Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics

Sun, Jianfei; Shi, Xianqing; Zhang, Hui; Xia, Lianming; Guo, Yangming; Sun, Xia

doi:10.1111/jfpe.13263

Cited by 31 publications

(10 citation statements)

References 35 publications

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“…[ 19 ] Previously, alginate microgels had been used to encapsulate nanoemulsions, liposomes, and double emulsions, resulting in improved physical stability and protection of the entrapped bioactives, as well as in enhanced control of bioactive release. [ 19–21 ]…”

Section: Introductionmentioning

confidence: 99%

Nanostructured Lipid Carriers in Alginate Microgels for the Delivery of Astaxanthin

Huang

Xie

Liu

et al. 2021

Euro J Lipid Sci & Tech

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This study aims to enhance the stability and bioavailability of astaxanthin by loading it into nanostructured lipid carriers (NLCs), then incorporating the NLCs into alginate microgels. The NLCs (about 200 nm particle size) and alginate microgels (about 1 mm particle size) are prepared using high‐pressure homogenization and injection‐gelation methods, respectively. Based on in vitro dissolution assessments, the astaxanthin in alginate microgels dissolves more slowly than that in NLCs. In simulated gastric digestive juice, minimal dissolution of microgel‐embedded astaxanthin is detected; however, in simulated intestinal digestive juice, the dissolution is appreciably accelerated. Moreover, the 2 h bio‐accessibility of astaxanthin in microgels is less than that in NLCs. At higher concentrations of sodium alginate and calcium ions, the internal pores of the microgels become smaller, resulting in slower dissolution and lower bio‐accessibility of astaxanthin. As for the chemical stability of the pigment, it is improved by the secondary coating effect of hydrogelation. Hence, NLC incorporation into alginate microgels constitutes a promising strategy for the encapsulation of astaxanthin in the food industry. Practical applications : Nanostructured lipid carriers (NLCs) incorporated in alginate microgels can increase the water solubility of astaxanthin, improve its chemical stability, increase its bioavailability, and allow for its controlled release. The high‐pressure homogenization method used to prepare NLCs can be easily applied on an industrial level. Moreover, the operation conditions of the injection‐gelation process needed to synthesize the alginate microgels are mild, nontoxic, and nonpolluting. The NLC‐alginate microgels can be used in yogurt, ice cream, and other foods.

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Section: Introductionmentioning

confidence: 99%

Nanostructured Lipid Carriers in Alginate Microgels for the Delivery of Astaxanthin

Huang

Xie

Liu

et al. 2021

Euro J Lipid Sci & Tech

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“…Therefore, kernel function was the key to solve the low‐dimensional linear indivisible problem. The common kernel functions of SVM model were radial basis function (RBF), sigmoid function, polynomial function, and so on (Sun et al, 2019). In addition, the SVM model has two important parameters C and Gamma (G).…”

Section: Methodsmentioning

confidence: 99%

Visual detection of moldy peanut kernels based on the combination of hyperspectral imaging technology and chemometrics

Sun

Liu

Sun

et al. 2021

J Food Process Engineering

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In order to facilitate the identification of moldy peanuts using hyperspectral imaging, in this study, hyperspectral images of four peanut kernels in a large dark box were acquired at the band range of 1,000–2,030 nm for classification of moldy peanut kernels. Then, the spectral data were extracted from hyperspectral images. Moreover, successive projection algorithm (SPA) was used to select effective wavelength. Next, three models including partial least squares discriminant analysis (PLS‐DA), support vector machine, and linear discriminant analysis (LDA) were established to identify moldy peanut kernels based on the optimal wavelengths selected by SPA. As a result, SPA–LDA generated the best effect with accuracy of 100% in both calibration set and prediction set. Finally, based on the SPA–LDA model, we successfully completed the visual detection of peanut seed mold using 1,120 nm wave band and threshold method. The results indicated that the combination of chemometrics and hyperspectral imaging technology provided an accurate, rapid, and nondestructive detecting method for the classification of moldy peanut kernels. Practical Applications It is well‐known that the moldy peanuts contain potent carcinogen. Therefore, the accuracy of moldy peanuts identification becomes very important. Traditional methods for detection of moldy peanuts are tedious, time‐consuming and strict requirements for operators, which cannot meet the requirements of intelligent agriculture. Hyperspectral imaging technology, providing both spectral and spatial information simultaneously, can detect moldy peanuts rapidly, accurately, and nondestructively. All these results show that the use of hyperspectral technology combined with appropriate chemometrics can facilitate the accurate identification of moldy peanuts, and the high accuracy of discrimination also provides theoretical support for the rapid and accurate detection of industrial application of moldy peanuts in the future.

