drying power of 350 W and drying time of 4 h. The dried samples were denoted as VMD.…”

Section: Vacuum Microwave Dryingmentioning

confidence: 99%

Effect of Different Drying Methods on the Quality and Nonvolatile Flavor Components of Oudemansiella raphanipes

Shen

Ding

et al. 2023

Foods

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Different drying methods affect the quality of foods. The aim of this study is to explore the effects of seven drying methods, including hot air drying at 60 °C and 80 °C, ultrasound-assisted hot air drying at 60 °C and 80 °C, microwave drying, vacuum microwave drying, and vacuum freeze-drying, on the quality and nonvolatile flavor components of Oudemansiella raphanipes. The vacuum freeze-drying resulted in minimal collapse, mild shrinkage at the macroscopic level, and the formation of uniform pores at the microscopic level on the surfaces of O. raphanipes mushrooms. In addition, vacuum freeze-drying can improve the color attributes of the mushrooms. Therefore, the appearance and shape of vacuum freeze-drying treated O. raphanipes were closest to those of fresh mushrooms. We found that ultrasound-assisted treatment can effectively shorten the drying time of O. raphanipes. The drying time of ultrasound-assisted hot air drying at 60 °C was 20% shorter than that of hot air drying at 60 °C, and the drying time of ultrasound-assisted hot air drying at 80 °C was 37.5% shorter than that of hot air drying at 80 °C. The analysis of the nonvolatile flavor components showed that the ultrasound-assisted hot air drying at 60 °C of the O. raphanipes sample had the highest content of free amino acids (83.78 mg/g) and an equivalent umami concentration value (1491.33 monosodium glutamate/100 g). The vacuum freeze-drying treated O. raphanipes had the highest 5′-nucleotide content of 2.44 mg/g. Therefore, vacuum freeze-drying and ultrasound-assisted hot air drying at 60 °C, followed by vacuum microwave drying, might protect the flavor components of O. raphanipes to the greatest extent. However, microwave drying, hot air drying at 80 °C, and ultrasound-assisted hot air drying at 80 °C could destroy the flavor components of O. raphanipes during drying. The results of this study provided data support for the industrial production of dried O. raphanipes.

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“…Panax notoginseng is a rare Chinese medicinal herb and the largest variety of Chinese traditional medicine (P. Liu et al, 2019; Y. Liu et al, 2019; F. Zhang, Xin, et al, 2022). The dried roots and rhizome of Panax notoginseng are the most medicinal parts of the plant, effective for improving blood circulation, removing blood stasis, managing inflammation, reducing swelling, and relieving pain (Liu, 2011; Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Panax notoginseng is a rare Chinese medicinal herb and the largest variety of Chinese traditional medicine (P. Y. Liu et al, 2019;F. Zhang, Xin, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Application of portable near‐infrared spectroscopy technology for grade identification of Panax notoginseng slices

Zhang

Shi

et al. 2023

Journal of Food Safety

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Panax notoginseng slices (PNS) are prepared from the taproot of a rare Chinese herbal plant, Panax notoginseng. The price and efficacy of PNS change depending on its grade, but substandard PNS are more prevalent in the Asian market. In this study, a portable near‐infrared spectrometer was used to collect the spectra of 240 PNS samples divided into four grades. The spectral data were preprocessed by the Savitzky–Golay (SG) filter to eliminate noise interference. Principal component analysis (PCA), competitive adaptive reweighted sampling, and variable combination population analysis were used to extract the feature variables of the spectral data. The selected feature variables were used to establish least squares support vector machine (LSSVM), support vector machine (SVM), and extreme learning machine (ELM) classification models. To further improve the classification accuracy of the most effective of these models, a grey wolf optimizer (GWO) was introduced, and particle swarm optimization (PSO) as well as genetic algorithm (GA) were used to conduct comparative analyses. The results showed that PCA provided accurate identification information of different PNS grades and that the classification effect of the LSSVM model was better than that of the ELM and SVM models. During the optimization process, the optimization accuracy of the GWO was better than that of the PSO and GA systems. Therefore, the optimal classification model was established as GWO–PCA–LSSVM, and the classification accuracy of the test set was 91.67%. Therefore, portable near‐infrared spectroscopy technology can be used to identify the grade of PNS effectively.

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Study on drying characteristics and moisture content prediction model of Panax notoginseng taproot by using segmented drying of microwave vacuum combined with hot air

Cited by 9 publications

References 32 publications

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

Effect of Different Drying Methods on the Quality and Nonvolatile Flavor Components of Oudemansiella raphanipes

Application of portable near‐infrared spectroscopy technology for grade identification of Panax notoginseng slices

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