Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue Data

Liu, Jingjing; Liu, Simeng; Shin, Sze; Liu, Fulong; Shi, Tingting; Lv, Chuang; Qiao, Qing; Fang, Hairui; Jiang, Wenjuan; Men, Hong

doi:10.18494/sam.2020.2715

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…At present, many scholars have made researches on identifying red jujube varieties based on HSI technology. Liu, He, et al (2020) and Liu, Liu, et al (2020) used CARS and other methods to select the characteristic wavelengths of red jujube samples. Although satisfactory results have been obtained, the number of characteristic wavelengths was large and the running time was long.…”

Section: Discussionmentioning

confidence: 99%

“…CARS (Liu, He, et al, 2020; Liu, Liu, et al, 2020) is an effective variable selection method involving MC sampling coupled with partial least squares (PLS) model regression coefficients. The wavelengths with a large absolute weights of regression coefficients in the PLS model are reserved as new subsets through ARS each time, the wavelengths with lower weights are got rid of.…”

Section: Methodsmentioning

confidence: 99%

“…Numerous studies were implemented for feature extraction. Liu, He, et al (2020) and Liu, Liu, et al (2020) chose red jujube as the experimental object and utilized competitive adaptive reweighted sampling (CARS), principal component analysis, and successive projections algorithm to screen characteristic wavelengths. The modeling results found that the SVM based on the result of CARS obtained the highest accuracy and the least number of characteristic wavelengths.…”

Section: Introductionmentioning

confidence: 99%

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Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

Wang

Sun

et al. 2022

J Food Process Engineering

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To identify the varieties of red jujube rapidly and nondestructively, hyperspectral imaging (HSI) technology was applied in this article. Hyperspectral data of 480 samples with four different varieties were acquired in the range of 400.68-1001.61 nm.First, Savitzky-Golay and standard normal variable were utilized to process raw spectra. Afterward, a novel method combining competitive adaptive reweighted sampling and iterative retained information variable (CARS-IRIV) was proposed to select feature wavelengths. The support vector machine (SVM) modeling results indicated that CARS-IRIV had better information extraction performance and simplified the model.Finally, to further improve the accuracy, sparrow search algorithm (SSA) was adopted to optimize the parameters (c, g) of SVM. The results showed that SSA-SVM exhibited greater accuracy than other compared models, and the accuracy of training and test sets were 100% and 96.68%, respectively. It confirmed that HSI technology coupled with CARS-IRIV-SSA-SVM can effectively identify varieties of red jujube. Practical ApplicationsThe traditional ways of classifying red jujube varieties are destructive and laborious. Therefore, HSI technology was adopted to overcome the above shortcomings. In this article, the best identification performance was based on the CARS-IRIV-SSA-SVM model with an identification accuracy of 96.68%. This study is helpful for the identification of other agricultural product varieties by HSI technology.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

Wang

Sun

et al. 2022

J Food Process Engineering

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“…(24) It is based on the principle of structural risk minimization and has many advantages for pattern recognition problems, such as a small sample size, nonlinearity, and a high-dimensional feature space. (25) In the process of pattern recognition, the kernel function maps the feature vector to a space with a higher dimension and establishes a hyperplane in this space. Previous studies have shown that the Radial Basis Function (RBF) kernel function exhibits a good classification performance.…”

Section: Gwo-svmmentioning

confidence: 99%

Feature Reduction Method Coupled with Electronic Nose for Quality Control of Tea

Wang¹,

Yang²,

Wu³

2021

Sensors and Materials

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An effective feature reduction method is a key issue to improve the detection performance of the electronic nose (e-nose). In this study, a feature reduction method coupled with a support vector machine (SVM) was proposed to enhance the detection performance of the e-nose for the quality detection of tea. Firstly, the time-domain features were extracted, which can represent the original gas information of different grades of tea. Secondly, to consider the importance of the relationship between each feature and output category, a subset of multiple features with the best variable importance of projection (VIP) score was generated to obtain the optimized feature set. Finally, kernel principal component analysis (KPCA) and kernel entropy component analysis (KECA) were performed to further reduce the correlation between features to obtain the best feature set. The results indicated that VIP-KECA can obtain the best feature set effectively, and a good classification accuracy of 98% was obtained. This study shows that the feature reduction method is effective for enhancing the detection performance of the e-nose. It also provides an effective technique to monitor the quality of tea.

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“…Moreover, artificial intelligence and machine learning (ML) models can be used for prediction and modeling in the food industry [14]. With spectral images, HSI has been widely applied to evaluate the traits of meat products, including color, surface defects, damage, texture, water-holding capacity, flavor, freshness, and ripeness [4,[15][16][17][18][19][20][21]. Ma et al used HSI based on 400-1000 nm wavelengths to predict the different textural parameters of grass carp fillets during vacuum freeze-drying [4].…”

Section: Introductionmentioning

confidence: 99%

Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning

Cao,

Zhang,

et al. 2023

Foods

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Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live common carp by scanning the corresponding skin regions using HSI. We collected skin hyperspectral information from four regions of 387 scaled and live common carp. Eight texture indicators of the muscle corresponding to each skin region were measured. With the skin HSI of live common carp, six machine learning (ML) models were used to predict the muscle texture indicators. Backpropagation artificial neural network (BP-ANN), partial least-square regression (PLSR), and least-square support vector machine (LS-SVM) were identified as the optimal models for predicting the texture parameters of the dorsal (coefficients of determination for prediction (rp) ranged from 0.9191 to 0.9847, and the root-mean-square error for prediction ranged from 0.1070 to 0.3165), pectoral (rp ranged from 0.9033 to 0.9574, and RMSEP ranged from 0.2285 to 0.3930), abdominal (rp ranged from 0.9070 to 0.9776, and RMSEP ranged from 0.1649 to 0.3601), and gluteal (rp ranged from 0.8726 to 0.9768, and RMSEP ranged from 0.1804 to 0.3938) regions. The optimal ML models and skin HSI data were employed to generate visual prediction maps of TPA values in common carp muscles. These results demonstrated that skin HSI and the optimal models can be used to rapidly and accurately determine the texture qualities of different muscle regions in common carp.

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Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue Data

Cited by 7 publications

References 25 publications

Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

Identification of red jujube varieties based on hyperspectral imaging technology combined with CARS‐IRIV and SSA‐SVM

Feature Reduction Method Coupled with Electronic Nose for Quality Control of Tea

Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning

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