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“…It was reported that backpropagation (BP) combined with UVE-CARS and least squares support vector machine (LS-SVM) combined with CARS-SPA showed a determination coefficient of prediction set (Rp 2 ) of the prediction set was .9784 and .99, and the root mean square error of prediction (RMSEP) of 0.2503 and 2.422, respectively. Sun et al (2019) used HSI technology to detect the moisture content of peanut kernels in different storage times. The results showed that the optimized prediction results were obtained by the successive projection algorithm-support vector regression (SPA-SVR) method, and there was a strong correlation between the predicted value and the measured value (Rp 2 = .9363, RMSEP = 0.7021%).…”

Section: Hyperspectral Imaging (Hsi) Technology Is a New Nondestructivementioning

confidence: 99%

Nondestructive prediction and visualization of total flavonoids content in Cerasus Humilis fruit during storage periods based on hyperspectral imaging technique

Bin

Zhang

2021

J Food Process Engineering

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In this study, the changes of total flavonoids content in Cerasus Humilis fruits during storage periods were detected by using hyperspectral imaging (HSI) technique over the spectral region of 895-1,700 nm, and the spatial distribution of flavonoids was visualized during 24 days storage periods. The hyperspectral images of 240 samples of Cerasus Humilis were collected at different storage times (0, 8, 16, and 24 days). The Monte-Carlo outlier detection method was applied to identify four abnormal samples, three spectral preprocessing methods were used to preprocess the original spectral data, including savitzky-golay, standard normal variate, and baseline correction (BC). The x-loading weights, competitive adaptive reweighed sampling (CARS), uninformative variable elimination (UVE), UVE-CARS and the combination of UVE and successive projections algorithm (UVE-SPA) were used to reduce the dimension of spectral data and extract the characteristic wavelength, respectively. Then multiple linear regression and nonlinear least squared support vector machine (LS-SVM) regression models were developed based on full-spectrum data and selected characteristic wavelength.The results showed that BC was the best preprocessing approach, the LS-SVM models based on the nine effective wavelengths selected by UVE-CARS achieved the best results with correlation coefficient of prediction of .9357, root mean square error of prediction of 2.0107, and residual prediction deviation of 2.2809, respectively. The overall results demonstrated that the HSI technology coupled with chemometric algorithms is feasible to determine total flavonoids content, UVE-CARS-LS-SVM model was the optimal model. Visualization of the total flavonoids content during storage periods distribution map was performed, which clearly showed that the total flavonoids content kept decreasing with storage time. Practical ApplicationsCerasus Humilis is a fruit tree resource endemic to China. The content of total flavonoids is one of the key factors that affect the edible quality of Cerasus Humilis fruit.With the increase of storage time, the active components of flavonoids are easy to be degraded by oxidation, the quality and commercial value of Cerasus Humilis fruit were negatively affected. Thus, the detection of total flavonoids content during the

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Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics

Cited by 31 publications

References 35 publications

Nanostructured Lipid Carriers in Alginate Microgels for the Delivery of Astaxanthin

Nanostructured Lipid Carriers in Alginate Microgels for the Delivery of Astaxanthin

Visual detection of moldy peanut kernels based on the combination of hyperspectral imaging technology and chemometrics

Nondestructive prediction and visualization of total flavonoids content in Cerasus Humilis fruit during storage periods based on hyperspectral imaging technique

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